Health Policy Spotlight Effects on Critical Time-Sensitive Diseases: Evidence from Taiwan Categorization of Hospital Emergency Capability Policy

Health Policy Spotlight Effects on Critical Time-Sensitive Diseases: Evidence from Taiwan Categorization of Hospital Emergency Capability Policy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Health Policy Spotlight Effects on Critical Time-Sensitive Diseases: Evidence from Taiwan Categorization of Hospital Emergency Capability Policy Chih-Yuan Lin, Chih-Chin Liu, Yu-Tung Huang, Yue-Chune Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4697511/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To investigate the effects of the Categorization of hospital emergency capability (CHEC) policy on critical time-sensitive diseases (CTSDs). Setting CHEC is a policy implemented worldwide to regionalize and guide the dispatch of critical patients to the nearest appropriate hospital. In 2009, Taiwan's CHEC policy was designed to improve the quality of emergent care for CTSDs. Research Design and Participants A nationwide observational quasi-experimental study was conducted to examine the quality of care for CTSD before (2006-2008) and after (2009-2012) the implementation of the CHEC policy. CHEC policy focused on acute ischemic stroke (AIS), ST-segment elevation myocardial infarction (STEMI), septic shock, and major trauma. A difference-in-differences estimation was used to assess the impact of the CHEC policy exposure (AIS and STEMI) on clinical practice and outcomes, compared with the unexposed counterfactual of septic shock. We selected diagnosis and treatment guideline adherence process quality measures as primary outcome and medical utilization, upward transfer rate, short-term and long-term mortality as secondary outcomes. Taiwan National Health Insurance 2005 Longitudinal Health Insurance Database contains one million random cases, including time-sensitive disease samples. Results In our cohort of 9,923 cases, refined through 1:1 propensity score matching, 56% were male, mostly older adults. The CHEC policy significantly reduced medical orders and major diagnostic indicators, yet diagnostic fees notably increased. This led to a decrease in mortality rates, ultimately lowering overall medical expenses. Septic shock cases showed marked reductions in both primary diagnosis indicators and medical orders. In contrast, primary treatment indicators for AIS and STEMI rose, supporting the hypothesis of a health policy spotlight effect. Conclusions This study highlights the CHEC policy's dual effects on reducing costs and enhancing patient outcomes. We observed a health policy spotlight effect, which led to a disproportionate improvement in guideline adherence and process quality for CTSDs that have time-based surveillance indicators. categorization of hospital emergency capability quality time-sensitive diseases emergency care difference-in-differences. Figures Figure 1 Highlights What is already known on this subject? ► Emergency care is a symptom-driven profession under significant uncertainty and time pressure. ►Critical time-sensitive diseases refer to life-threatening illnesses or injuries that require immediate emergency care, where rapid intervention is paramount to mitigate morbidity and mortality. ► The hospital emergency capability categorization policy aims to classify hospital care capacities, guide emergent patient transport to the nearest appropriate facilities prevent preventable deaths. The hospital emergency capability categorization policy aims to classify hospital care capacities, guide emergent patient transport to the nearest appropriate facilities prevent preventable deaths. What this study adds? ►The categorization of hospital emergency capability policy implementation demonstrates a dual capability to reduce costs and improve patient outcomes. ► Disease entities not fully encompassed in the surveillance indicators may be jeopardized with a decrease in diagnosis and treatment process quality. ► Health policy spotlight effect exists in critical time-sensitive diseases with time-based quality indicators, resulting in a disproportional improvement in disease guideline adherence and process quality. INTRODUCTION Emergency care is a symptom-driven profession delivered by emergency department (ED) physicians under significant time pressure and uncertainty 1 . Emergency care providers tentatively diagnose specific diseases based on at least 70 common disease patterns encountered in the ED, often dedicating only 3% of their time for diagnosis 2 , despite this step representing the most important in terms of cost 2 . Furthermore, medical care providers are the most influential decision-makers and drive approximately 70–80% of medical utilization 3 . Critical time-sensitive diseases (CTSD) refer to life-threatening illnesses or injuries that require immediate emergency care, where rapid intervention is paramount to mitigate morbidity and mortality 4 . The Agency for Healthcare Research and Quality proposed the concept of time-sensitive diseases 5 using scientific data to maintain up-to-date guidelines and launched the "get with the guidelines" campaign to establish it as the basis for surveillance indicators of process and outcome quality 6 . Critical time-sensitive disease (CTSD) refers to life-threatening illnesses or injuries that require immediate emergency care, where rapid intervention is paramount to mitigate morbidity and mortality 4 . The various guidelines for managing time-sensitive events emphasize the crucial importance of time. In the context of acute ischemic stroke (AIS), the "time is brain" 7 goal focuses on the timely administration of thrombolytic therapy; in ST-segment elevation myocardial infarction (STEMI), the "time is muscle" goal focuses on early reperfusion 8 ; in septic shock events, the "early goal" focuses on early resuscitation 9 ; and in major trauma cases, the "golden hour" goal focuses on the window of opportunity in which patients can undergo rescue operations 10 . American Medical Association issued the categorization of hospital emergency capability (CHEC) guidelines 11 to classify hospitals according to their emergency care capabilities, thereby regionalization and providing emergency medical services with references to transport emergent patients to the nearest appropriate hospitals 12 , aiming to reduce preventable deaths. Most studies investigating the effects of this categorization, designation, and regionalization policy reported positive findings 13–16 . However, studies mainly focused on a single disease entity 13–16 or region 15 16 . The CHEC policy often implements rigid time-based surveillance indicators. These indicators can affect disease-specific guideline adherence in clinical practice because they may reshape the behaviors of ED medical providers 17 . This phenomenon is related to the so-called "policy spotlight effect", which influences medical care providers' assessment of how others perceive them 18 . More specifically, the policy spotlight effect refers to how medical care providers perceive how policymakers interpret surveillance indicators and adjust their process-related behaviors accordingly 19 . Current emergency care policies often use time-based criteria as process quality indicators, which may exacerbate the policy spotlight effect 18 ; however, the unintended effects or safety concerns generated by this effect remain unclear. Therefore, this study targeted four CTSDs: AIS, STEMI, septic shock, and major trauma 10 . Our research presents the hypothesis that emergency care providers might inadvertently give more attention to diseases under active surveillance while potentially neglecting those not thoroughly incorporated in this observation. This focus might be based on their perception of observer expectations 20 . The primary aim of our research is to examine the effects of the CHEC policy on process quality and outcomes for CTSDs, addressing three research questions: 1. how does the CHEC policy impact the quality of diagnosis, treatment, and outcomes for these diseases? 2. Does a policy spotlight effect exist in this context? 3. What are the potential consequences of this policy spotlight effect? METHODS Setting, study design and data source Taiwan's National Health Insurance (NHI) is a single-payer compulsory social insurance system that primarily operates on a fee-for-service basis. This study is based on the NHI 2005 Longitudinal Health Insurance Database (LHID2005), which contains one million random cases, including medical records and hospital information, collected since 1995. The LHID2005 was validated as representative of medical utilization, as well as of diagnosis and treatment process and outcome quality for CTSDs 21 . This nationwide observational study investigates the impact of the CHEC policy, initiated in August 2009, which integrated 190 hospitals into a network focusing on acute conditions like stroke, myocardial infarction, major trauma, and perinatal care 22 . We divided our analysis into two periods: pre-CHEC (August 1, 2005 - July 31, 2009) and post-CHEC (August 1, 2009 - July 31, 2011). This division aims to distinctly assess the CHEC policy's effects, distinctly from the ED Quality Improvement Plan introduced in 2012. Well-established guidelines exist for AIS, STEMI, and septic shock, whereas the guidelines for major trauma are continuously evolving due to the variability in injury mechanisms, locations, and severity. Moreover, AIS and STEMI events are stringently monitored under the CHEC policy with specific time-based quality indicators, whereas septic shock and major trauma events are not (Table 1 ). Thus, we selected major trauma events as a reference for our study because they were not monitored under the CHEC policy with rigid indicators. The study uses a quasi-experimental design to evaluate the quality of CTSDs care. We adopted pre and post-implementation of a CHEC policy, using 1:1 propensity score matching (PSM) to control for confounding variables. To estimate the association of the CHEC policy on process and outcomes for AIS and STEMI, we employed a difference-in-differences (DID) estimation approach. For the counterfactual, we used major trauma cases unexposed to the clinical guideline or CHEC policy time-based quality indicators as a comparison group. Table 1 Critical time-sensitive diseases and categorization hospital emergency capability policy indicators in Taiwan Quality indicator Acute ischemic stroke ST-segment elevation MI Septic shock Major trauma Guidelines development Well developed Well developed Well developed Developing Major diagnosis indicator Brain imaging (CT&MRI) EKG Blood Culture Image Study Major treatment indicator iv-TPA PCI Antibiotics Rescue operation Guideline’s major goal Early thrombolysis (Time is brain) Early reperfusion (Time is muscle) Goal-directed therapy (Early goal) Rescue operation (Golden hour) Guideline time-based criteria 60 min 90 min 3–6 hours 1 hour CHEC policy indicators Stroke team NIHSS score evaluation Intravenous t-PA PCI team Give Aspirin and Clopidogrel ICU critical care team Trauma team ISS evaluation CHEC policy time-based criteria Neurologist consultation < 30 mins Door to CT < 30 mins Door to CT read < 45 mins Onset to needle < 3 hours Cardiologist consultation < 30 min Door to EKG < 10 mins Door to needle < 30 mins Door to balloon < 90 mins Admission < 24 hours Trauma team activation < 30 mins Source: Taiwan Ministry of Health and Welfare: The Statistics and Trends in Health and Welfare 2014. CHEC: Categorization Hospital Emergency Capability; CT: Computed tomography; EKG: Electrocardiography; ED: emergency department; ICU: Intensive Care Unit; ISS: Injury Severity Score; IV-tPA: intravenous tissue plasminogen activator; NIHSS: National Institute of Health Stroke Scale/Score; PCI: Percutaneous coronary intervention Identification of study cohort This study identified CTSD based on ED visits accompanied by a primary diagnosis using the appropriate disease code. The identification of AIS (codes 433 and 434), STEMI (code 410), and septic shock (codes 038, 785, and 995) was based on the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9‐CM). Major trauma cases were classified following the American Academy of Surgery Committee guidelines (codes 800–959) 23 . Due to the absence of trauma severity data in the LHID2005 database, primary ICD-9-CM codes served as our initial method for identifying major trauma incidents. This identification was further refined by including cases where patients received rescue surgery or were admitted to the ICU, serving as additional criteria for major trauma 24 . We excluded cases that occurred before the start of the study period and those that lacked hospital or patient sociodemographic information. We also excluded hospitals with a volume of CTSD cases lower than five per year 25 . We used the date of the first ED visit as the index date. Variable definitions Independent variable in this study was exposure to the CHEC policy intervention. Events related to AIS and STEMI were specifically subject to rigid time-based quality indicators and regular surveillance under the CHEC policy. While septic shock has well-developed clinical guidelines, it is not subject to the CHEC policy's rigid, time-based quality indicators. Similarly, major trauma cases, which lack well-developed clinical guidelines, are also not subject to these CHEC policy indicators and are unexposed as counterfactual. Dependent variables included guideline adherence rate of diagnosis and treatment process quality indicator as primary outcomes (Table 1 ), upward transfer rate, diagnostic fees, medical orders and expense, and 30-day and one-year mortality as secondary outcomes. Covariates including patients' predisposing factors included age, sex, and occupation; enable factor was insured salary, and Charlson Comorbidity Index (CCI) score one year before the index date as a need factor. External environmental factors included urbanization and regional emergency resources. Hospital-level variables followed the input-throughput-output model of Asplin et al. 26 , focusing on the rate of ED visits with triage severity levels Ⅰ and Ⅱ to gauge input. We assessed throughput and output efficiency via the ED's occupancy rate. The CCI score used to categorize patient comorbidities, was calculated using ICD-9-CM codes from primary diagnoses in both inpatient and outpatient claims data up to a year before the index date. Statistical Analysis Patients' characteristics, process quality, and outcomes were presented using descriptive statistics. Continuous data were described using mean ± standard deviation, and categorical data were presented using numbers and percentages. To enhance the robustness of the outcomes, propensity score was calculated using a multivariable logistic regression that included all baseline covariates in Table 2 . The standardized mean difference (SMD) was calculated to confirm the balance of potential confounders at baseline between groups before and after matching. An SMD of less than 0.1 was considered to represent a negligible difference 27 . Table 2 Propensity Score Matched Comparison of Patient Characteristics Pre- and Post-CHEC Policy Before propensity score matching After propensity score matching Before CHEC policy (N = 28829) After CHEC policy (N = 14534) ASMD a Before CHEC policy (N = 9923) After CHEC policy (N = 9923) ASMD Sex Female 12345 (42.82%) 6250 (43.00%) 0.004 4357 (43.91%) 4268 (43.01%) 0.018 Male 16484 (57.18%) 8284 (57.00%) 0.004 5566 (56.09%) 5655 (56.99%) 0.018 Age ≤ 45 4105 (14.24%) 1967 (13.53%) 0.021 1289 (12.99%) 1306 (13.16%) 0.005 45–64 7173 (24.88%) 3795 (26.11%) 0.028 2550 (25.70%) 2590 (26.10%) 0.009 ≥ 65 17551 (60.88%) 8772 (60.36%) 0.011 6084 (61.31%) 6027 (60.74%) 0.012 Charlson Comorbidity Index ≤ 1 13215 (45.84%) 6546 (45.04%) 0.016 4473 (45.08%) 4434 (44.68%) 0.008 > 1 15614 (54.16%) 7988 (54.96%) 0.016 5450 (54.92%) 5489 (55.32%) 0.008 Income 22800 12868 (44.64%) 8331 (57.32%) 0.256 5495 (55.38%) 5319 (53.60%) 0.036 Occupation Dependents of the insured 10095 (35.02%) 4952 (34.07%) 0.020 3550 (35.78%) 3326 (33.52%) 0.048 Civil servants, teachers, military 2792 (9.68%) 1529 (10.52%) 0.028 986 (9.94%) 1053 (10.61%) 0.022 Nonmanual workers and professionals 2231 (7.74%) 1240 (8.53%) 0.029 748 (7.54%) 796 (8.02%) 0.018 Manual workers 10250 (35.55%) 5154 (35.46%) 0.002 3422 (34.49%) 3708 (37.37%) 0.060 Others 3461 (12.01%) 1659 (11.41%) 0.019 1217 (12.26%) 1040 (10.48%) 0.056 Hospital categorization Severe level 11371 (39.44%) 5924 (40.76%) 0.027 3331 (33.57%) 3502 (35.29%) 0.036 Moderate level 10921 (37.88%) 5496 (37.81%) 0.001 4030 (40.61%) 4052 (40.83%) 0.004 General level 6537 (22.68%) 3114 (21.43%) 0.030 2562 (25.82%) 2369 (23.87%) 0.045 ESI triage level Ⅰ and Ⅱ rate 5.8 ± 8.98 16.63 ± 10.06 1.136 14.01 ± 10.39 14.37 ± 10.50 0.034 Length of ED stay 5.33 ± 10.11 4.49 ± 9.30 0.086 6.25 ± 9.30 5.36 ± 10.26 0.091 ED observation ≧ 1-day rate 11.37 ± 9.97 12.32 ± 10.06 0.095 10.53 ± 9.34 11.32 ± 10.12 0.081 Place of ED resources Sufficiency 22770 (78.98%) 11460 (78.85%) 0.003 7768 (78.28%) 7834 (78.95%) 0.016 Not sufficiency 6059 (21.02%) 3074 (21.15%) 0.003 2155 (21.72%) 2089 (21.05%) 0.016 Time-sensitive disease Acute ischemic stroke 8660 (30.04%) 3814 (26.24%) 0.085 2895 (29.17%) 2895 (29.17%) 0.000 ST-segment elevation MI 2481 (8.61%) 1141 (7.85%) 0.028 723 (7.29%) 723 (7.29%) 0.000 Septic shock 14896 (51.67%) 8275 (56.94%) 0.106 5441 (54.83%) 5441 (54.83%) 0.000 Major trauma 2792 (9.68%) 1304 (8.97%) 0.024 864 (8.71%) 864 (8.71%) 0.000 Care delivered by : Specialty Consultant 18692 (64.84%) 9245 (63.61%) 0.026 6738 (67.9%) 6570 (66.21%) 0.036 Emergency physician 7788 (27.01%) 4236 (29.15%) 0.048 2449 (24.68%) 2585 (26.05%) 0.031 Others 2349 (8.15%) 1053 (7.25%) 0.034 736 (7.42%) 768 (7.74%) 0.012 ASMD: absolute standardized mean difference; CHEC: Categorization Hospital Emergency Capability; ED: emergency department; ESI: Emergency Severity Index; Specialty Consultant: 1. acute ischemic stroke is treated by neurologists, 2. acute myocardial infarction is treated by cardiologists, 3. septic shock is managed by internal medicine physicians or critical care intensivists, 4. major trauma conditions are managed by surgeons or critical care intensivists. We evaluated the impact of the CHEC intervention on each outcome, including overall, within individual diseases, and between-disease differences in change from baseline (group-by-disease interaction effects) by generalized estimating equation (GEE) models. The β coefficients of the group-by-disease interaction terms, estimated from the GEE models, indicate the difference in outcome change in each disease relative to the reference group (major trauma) between pre- and post-CHEC. A positive group-by-disease interaction β coefficient indicates that the outcome change for that disease is greater compared to the reference group. All analyses were performed using SAS version 9.4. All statistical tests were 2-sided; p-value < 0.05 was considered statistically significant. RESULTS Participants characteristics During the study period, we analyzed emergency presentations related to four CTSDs, originally encompassing 288,443 cases. Exclusion criteria included the diagnosis of CTSD before 2005 (n = 99,768), patients with transient ischemic attack or intracranial hemorrhage (n = 878), non-STEMI (n = 1,315), and individuals with major traumas defined by ICD codes that did not necessitate a rescue operation or ICU admission (n = 142,446), and cases lacking hospital or living area information, or where the hospital's volume of CTSD was less than five visits per year (n = 673). These criteria refined the total sample size to 43,363 cases. Considering the extended period before the policy intervention, this research adopted a 1:1 PSM technique, resulting in a final matched sample of 9,923. The flow chart and baseline table (Fig. 1 ) display the initial count of emergency CTSD patients and the numbers post-PSM, broken down by each of the four diseases. Table 2 presents the PSM of CTSD participants before and after the PSM. After the matching process, each variable baseline characteristic demonstrates almost complete congruity. Additionally, uniformity is achieved within each disease sub-group post-matching (supplementary table 1 ) . The distribution for each disease pre and post-PSM was as follows: AIS (n = 2,895), STEMI (n = 723), septic shock (n = 5,441), and major trauma (n = 864). Septic shock was observed to be more pervasive, representing 54% of all cases. The patient population was male-dominated (56%), with the most represented age group being those aged 65 and above (60%). Nearly three-fourths of the incidents involving CTSD were handled in hospitals that were categorized as moderate (40%) or severe (35%) levels. Consultant specialists delivered care accounted for two-thirds of cases. Impact of CHEC policy on overall and individual four CTSDs’ process and outcome before and after implementation In examining individual diseases, primary diagnostic indicators for AIS, septic shock, and major trauma decreased post-intervention, while only STEMI increased (Table 3 ). Diagnostic fees increased for AIS, STEMI, and major trauma but decreased for septic shock. A similar trend was observed in primary treatment indicators, with AIS and STEMI increased and septic shock and major trauma decreased. In contrast, medical orders showed a universal decline. Upward transfer rates rose for AIS and major trauma, while a decrease in STEMI and septic shock. Regarding outcome indicators, short-term mortality rates displayed a universal decline, and long-term mortality rates followed suit, except for AIS, which showed an increase. The medical expenses were higher for AIS and STEMI but lower for septic shock and major trauma. Table 3 Comparative analysis of the individual and overall impact of CHEC policy effects for critical time-sensitive diseases Change between pre- & post-CHEC Before CHEC policy (N = 9923) After CHEC policy (N = 9923) Multivariable model Β [95% CI] p-value Outcome Acute ischemic stroke a STEMI b Septic shock c Major trauma d Process quality Major diagnosis indicator -3.52 0.55 -2.28 -3.01 8682 (87.49%) 8434 (84.99%) -0.21 (-0.29 to -0.13) < .0001 Diagnostic fees 460.66 2746.6 -44.8 762.85 7166.94 (10018.69) 7543.31 (10833.26) 376.37 (92.42 to 660.33) 0.0094 Major treatment indicator 0.62 2.07 -1.25 -3.12 4334 (43.68%) 4272 (43.05%) -0.03 (-0.07 to 0.02) 0.2415 Medical orders per case -0.93 -4.21 -9.67 -16.13 102.13 (109.19) 94.84 (101.07) -7.29 (-10.09 to -4.48) < .0001 Upward transfer rate 0.59 -1.24 -0.1 2.43 148 (1.49%) 172 (1.73%) 0.15 (-0.06 to 0.37) 0.1648 Outcomes 30-days mortality -0.04 -1.52 -1.84 -1.27 1631 (16.44%) 1508 (15.20%) -0.09 (-0.17 to -0.02) 0.0137 One-year mortality 0.28 -0.83 -3.64 -0.58 3242 (32.67%) 3041 (30.65%) -0.09 (-0.15 to -0.04) 0.0013 Total medical expense per case 3616.16 11219.01 -11059.09 -13056.54 100875.96 (192912.94) 95547.60 (176487.79) -5328.35 (-10387.10 to -269.60) 0.0390 a Acute ischemic stroke major diagnosis indicator: head image; major treatment indicator: IV-tPA thrombolysis b ST-elevation MI major diagnosis indicator: EKG; major treatment indicator: PCI c Septic shock major diagnosis indicator: culture; major treatment indicator: antipathogen medication d Major trauma major diagnosis indicator: CT or MRI or sonography study; major treatment indicator: rescue operation CHEC: Categorization Hospital Emergency Capability; CI: confident confidence interval; EKG: Electrocardiography; IV-tPA: intravenous tissue plasminogen activator; PCI: Percutaneous coronary intervention; STEMI: ST-segment elevation myocardial infarction In assessing the overall policy effects on four CTSD cohorts, notably, the primary diagnosis indicator significantly decreased by 0.21 percentage points (95% CI: -0.29 to -0.13, p < 0.0001); medical orders per case dropped by an average of 7.29 items (95% CI: -10.09 to -4.48, p < 0.0001). In comparison, diagnostic fees demonstrated an average increase of $ 376.37 (95% CI: 92.42 to 660.33, p = 0.0094). The 30-day mortality rate saw a notable reduction of 0.09 percentage points (95% CI: -0.17 to -0.02, p = 0.0137), one-year mortality significantly decreased by 0.09 percentage points (95% CI: -0.15 to -0.04, p = 0.0013) and medical expense per case significantly decreased by $ 5328.35 (95% CI: -10387.10 to -269.60, p = 0.0390). Association of CHEC policy with process and outcomes quality in four CTSDs In model 1, we analyze the variations in several indicators before and after the implementation of CHEC for individual diseases. For AIS, following the implementation of CHEC, there was a significant decrease in major diagnosis indicators by 0.23 percentage points (95% CI: -0.36 to -0.10, p = 0.0005) (Table 4 ). Conversely, major treatment indicator experienced a significant rise of 0.57 percentage points (95% CI: 0.07 to 1.07, p = 0.0263), and upward transfer rate also significantly increased by 0.52 percentage points (95% CI: 0.02 to 1.03, p = 0.0399). Meanwhile, there was also a trend of increasing diagnostic costs, with a rise of $ 460.66 (95% CI: -3.44 to 924.76, p = 0.0517). For STEMI, the diagnostic fees significantly increased by $ 2746.59 (95% CI: 1141.67 to 4351.51, p = 0.0008). When examining septic shock, the major diagnosis indicator saw a significant decrease of 0.25 percentage points (95% CI: -0.37 to -0.12, p < 0.0001) following the introduction of CHEC. 30-day mortality decreased by 0.11% (95% CI: -0.20 to -0.02, p = 0.0189), and 1-year mortality decreased by 0.15% (95% CI: -0.22 to -0.07, p = 0.0001). Additionally, medical orders significantly dropped by 9.67 items (95% CI: -13.99 to -5.35, p < 0.0001), and average medical expenses significantly fell by $ 11059.10 (95% CI: -18603.60 to -3514.55, p = 0.0041). Finally, in the case of major trauma, post-CHEC implementation, the average medical orders significantly decreased by 16.13 items (95% CI: -25.32 to -6.94, p = 0.0006). Table 4 Association of CHEC policy with process and outcomes quality in four critical time-sensitive diseases Before CHEC policy After CHEC policy Change between pre & post CHEC Model 1 α (95% CI) a P value Model 2 β (95% CI) b P value Acute ischemic stroke a (N = 2895) Major diagnosis indicator 2391 (82.59%) 2289 (79.07%) -3.52 -0.23 (-0.36 to -0.10) 0.0005 -0.06 (-0.32 to 0.20) 0.6635 Diagnostic fees 6050.63 (10934.56) 6511.29 (6895.16) 460.66 460.66 (-3.44 to 924.76) 0.0517 -302.19 (-1419.15 to 814.77) 0.5959 Major treatment indicator 24 (0.83%) 42 (1.45%) 0.62 0.57 (0.07 to 1.07) 0.0263 0.77 (0.21 to 1.33) 0.0068 Medical orders per case 70.44 (71.03) 69.51 (74.08) -0.93 -0.93 (-4.64 to 2.78) 0.6228 15.20 (5.28 to 25.11) 0.0027 Upward transfer rate 25 (0.86%) 42 (1.45%) 0.59 0.52 (0.02 to 1.03) 0.0399 0.21 (-0.39 to 0.81) 0.4929 Short-term mortality (30 days) 140 (4.84%) 139 (4.80%) -0.04 -0.01 (-0.25 to 0.23) 0.951 0.11 (-0.27 to 0.49) 0.5687 Long-term mortality (365 days) 441 (15.23%) 449 (15.51%) 0.28 0.02 (-0.12 to 0.16) 0.7668 0.06 (-0.22 to .033) 0.6874 Total medical expense per case 58995.28 (161260.94) 62611.44 (113039.39) 3616.16 3616.15 (-3524.26 to 10756.56) 0.3209 16672.69 (-3581.75 to 36927.12) 0.1067 ST-segment elevation MI b (N = 723) Major diagnosis indicator 671 (92.81%) 675 (93.36%) 0.55 0.09 (-0.32 to 0.49) 0.6767 0.26 (-0.21 to 0.72) 0.2767 Diagnostic fees 6269.07 (11425.79) 9015.67 (19940.85) 2746.6 2746.59 (1141.67 to 4351.51) 0.0008 1983.75 (84.28 to 3883.21) 0.0407 Major treatment indicator 240 (33.20%) 255 (35.27%) 2.07 0.09 (-0.11 to 0.30) 0.3824 0.30 (-0.03 to 0.62) 0.0729 Medical orders per case 92.31 (90.33) 88.10 (88.05) -4.21 -4.21 (-13.14 to 4.72) 0.3556 11.92 (-0.90 to 24.73) 0.068 4 Upward transfer rate 39 (5.39%) 30 (4.15%) -1.24 -0.28 (-0.76 to 0.21) 0.2654 -0.59 (-1.18 to -0.001) 0.0496 Short-term mortality (30 days) 144 (19.92%) 133 (18.40%) -1.52 -0.10 (-0.36 to 0.16) 0.4531 -0.02 (-0.37 to 0.41) 0.9247 Long-term mortality (365 days) 217 (30.01%) 211 (29.18%) -0.83 -0.04 (-0.26 to 0.18) 0.7227 -0.01 (-0.33 to .032) 0.9745 Total medical expense per case 108481.43 (144206.04) 119700.44 (166898.18) 11219.01 11219.00 (-4953.90 to 27391.90) 0.174 24275.54 (-640.71 to 49191.78) 0.0562 Septic shock c (N = 5441) Major diagnosis indicator 4941 (90.81%) 4817 (88.53%) -2.28 -0.25 (-0.37 to -0.12) < .0001 -0.08 (-0.33 to 0.18) 0.5573 Diagnostic fees 7360.02 (9079.06) 7315.22 (10611.73) -44.8 -44.80 (-412.26 to 322.66) 0.8111 -807.65 (-1888.03 to 272.74) 0.1429 Major treatment indicator 3894 (71.57%) 3826 (70.32%) -1.25 -0.06 (-0.14 to 0.02) 0.1434 0.14 (-0.12 to 0.41) 0.2824 Medical orders per case 120.70 (124.08) 111.03 (112.49) -9.67 -9.67 (-13.99 to -5.35) < .0001 6.45 (-3.70 to 16.61) 0.2129 Upward transfer rate 21 (0.39%) 16 (0.29%) -0.1 -0.27 (-0.93 to 0.38) 0.4125 -0.59 (-1.32 to 0.15) 0.1165 Short-term mortality (30 days) 1234 (22.68%) 1134 (20.84%) -1.84 -0.11 (-0.20 to -0.02) 0.0189 -0.01 (-0.29 to 0.31) 0.9539 Long-term mortality (365 days) 2398 (44.07%) 2200 (40.43%) -3.64 -0.15 (-0.22 to -0.07) 0.0001 -0.11 (-0.36 to 0.13) 0.3588 Total medical expense per case 115832.23 (206098.13) 104773.14 (198516.37) -11059.09 -11059.10 (-18603.60 to -3514.55) 0.0041 1997.45 (-18403.00 to 22397.86) 0.8478 Major trauma d (N = 864) Major diagnosis indicator 679 (78.59%) 653 (75.58%) -3.01 -0.17 (-0.39 to 0.05) 0.1359 Major trauma as reference group Diagnostic fees 10442.74 (10411.94) 11205.59 (11322.87) 762.85 762.85 (-253.13 to 1778.82) 0.1411 Major treatment indicator 176 (20.37%) 149 (17.25%) -3.12 -0.21 (-0.46 to 0.05) 0.1086 Medical orders per case 99.56 (103.19) 83.43 (93.90) -16.13 -16.13 (-25.32 to -6.94) 0.0006 Upward transfer rate 63 (7.29%) 84 (9.72%) 2.43 0.31 (-0.02 to 0.65) 0.0650 Short-term mortality (30 days) 113 (13.08%) 102 (11.81%) -1.27 -0.12 (-0.41 to 0.17) 0.4286 Long-term mortality (365 days) 186 (21.53%) 181 (20.95%) -0.58 -0.03 (-0.27 to 0.20) 0.7710 Total medical expense per case 140654.57 (215834.78) 127598.03 (194545.71) -13056.54 -13056.50 (-32010.60 to 5897.53) 0.1770 α Model 1: Compare the differences between post-policy and pre-policy. β Model 2: The model adjusted estimates for an interaction between a binary measure of CHEC policy (post-implementation vs. pre-implementation) and critical time-sensitive diseases compared with major trauma (e.g.. acute ischemic stroke vs. major trauma; ST-segment elevation MI vs. major trauma; septic shock vs. major trauma) CHEC: Categorization Hospital Emergency Capability. a Acute ischemic stroke major diagnosis indicator: head image; major treatment indicator: IV-tPA thrombolysis b ST-elevation MI major diagnosis indicator: EKG; major treatment indicator: PCI c Septic shock major diagnosis indicator: culture; major treatment indicator: antipathogen medication d Major trauma major diagnosis indicator: CT or MRI or sonography study; major treatment indicator: rescue operation CHEC: Categorization Hospital Emergency Capability; CI: confident confidence interval; EKG: Electrocardiography; IV-tPA: intravenous tissue plasminogen activator; S TEMI: ST-segment elevation myocardial infarction; PCI: Percutaneous coronary intervention In Model 2 results from the GEE model highlight the CHEC policy's varied effects across different disease outcomes. Compared to the major trauma, AIS exhibited a significant increase in the major treatment indicator (Interaction β = 0.77; 95% CI = 0.21 to 1.33; p = 0.0068) and medical orders (Interaction β = 15.20; 95% CI = 5.28 to 25.11; p = 0.0027) between pre- & post-CHEC. Meanwhile, STEMI demonstrated diagnostic fees significantly increased (Interaction β = 1983.75; 95% CI = 84.28 to 3883.21; p = 0.0407) and significant decrease in upward transfer rate (Interaction β=-0.59; 95% CI=-1.18 to -0.001; p = 0.0496) compared to the major trauma, additionally, there was a trend of increasing major treatment indicators (Interaction β = 0.30; 95% CI: -0.03 to 0.62, p = 0.0729), medical orders (Interaction β = 11.92; 95% CI=-0.90 to 24.73; p = 0.0684), and medical expense (Interaction β = 24275.54; 95% CI=-640.71 to 4991991.78; p = 0.0562). In septic shock, compared to major trauma, there were no significant differences observed in either process or outcome quality. DISCUSSION Evaluating the impact of a health policy exposed to an entire nationwide population presents challenges due to the absence of a control group. This restriction only permits pre- and post-implementation comparisons and complicates understanding the potential interplay of various disease shifts under the influence of policy is a significant challenge. Nevertheless, our study aims to thoroughly analyze such a policy, investigating its differential impacts across various time-sensitive diseases. By carefully examining each disease's guidelines, we discern the unique effects of the policy on each one. This process allows us to identify the groups affected by the policy and those serving as relatively unaffected counterfactuals, thereby giving us insights into the policy's impacts and difference-in-differences comparison. The overall impact of the CHEC policy Our study utilized real-world data to illustrate that the enforcement of the CHEC policy has led to a substantial reduction in overall medical orders and major diagnostic indicators. Nevertheless, there has been a conspicuous increase in diagnostic fees; this could be potentially attributed to stringent time indicators, as expedited diagnoses are crucial. Consequently, there could be an augmentation in the charges associated with other differential diagnoses. Moreover, the implementation of the CHEC policy has also resulted in a decrease in mortality rates, ultimately contributing to a reduction in overall medical expenses. Thus, the effectiveness and efficiency of the CHEC policy underscore its dual ability to decrease costs while improving patient outcomes. However, a crucial question remains unanswered: Why has the CHEC policy been effective in significantly reducing medical costs and mortality rates, even when the overall major diagnosis indicator has declined and no substantial change has been observed in treatment quality indicators? The following sections will delve further into diseases' individual and interactive effects to provide an analysis. Individual disease response to CHEC policy Implementing the CHEC policy aims to improve the quality of the diagnosis and treatment process, enable rapid diagnosis and timely treatment, efficiently reduce unnecessary examinations, and streamline management, leading to lower diagnostic cost and medical orders. As hospitals improve their capabilities in emergency medical care, the need for patient transfers is anticipated to reduce. Through the provision of timely and appropriate treatment, the ultimate objectives of the policy are to lower mortality rates and curtail overall healthcare utilization. When examining individual disease policy effects, in the case of AIS, we observed a decrease in diagnostic indicators, which might be attributable to an increase in patient upward transfer rates and possibly suggests that hospital physicians may be more inclined to transfer AIS patients to higher-level hospitals to increase their chances of receiving thrombolytic treatment 28 . Therefore, it presents a significant increase in the primary treatment indicators. There has been a substantial increase in diagnostic fees. c seemed to be adversely affected by the policy intervention, with increased medical orders, which led to increased diagnostic costs, upper transfer rates, long-term mortality rates, and overall medical costs. In the context of STEMI, there is an increase in diagnostic fees and a tendency to increase diagnostic and treatment indicators while transfer rates have decreased. This suggests a more intensive treatment approach for STEMI, correlating with decreased mortality rates. Conversely, there was a decrease in major diagnostic and treatment indicators and medical orders for septic shock and major trauma exhibited a decline in primary diagnosis indicators and primary treatment indicators also saw an increase in transfer rates. The differential impact on diagnostic costs, treatment indicators, transfer rates, long-term mortality rates, and medical costs among different diseases highlights the differential influence of policy effects. This could indirectly suggest the impact of the policy spotlight effect on AIS and STEMI conditions, while septic shock and major trauma may deteriorate due to the absence of policy monitoring. Association of diseases with policy spotlight effect Given the setting of this study, all emergency medical services are exposed to the CHEC policy, potentially subjecting emergency medical personnel to effects ranging from the Hawthorne effect and the policy spotlight effect. To differentiate the behaviors of healthcare providers under the influence of the Hawthorne effect or the policy spotlight effect, we selected AIS and STEMI diseases with well-established guidelines and time-based quality indicators under the CHEC policy. Septic shock, a disease with well-established guidelines but without specific time-based quality indicators, and major trauma, a disease without well-established guidelines or specific time-based quality indicators, were selected as external controls. Based on these natural quasi-experimental conditions, we hypothesize that the response observed may suggest varying levels of awareness among emergency care providers 29 . The Hawthorne effect demonstrates that the productivity of individuals in an experiment may increase simply because they are being observed, reflecting the influence of human attention and intervention on behavior 30 . The policy spotlight effect may be intensified by factors such as time constraints, ambiguous symptom patterns, and time-based quality surveillance indicators, prompting emergency care providers to unconsciously adopt selective behaviors concentrating on specific diseases according to policy targets 30 . Consequently, diseases not prioritized by the policy, such as septic shock and major trauma in this study, may experience a significant decrease in process quality and outcome. When using major trauma as a reference group, both AIS and STEMI, diseases monitored by time-based indicators, exhibited an increase in the medical orders corresponding to treatment indicators. Moreover, there was an apparent increase in diagnostic and overall healthcare expenses in the STEMI cohort and a decreased transfer rate. In contrast, no significant changes were observed for the septic shock group, which was not under time indicator monitoring when compared to the major trauma reference group. These contrasting developments illustrate the divergent trends between time-based and non-time-based monitored time-sensitive diseases. We attribute these differences to the potential influence of the policy spotlight effect. The two diseases, AIS and STEMI, which are under the spotlight effect of policy, have shown an increase in the overall medical orders, diagnostic fees and medical expenses compared to the reference group, subsequently increasing the treatment indicators. As hypothesized, the septic shock group did not demonstrate significant changes in comparison to the major trauma reference group, underscoring the influence of time-based monitoring on these particular outcomes. Discrepancy and unintended consequences While both AIS and STEMI appeared to be influenced by the policy spotlight effect, they showed entirely opposite results in diagnostic indicators and transfer rates. This could potentially be attributed to the inherent differences between AIS and STEMI. For instance, only about 1%~2% of all AIS patients underwent primary treatment with intravenous thrombolytic agents, which is far lower than STEMI major treatment rate. Unexpectedly, in less policy spotlight affected groups such as septic shock and major trauma, despite significant decreases in primary diagnostic indicators, total medical orders, and diagnostic fees, there were unexpected reductions in 30-day and one-year mortality rates and overall medical expenses per event. These findings may align with the American 'less is more' initiative, suggesting that minimizing excessive diagnoses and unnecessary procedures could potentially enhance patient outcomes and yield cost reductions. Policy implications Currently, many emergency care policies implement time-based criteria 31–33 , such as the UK’s 4-hour standard 33 and the four-hour rule 31 . Australia's experience showed that an emergency care policy using time-based criteria can improve emergency congestion without increasing the rate of ED re-visits. However, in New Zealand, a policy in effect during 2006–2012 dictated that emergent patients must be hospitalized, transferred, or discharged within six hours of visiting the ED. After emergency care policy intervention, the length of ED stay decreased while the treatment outcomes of acute myocardial infarction, severe septic shock, and acute appendicitis did not improve significantly 32 . Similarly, after the Canadian Emergency Observation Reduction Program implementation, the length of ED stay decreased while the treatment quality indicators for acute myocardial infarction, asthma, and upper limb fractures can only be treated in time for the above-mentioned time-sensitive diseases during the non-congested emergency period 34 . Emergency care quality is closely related to the practices of medical care providers 17 . Policymakers and medical care providers must reconsider the conventional emphasis on time-based process indicators. This approach may have unintended consequences for diseases that are outside the "spotlight" of rigid time-sensitive evaluation. Instead, a broader and more nuanced evaluation of emergency care quality is needed to incorporate the complexity and ambiguity of various time-sensitive diseases that emergency care providers often manage 35 . Therefore, we propose replacing time-based indicators with performance-based indicators, such as those exemplified in the NHS's Best Practice Tariff policy 36 . This shift towards 'best practice' can create a more flexible approach that goes beyond merely relying on time-based or diagnosis-based practices 37 . Such a transition is crucial, as an excessive reliance on diagnostic tests may lead to ED crowding, a decline in emergency care quality, and increased safety issues 38 . The complexities of emergency care, including diverse patient origins and multiple routes to specialized units like stroke units, demand more sophisticated quality measures. These may encompass factors such as the presence of a consultant and the need for critical care admissions. In the case of specific procedures like diagnostic angiography and PCI (where indicated) within 72 hours of admission with NSTEMI, rigid time-based quality indicators may be especially inadequate. Performance-based measures, such as those used in the "Best Practice Tariff," reward best practices in care delivery and foster a higher standard of healthcare. Strengths To the best of our knowledge, this was the first nationwide retrospective cohort study using data on different CTSDs the effects of a policy exposed to an entire population. In the scenario that often presents challenges due to the lack of a control group, we identified differential impacts on various diseases through detailed analysis, establishing affected and counterfactual groups. This process allowed us to explore pre- and post-intervention effects, comprehending the policy's overall impact. This structure provides essential insights into the impact of hospital categorization on the processes and outcomes of emergency care. Further, it offers crucial empirical evidence that could be instrumental in refining policies and optimizing emergency care systems. This study explored emergency care providers' behaviors under time constraints and how they interacted with strict time-based quality surveillance indicators and “get with the guidelines” adherence. Limitations Because the study data were retrieved from a secondary dataset of insurance claims not a randomized controlled trial, this study has the following limitations: ( 1 ) our analysis lacked detailed information on time-related quality indicators, such as door-to-evaluation and door-to-treatment times measurement; ( 2 ) Our study's sample definitions were based on emergency primary diagnosis, making the emergency ICD code diagnosis potentially imprecise highlighting the need for a more comprehensive approach 39 ( 3 ) The effectiveness of emergency medical care often hinges on the cooperation between emergency physicians and the consulting physicians. This study is unable to dissect this relationship explicitly. Evaluating how emergency physicians and consulting physicians collaborate and coordinate is a key focus for future research.; ( 4 ) we need more qualitative research to elucidate the psychological mechanisms through which the policy spotlight effect influences emergency care providers' behaviour. CONCLUSION Our study used real-world evidence to demonstrate that CHEC policy implementation demonstrates a dual capability to reduce costs and improve patient outcomes. Health policy spotlight effect resulting in a disproportional improvement in disease guideline adherence rates and process quality of CTSD with time-based surveillance indicators. In contrast, disease entities not fully encompassed in the surveillance indicators may be jeopardized with a decrease in diagnosis and treatment process quality, thereby highlighting an unintended consequence of the policy. We propose a transition from time-based process indicators to performance-based indicators that may potentially improve efficiency and quality of care. Abbreviations AIS acute ischemic stroke ASMD absolute standardized mean difference CCI Charlson Comorbidity Index CHEC categorization of hospital emergency capability CI confident confidence interval CT computed tomography CTSD critical time-sensitive diseases DID difference-in-differences ED emergency department EKG electrocardiography ESI Emergency Severity Index ICD-9-CM International Classification of Diseases, 9th Revision, Clinical Modification ICU intensive care unit ISS Injury Severity Score IV-tPA intravenous tissue plasminogen activator JMIR Journal of Medical Internet Research LHID2005 NHI 2005 Longitudinal Health Insurance Database MRI Magnetic Resonance Imaging NHI National Health Insurance NIHSS National Institute of Health Stroke Scale PCI percutaneous coronary intervention PSM propensity score matching SMD standardized mean difference STEMI ST-segment elevation myocardial infarction Declarations Ethics approval and consent to participate This study approved by Institutional Review Board of National Yang-Ming University-YM107035E on May, 5 2018. In accordance with regulations of the National Health Research Institutes, patient identification information was anonymized, such that informed consent was not required. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available from Taiwan National Health Insurance Research Database but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the academic request and with permission of Taiwan National Health Insurance Administration. Competing interests The authors declare that they have no competing interests. Funding The authors declare that they have no funding. Authors' contributors CY Lin took a lead role in conceptualizing the study and writing the original draft, and was responsible for formal data analysis. CY Lin also verified the underlying data in the manuscript. CC Liu contributed to study design, data curation, and formal data analysis, and was in charge of data collection. CC Liu YT Huang ensured accurate data analysis and interpretation, and verified the manuscript's underlying data. YC Lee supervised the study, validated the results, and significantly contributed to reviewing and editing the manuscript. All authors participated in developing the study concept and design, analysing and interpreting data, and preparing the manuscript. We have all approved the final manuscript and agree to be accountable for all aspects of the work, promising to appropriately investigate and resolve any question related to the work's accuracy or integrity. Acknowledgments We acknowledge the Taiwan National Health Insurance Research Database, which was provided by the National Health Insurance Administration and is managed by National Health Research Institutes. References Counselman FL, Borenstein MA, Chisholm CD, et al. 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Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYFAC5oYDQEIOxDzwgDgtjGAtxmAtCcRqAVmVCCIZiNLCPyOx8XBhjnX6/LDDD4G22MnpNhDQInEjseHwzG3puRtvpxkAtSQbmx0goMVAAqiFd9vh3I2zE0BaDiRuI1ZLuuHs9A+kaUmQl84h0haJMw9BWtINN0jnFBxIMCDCL/ztyYc/826zlpefnb75w4cKOzmCWhgEEqAuBKs0IKQcbA3UUPkGYlSPglEwCkbBiAQApZ5KJfyQvxkAAAAASUVORK5CYII=","orcid":"","institution":"Taipei City Hospital Linsen Chinese Medicine Branch","correspondingAuthor":true,"prefix":"","firstName":"Chih-Yuan","middleName":"","lastName":"Lin","suffix":""},{"id":332016732,"identity":"6644e8e6-3867-4827-8041-4a061557c2bb","order_by":1,"name":"Chih-Chin Liu","email":"","orcid":"","institution":"Asia University","correspondingAuthor":false,"prefix":"","firstName":"Chih-Chin","middleName":"","lastName":"Liu","suffix":""},{"id":332016733,"identity":"fd78bc50-74e2-4c21-8490-9d46062637ac","order_by":2,"name":"Yu-Tung Huang","email":"","orcid":"","institution":"Chang Gung Memorial Hospital Linkou Main Branch","correspondingAuthor":false,"prefix":"","firstName":"Yu-Tung","middleName":"","lastName":"Huang","suffix":""},{"id":332016734,"identity":"74713961-5b30-4857-8bfb-b836413511f3","order_by":3,"name":"Yue-Chune Lee","email":"","orcid":"","institution":"Chang Gung University","correspondingAuthor":false,"prefix":"","firstName":"Yue-Chune","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2024-07-06 15:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4697511/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4697511/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62151678,"identity":"cbfcf819-a16e-418c-b1a1-9c6559408be6","added_by":"auto","created_at":"2024-08-09 20:45:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34929,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4697511/v1/906f98a9038f9cba2dfdde2d.png"},{"id":75032040,"identity":"46398b35-6b3f-4848-984a-4bd4d7b46caa","added_by":"auto","created_at":"2025-01-29 16:01:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2017919,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4697511/v1/3d141921-be93-4462-b97c-1e960181612f.pdf"},{"id":62151680,"identity":"c8e58ba3-a2c0-4751-824a-721fb5c5a843","added_by":"auto","created_at":"2024-08-09 20:45:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":34144,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistv4combinedBMC.docx","url":"https://assets-eu.researchsquare.com/files/rs-4697511/v1/f6f042380be5395f4afd52e8.docx"},{"id":62153327,"identity":"23f456d4-de76-42b6-ad50-10c206381902","added_by":"auto","created_at":"2024-08-09 20:53:12","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":31997,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-4697511/v1/7080b76b375df6201049beed.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Health Policy Spotlight Effects on Critical Time-Sensitive Diseases: Evidence from Taiwan Categorization of Hospital Emergency Capability Policy","fulltext":[{"header":"Highlights","content":"\u003ch3\u003eWhat is already known on this subject?\u003c/h3\u003e\n\u003cp\u003e►\u0026nbsp;Emergency care is a symptom-driven profession under significant uncertainty and time pressure.\u003c/p\u003e\n\u003cp\u003e►Critical time-sensitive diseases refer to life-threatening illnesses or injuries that require immediate emergency care, where rapid intervention is paramount to mitigate morbidity and mortality.\u003c/p\u003e\n\u003cp\u003e►\u0026nbsp;The hospital emergency capability categorization policy aims to classify hospital care capacities, guide emergent patient transport to the nearest appropriate facilities prevent preventable deaths.\u003c/p\u003e\n\u003cp\u003eThe hospital emergency capability categorization policy aims to classify hospital care capacities, guide emergent patient transport to the nearest appropriate facilities prevent preventable deaths.\u003c/p\u003e\n\u003ch3\u003eWhat this study adds?\u003c/h3\u003e\n\u003cp\u003e►The categorization of hospital emergency capability policy implementation demonstrates a dual capability to reduce costs and improve patient outcomes.\u003c/p\u003e\n\u003cp\u003e►\u0026nbsp;Disease entities not fully encompassed in the surveillance indicators may be jeopardized with a decrease in diagnosis and treatment process quality.\u003c/p\u003e\n\u003cp\u003e► Health policy spotlight effect exists in critical time-sensitive diseases with time-based quality indicators, resulting in a disproportional improvement in disease guideline adherence and process quality.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eEmergency care is a symptom-driven profession delivered by emergency department (ED) physicians under significant time pressure and uncertainty \u003csup\u003e1\u003c/sup\u003e. Emergency care providers tentatively diagnose specific diseases based on at least 70 common disease patterns encountered in the ED, often dedicating only 3% of their time for diagnosis \u003csup\u003e2\u003c/sup\u003e, despite this step representing the most important in terms of cost \u003csup\u003e2\u003c/sup\u003e. Furthermore, medical care providers are the most influential decision-makers and drive approximately 70\u0026ndash;80% of medical utilization \u003csup\u003e3\u003c/sup\u003e. Critical time-sensitive diseases (CTSD) refer to life-threatening illnesses or injuries that require immediate emergency care, where rapid intervention is paramount to mitigate morbidity and mortality \u003csup\u003e4\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Agency for Healthcare Research and Quality proposed the concept of time-sensitive diseases \u003csup\u003e5\u003c/sup\u003e using scientific data to maintain up-to-date guidelines and launched the \"get with the guidelines\" campaign to establish it as the basis for surveillance indicators of process and outcome quality \u003csup\u003e6\u003c/sup\u003e. Critical time-sensitive disease (CTSD) refers to life-threatening illnesses or injuries that require immediate emergency care, where rapid intervention is paramount to mitigate morbidity and mortality \u003csup\u003e4\u003c/sup\u003e. The various guidelines for managing time-sensitive events emphasize the crucial importance of time. In the context of acute ischemic stroke (AIS), the \"time is brain\" \u003csup\u003e7\u003c/sup\u003e goal focuses on the timely administration of thrombolytic therapy; in ST-segment elevation myocardial infarction (STEMI), the \"time is muscle\" goal focuses on early reperfusion \u003csup\u003e8\u003c/sup\u003e; in septic shock events, the \"early goal\" focuses on early resuscitation \u003csup\u003e9\u003c/sup\u003e; and in major trauma cases, the \"golden hour\" goal focuses on the window of opportunity in which patients can undergo rescue operations \u003csup\u003e10\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAmerican Medical Association issued the categorization of hospital emergency capability (CHEC) guidelines \u003csup\u003e11\u003c/sup\u003e to classify hospitals according to their emergency care capabilities, thereby regionalization and providing emergency medical services with references to transport emergent patients to the nearest appropriate hospitals \u003csup\u003e12\u003c/sup\u003e, aiming to reduce preventable deaths. Most studies investigating the effects of this categorization, designation, and regionalization policy reported positive findings \u003csup\u003e13\u0026ndash;16\u003c/sup\u003e. However, studies mainly focused on a single disease entity \u003csup\u003e13\u0026ndash;16\u003c/sup\u003e or region\u003csup\u003e15 16\u003c/sup\u003e. The CHEC policy often implements rigid time-based surveillance indicators. These indicators can affect disease-specific guideline adherence in clinical practice because they may reshape the behaviors of ED medical providers \u003csup\u003e17\u003c/sup\u003e. This phenomenon is related to the so-called \"policy spotlight effect\", which influences medical care providers' assessment of how others perceive them \u003csup\u003e18\u003c/sup\u003e. More specifically, the policy spotlight effect refers to how medical care providers perceive how policymakers interpret surveillance indicators and adjust their process-related behaviors accordingly \u003csup\u003e19\u003c/sup\u003e. Current emergency care policies often use time-based criteria as process quality indicators, which may exacerbate the policy spotlight effect \u003csup\u003e18\u003c/sup\u003e; however, the unintended effects or safety concerns generated by this effect remain unclear. Therefore, this study targeted four CTSDs: AIS, STEMI, septic shock, and major trauma\u003csup\u003e10\u003c/sup\u003e. Our research presents the hypothesis that emergency care providers might inadvertently give more attention to diseases under active surveillance while potentially neglecting those not thoroughly incorporated in this observation. This focus might be based on their perception of observer expectations \u003csup\u003e20\u003c/sup\u003e. The primary aim of our research is to examine the effects of the CHEC policy on process quality and outcomes for CTSDs, addressing three research questions: 1. how does the CHEC policy impact the quality of diagnosis, treatment, and outcomes for these diseases? 2. Does a policy spotlight effect exist in this context? 3. What are the potential consequences of this policy spotlight effect?\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSetting, study design and data source\u003c/h2\u003e \u003cp\u003eTaiwan's National Health Insurance (NHI) is a single-payer compulsory social insurance system that primarily operates on a fee-for-service basis. This study is based on the NHI 2005 Longitudinal Health Insurance Database (LHID2005), which contains one million random cases, including medical records and hospital information, collected since 1995. The LHID2005 was validated as representative of medical utilization, as well as of diagnosis and treatment process and outcome quality for CTSDs \u003csup\u003e21\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis nationwide observational study investigates the impact of the CHEC policy, initiated in August 2009, which integrated 190 hospitals into a network focusing on acute conditions like stroke, myocardial infarction, major trauma, and perinatal care \u003csup\u003e22\u003c/sup\u003e. We divided our analysis into two periods: pre-CHEC (August 1, 2005 - July 31, 2009) and post-CHEC (August 1, 2009 - July 31, 2011). This division aims to distinctly assess the CHEC policy's effects, distinctly from the ED Quality Improvement Plan introduced in 2012. Well-established guidelines exist for AIS, STEMI, and septic shock, whereas the guidelines for major trauma are continuously evolving due to the variability in injury mechanisms, locations, and severity. Moreover, AIS and STEMI events are stringently monitored under the CHEC policy with specific time-based quality indicators, whereas septic shock and major trauma events are not (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Thus, we selected major trauma events as a reference for our study because they were not monitored under the CHEC policy with rigid indicators. The study uses a quasi-experimental design to evaluate the quality of CTSDs care. We adopted pre and post-implementation of a CHEC policy, using 1:1 propensity score matching (PSM) to control for confounding variables. To estimate the association of the CHEC policy on process and outcomes for AIS and STEMI, we employed a difference-in-differences (DID) estimation approach. For the counterfactual, we used major trauma cases unexposed to the clinical guideline or CHEC policy time-based quality indicators as a comparison group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCritical time-sensitive diseases and categorization hospital emergency capability policy indicators in Taiwan\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality indicator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcute ischemic stroke\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eST-segment elevation MI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSeptic shock\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMajor trauma\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGuidelines development\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWell developed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWell developed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWell developed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDeveloping\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMajor diagnosis indicator\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBrain imaging (CT\u0026amp;MRI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEKG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBlood Culture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImage Study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMajor treatment indicator\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eiv-TPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAntibiotics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRescue operation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGuideline\u0026rsquo;s major goal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEarly thrombolysis\u003c/p\u003e \u003cp\u003e(Time is brain)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEarly reperfusion\u003c/p\u003e \u003cp\u003e(Time is muscle)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGoal-directed therapy\u003c/p\u003e \u003cp\u003e(Early goal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRescue operation\u003c/p\u003e \u003cp\u003e(Golden hour)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGuideline time-based criteria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 min\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u0026ndash;6 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 hour\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCHEC policy indicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStroke team\u003c/p\u003e \u003cp\u003eNIHSS score evaluation\u003c/p\u003e \u003cp\u003eIntravenous t-PA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCI team\u003c/p\u003e \u003cp\u003eGive Aspirin and Clopidogrel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICU critical care team\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTrauma team\u003c/p\u003e \u003cp\u003eISS evaluation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCHEC policy time-based criteria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeurologist consultation\u0026thinsp;\u0026lt;\u0026thinsp;30 mins\u003c/p\u003e \u003cp\u003eDoor to CT\u0026thinsp;\u0026lt;\u0026thinsp;30 mins\u003c/p\u003e \u003cp\u003eDoor to CT read\u0026thinsp;\u0026lt;\u0026thinsp;45 mins\u003c/p\u003e \u003cp\u003eOnset to needle\u0026thinsp;\u0026lt;\u0026thinsp;3 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCardiologist consultation\u0026thinsp;\u0026lt;\u0026thinsp;30 min\u003c/p\u003e \u003cp\u003eDoor to EKG\u0026thinsp;\u0026lt;\u0026thinsp;10 mins\u003c/p\u003e \u003cp\u003eDoor to needle\u0026thinsp;\u0026lt;\u0026thinsp;30 mins\u003c/p\u003e \u003cp\u003eDoor to balloon\u0026thinsp;\u0026lt;\u0026thinsp;90 mins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdmission\u0026thinsp;\u0026lt;\u0026thinsp;24 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTrauma team activation\u0026thinsp;\u0026lt;\u0026thinsp;30 mins\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Taiwan Ministry of Health and Welfare: The Statistics and Trends in Health and Welfare 2014.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eCHEC: Categorization Hospital Emergency Capability; CT: Computed tomography; EKG: Electrocardiography; ED: emergency department; ICU: Intensive Care Unit; ISS: Injury Severity Score; IV-tPA: intravenous tissue plasminogen activator; NIHSS: National Institute of Health Stroke Scale/Score; PCI: Percutaneous coronary intervention\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of study cohort\u003c/h2\u003e \u003cp\u003eThis study identified CTSD based on ED visits accompanied by a primary diagnosis using the appropriate disease code. The identification of AIS (codes 433 and 434), STEMI (code 410), and septic shock (codes 038, 785, and 995) was based on the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9‐CM). Major trauma cases were classified following the American Academy of Surgery Committee guidelines (codes 800\u0026ndash;959) \u003csup\u003e23\u003c/sup\u003e. Due to the absence of trauma severity data in the LHID2005 database, primary ICD-9-CM codes served as our initial method for identifying major trauma incidents. This identification was further refined by including cases where patients received rescue surgery or were admitted to the ICU, serving as additional criteria for major trauma \u003csup\u003e24\u003c/sup\u003e. We excluded cases that occurred before the start of the study period and those that lacked hospital or patient sociodemographic information. We also excluded hospitals with a volume of CTSD cases lower than five per year \u003csup\u003e25\u003c/sup\u003e. We used the date of the first ED visit as the index date.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eVariable definitions\u003c/h2\u003e \u003cp\u003eIndependent variable in this study was exposure to the CHEC policy intervention. Events related to AIS and STEMI were specifically subject to rigid time-based quality indicators and regular surveillance under the CHEC policy. While septic shock has well-developed clinical guidelines, it is not subject to the CHEC policy's rigid, time-based quality indicators. Similarly, major trauma cases, which lack well-developed clinical guidelines, are also not subject to these CHEC policy indicators and are unexposed as counterfactual. Dependent variables included guideline adherence rate of diagnosis and treatment process quality indicator as primary outcomes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), upward transfer rate, diagnostic fees, medical orders and expense, and 30-day and one-year mortality as secondary outcomes. Covariates including patients' predisposing factors included age, sex, and occupation; enable factor was insured salary, and Charlson Comorbidity Index (CCI) score one year before the index date as a need factor. External environmental factors included urbanization and regional emergency resources. Hospital-level variables followed the input-throughput-output model of Asplin et al. \u003csup\u003e26\u003c/sup\u003e, focusing on the rate of ED visits with triage severity levels Ⅰ and Ⅱ to gauge input. We assessed throughput and output efficiency via the ED's occupancy rate. The CCI score used to categorize patient comorbidities, was calculated using ICD-9-CM codes from primary diagnoses in both inpatient and outpatient claims data up to a year before the index date.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003ePatients' characteristics, process quality, and outcomes were presented using descriptive statistics. Continuous data were described using mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and categorical data were presented using numbers and percentages. To enhance the robustness of the outcomes, propensity score was calculated using a multivariable logistic regression that included all baseline covariates in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The standardized mean difference (SMD) was calculated to confirm the balance of potential confounders at baseline between groups before and after matching. An SMD of less than 0.1 was considered to represent a negligible difference \u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePropensity Score Matched Comparison of Patient Characteristics Pre- and Post-CHEC Policy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBefore propensity score matching\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAfter propensity score matching\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBefore CHEC policy\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;28829)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eAfter CHEC policy\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;14534)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eASMD\u003c/b\u003e\u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eBefore CHEC policy\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;9923)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eAfter CHEC policy\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(N\u0026thinsp;=\u0026thinsp;9923)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eASMD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12345 (42.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6250 (43.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4357 (43.91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4268 (43.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16484 (57.18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8284 (57.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5566 (56.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5655 (56.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4105 (14.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1967 (13.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1289 (12.99%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1306 (13.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7173 (24.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3795 (26.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2550 (25.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2590 (26.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17551 (60.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8772 (60.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6084 (61.31%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6027 (60.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharlson Comorbidity Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13215 (45.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6546 (45.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4473 (45.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4434 (44.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15614 (54.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7988 (54.96%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5450 (54.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5489 (55.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIncome\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;=22800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15961 (55.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6203 (42.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4428 (44.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4604 (46.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;22800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12868 (44.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8331 (57.32%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5495 (55.38%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5319 (53.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependents of the insured\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10095 (35.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4952 (34.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3550 (35.78%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3326 (33.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCivil servants, teachers, military\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2792 (9.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1529 (10.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e986 (9.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1053 (10.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonmanual workers and professionals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2231 (7.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1240 (8.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e748 (7.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e796 (8.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManual workers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10250 (35.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5154 (35.46%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3422 (34.49%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3708 (37.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3461 (12.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1659 (11.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1217 (12.26%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1040 (10.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital categorization\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11371 (39.44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5924 (40.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3331 (33.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3502 (35.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10921 (37.88%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5496 (37.81%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4030 (40.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4052 (40.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6537 (22.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3114 (21.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2562 (25.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2369 (23.87%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eESI triage level Ⅰ and Ⅱ rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.63\u0026thinsp;\u0026plusmn;\u0026thinsp;10.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.01\u0026thinsp;\u0026plusmn;\u0026thinsp;10.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.37\u0026thinsp;\u0026plusmn;\u0026thinsp;10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of ED stay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.33\u0026thinsp;\u0026plusmn;\u0026thinsp;10.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;9.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.25\u0026thinsp;\u0026plusmn;\u0026thinsp;9.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.36\u0026thinsp;\u0026plusmn;\u0026thinsp;10.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eED observation\u0026thinsp;≧\u0026thinsp;1-day rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.37\u0026thinsp;\u0026plusmn;\u0026thinsp;9.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.32\u0026thinsp;\u0026plusmn;\u0026thinsp;10.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.53\u0026thinsp;\u0026plusmn;\u0026thinsp;9.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.32\u0026thinsp;\u0026plusmn;\u0026thinsp;10.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of ED resources\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSufficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22770 (78.98%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11460 (78.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7768 (78.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7834 (78.95%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot sufficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6059 (21.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3074 (21.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2155 (21.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2089 (21.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime-sensitive disease\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute ischemic stroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8660 (30.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3814 (26.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2895 (29.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2895 (29.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST-segment elevation MI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2481 (8.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1141 (7.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e723 (7.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e723 (7.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptic shock\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14896 (51.67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8275 (56.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5441 (54.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5441 (54.83%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMajor trauma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2792 (9.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1304 (8.97%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e864 (8.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e864 (8.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCare delivered by\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecialty Consultant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18692 (64.84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9245 (63.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6738 (67.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6570 (66.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency physician\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7788 (27.01%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4236 (29.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2449 (24.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2585 (26.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2349 (8.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1053 (7.25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e736 (7.42%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e768 (7.74%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eASMD: absolute standardized mean difference; CHEC: Categorization Hospital Emergency Capability; ED: emergency department; ESI: Emergency Severity Index; Specialty Consultant: 1. acute ischemic stroke is treated by neurologists, 2. acute myocardial infarction is treated by cardiologists, 3. septic shock is managed by internal medicine physicians or critical care intensivists, 4. major trauma conditions are managed by surgeons or critical care intensivists.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe evaluated the impact of the CHEC intervention on each outcome, including overall, within individual diseases, and between-disease differences in change from baseline (group-by-disease interaction effects) by generalized estimating equation (GEE) models. The β coefficients of the group-by-disease interaction terms, estimated from the GEE models, indicate the difference in outcome change in each disease relative to the reference group (major trauma) between pre- and post-CHEC. A positive group-by-disease interaction β coefficient indicates that the outcome change for that disease is greater compared to the reference group. All analyses were performed using SAS version 9.4. All statistical tests were 2-sided; p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipants characteristics\u003c/h2\u003e\n \u003cp\u003eDuring the study period, we analyzed emergency presentations related to four CTSDs, originally encompassing 288,443 cases. Exclusion criteria included the diagnosis of CTSD before 2005 (n\u0026thinsp;=\u0026thinsp;99,768), patients with transient ischemic attack or intracranial hemorrhage (n\u0026thinsp;=\u0026thinsp;878), non-STEMI (n\u0026thinsp;=\u0026thinsp;1,315), and individuals with major traumas defined by ICD codes that did not necessitate a rescue operation or ICU admission (n\u0026thinsp;=\u0026thinsp;142,446), and cases lacking hospital or living area information, or where the hospital\u0026apos;s volume of CTSD was less than five visits per year (n\u0026thinsp;=\u0026thinsp;673). These criteria refined the total sample size to 43,363 cases. Considering the extended period before the policy intervention, this research adopted a 1:1 PSM technique, resulting in a final matched sample of 9,923. The flow chart and baseline table (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) display the initial count of emergency CTSD patients and the numbers post-PSM, broken down by each of the four diseases. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the PSM of CTSD participants before and after the PSM. After the matching process, each variable baseline characteristic demonstrates almost complete congruity. Additionally, uniformity is achieved within each disease sub-group post-matching (supplementary table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. The distribution for each disease pre and post-PSM was as follows: AIS (n\u0026thinsp;=\u0026thinsp;2,895), STEMI (n\u0026thinsp;=\u0026thinsp;723), septic shock (n\u0026thinsp;=\u0026thinsp;5,441), and major trauma (n\u0026thinsp;=\u0026thinsp;864).\u003c/p\u003e\n \u003cp\u003eSeptic shock was observed to be more pervasive, representing 54% of all cases. The patient population was male-dominated (56%), with the most represented age group being those aged 65 and above (60%). Nearly three-fourths of the incidents involving CTSD were handled in hospitals that were categorized as moderate (40%) or severe (35%) levels. Consultant specialists delivered care accounted for two-thirds of cases.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eImpact of CHEC policy on overall and individual four CTSDs\u0026rsquo; process and outcome before and after implementation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn examining individual diseases, primary diagnostic indicators for AIS, septic shock, and major trauma decreased post-intervention, while only STEMI increased (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Diagnostic fees increased for AIS, STEMI, and major trauma but decreased for septic shock. A similar trend was observed in primary treatment indicators, with AIS and STEMI increased and septic shock and major trauma decreased. In contrast, medical orders showed a universal decline. Upward transfer rates rose for AIS and major trauma, while a decrease in STEMI and septic shock. Regarding outcome indicators, short-term mortality rates displayed a universal decline, and long-term mortality rates followed suit, except for AIS, which showed an increase. The medical expenses were higher for AIS and STEMI but lower for septic shock and major trauma.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparative analysis of the individual and overall impact of CHEC policy effects for critical time-sensitive diseases\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"12\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eChange between pre- \u0026amp; post-CHEC\u003c/span\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eBefore CHEC policy\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;9923)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003eAfter CHEC policy\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;9923)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMultivariable model\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u0026Beta; [95% CI]\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcute ischemic stroke\u003c/strong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSTEMI\u003c/strong\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeptic shock\u003c/strong\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor trauma\u003c/strong\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProcess quality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMajor diagnosis indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(87.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(84.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.29 to -0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnostic fees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e460.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2746.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e762.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7166.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(10018.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7543.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(10833.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e376.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(92.42 to 660.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMajor treatment indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(43.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(43.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.07 to 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2415\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical orders per case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-16.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(109.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(101.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-10.09 to -4.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpward transfer rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.06 to 0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30-days mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(16.44%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(15.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.17 to -0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOne-year mortality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(32.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(30.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.15 to -0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal medical expense per case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3616.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11219.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-11059.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-13056.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100875.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(192912.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95547.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(176487.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5328.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-10387.10 to -269.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0390\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\u003csup\u003ea\u003c/sup\u003e Acute ischemic stroke major diagnosis indicator: head image; major treatment indicator: IV-tPA thrombolysis\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\u003csup\u003eb\u003c/sup\u003e ST-elevation MI major diagnosis indicator: EKG; major treatment indicator: PCI\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\u003csup\u003ec\u003c/sup\u003e Septic shock major diagnosis indicator: culture; major treatment indicator: antipathogen medication\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003e\u003csup\u003ed\u003c/sup\u003e Major trauma major diagnosis indicator: CT or MRI or sonography study; major treatment indicator: rescue operation\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"12\"\u003eCHEC: Categorization Hospital Emergency Capability; CI: confident confidence interval; EKG: Electrocardiography; IV-tPA: intravenous tissue plasminogen activator; PCI: Percutaneous coronary intervention; STEMI: ST-segment elevation myocardial infarction\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003eIn assessing the overall policy effects on four CTSD cohorts, notably, the primary diagnosis indicator significantly decreased by 0.21 percentage points (95% CI: -0.29 to -0.13, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001); medical orders per case dropped by an average of 7.29 items (95% CI: -10.09 to -4.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). In comparison, diagnostic fees demonstrated an average increase of \u003cspan\u003e$\u003c/span\u003e376.37 (95% CI: 92.42 to 660.33, p\u0026thinsp;=\u0026thinsp;0.0094). The 30-day mortality rate saw a notable reduction of 0.09 percentage points (95% CI: -0.17 to -0.02, p\u0026thinsp;=\u0026thinsp;0.0137), one-year mortality significantly decreased by 0.09 percentage points (95% CI: -0.15 to -0.04, p\u0026thinsp;=\u0026thinsp;0.0013) and medical expense per case significantly decreased by \u003cspan\u003e$\u003c/span\u003e5328.35 (95% CI: -10387.10 to -269.60, p\u0026thinsp;=\u0026thinsp;0.0390).\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation of CHEC policy with process and outcomes quality in four CTSDs\u003c/h2\u003e\n \u003cp\u003eIn model 1, we analyze the variations in several indicators before and after the implementation of CHEC for individual diseases. For AIS, following the implementation of CHEC, there was a significant decrease in major diagnosis indicators by 0.23 percentage points (95% CI: -0.36 to -0.10, p\u0026thinsp;=\u0026thinsp;0.0005) (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Conversely, major treatment indicator experienced a significant rise of 0.57 percentage points (95% CI: 0.07 to 1.07, p\u0026thinsp;=\u0026thinsp;0.0263), and upward transfer rate also significantly increased by 0.52 percentage points (95% CI: 0.02 to 1.03, p\u0026thinsp;=\u0026thinsp;0.0399). Meanwhile, there was also a trend of increasing diagnostic costs, with a rise of \u003cspan\u003e$\u003c/span\u003e460.66 (95% CI: -3.44 to 924.76, p\u0026thinsp;=\u0026thinsp;0.0517). For STEMI, the diagnostic fees significantly increased by \u003cspan\u003e$\u003c/span\u003e2746.59 (95% CI: 1141.67 to 4351.51, p\u0026thinsp;=\u0026thinsp;0.0008). When examining septic shock, the major diagnosis indicator saw a significant decrease of 0.25 percentage points (95% CI: -0.37 to -0.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) following the introduction of CHEC. 30-day mortality decreased by 0.11% (95% CI: -0.20 to -0.02, p\u0026thinsp;=\u0026thinsp;0.0189), and 1-year mortality decreased by 0.15% (95% CI: -0.22 to -0.07, p\u0026thinsp;=\u0026thinsp;0.0001). Additionally, medical orders significantly dropped by 9.67 items (95% CI: -13.99 to -5.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and average medical expenses significantly fell by \u003cspan\u003e$\u003c/span\u003e11059.10 (95% CI: -18603.60 to -3514.55, p\u0026thinsp;=\u0026thinsp;0.0041). Finally, in the case of major trauma, post-CHEC implementation, the average medical orders significantly decreased by 16.13 items (95% CI: -25.32 to -6.94, p\u0026thinsp;=\u0026thinsp;0.0006).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation of CHEC policy with process and outcomes quality in four critical time-sensitive diseases\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBefore CHEC policy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAfter CHEC policy\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChange between pre \u0026amp; post CHEC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel 1\u003c/span\u003e\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026alpha;\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eModel 2\u003c/span\u003e\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026beta;\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcute ischemic stroke\u003c/strong\u003e\u003csup\u003ea\u003c/sup\u003e \u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;2895)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMajor diagnosis indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2391 (82.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2289 (79.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.23 (-0.36 to -0.10)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06 (-0.32 to 0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnostic fees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6050.63 (10934.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6511.29 (6895.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e460.66\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e460.66 (-3.44 to 924.76)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0517\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-302.19 (-1419.15 to 814.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5959\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMajor treatment indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24 (0.83%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 (1.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.62\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.57 (0.07 to 1.07)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0263\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.77 (0.21 to 1.33)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0068\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical orders per case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70.44 (71.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.51 (74.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.93 (-4.64 to 2.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.20 (5.28 to 25.11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpward transfer rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25 (0.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42 (1.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.52 (0.02 to 1.03)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0399\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21 (-0.39 to 0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4929\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShort-term mortality (30 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140 (4.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139 (4.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01 (-0.25 to 0.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11 (-0.27 to 0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLong-term mortality (365 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e441 (15.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e449 (15.51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02 (-0.12 to 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06 (-0.22 to .033)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6874\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal medical expense per case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58995.28 (161260.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62611.44 (113039.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3616.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3616.15 (-3524.26 to 10756.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16672.69 (-3581.75 to 36927.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eST-segment elevation MI\u003c/strong\u003e\u003csup\u003eb\u003c/sup\u003e \u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;723)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMajor diagnosis indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e671 (92.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e675 (93.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09 (-0.32 to 0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26 (-0.21 to 0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnostic fees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6269.07 (11425.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9015.67 (19940.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2746.6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2746.59 (1141.67 to 4351.51)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1983.75 (84.28 to 3883.21)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0407\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMajor treatment indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240 (33.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e255 (35.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09 (-0.11 to 0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.30 (-0.03 to 0.62)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0729\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical orders per case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.31 (90.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88.10 (88.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.21 (-13.14 to 4.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.92 (-0.90 to 24.73)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.068\u003c/strong\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpward transfer rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39 (5.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30 (4.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.28 (-0.76 to 0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.59 (-1.18 to -0.001)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0496\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShort-term mortality (30 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144 (19.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133 (18.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10 (-0.36 to 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02 (-0.37 to 0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLong-term mortality (365 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e217 (30.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e211 (29.18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.04 (-0.26 to 0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01 (-0.33 to .032)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9745\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal medical expense per case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108481.43 (144206.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119700.44 (166898.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11219.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11219.00 (-4953.90 to 27391.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e24275.54 (-640.71 to 49191.78)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0562\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSeptic shock\u003c/strong\u003e\u003csup\u003ec\u003c/sup\u003e \u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;5441)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMajor diagnosis indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4941 (90.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4817 (88.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.28\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.25 (-0.37 to -0.12)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.08 (-0.33 to 0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnostic fees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7360.02 (9079.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7315.22 (10611.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-44.80 (-412.26 to 322.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-807.65 (-1888.03 to 272.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMajor treatment indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3894 (71.57%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3826 (70.32%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.06 (-0.14 to 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14 (-0.12 to 0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2824\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical orders per case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120.70 (124.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111.03 (112.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-9.67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-9.67 (-13.99 to -5.35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.45 (-3.70 to 16.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpward transfer rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21 (0.39%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (0.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.27 (-0.93 to 0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.59 (-1.32 to 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShort-term mortality (30 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1234 (22.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1134 (20.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.84\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.11 (-0.20 to -0.02)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0189\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01 (-0.29 to 0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLong-term mortality (365 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2398 (44.07%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2200 (40.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-3.64\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.15 (-0.22 to -0.07)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11 (-0.36 to 0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3588\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal medical expense per case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115832.23 (206098.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104773.14 (198516.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-11059.09\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-11059.10 (-18603.60 to -3514.55)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0041\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1997.45 (-18403.00 to 22397.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8478\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMajor trauma\u003c/strong\u003e\u003csup\u003ed\u003c/sup\u003e \u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;864)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMajor diagnosis indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e679 (78.59%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e653 (75.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.17 (-0.39 to 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMajor trauma as reference group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnostic fees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10442.74 (10411.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11205.59 (11322.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e762.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e762.85 (-253.13 to 1778.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMajor treatment indicator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e176 (20.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149 (17.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.21 (-0.46 to 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical orders per case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.56 (103.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.43 (93.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e-16.13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-16.13 (-25.32 to -6.94)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpward transfer rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63 (7.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84 (9.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.43\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.31 (-0.02 to 0.65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0650\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShort-term mortality (30 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e113 (13.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102 (11.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12 (-0.41 to 0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLong-term mortality (365 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e186 (21.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e181 (20.95%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03 (-0.27 to 0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal medical expense per case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e140654.57 (215834.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e127598.03 (194545.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-13056.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13056.50 (-32010.60 to 5897.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026alpha;\u003c/sup\u003eModel 1: Compare the differences between post-policy and pre-policy.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003e\u0026beta;\u003c/sup\u003eModel 2: The model adjusted estimates for an interaction between a binary measure of CHEC policy (post-implementation vs. pre-implementation) and critical time-sensitive diseases compared with major trauma (e.g.. acute ischemic stroke vs. major trauma; ST-segment elevation MI vs. major trauma; septic shock vs. major trauma)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eCHEC: Categorization Hospital Emergency Capability.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e Acute ischemic stroke major diagnosis indicator: head image; major treatment indicator: IV-tPA thrombolysis\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e ST-elevation MI major diagnosis indicator: EKG; major treatment indicator: PCI\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003ec\u003c/sup\u003e Septic shock major diagnosis indicator: culture; major treatment indicator: antipathogen medication\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003ed\u003c/sup\u003e Major trauma major diagnosis indicator: CT or MRI or sonography study; major treatment indicator: rescue operation\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eCHEC: Categorization Hospital Emergency Capability; CI: confident confidence interval; EKG: Electrocardiography; IV-tPA: intravenous tissue plasminogen activator; \u003cstrong\u003eS\u003c/strong\u003eTEMI: ST-segment elevation myocardial infarction; PCI: Percutaneous coronary intervention\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn Model 2 results from the GEE model highlight the CHEC policy\u0026apos;s varied effects across different disease outcomes. Compared to the major trauma, AIS exhibited a significant increase in the major treatment indicator (Interaction \u0026beta;\u0026thinsp;=\u0026thinsp;0.77; 95% CI\u0026thinsp;=\u0026thinsp;0.21 to 1.33; p\u0026thinsp;=\u0026thinsp;0.0068) and medical orders (Interaction \u0026beta;\u0026thinsp;=\u0026thinsp;15.20; 95% CI\u0026thinsp;=\u0026thinsp;5.28 to 25.11; p\u0026thinsp;=\u0026thinsp;0.0027) between pre- \u0026amp; post-CHEC. Meanwhile, STEMI demonstrated diagnostic fees significantly increased (Interaction \u0026beta;\u0026thinsp;=\u0026thinsp;1983.75; 95% CI\u0026thinsp;=\u0026thinsp;84.28 to 3883.21; p\u0026thinsp;=\u0026thinsp;0.0407) and significant decrease in upward transfer rate (Interaction \u0026beta;=-0.59; 95% CI=-1.18 to -0.001; p\u0026thinsp;=\u0026thinsp;0.0496) compared to the major trauma, additionally, there was a trend of increasing major treatment indicators (Interaction \u0026beta;\u0026thinsp;=\u0026thinsp;0.30; 95% CI: -0.03 to 0.62, p\u0026thinsp;=\u0026thinsp;0.0729), medical orders (Interaction \u0026beta;\u0026thinsp;=\u0026thinsp;11.92; 95% CI=-0.90 to 24.73; p\u0026thinsp;=\u0026thinsp;0.0684), and medical expense (Interaction \u0026beta;\u0026thinsp;=\u0026thinsp;24275.54; 95% CI=-640.71 to 4991991.78; p\u0026thinsp;=\u0026thinsp;0.0562). In septic shock, compared to major trauma, there were no significant differences observed in either process or outcome quality.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eEvaluating the impact of a health policy exposed to an entire nationwide population presents challenges due to the absence of a control group. This restriction only permits pre- and post-implementation comparisons and complicates understanding the potential interplay of various disease shifts under the influence of policy is a significant challenge. Nevertheless, our study aims to thoroughly analyze such a policy, investigating its differential impacts across various time-sensitive diseases. By carefully examining each disease's guidelines, we discern the unique effects of the policy on each one. This process allows us to identify the groups affected by the policy and those serving as relatively unaffected counterfactuals, thereby giving us insights into the policy's impacts and difference-in-differences comparison.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe overall impact of the CHEC policy\u003c/h2\u003e \u003cp\u003eOur study utilized real-world data to illustrate that the enforcement of the CHEC policy has led to a substantial reduction in overall medical orders and major diagnostic indicators. Nevertheless, there has been a conspicuous increase in diagnostic fees; this could be potentially attributed to stringent time indicators, as expedited diagnoses are crucial. Consequently, there could be an augmentation in the charges associated with other differential diagnoses. Moreover, the implementation of the CHEC policy has also resulted in a decrease in mortality rates, ultimately contributing to a reduction in overall medical expenses. Thus, the effectiveness and efficiency of the CHEC policy underscore its dual ability to decrease costs while improving patient outcomes. However, a crucial question remains unanswered: Why has the CHEC policy been effective in significantly reducing medical costs and mortality rates, even when the overall major diagnosis indicator has declined and no substantial change has been observed in treatment quality indicators? The following sections will delve further into diseases' individual and interactive effects to provide an analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIndividual disease response to CHEC policy\u003c/h2\u003e \u003cp\u003eImplementing the CHEC policy aims to improve the quality of the diagnosis and treatment process, enable rapid diagnosis and timely treatment, efficiently reduce unnecessary examinations, and streamline management, leading to lower diagnostic cost and medical orders. As hospitals improve their capabilities in emergency medical care, the need for patient transfers is anticipated to reduce. Through the provision of timely and appropriate treatment, the ultimate objectives of the policy are to lower mortality rates and curtail overall healthcare utilization.\u003c/p\u003e \u003cp\u003eWhen examining individual disease policy effects, in the case of AIS, we observed a decrease in diagnostic indicators, which might be attributable to an increase in patient upward transfer rates and possibly suggests that hospital physicians may be more inclined to transfer AIS patients to higher-level hospitals to increase their chances of receiving thrombolytic treatment \u003csup\u003e28\u003c/sup\u003e. Therefore, it presents a significant increase in the primary treatment indicators. There has been a substantial increase in diagnostic fees. c seemed to be adversely affected by the policy intervention, with increased medical orders, which led to increased diagnostic costs, upper transfer rates, long-term mortality rates, and overall medical costs. In the context of STEMI, there is an increase in diagnostic fees and a tendency to increase diagnostic and treatment indicators while transfer rates have decreased. This suggests a more intensive treatment approach for STEMI, correlating with decreased mortality rates. Conversely, there was a decrease in major diagnostic and treatment indicators and medical orders for septic shock and major trauma exhibited a decline in primary diagnosis indicators and primary treatment indicators also saw an increase in transfer rates. The differential impact on diagnostic costs, treatment indicators, transfer rates, long-term mortality rates, and medical costs among different diseases highlights the differential influence of policy effects. This could indirectly suggest the impact of the policy spotlight effect on AIS and STEMI conditions, while septic shock and major trauma may deteriorate due to the absence of policy monitoring.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of diseases with policy spotlight effect\u003c/h2\u003e \u003cp\u003eGiven the setting of this study, all emergency medical services are exposed to the CHEC policy, potentially subjecting emergency medical personnel to effects ranging from the Hawthorne effect and the policy spotlight effect. To differentiate the behaviors of healthcare providers under the influence of the Hawthorne effect or the policy spotlight effect, we selected AIS and STEMI diseases with well-established guidelines and time-based quality indicators under the CHEC policy. Septic shock, a disease with well-established guidelines but without specific time-based quality indicators, and major trauma, a disease without well-established guidelines or specific time-based quality indicators, were selected as external controls. Based on these natural quasi-experimental conditions, we hypothesize that the response observed may suggest varying levels of awareness among emergency care providers \u003csup\u003e29\u003c/sup\u003e. The Hawthorne effect demonstrates that the productivity of individuals in an experiment may increase simply because they are being observed, reflecting the influence of human attention and intervention on behavior \u003csup\u003e30\u003c/sup\u003e. The policy spotlight effect may be intensified by factors such as time constraints, ambiguous symptom patterns, and time-based quality surveillance indicators, prompting emergency care providers to unconsciously adopt selective behaviors concentrating on specific diseases according to policy targets \u003csup\u003e30\u003c/sup\u003e. Consequently, diseases not prioritized by the policy, such as septic shock and major trauma in this study, may experience a significant decrease in process quality and outcome.\u003c/p\u003e \u003cp\u003eWhen using major trauma as a reference group, both AIS and STEMI, diseases monitored by time-based indicators, exhibited an increase in the medical orders corresponding to treatment indicators. Moreover, there was an apparent increase in diagnostic and overall healthcare expenses in the STEMI cohort and a decreased transfer rate. In contrast, no significant changes were observed for the septic shock group, which was not under time indicator monitoring when compared to the major trauma reference group. These contrasting developments illustrate the divergent trends between time-based and non-time-based monitored time-sensitive diseases. We attribute these differences to the potential influence of the policy spotlight effect. The two diseases, AIS and STEMI, which are under the spotlight effect of policy, have shown an increase in the overall medical orders, diagnostic fees and medical expenses compared to the reference group, subsequently increasing the treatment indicators. As hypothesized, the septic shock group did not demonstrate significant changes in comparison to the major trauma reference group, underscoring the influence of time-based monitoring on these particular outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDiscrepancy and unintended consequences\u003c/h2\u003e \u003cp\u003eWhile both AIS and STEMI appeared to be influenced by the policy spotlight effect, they showed entirely opposite results in diagnostic indicators and transfer rates. This could potentially be attributed to the inherent differences between AIS and STEMI. For instance, only about 1%~2% of all AIS patients underwent primary treatment with intravenous thrombolytic agents, which is far lower than STEMI major treatment rate. Unexpectedly, in less policy spotlight affected groups such as septic shock and major trauma, despite significant decreases in primary diagnostic indicators, total medical orders, and diagnostic fees, there were unexpected reductions in 30-day and one-year mortality rates and overall medical expenses per event. These findings may align with the American 'less is more' initiative, suggesting that minimizing excessive diagnoses and unnecessary procedures could potentially enhance patient outcomes and yield cost reductions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePolicy implications\u003c/h2\u003e \u003cp\u003eCurrently, many emergency care policies implement time-based criteria \u003csup\u003e31\u0026ndash;33\u003c/sup\u003e, such as the UK\u0026rsquo;s 4-hour standard \u003csup\u003e33\u003c/sup\u003e and the four-hour rule \u003csup\u003e31\u003c/sup\u003e. Australia's experience showed that an emergency care policy using time-based criteria can improve emergency congestion without increasing the rate of ED re-visits. However, in New Zealand, a policy in effect during 2006\u0026ndash;2012 dictated that emergent patients must be hospitalized, transferred, or discharged within six hours of visiting the ED. After emergency care policy intervention, the length of ED stay decreased while the treatment outcomes of acute myocardial infarction, severe septic shock, and acute appendicitis did not improve significantly \u003csup\u003e32\u003c/sup\u003e. Similarly, after the Canadian Emergency Observation Reduction Program implementation, the length of ED stay decreased while the treatment quality indicators for acute myocardial infarction, asthma, and upper limb fractures can only be treated in time for the above-mentioned time-sensitive diseases during the non-congested emergency period \u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEmergency care quality is closely related to the practices of medical care providers \u003csup\u003e17\u003c/sup\u003e. Policymakers and medical care providers must reconsider the conventional emphasis on time-based process indicators. This approach may have unintended consequences for diseases that are outside the \"spotlight\" of rigid time-sensitive evaluation. Instead, a broader and more nuanced evaluation of emergency care quality is needed to incorporate the complexity and ambiguity of various time-sensitive diseases that emergency care providers often manage \u003csup\u003e35\u003c/sup\u003e. Therefore, we propose replacing time-based indicators with performance-based indicators, such as those exemplified in the NHS's Best Practice Tariff policy \u003csup\u003e36\u003c/sup\u003e. This shift towards 'best practice' can create a more flexible approach that goes beyond merely relying on time-based or diagnosis-based practices \u003csup\u003e37\u003c/sup\u003e. Such a transition is crucial, as an excessive reliance on diagnostic tests may lead to ED crowding, a decline in emergency care quality, and increased safety issues \u003csup\u003e38\u003c/sup\u003e. The complexities of emergency care, including diverse patient origins and multiple routes to specialized units like stroke units, demand more sophisticated quality measures. These may encompass factors such as the presence of a consultant and the need for critical care admissions. In the case of specific procedures like diagnostic angiography and PCI (where indicated) within 72 hours of admission with NSTEMI, rigid time-based quality indicators may be especially inadequate. Performance-based measures, such as those used in the \"Best Practice Tariff,\" reward best practices in care delivery and foster a higher standard of healthcare.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStrengths\u003c/h2\u003e \u003cp\u003eTo the best of our knowledge, this was the first nationwide retrospective cohort study using data on different CTSDs the effects of a policy exposed to an entire population. In the scenario that often presents challenges due to the lack of a control group, we identified differential impacts on various diseases through detailed analysis, establishing affected and counterfactual groups. This process allowed us to explore pre- and post-intervention effects, comprehending the policy's overall impact. This structure provides essential insights into the impact of hospital categorization on the processes and outcomes of emergency care. Further, it offers crucial empirical evidence that could be instrumental in refining policies and optimizing emergency care systems. This study explored emergency care providers' behaviors under time constraints and how they interacted with strict time-based quality surveillance indicators and \u0026ldquo;get with the guidelines\u0026rdquo; adherence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eBecause the study data were retrieved from a secondary dataset of insurance claims not a randomized controlled trial, this study has the following limitations: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) our analysis lacked detailed information on time-related quality indicators, such as door-to-evaluation and door-to-treatment times measurement; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Our study's sample definitions were based on emergency primary diagnosis, making the emergency ICD code diagnosis potentially imprecise highlighting the need for a more comprehensive approach \u003csup\u003e39\u003c/sup\u003e (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The effectiveness of emergency medical care often hinges on the cooperation between emergency physicians and the consulting physicians. This study is unable to dissect this relationship explicitly. Evaluating how emergency physicians and consulting physicians collaborate and coordinate is a key focus for future research.; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) we need more qualitative research to elucidate the psychological mechanisms through which the policy spotlight effect influences emergency care providers' behaviour.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOur study used real-world evidence to demonstrate that CHEC policy implementation demonstrates a dual capability to reduce costs and improve patient outcomes. Health policy spotlight effect resulting in a disproportional improvement in disease guideline adherence rates and process quality of CTSD with time-based surveillance indicators. In contrast, disease entities not fully encompassed in the surveillance indicators may be jeopardized with a decrease in diagnosis and treatment process quality, thereby highlighting an unintended consequence of the policy. We propose a transition from time-based process indicators to performance-based indicators that may potentially improve efficiency and quality of care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eacute ischemic stroke\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eabsolute standardized mean difference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCHEC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecategorization of hospital emergency capability\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfident confidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecomputed tomography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCTSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecritical time-sensitive diseases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003edifference-in-differences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eemergency department\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEKG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eelectrocardiography\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eESI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEmergency Severity Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD-9-CM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Classification of Diseases, 9th Revision, Clinical Modification\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintensive care unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eISS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInjury Severity Score\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIV-tPA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintravenous tissue plasminogen activator\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eJMIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eJournal of Medical Internet Research\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLHID2005\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNHI 2005 Longitudinal Health Insurance Database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetic Resonance Imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health Insurance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNIHSS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Institute of Health Stroke Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epercutaneous coronary intervention\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epropensity score matching\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandardized mean difference\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTEMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eST-segment elevation myocardial infarction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study approved by Institutional Review Board of National Yang-Ming University-YM107035E on May, 5 2018. In accordance with regulations of the National Health Research Institutes, patient identification information was anonymized, such that informed consent was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from Taiwan National Health Insurance Research Database but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the academic request and with permission of Taiwan National Health Insurance Administration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributors\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCY Lin took a lead role in conceptualizing the study and writing the original draft, and was responsible for formal data analysis. CY Lin also verified the underlying data in the manuscript. CC Liu contributed to study design, data curation, and formal data analysis, and was in charge of data collection. CC Liu YT Huang ensured accurate data analysis and interpretation, and verified the manuscript\u0026apos;s underlying data. YC Lee supervised the study, validated the results, and significantly contributed to reviewing and editing the manuscript. All authors participated in developing the study concept and design, analysing and interpreting data, and preparing the manuscript. We have all approved the final manuscript and agree to be accountable for all aspects of the work, promising to appropriately investigate and resolve any question related to the work\u0026apos;s accuracy or integrity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the Taiwan National Health Insurance Research Database, which was provided by the National Health Insurance Administration and is managed by National Health Research Institutes.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCounselman FL, Borenstein MA, Chisholm CD, et al. The 2013 Model of the Clinical Practice of Emergency Medicine. Acad Emerg Med 2014;21(5):574-98. doi: 10.1111/acem.12373\u003c/li\u003e\n \u003cli\u003eFunctional analysis for operating emergency department of a general hospital. Simulation Conference, 2004 Proceedings of the 2004 Winter; 2004. IEEE.\u003c/li\u003e\n \u003cli\u003eEddy DM. Clinical Decision Making: From Theory to Practice : a Collection of Essays from JAMA. Boston: Jones and Bartlett Publishers 1996.\u003c/li\u003e\n \u003cli\u003ePanchavati S, Lam C, Zelin NS, et al. Retrospective validation of a machine learning clinical decision support tool for myocardial infarction risk stratification. Healthc Technol Lett 2021;8(6):139-47. doi: 10.1049/htl2.12017 [published Online First: 20210831]\u003c/li\u003e\n \u003cli\u003eAgency for Healthcare Research Quality. AHRQ quality indicators\u0026mdash;guide to prevention quality indicators: hospital admission for ambulatory care sensitive conditions. Rockville, MD: Department of Health and Human Services, Agency for Healthcare Research and Quality, 2001.\u003c/li\u003e\n \u003cli\u003eCombes J, Arespacochaga E. Appropriate use of medical resources. Chicago, IL.: American Hospital Association, Physician Leadership Forum, 2013.\u003c/li\u003e\n \u003cli\u003eDaley S, Braimah J, Sailor S, et al. Education to improve stroke awareness and emergent response. The NINDS rt-PA Stroke Study Group. J Neurosci Nurs 1997;29(6):393-6. [published Online First: 1998/02/28]\u003c/li\u003e\n \u003cli\u003eFesmire FM, Brady WJ, Hahn S, et al. Clinical policy: indications for reperfusion therapy in emergency department patients with suspected acute myocardial infarction. American College of Emergency Physicians Clinical Policies Subcommittee (Writing Committee) on Reperfusion Therapy in Emergency Department Patients with Suspected Acute Myocardial Infarction. Ann Emerg Med 2006;48(4):358-83. doi: 10.1016/j.annemergmed.2006.07.006 [published Online First: 2006/09/26]\u003c/li\u003e\n \u003cli\u003eRivers E. Early goal-direvted therapy in the treatment of severe sepsis and septic shock. N Eng J Med 2001;345:1368-77.\u003c/li\u003e\n \u003cli\u003eCarr BG, Kilaru AS, Karp DN, et al. Quality Through Coopetition: An Empiric Approach to Measure Population Outcomes for Emergency Care\u0026ndash;Sensitive Conditions. Ann Emerg Med 2018;72(3):237-45. doi: https://doi.org/10.1016/j.annemergmed.2018.03.004\u003c/li\u003e\n \u003cli\u003eAmerican Medical Association. Recommendations of the Conference on the Guidelines for the Categorization of Hospital Emergency Capabilities: American Medical Association 1971.\u003c/li\u003e\n \u003cli\u003eHaupt MT, Bekes CE, Brilli RJ, et al. Guidelines on critical care services and personnel: Recommendations based on a system of categorization of three levels of care. Crit Care Med 2003;31(11):2677-83. doi: 10.1097/01.ccm.0000094227.89800.93 [published Online First: 2003/11/08]\u003c/li\u003e\n \u003cli\u003eMacKenzie EJ, Rivara FP, Jurkovich GJ, et al. A national evaluation of the effect of trauma-center care on mortality. N Engl J Med 2006;354(4):366-78.\u003c/li\u003e\n \u003cli\u003eGlickman SW, Lytle BL, Ou FS, et al. Care processes associated with quicker door-in-door-out times for patients with ST-elevation-myocardial infarction requiring transfer: results from a statewide regionalization program. Circ Cardiovasc Qual Outcomes 2011;4(4):382-8. doi: 10.1161/circoutcomes.110.959643 [published Online First: 2011/06/30]\u003c/li\u003e\n \u003cli\u003eJollis JG, Al-Khalidi HR, Roettig ML, et al. Impact of Regionalization of ST-Segment-Elevation Myocardial Infarction Care on Treatment Times and Outcomes for Emergency Medical Services-Transported Patients Presenting to Hospitals With Percutaneous Coronary Intervention: Mission: Lifeline Accelerator-2. Circulation 2018;137(4):376-87. doi: 10.1161/circulationaha.117.032446 [published Online First: 2017/11/16]\u003c/li\u003e\n \u003cli\u003eGovindarajan P, Shiboski S, Grimes B, et al. Effect of Acute Stroke Care Regionalization on Intravenous Alteplase Use in Two Urban Counties. Prehosp Emerg Care 2019:1-10. doi: 10.1080/10903127.2019.1679303 [published Online First: 2019/10/11]\u003c/li\u003e\n \u003cli\u003eRubenstein LV, Mittman BS, Yano EM, et al. From understanding health care provider behavior to improving health care: the QUERI framework for quality improvement. Med Care 2000:I129-I41.\u003c/li\u003e\n \u003cli\u003eGilovich T, Savitsky K. The spotlight effect and the illusion of transparency: Egocentric assessments of how we are seen by others. Curr Dir Psychol Sci 1999;8(6):165-68.\u003c/li\u003e\n \u003cli\u003eSalisbury KM. National and state policies influencing the care of children affected by AIDS. Child Adolesc Psychiatr Clin N Am 2000;9(2):425-49.\u003c/li\u003e\n \u003cli\u003eSavitsky K, Epley N, Gilovich T. Do others judge us as harshly as we think? Overestimating the impact of our failures, shortcomings, and mishaps. J Pers Soc Psychol 2001;81(1):44.\u003c/li\u003e\n \u003cli\u003eNational Health Research Institutes. National Health Insurance Research Database. Accessed August 20, 2018. https://nhird.nhri.org.tw/en/ [Available from: https://nhird.nhri.org.tw/en/ accessed Accessed August 20, 2018.\u003c/li\u003e\n \u003cli\u003eLin C-Y, Lee Y-C. Effectiveness of Hospital Emergency Department Regionalization and Categorization Policy on Appropriate Patient Emergency Care Use-A nationwide long-term observational study in Taiwan. 2020\u003c/li\u003e\n \u003cli\u003eShafi S, Nathens AB, Cryer HG, et al. The trauma quality improvement program of the American College of Surgeons Committee on Trauma. J Am Coll Surg 2009;209(4):521-30. e1.\u003c/li\u003e\n \u003cli\u003eCox S, Smith K, Currell A, et al. Differentiation of confirmed major trauma patients and potential major trauma patients using pre-hospital trauma triage criteria. Injury 2011;42(9):889-95. doi: 10.1016/j.injury.2010.03.035 [published Online First: 2010/05/01]\u003c/li\u003e\n \u003cli\u003eDobaria V, Kwon OJ, Hadaya J, et al. Impact of center volume on outcomes of surgical repair for type A acute aortic dissections. Surgery 2020;168(1):185-92. doi: 10.1016/j.surg.2020.04.007 [published Online First: 20200604]\u003c/li\u003e\n \u003cli\u003eAsplin BR, Magid DJ, Rhodes KV, et al. A conceptual model of emergency department crowding. Ann Emerg Med 2003A;42(2):173-80.\u003c/li\u003e\n \u003cli\u003eAustin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate behavioral research 2011;46(3):399-424.\u003c/li\u003e\n \u003cli\u003eZachrison KS, Amati V, Schwamm LH, et al. Influence of hospital characteristics on hospital transfer destinations for patients with stroke. Circ Cardiovasc Qual Outcomes 2022;15(5):e008269.\u003c/li\u003e\n \u003cli\u003eLied TR, Kazandjian VA. Performance Measurement and Improvement. Health Care (Don Mills) 1998;6:201-04.\u003c/li\u003e\n \u003cli\u003eFranke RH, Kaul JD. The Hawthorne experiments: First statistical interpretation. Am Sociol Rev 1978:623-43.\u003c/li\u003e\n \u003cli\u003eNgo H, Forero R, Mountain D, et al. Impact of the Four-Hour Rule in Western Australian hospitals: Trend analysis of a large record linkage study 2002-2013. PLoS One 2018;13(3):e0193902.\u003c/li\u003e\n \u003cli\u003eJones P, Le Fevre J, Harper A, et al. Effect of the Shorter Stays in Emergency Departments time target policy on key indicators of quality of care. N Z Med J 2017;130(1455):35-44. [published Online First: 2017/05/12]\u003c/li\u003e\n \u003cli\u003eCooke MW. Reforming the UK emergency care system. Emerg Med J 2003;20(2):113-4. [published Online First: 2003/03/19]\u003c/li\u003e\n \u003cli\u003eVermeulen MJ, Guttmann A, Stukel TA, et al. Are reductions in emergency department length of stay associated with improvements in quality of care? A difference-in-differences analysis. BMJ Quality \u0026amp;amp; Safety 2016;25(7):489-98. doi: 10.1136/bmjqs-2015-004189\u003c/li\u003e\n \u003cli\u003eGlickman SW, Kit Delgado M, Hirshon JM, et al. Defining and measuring successful emergency care networks: a research agenda. Acad Emerg Med 2010;17(12):1297-305. doi: 10.1111/j.1553-2712.2010.00930.x\u003c/li\u003e\n \u003cli\u003eCopas D, Moran C. Major trauma care in England: Changing the state of a nation\u0026rsquo;s healthcare system. Bone \u0026amp; Joint 360 2014;3(2):2-5.\u003c/li\u003e\n \u003cli\u003eCarrier ER, Reschovsky JD, Katz DA, et al. High physician concern about malpractice risk predicts more aggressive diagnostic testing in office-based practice. Health Aff (Millwood) 2013;32(8):1383-91. doi: 10.1377/hlthaff.2013.0233\u003c/li\u003e\n \u003cli\u003eKawano T, Nishiyama K, Hayashi H. Execution of diagnostic testing has a stronger effect on emergency department crowding than other common factors: a cross-sectional study. PLoS One 2014;9(10):e108447.\u003c/li\u003e\n \u003cli\u003eLutz M, Mockel M, Lindner T, et al. The accuracy of initial diagnoses in coma: an observational study in 835 patients with non-traumatic disorder of consciousness. Scand J Trauma Resusc Emerg Med 2021;29(1):15. doi: 10.1186/s13049-020-00822-w\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"categorization of hospital emergency capability, quality, time-sensitive diseases, emergency care, difference-in-differences. ","lastPublishedDoi":"10.21203/rs.3.rs-4697511/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4697511/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective \u003c/strong\u003eTo investigate the effects of the Categorization of hospital emergency capability (CHEC) policy on critical time-sensitive diseases (CTSDs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSetting \u003c/strong\u003eCHEC is a policy implemented worldwide to regionalize and guide the dispatch of critical patients to the nearest appropriate hospital. In 2009, Taiwan's CHEC policy was designed to improve the quality of emergent care for CTSDs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResearch Design and Participants \u003c/strong\u003eA nationwide observational quasi-experimental study was conducted to examine the quality of care for CTSD before (2006-2008) and after (2009-2012) the implementation of the CHEC policy. CHEC policy focused on acute ischemic stroke (AIS), ST-segment elevation myocardial infarction (STEMI), septic shock, and major trauma. A difference-in-differences estimation was used to assess the impact of the CHEC policy exposure (AIS and STEMI) on clinical practice and outcomes, compared with the unexposed counterfactual of septic shock. We selected diagnosis and treatment guideline adherence process quality measures as primary outcome and medical utilization, upward transfer rate, short-term and long-term mortality as secondary outcomes. Taiwan National Health Insurance 2005 Longitudinal Health Insurance Database contains one million random cases, including time-sensitive disease samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e In our cohort of 9,923 cases, refined through 1:1 propensity score matching, 56% were male, mostly older adults. The CHEC policy significantly reduced medical orders and major diagnostic indicators, yet diagnostic fees notably increased. This led to a decrease in mortality rates, ultimately lowering overall medical expenses. Septic shock cases showed marked reductions in both primary diagnosis indicators and medical orders. In contrast, primary treatment indicators for AIS and STEMI rose, supporting the hypothesis of a health policy spotlight effect.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eThis study highlights the CHEC policy's dual effects on reducing costs and enhancing patient outcomes. We observed a health policy spotlight effect, which led to a disproportionate improvement in guideline adherence and process quality for CTSDs that have time-based surveillance indicators.\u003c/p\u003e","manuscriptTitle":"Health Policy Spotlight Effects on Critical Time-Sensitive Diseases: Evidence from Taiwan Categorization of Hospital Emergency Capability Policy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 20:45:08","doi":"10.21203/rs.3.rs-4697511/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4515a87e-1924-4804-b8ad-ce9bb8297d48","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-29T15:53:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-09 20:45:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4697511","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4697511","identity":"rs-4697511","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}