Staffing level in the discharge planning department and average length of stay in acute care wards: A cross-sectional study using a nationwide hospital- and ward-level data in Japan

Staffing level in the discharge planning department and average length of stay in acute care wards: A cross-sectional study using a nationwide hospital- and ward-level data in Japan | 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 Staffing level in the discharge planning department and average length of stay in acute care wards: A cross-sectional study using a nationwide hospital- and ward-level data in Japan Ako Machida, Noriko Morioka, Masayo Kashiwagi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4302724/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 Background The transition from hospital to the next care setting is when care fragmentations are likely to occur, making discharge planning essential; however, the relationship between discharge planning and length of stay is unclear. This study aimed to investigate the association between staffing levels, particularly the number of nurses and medical social workers in the discharge planning department, and the average length of stay at the ward level in acute care hospitals in Japan. Methods Applying a cross-sectional approach, we used nationwide administrative hospital- and ward-level data from the fiscal year 2021. A total of 5,580 acute care wards in 1,101 hospitals across 206 secondary medical areas were included. A two-level multilevel regression analysis with random intercept stratified by three types of acute care ward functions was performed by adjusting ward, hospital, and regional characteristics. Results A total of 1,017 wards in 70 designated special function hospitals, 3,828 general acute care wards with a 7:1 patient-to-nurse ratio in 596 hospitals, and 735 general acute care wards with a 10:1 patient-to-nurse ratio in 435 hospitals were included in the final analysis. The average length of stay was 12.5 days, 11.5 days, and 18.0 days, respectively. There was a significant association between the total number of nurses and medical social workers per 100 hospital beds in acute care wards with a 7:1 patient-to-nurse ratio, but not in special function wards or in acute care wards with a 10:1 patient-to-nurse ratio. Sensitivity analysis that separately analysed the number of nurses and medical social workers showed that the number of nurses per 100 hospital beds was associated with the average length of stay in acute care wards with a 7:1 patient-to-nurse ratio. Medical social workers per 100 hospital beds showed no association in any ward model. Conclusions A greater number of nurses and medical social workers per 100 hospital beds in the discharge planning department, especially greater nurse staffing, was associated with short lengths of stay in acute care wards with a 7:1 patient-to-nurse ratio. discharge planning health service research acute care quantitative approach length of stay Figures Figure 1 Figure 2 Figure 3 1. Introduction The transition period from hospital to the next care setting is a vulnerable and unstable time when care fragmentations are likely to occur [1], making discharge planning essential. Since most patients who require discharge planning are elderly with chronic diseases and functionally dependent [2], their hospitalisations often tend to be prolonged [3,4]. Consequently, meticulous collaboration with the next phase of care is indispensable to prevent adverse events and rehospitalisation. Various interventions and outcomes have been conducted on discharge planning. As for intervention strategies, multidisciplinary collaboration or a dedicated nurse who serves as a key coordinator for discharge planning is considered effective [5]. From an outcome perspective, discharge planning has been effective in reducing mortality rates, readmission rates, and time to readmission and improving patient and family satisfaction [6,7]. The involvement of professionals may have led to good outcomes. However, the relationship between discharge planning and length of stay remains inconclusive. A recent systematic review of the effectiveness of discharge planning in acute care settings reported a reduction in the length of stay [8]. Another systematic review noted inconsistent associations with the length of stay owing to the heterogeneity of approaches and the lack of a unified component for discharge planning [9–11]. Japan’s population aged 65 years or older accounted for 29.1% of the total population in 2023, marking the highest percentage on record [12]. To overcome this super-aged society, the government has put forward integrated community care as part of its healthcare system, aiming for smooth transitions from hospitals to communities, especially from acute care hospitals to home care [13]. To effectively implement this system, policies such as the addition of discharge planning fees in 2008 and reports on the medical functions of hospital beds since 2014 have been boosted. The purpose of the discharge planning fee was expected to include the goal of reducing the length of stay, along with the promotion of integrated community care systems. An incentive is provided to hospitals by assigning at least one nurse and one medical social worker (MSW) to the discharge planning department. Relatively large hospitals had 10 or more nurses or MSWs allocated [14]. The practice status of discharge planning nurses [15,16] and readmissions within 30 days after discharge [17] have been examined in Japan; however, the impact on the length of stay has not been verified. Further, the relationship between increased staffing in the discharge planning department and length of stay is not well understood. 2. Background 2.1 Types of hospitals in Japan In Japan, hospitals and bed types are regulated by the Medical Care Act. Hospitals are classified into six types, including special function hospitals and general hospitals, which were the scope of this study [ 18 ]. Special function hospitals’ main role is to provide, develop, evaluate, and train in advanced medical care. As of 2023, there are 88 such hospitals nationwide. Contrastingly, the ward classification is determined by the medical fee system, not by the Medical Care Act, and this study focused on special function hospital admission fees and general ward admission fees. General ward admission fees are categorised into six types, ranging from Type 1 to Type 6. As the type increases to 6, some calculation criteria are eased. General ward admission fees for Type 1 require a 7:1 patient-to-nurse ratio, an average length of stay of 18 days or less, and a discharge-to-home rate of 80% or more. Basic hospitalisation fees for Type 2 to Type 6 require a 10:1 patient-to-nurse ratio and an average length of stay of 21 days or less (Ministry of Health, Labour and Welfare, 2023)[ 19 ]. Details on hospitals and wards are shown in Appendix 1. There is ongoing concern about the myriad of Type 1 general ward admissions fees (equates to a 7:1 patient-to-nurse ratio), and policies are being taken to reduce these admissions by strictly limiting the length of stay [ 20 ]. 2.2 Policies to improve discharge planning in the acute care setting in Japan In Japan, discharge planning initiatives in acute care hospitals began in earnest with the 2006 reform of the healthcare system. A new discharge planning fee payment was introduced following the 2008 revision of the medical fee system [ 21 ]. To receive this payment, the establishment of the discharge planning department became mandatory in 2010. The only requirement regarding discharge planning department personnel, regardless of hospital volume, is a minimum of one dedicated or full-time registered nurse and MSW, with at least one of each profession forming a pair [ 22 ]. There are two types of discharge planning fees. With discharge planning fee Type 1, at least one dedicated discharge planning staff member must be assigned to each of the two wards. Additionally, interventions include identifying patients with discharge difficulties, supporting patients’ and caregivers’ decision-making, holding conferences with other necessary professionals, and initiating a discharge support plan within seven days of patient admission. Discharge planning fee Type 2 has no staffing standards, but the same steps as for fee Type 1 must be performed as soon as possible. Moreover, some add-ons incentivise both the hospital and home care provider sides to conduct joint conferences before discharge. Length of stay is a key indicator of smooth coordination. 3. The study 3.1 Aim This study aimed to clarify the association between staffing in the discharge planning department at the hospital level and the average length of stay in acute care wards using nationwide data in Japan. 3.2 Hypothesis We hypothesise that higher staffing levels in the discharge planning department in hospitals are associated with shorter lengths of stay on a ward basis. 4. Methods 4.1 Study design A cross-sectional study was conducted using nationwide administrative data in Japan. 4.2 Samples and data source We obtained ward- and hospital-level data on adult acute care wards and hospitals from reports on the medical functions of hospital beds in fiscal year 2021. The 2014 revision of the Medical Care Act introduced the annual reporting system for all hospitals in Japan [ 14 ]. This reporting system mandates hospitals with general or long-term care beds to report annually to their local governments (prefectures) for the allocation of appropriate medical resources for each region. Items related to hospital characteristics, such as establishment status and personnel, are reported at the point of July 1, 2020, whereas other items are reported in full-year units from April 1, 2020, to March 31, 2021. The reporting system comprises ward- and hospital-level datasets: the ward-level dataset includes the function name based on the medical fee system, number of beds, number of inpatients, number of full-time equivalent nursing staff, number of rehabilitation staff, percentage of inpatients that meet the criteria of the Severity of a Patient’s Condition and Extent of a Patient’s Need for Medical/Nursing Care tool [ 23 ], and annual number of admissions and discharges; the hospital-level dataset includes the establishment category, function type, number of medical staff by profession, and number of staff by profession in the discharge planning department, among others. We included all nationwide general acute care wards for adults except critical care units such as intensive care units, high care units, and stroke care units or emergency room and their corresponding hospitals (Fig. 1 ). 4.3 Outcome The outcome variable for this study was the average length of stay at the ward level. Length of stay was defined as the number of hospitalisation days from the total number of admissions by prior location to the total number of discharge destinations, calculated as follows [ 24 ]: number of annual inpatients in total/{(total number of admissions by prior location + total number of discharge destinations)/2}. 4.4 Independent variable For the independent variable, we used staffing levels of nurses and MSWs affiliated with the discharge planning department per 100 hospital beds in the hospital. In Japan, the discharge planning fee in medical reimbursement was established in 2008 [ 21 ]. 4.5 Covariates We used ward-, hospital-, and regional-level characteristics from literature reviews as covariates [ 25 , 26 ]. 4.5.1 Ward-level characteristics To represent the ward-level characteristics, the following variables were employed: type of ward (special function, acute care wards with 7:1 and 10:1 patient-to-nurse ratios, respectively), the annual number of inpatient admissions, number of beds, number of full-time equivalent nursing staff, presence or absence of full-time rehabilitation staff, percentage by admission status (planned, unplanned, emergency), percentage by pre-admission location (home, inpatient transfer, patient transfer, long-term facilities, others), percentage by destination (home, inpatient transfer, patient transfer, long-term care facilities, others), in-hospital mortality rate, presence or absence of discharge planning fee (none, fee Type 1, fee Type 2), and percentage of inpatients meeting the criteria of the Severity of a Patient’s Condition and Extent of a Patient’s Need for Medical/Nursing Care tool. This assessment tool involves checking the presence or absence of items in three categories and scoring accordingly: Item A primarily focuses on monitoring and treatment, Item B on patients' conditions, and Item C on medical conditions, with eight, seven, and seven items, respectively (for more details, see [ 23 ]). To ensure the appropriate allocation of healthcare resources and nurse staffing, the Severity of a Patient’s Condition and the Extent of a Patient’s Need for Medical/Nursing Care tool has been a requirement for acute care wards to receive medical reimbursement since 2008 [ 19 ]. In acute care wards, nursing staff must record the nursing care needs on a 24-hour basis. Since individual patient records were not available, the following variables were utilised as approximations: the total number of annual general anaesthesia surgeries, the total number of annual cancer treatments (chemotherapy and radiation therapy), and the total number of annual rehabilitations. Because of the nature of the data, the numbers ranging from 0 to 9 were represented using symbolic notation and, therefore, were replaced with an average value of 4.5. 4.5.2 Hospital-level characteristics The characteristics of hospitals were represented by the following variables: type of ownership (national government, public medical institutions, social insurance bodies, private, others), implementation of the Diagnosis Procedure Combination payment system, adoption of special functions, establishment of regional medical care support, establishment of home health clinic support, adoption of emergency medical service, number of beds, number of physicians per 100 hospital beds, number of nursing staff per hospital 100 hospital beds, and number of rehabilitation staff per hospital 100 hospital beds. Nursing staff comprises registered and licensed practical nurses, excluding nursing assistants, while rehabilitation staff comprises physical, occupational, and speech-language therapists. 4.5.3 Regional-level characteristics The regional-level factors were adopted on the basis of secondary medical area (SMA) units. In Japan, an SMA is a regional unit defined by the Medical Care Act, which is a system that provides general inpatient care while considering geographical conditions, infrastructure, and other social factors [ 13 ]. An SMA is commonly employed to investigate the healthcare resources within a region [ 27 ]. As of 2021, 334 SMAs had been established for all 47 prefectures [ 14 ]. The variables—number of community care beds per 10,000 people aged 65 years or older, number of rehabilitation beds per 10,000 people aged 65 years or older, and number of long-term care beds per 10,000 people aged 65 years or older—were originally derived from reports of medical functions of hospital beds [ 14 ]. Subsequently, we calculated the number of beds by obtaining the population aged 65 years or older from the 2021 Resident Basic Registry [ 28 ]. The number of home healthcare support clinics was retrieved from publicly available data in the 2021 datasets [ 29 ]. For in-home service agencies, publicly disclosed data for long-term care information in 2021 was utilised [ 30 ]. The in-home service agencies variable was created by aggregating the number of service providers for care managers, home-visit care, home-visit night care, 24-hour home-visit service, home-visit rehabilitation, home-visit bathing, and home-visit nursing care. 4.6 Statistical analysis We excluded all cases with missing data in the selected covariates. We described the mean with standard deviation (SD) or the medians with interquartile ranges (IQR) for the numeric variables and the percentages for the categorical variables of the characteristics of the eligible samples. We depicted boxplots by ward functions to illustrate the distribution of the average length of stay at each ward and the staffing levels in the discharge planning department at the hospital level. To compare the average length of stay among the three wards’ functions, analysis of variance and student’s t -test were conducted. To compare the staffing levels at the discharge planning department among the three wards' functions, the Kruskal–Wallis test and Mann–Whitney U test were performed. To investigate the relationship between the staffing level in the discharge planning department and the average length of hospital stay in the ward, we performed a two-level (level 1 being the ward and level 2 being the hospital) multilevel regression analysis with random intercept stratified by the ward functions, since our data were nested within hospitals. The residual analysis using a null model and adding the regional-level as the third level suggested no significant difference in residual between level 2 and level 3; therefore, we proceeded with a two-level multilevel analysis. The covariates employed in the models were selected based on a univariate analysis that potentially correlated with outcome, identification in previous studies [ 31 , 32 ], and clinical significance. To test for multicollinearity, variance inflation factors were computed for each independent variable. P -values below .05 were considered significant. To check whether the results would change the model’s results, sensitivity analyses were conducted by segregating the number of nurses and MSWs in the discharge planning department. All analyses used Stata version 16.1 (Stata Corp. College Station, TX, USA). 5. Results 5.1 Characteristics of study samples We selected 5,580 acute care wards in 1,101 hospitals in 260 SMAs out of 28,030 wards in 7,019 hospitals in 334 SMAs nationwide as of 2021 (Fig. 1 ). Of these, there were 1,017 wards in 70 designated special function hospitals, 3,828 general acute care wards with a 7:1 patient-to-nurse ratio in 596 hospitals, and 735 general acute care wards with a 10:1 patient-to-nurse ratio in 435 hospitals. The characteristics of wards, hospitals, and SMAs are shown in Table 1 . The median (IQR) number of beds and the annual number of inpatients per ward were approximately 46 (40–50) beds and 13,070 (10,445–14,997) inpatients, respectively—almost the same among the three ward types. The percentage of inpatients who met two or more criteria in Item A and three or more criteria in Item B in the Severity of a Patient’s Condition and Extent of a Patient's Need for Medical/Nursing Care tool was highest in acute care wards with a 7:1 patient-to-nurse ration, at 19.0%. The admission rate from long-term care facilities was 0.2% (IQR 0.1–0.7) for special function wards, 1.8% (IQR 0.7–3.7) for acute care wards with a 7:1 patient-to-nurse ratio, and 5.3% (IQR 2.0–11.9) for acute care wards with a 10:1 patient-to-nurse ratio. Regarding the discharge destination by ward type, the discharge rate to home was 83.3% (IQR 71.8–90.4) for special function wards, 78.0% (IQR 66.6–86.3) for acute care wards with a 7:1 patient-to-nurse ratio, and 75.5% (IQR 61.1–85.7) for acute care wards with a 10:1 patient-to-nurse ratio. The discharge rate to long-term care facilities was 0.3% (IQR 0.1–0.7) for special function wards, 1.9% (IQR 0.8–3.5) for acute care wards with a 7:1 patient-to-nurse ratio, and 6.2% (IQR 2.5–12.9) for acute care wards with a 10:1 patient-to-nurse ratio. Regarding discharge planning fees, it was calculated in all wards only for special function beds. (Table 1 ). The means (SDs) number of hospital beds were 757 (184.62) for designated special function hospitals, 345 (96.93) for acute care hospitals with a 7:1 patient-to-nurse ratio, and 101 (79.33) for acute care hospitals with a 10:1 patient-to-nurse ratio. For type of establishment, national or public medical institution hospitals accounted for the largest proportion of hospitals with special function wards (68.6%, 48 hospitals). Private hospitals had the highest proportions of acute wards with a 7:1 (41.9%, 250 hospitals) and 10:1 (68.7%, 299 hospitals) patient-to-nurse ratios. Special function hospitals were located in areas with a greater number of SMAs than were wards with acute care wards with 7:1 or 10:1 patient-to-nurse ratios. Table 1 Characteristics of study participants Total Special function wards Acute care wards with a 7:1 patient-to-nurse ratio Acute care wards with a 10:1 patient-to-nurse ratio ( n = 5,580) ( n = 1,017) ( n = 3,828) ( n = 735) Variables n /median %/IQR n /median %/IQR n /median %/IQR n /median %/IQR Annual total number of inpatient admissions in the ward 13,070 10,445–14,997 13,033 11,059–14,726 13,205 10,514–15,155 12,394 9,684–14,572 Number of beds per ward 46 40–50 45 41–50 46 41–50 47 40–52 Full-time equivalent nursing staff per ward 32 28–94 30 26–33 29 25–33 22 18–26 Presence or absence of full-time rehabilitation staff assigned to the ward ( n , %) No 5119.0 91.7 1008.0 99.1 3567.0 93.2 544.0 74.0 Yes 461.0 8.3 9.0 0.9 261.0 6.8 191.0 26.0 Percentage of admissions considering annual total number of inpatients by route Planned admissions 67.9 51.5–80.2 83.1 74.9–89.1 66.0 52.5–77.1 45.0 27.6–61.8 Unplanned admissions 10.0 4.1–20.4 7.6 3.9–13.6 9.6 3.9–18.9 26.3 10.2–45.7 Emergency admissions 16.6 7.8–29.7 6.7 3.1–13.0 19.6 10.8–31.8 18.5 6.0–37.8 Percentage of admissions considering annual total number of inpatients by pre-admission location Admissions from home 80.5 69.0–88.5 86.5 77.0–93.0 79.1 68.0–86.9 79.9 66.3–89.0 Inpatient transfers 11.7 4.3–22.8 9.4 4.0–18.2 14.3 6.8–24.9 2.1 0.0–8.4 Transfers from other hospitals 1.8 1.0–3.2 1.6 0.8–2.9 1.7 0.9–2.8 4.1 1.9–8.5 Admissions from long-term facilities 1.5 0.4–3.7 0.2 0.1–0.7 1.8 0.7–3.7 5.3 2.0–11.9 Other admissions 0.0 0.0–0.1 0.0 0.0–0.1 0.0 0.0–0.1 0.0 0.0–0.0 Percentage of discharges considering annual total number of inpatient discharges Discharges to home 78.6 66.9–87.2 83.3 71.8–90.4 78.0 66.6–86.3 75.5 61.1–85.7 Inpatient transfers 8.9 4.1–16.6 9.3 4.7–18.0 9.5 4.8–16.7 3.6 0.0–13.3 Transfers to other hospitals 5.6 3.1–10.6 4.0 2.1–7.1 6.1 3.4–11.5 6.1 3.8–10.7 Discharges to long-term facilities 1.6 0.5–3.7 0.3 0.1–0.7 1.9 0.8–3.5 6.2 2.5–12.9 Other discharges 0.0 0.0–0.0 0.0 0.0–0.1 0.0 0.0–0.0 0.0 0.0–0.0 Percentage of in-hospital mortality 2.0 0.8–3.9 1.0 0.4–1.9 2.1 1.0–3.8 4.4 1.8–7.4 Percentage of patients meeting the criteria in the Severity of a Patient’s Condition and Extent of a Patient’s Need for Medical/Nursing Care Tool among the annual total number of patients in the ward Two or more criteria in Item A and three or more criteria in Item B 18.0 12.5–23.1 15.2 10.1–20.1 19.0 14–23.9 15.9 8.1–22.5 Three or more criteria in Item C 11.8 2.4–21.55 15.6 3.5–22.6 12.9 3.8–22.3 2.7 0.0–10.0 Annual total number of general anesthesia surgeries 54.0 13.5–247.8 87.5 31.5–295 56.0 16.0–259.3 31.5 0.0–101 Annual total number of cancer treatments (chemotherapy and radiation therapy) 72.0 22.5–205.3 142.0 54–338.5 72.0 22.5–197.75 22.5 4.5–63.0 Annual total number of rehabilitations 327.0 98–590 228.0 82.0–411.0 369.0 107.0–636.5 322.0 78–552.0 Presence or absence of a discharge planning fee § None ( n , %) 288 5.2 0.0 0.0 79 2.1 209 28.4 Fee Type 1 ( n , %) 923 16.5 322 31.7 326 8.5 275 37.4 Fee Type 2 ( n , %) 4369 78.3 695 68.3 3423 89.4 251 34.2 Type of ownership* National government ( n , %) 1010 18.1 526 51.7 428 11.2 56 7.6 Public medical institutions ( n , %) 2152 38.6 117 11.5 1854 48.4 181 24.6 Social insurance bodies ( n , %) 126 2.3 0 0.0 125 3.2 1 0.2 Private ( n , %) 2101 37.7 374 36.8 1256 32.8 471 64.1 Other ( n , %) 191 3.4 0 0.0 165 4.3 26 3.5 Hospital adopted the DPC payment system ( n , %) No 696 12.5 0 0.0 151 3.9 545 74.2 Yes 4884 87.5 1017 100.0 3677 96.1 190 25.9 Hospital adopted special functions ( n , %) No 4563 81.8 0 0.0 3828 100.0 735 100.0 Yes 1017 18.2 1017 100.0 0 0.0 0 0.0 Hospital adopted regional medical care support ( n , %) No 2581 46.3 964 94.8 931 24.3 686 93.3 Yes 2999 53.8 53 5.2 2897 75.7 49 6.7 Hospital adopted home medical care support ( n , %) No 4774 85.6 1001 98.4 3245 84.8 528 71.8 Yes 806 14.4 16 1.6 583 15.2 207 28.2 Hospital adopted emergency medical services ( n , %) No 3184 57.1 257 25.3 2202 57.5 725 98.6 Yes 2396 42.9 760 74.7 1626 42.5 10 1.4 Population (100,000 people) 7.5 4.2–14.3 9.1 5.1–14.1 7.5 4.3–15.0 5.2 2.1–11.7 Proportion of individuals aged 65 years or older 28.0 24.8–30.5 26.9 22.5–29.3 28.0 24.8–30.6 28.9 26.3–32.8 Number of community care beds per 10,000 people aged 65 years or older 0.4 0.3–0.5 0.4 0.3–0.6 0.4 0.3–0.5 0.4 0.3–0.5 Number of rehabilitation beds per 10,000 people aged 65 years or older 0.6 0.4–0.6 0.6 0.4–0.6 0.5 0.4–0.6 0.5 0.4–0.6 Number of long-term care beds per 10,000 people aged 65 years or older 254.1 220.0–291.8 232.4 203.1–271.0 255.6 220.5–291.8 272.3 230.0–313.7 Number of home health care support clinics per 10,000 people aged 65 years or older 4.3 2.6–6.0 5.3 3.4–7.0 4.0 2.6–5.8 3.7 2.5–5.2 Number of in-home service agencies per 10,000 people aged 65 years or older 126.4 80.0–183.2 153.7 83.6–198.1 121.3 65.9–183.2 117.8 81.2–183.2 DPC: diagnosis procedure combination; IQR: interquartile range § With discharge planning fee Type 1, at least one dedicated discharge planning staff member must be assigned to each of the two wards. Additional interventions include identifying patients with difficulty in discharge, supporting patients' and caregivers’ decision-making, holding conferences with other necessary professionals, and initiating a discharge support plan within seven days of patient admission. Discharge planning fee Type 2 has no staffing standards, but the same steps as those for fee Type 1 must be performed as soon as possible. * The detailed classification of types of ownership is as follows. National government: Ministry of Health, Labour and Welfare, National Hospital Organization, National University Corporation, National Institute of Occupational Safety and Health, National Research Center for Advanced and Specialized Medical Care, and Japan Community Health Care Organizations. Public medical institutions: prefectures, municipalities, local incorporated administrative agencies, Japanese Red Cross, Saiseikai Imperial Gift Foundation, Hokkaido Social Service Association, National Welfare Federation, and Federation of National Health Insurance Organizations. Social insurance bodies: health insurance societies and their federations, mutual aid associations and their federations, and national health insurance societies. Private: medical corporations, public interest corporations, private university corporations, social welfare corporations, medical co-ops, and companies. 5.2 Average length of stay The average length of stay for all 5,580 wards was 12.5 days (SD 5.26). When stratified by ward type, the average length of stay was 12.5 days (SD 4.65) for special function wards, 11.5 days (SD 4.36) for acute care wards with a 7:1 patient-to-nurse ratio, and 18.0 days (SD 7.01) for acute care wards with a 10:1 patient-to-nurse ratio. The length of stay for acute care wards with a 10:1 patient-to-nurse ratio was significantly longer ( p < .001) compared with that for other wards (Fig. 2 ). 5.3 Staffing level in the discharge planning department The median number of nurses and MSWs per 100 hospital beds in the discharge planning department across all 1,101 hospitals was 2.9 (IQR 2.0–4.0). When stratified by ward type, median values were 2.1 (IQR 1.6–2.7) per 100 hospital beds for special function wards, 2.7 (IQR 2.0–3.7) per 100 hospital beds for acute care wards with a 7:1 patient-to-nurse ratio, and 3.5 (IQR 2.3–5) per 100 hospital beds for acute care wards with a 10:1 patient-to-nurse ratio. The number of nurses and MSWs in acute care wards with a 10:1 patient-to-nurse ratio was significantly higher ( p < .001). The proportion of nurses in the discharge planning department was 56.1% for hospitals with special function wards and 50% for hospitals with acute care wards with a 7:1 and 10:1 patient-to-nurse ratio, respectively. When considering the individual counts of nurses and MSWs per 100 hospital beds, there was a trend for higher staffing numbers for both nurses and MSWs in hospitals with acute care wards with a 10:1 patient-to-nurse ratio, followed by acute care wards with a 7:1 patient-to-nurse ratio and special function wards (Fig. 3 ). 5.4 Multilevel linear regression analysis for the length of stay and the discharge planning department structure Stratified analysis by ward type, while adjusting for covariates, revealed that an increase in one nurse or MSW per 100 hospital beds impacted the length of stay as follows: in hospitals with special function wards, coefficient (coef.) = -0.32, 95% confidence interval (CI) = -0.80 to 0.15, p = .18; in hospitals with acute care wards with a 7:1 patient-to-nurse ratio, coef. = -0.19, 95% CI = -0.33 to -0.06, p < .001; and in hospitals with acute care wards with a 10:1 patient-to-nurse ratio, coef. = -0.12, 95% CI = -0.38 to 0.13, p = .33 (Table 2 ). Sensitivity analysis separately analysing the number of nurses and MSWs showed that an increase of one nurse per 100 hospital beds was significantly associated with the average length of stay in acute care wards with a 7:1 patient-to-nurse ratio (coef. = -0.21, 95% CI = -0.38 to -0.04, p = .02), whereas an increase of one MSW per 100 hospital beds was non-significantly associated with the average length of stay in any ward model (Appendix 2–4). Table 2 Results of random intercept multilevel linear regression analysis for length of stay Model 1 Model 2 Model 3 Special function wards Acute wards with a 7:1 patient-to-nurse ratio Acute wards with a 10:1 patient-to-nurse ratio ( n = 1,017) ( n = 3,828) ( n = 735) Variables Coef. 95% CI P Coef. 95% CI P Coef. 95% CI P Total number of nurses and MSWs in the discharge planning sector per 100 hospital beds -0.32 -0.80 0.15 .18 -0.19 -0.33 -0.06 < .001 -0.12 -0.38 0.13 .33 Total number of nurses in the discharge planning sector per 100 hospital beds -0.10 -0.62 0.41 .70 -0.21 -0.38 -0.04 .02 -0.24 -0.54 0.07 .13 Total number of MSWs in the discharge planning sector per 100 hospital beds -0.47 -1.33 0.39 .28 -0.18 -0.39 0.03 .09 0.01 -0.39 0.41 .95 MSWs: medical social workers; Coef.: coefficient; 95% CI: 95% confidence interval Model 1: Multiple regression analysis was conducted using the following adjusting variables: discharge planning nurse ratio in the discharge planning sector; planned admissions in the annual total number of inpatients by route; admissions from home in the annual total by pre-admission location; transfers from other hospitals in the annual total pre-admission location; admissions from long-term facilities in the annual total pre-admission location; discharges to home in the annual total discharges; transfers to other hospitals in the annual total discharges; discharges to long-term facilities in the annual total discharges; patients meeting two or more criteria in item A and three or more criteria in item B in the Severity of a Patient’s Condition and Extent of a Patient’s Need for Medical/Nursing Care tool; the annual total number of general anesthesia surgeries; types of ownership (national government, public medical institutions, privates, other); hospital-adopted regional and medical care support and emergency medical service; number of beds per hospital; physicians, nursing staff, and rehabilitation staff per 100 beds; discharge planning fee (none and Type 2); population (100,000 people); proportion of individuals aged 65 years or older; number of community care beds, rehabilitation beds, and long-term beds per 10,000 people aged 65 years or older; and number of home health care support clinics and in-home service agencies per 10,000 people aged 65 years or older. Models 2 and 3: All types of ownership, discharge planning fee Type 1, and hospital-adopted diagnosis procedure combination were added to the adjusting variables in Model 1. 6. Discussion A higher number of nurses and MSWs per 100 hospital beds in the discharge planning department, especially higher nurse staffing, was associated with a lower length of stay in acute care wards with a 7:1 patient-to-nurse ratio, but with no significant associations in the special function wards and acute care wards with a 10:1 patient-to-nurse ratio. Previous studies have examined the effect of discharge planning on the decrease in the length of hospital stay and showed mixed results: discharge planning interventions conducted by a multidisciplinary team including clinical nurse specialists [ 33 ] and one systematic review [ 34 ] reported no difference or prolonged length of stay. Other randomised controlled trials or comparative studies targeting specific settings, such as exacerbated chronic obstructive pulmonary disease [ 35 ], elderly patients [ 36 ], and general medical unit interventions [ 37 ] revealed a significant reduction in the length of stay. Our findings support the potential contribution of higher staffing levels in the discharge planning department, particularly in terms of nurse staffing, in reducing the length of stay at the ward level in acute care wards with a 7:1 patient-to-nurse ratio. Although this study could not determine the mechanism owing to the study design, this result might be explained by two reasons: strengthening the activities for discharge support for patients and fostering a culture of discharge planning throughout the hospital. Regarding the former, in acute care hospitals in Japan, the specific interventions conducted by discharge planning nurses include screening patients who face challenges with discharge, formulating discharge support plans, collecting information from relevant local professionals during hospitalisation, supporting decision-making for patients and their families, and coordinating pre-discharge conferences with visiting physicians and home care nurses [ 38 , 39 ]. Additionally, some discharge planning nurses provide follow-up services such as home visiting care after patients are discharged if needed [ 16 ]. Thus, more staffing per inpatient in the discharge planning department improved the process and might contribute to shortening the average length of stay as a result. Regarding the second reason, hospitals in which discharge planning nurses are well-staffed, there is likely capacity to expand the scope of activities beyond those mentioned earlier, such as providing education and training on discharge planning for ward nurses [ 40 , 41 ] and other professionals. Consequently, by increasing cross-organisational activities, discharge planning nurses had a spillover effect in fostering a culture of discharge planning throughout the hospital, which may have influenced the positive outcomes of this study. This finding reinforces the need for appropriate staffing levels in discharge planning departments tailored to hospital functions. Noteworthy, this study showed the association between higher staffing in the discharge planning department and the shorter average length of stay only in acute care wards with a 7:1 patient-to-nurse ratio, but not in special function hospitals or hospitals with a 10:1 patient-to-nurse ratio. This may be influenced by the difference in hospital types and policies for differentiation of bed functions. As for special function hospitals, because of the nature of providing advanced medical care in a national or large university hospital with a substantial number of beds, special function hospitals may prioritise improving treatment outcomes rather than shortening hospital days [ 42 , 43 ]. Additionally, as many patients with complex medical conditions are referred from across the country, the hospital itself is not tied to a specific region. Therefore, since the transition to the alternative phase of care often starts from scratch, reliance on post-discharge local resources, rather than the number of staff in the discharge planning department, may have a significant impact. In terms of the length of stay criteria set for each ward under the medical fee system, wards with a 7:1 patient-to-nurse ratio for the length of stay calculation typically have a period of 18 days; this is much shorter than the criteria for special function hospitals (within 26–28 days) and acute care wards with a 10:1 patient-to-nurse ratio (21 days). The revision of the medical fee schedule in 2024 is expected to further shorten the length of stay to within 16 days. This pressure to reduce the average length of stay limit on reimbursement may make staff in discharge planning departments place even more importance on reducing the length of stay as an outcome of their activities. Contrastingly, in acute care wards with a patient-to-nurse ratio of 10:1, there may be patients with relatively mild conditions, or continue their hospitalisation owing to reasons related to the absence of caregivers or other social factors, despite not needing hospitalisation from a medical standpoint. This situation—'social admission’ [ 44 ]—could potentially contribute to an extended length of stay. Staff in discharge planning departments in acute wards with a 10:1 patient-to-nurse ratio may be more concerned with securing a discharge destination and consuming more time for coordination with caregivers and staff at the next care setting than with getting patients discharged quickly. 6.1 Strengths and limitations The major strength of this study is its focus on acute care hospitals nationwide in Japan, making it highly generalisable within the country. However, due to differences in systems and policies among countries, it is challenging to directly adapt them to overseas contexts. This study has two main limitations. First, the data were collected during the COVID-19 pandemic, resulting in potential deviations from the figures seen in typical years. Second, in the association between the number of nurses and MSWs per 100 hospital beds and length of stay in acute care wards with a 7:1 patient-to-nurse ratio, the coefficient was extremely small (-0.19). However, even though it may be slight, for patients with prolonged hospitalisations for non-medical reasons, in hospitals experiencing patient overcrowding owing to bed shortage (although bed occupancy rates were not examined in this study), and in countries such as Japan where policies for reducing the length of stay will continue in the case of acute care wards, a coefficient of -0.19 is considered clinically significant. To increase the accuracy of evaluating the effectiveness of discharge planning, it is necessary to collect more detailed information on structural aspects such as the organisational positioning of the discharge planning department, the years of experience of nurses and MSWs, the number of cases they handle, the presence of a certified nurse or certified nurse specialist qualifications, and information on other tasks besides discharge planning. Furthermore, combining this information with actual specialised interventions and patient characteristics would help clarify the mechanism of discharge planning. 7. Conclusion This study examined the association between staffing in the discharge planning department at the hospital level and the average length of stay in acute care wards using nationwide data. Higher staffing in the discharge planning department, particularly of nurses in acute care wards with a 7:1 patient-to-nurse ratio, potentially contributed to the reduction in the length of stay. Further research by obtaining more detailed information on the structure of the discharge planning department and comparing this information with combined patient data is needed. Abbreviations MSW medical social worker SMA secondary medical area SD standard deviation IQR interquartile ranges Declarations Ethics approval and consent to participate This study did not use individual data. All data were obtained from open source and are available on the website. This study adhered to the principles of the Declaration of Helsinki and the study protocol was approved by the Tokyo Medical and Dental University Ethics Review Board on April 17, 2024 (No. C2024-02). Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution AM designed the study, acquired the data, conducted the statistical analyses, and drafted and revised the manuscript. NM and MK supervised the study and statistical analyses and revised the manuscript. All authors agreed to be accountable for all aspects of the work and gave final approval of the manuscript to be published. Acknowledgments Not Applicable. Data Availability The datasets generated and analysed during the current study are publicly available in the Ministry of Health, Labour and Welfare (http://www.mhlw.go.jp/index.html, in Japanese), Ministry of Internal Affairs and Communications (http://www.soumu.go.jp/, in Japanese), and Japanses government repository (http://www.e-stat.go.jp/en). The detailed data are listed in the references. References Coleman EA. 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Supplementary Files Appendix1Theclassificationofhospitals0420.docx Appendix2Resultsspecialfunctionwards0420.docx Appendix3Resultsacutewards70420.docx Appendix4Resultsacutewards10.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4302724","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":295419747,"identity":"05f10089-ac24-4f54-b162-f2230f8528ea","order_by":0,"name":"Ako Machida","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYHACNoYENgYG9gbmBoYPDAyMDSAxCWK08BxgbGCckUCsFgaoFmYemBZ8QLf98LMHD8rsGHikG5s/2/7YJtvAfvgBg+UO3FrMzqSZGyScS2bgkTnYJp2TcNu4gSfNgEHyDB4tB3LYJBLbmBnsQSRQS2IDQw4Dg2QbHi3n34C01DPwSCQ2f7YAaeF/Q0DLDbAth0FaGqQZQFokCNly45mZRMK54zxALW2SPWm3jdsknhkcwOuX88nPJH+UVcvxSCQf/vDD5rZsP3/yw8eSeEIMBnjgLGAcMRyWbCCsBRUwfiRZyygYBaNgFAxjAAAOqk+4G8aUUQAAAABJRU5ErkJggg==","orcid":"","institution":"Tokyo Medical and Dental University","correspondingAuthor":true,"prefix":"","firstName":"Ako","middleName":"","lastName":"Machida","suffix":""},{"id":295419748,"identity":"e1dd8da7-4766-435a-8f48-c0da24adaba1","order_by":1,"name":"Noriko Morioka","email":"","orcid":"","institution":"Tokyo Medical and Dental University","correspondingAuthor":false,"prefix":"","firstName":"Noriko","middleName":"","lastName":"Morioka","suffix":""},{"id":295419749,"identity":"a5a9f74d-fa5b-454e-95e5-a54e6819d711","order_by":2,"name":"Masayo Kashiwagi","email":"","orcid":"","institution":"Tokyo Medical and Dental University","correspondingAuthor":false,"prefix":"","firstName":"Masayo","middleName":"","lastName":"Kashiwagi","suffix":""}],"badges":[],"createdAt":"2024-04-22 03:01:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4302724/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4302724/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55630573,"identity":"86bd4d64-5df3-4cb4-8fcb-385db91f8477","added_by":"auto","created_at":"2024-04-30 19:35:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":98184,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of sample selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSMA: secondary medical area\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4302724/v1/b8ab5da9efd463f61af9f765.png"},{"id":55631527,"identity":"dfdd6832-3389-42fd-b057-a8523f99cce3","added_by":"auto","created_at":"2024-04-30 19:43:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25836,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAverage length of stay by ward category\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e* Student’s t-test\u003c/p\u003e\n\u003cp\u003eANOVA p \u0026lt; .001\u003c/p\u003e\n\u003cp\u003eSpecial function: special function wards. Under the medical fee system, special function hospitals are required to provide advanced medical care, develop advanced medical technologies, and conduct advanced care training. In FY2021, there were no hospitals other than a 7:1 patient-to-nurse ratio (Ministry of Health, Labour and Welfare, 2021).\u003c/p\u003e\n\u003cp\u003eAcute care 7:1: acute care wards with a 7:1 patient-to-nurse ratio.\u003c/p\u003e\n\u003cp\u003eAcute care 10:1: acute care wards with a 10:1 patient-to-nurse ratio.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4302724/v1/e223b41c148fcdd89e0e1751.png"},{"id":55630574,"identity":"caca902c-2328-4810-9173-f0e1e790a33b","added_by":"auto","created_at":"2024-04-30 19:35:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85838,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNumber of nurses and MSWs per 100 hospital beds in the discharge planning sector\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMSW: medical social worker\u003c/p\u003e\n\u003cp\u003e* Mann–Whitney U-test\u003c/p\u003e\n\u003cp\u003eKruskal–Wallis test: Total number of nurses and MSWs \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, total number of nurses \u003cem\u003ep\u003c/em\u003e \u0026lt; .05, and total number of MSWs \u003cem\u003ep\u003c/em\u003e \u0026lt; .001.\u003c/p\u003e\n\u003cp\u003eSpecial function: special function wards. Under the medical fee system, special function hospitals are required to provide advanced medical care, develop advanced medical technologies, and conduct advanced care training. In Fiscal Year 2021, there were no hospitals other than a 7:1 patient-to-nurse ratio [18].\u003c/p\u003e\n\u003cp\u003eAcute care 7:1: Acute care wards with a 7:1 patient-to-nurse ratio.\u003c/p\u003e\n\u003cp\u003eAcute care 10:1: Acute care wars with a 10:1 patient-to-nurse ratio.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4302724/v1/9cea55cb09f68bbeaa12ec0e.png"},{"id":69261360,"identity":"40313c0e-c938-471e-99e7-eead14fd1d48","added_by":"auto","created_at":"2024-11-18 13:47:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1320925,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4302724/v1/49d4dc62-ee99-4f4e-a6b1-2530bb6e641e.pdf"},{"id":55630578,"identity":"fd5c3f54-3a07-44bd-b35a-d16af5e61980","added_by":"auto","created_at":"2024-04-30 19:35:58","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":23247,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1Theclassificationofhospitals0420.docx","url":"https://assets-eu.researchsquare.com/files/rs-4302724/v1/158a30e492bd31c5a0c6b2c9.docx"},{"id":55630576,"identity":"2b27032d-c5af-4f21-9a51-271a685d37fe","added_by":"auto","created_at":"2024-04-30 19:35:57","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":40119,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix2Resultsspecialfunctionwards0420.docx","url":"https://assets-eu.researchsquare.com/files/rs-4302724/v1/bdb639fca9b262e2957106de.docx"},{"id":55631528,"identity":"78927a5e-6e2b-4128-ace8-bbb806b4c3a2","added_by":"auto","created_at":"2024-04-30 19:43:57","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":40037,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix3Resultsacutewards70420.docx","url":"https://assets-eu.researchsquare.com/files/rs-4302724/v1/6682233de833b528d4846c8c.docx"},{"id":55630579,"identity":"c81311ac-eb1e-4eb9-b679-00d470992136","added_by":"auto","created_at":"2024-04-30 19:35:58","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":40706,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix4Resultsacutewards10.docx","url":"https://assets-eu.researchsquare.com/files/rs-4302724/v1/f3e1e5b1e94281042db29eb6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Staffing level in the discharge planning department and average length of stay in acute care wards: A cross-sectional study using a nationwide hospital- and ward-level data in Japan","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe transition period from hospital to the next care setting is a vulnerable and unstable time when care fragmentations are likely to occur [1], making discharge planning essential. Since most patients who require discharge planning are elderly with chronic diseases and functionally dependent [2], their hospitalisations often tend to be prolonged [3,4]. Consequently, meticulous collaboration with the next phase of care is indispensable to prevent adverse events and rehospitalisation.\u003c/p\u003e\n\u003cp\u003eVarious interventions and outcomes have been conducted on discharge planning. As for intervention strategies, multidisciplinary collaboration or a dedicated nurse who serves as a key coordinator for discharge planning is considered effective [5]. From an outcome perspective, discharge planning has been effective in reducing mortality rates, readmission rates, and time to readmission and improving patient and family satisfaction [6,7]. The involvement of professionals may have led to good outcomes. However, the relationship between discharge planning and length of stay remains inconclusive. A recent systematic review of the effectiveness of discharge planning in acute care settings reported a reduction in the length of stay [8]. Another systematic review noted inconsistent associations with the length of stay owing to the heterogeneity of approaches and the lack of a unified component for discharge planning [9\u0026ndash;11].\u003c/p\u003e\n\u003cp\u003eJapan\u0026rsquo;s population aged 65 years or older accounted for 29.1% of the total population in 2023, marking the highest percentage on record [12]. To overcome this super-aged society, the government has put forward integrated community care as part of its healthcare system, aiming for smooth transitions from hospitals to communities, especially from acute care hospitals to home care [13]. To effectively implement this system, policies such as the addition of discharge planning fees in 2008 and reports on the medical functions of hospital beds since 2014 have been boosted. The purpose of the discharge planning fee was expected to include the goal of reducing the length of stay, along with the promotion of integrated community care systems. An incentive is provided to hospitals by assigning at least one nurse and one medical social worker (MSW) to the discharge planning department. Relatively large hospitals had 10 or more nurses or MSWs allocated [14]. The practice status of discharge planning nurses [15,16] and readmissions within 30 days after discharge [17] have been examined in Japan; however, the impact on the length of stay has not been verified. Further, the relationship between increased staffing in the discharge planning department and length of stay is not well understood.\u003c/p\u003e"},{"header":"2. Background","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Types of hospitals in Japan\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn Japan, hospitals and bed types are regulated by the Medical Care Act. Hospitals are classified into six types, including special function hospitals and general hospitals, which were the scope of this study [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Special function hospitals\u0026rsquo; main role is to provide, develop, evaluate, and train in advanced medical care. As of 2023, there are 88 such hospitals nationwide. Contrastingly, the ward classification is determined by the medical fee system, not by the Medical Care Act, and this study focused on special function hospital admission fees and general ward admission fees. General ward admission fees are categorised into six types, ranging from Type 1 to Type 6. As the type increases to 6, some calculation criteria are eased. General ward admission fees for Type 1 require a 7:1 patient-to-nurse ratio, an average length of stay of 18 days or less, and a discharge-to-home rate of 80% or more. Basic hospitalisation fees for Type 2 to Type 6 require a 10:1 patient-to-nurse ratio and an average length of stay of 21 days or less (Ministry of Health, Labour and Welfare, 2023)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Details on hospitals and wards are shown in Appendix 1. There is ongoing concern about the myriad of Type 1 general ward admissions fees (equates to a 7:1 patient-to-nurse ratio), and policies are being taken to reduce these admissions by strictly limiting the length of stay [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Policies to improve discharge planning in the acute care setting in Japan\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn Japan, discharge planning initiatives in acute care hospitals began in earnest with the 2006 reform of the healthcare system. A new discharge planning fee payment was introduced following the 2008 revision of the medical fee system [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. To receive this payment, the establishment of the discharge planning department became mandatory in 2010. The only requirement regarding discharge planning department personnel, regardless of hospital volume, is a minimum of one dedicated or full-time registered nurse and MSW, with at least one of each profession forming a pair [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. There are two types of discharge planning fees. With discharge planning fee Type 1, at least one dedicated discharge planning staff member must be assigned to each of the two wards. Additionally, interventions include identifying patients with discharge difficulties, supporting patients\u0026rsquo; and caregivers\u0026rsquo; decision-making, holding conferences with other necessary professionals, and initiating a discharge support plan within seven days of patient admission. Discharge planning fee Type 2 has no staffing standards, but the same steps as for fee Type 1 must be performed as soon as possible. Moreover, some add-ons incentivise both the hospital and home care provider sides to conduct joint conferences before discharge. Length of stay is a key indicator of smooth coordination.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. The study","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Aim\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study aimed to clarify the association between staffing in the discharge planning department at the hospital level and the average length of stay in acute care wards using nationwide data in Japan.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Hypothesis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe hypothesise that higher staffing levels in the discharge planning department in hospitals are associated with shorter lengths of stay on a ward basis.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003e4.1 Study design\u003c/h2\u003e\n\u003cp\u003eA cross-sectional study was conducted using nationwide administrative data in Japan.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003e4.2 Samples and data source\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eWe obtained ward- and hospital-level data on adult acute care wards and hospitals from reports on the medical functions of hospital beds in fiscal year 2021. The 2014 revision of the Medical Care Act introduced the annual reporting system for all hospitals in Japan [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. This reporting system mandates hospitals with general or long-term care beds to report annually to their local governments (prefectures) for the allocation of appropriate medical resources for each region. Items related to hospital characteristics, such as establishment status and personnel, are reported at the point of July 1, 2020, whereas other items are reported in full-year units from April 1, 2020, to March 31, 2021. The reporting system comprises ward- and hospital-level datasets: the ward-level dataset includes the function name based on the medical fee system, number of beds, number of inpatients, number of full-time equivalent nursing staff, number of rehabilitation staff, percentage of inpatients that meet the criteria of the Severity of a Patient\u0026rsquo;s Condition and Extent of a Patient\u0026rsquo;s Need for Medical/Nursing Care tool [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], and annual number of admissions and discharges; the hospital-level dataset includes the establishment category, function type, number of medical staff by profession, and number of staff by profession in the discharge planning department, among others. We included all nationwide general acute care wards for adults except critical care units such as intensive care units, high care units, and stroke care units or emergency room and their corresponding hospitals (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e4.3 Outcome\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThe outcome variable for this study was the average length of stay at the ward level. Length of stay was defined as the number of hospitalisation days from the total number of admissions by prior location to the total number of discharge destinations, calculated as follows [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]: number of annual inpatients in total/{(total number of admissions by prior location\u0026thinsp;+\u0026thinsp;total number of discharge destinations)/2}.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e4.4 Independent variable\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eFor the independent variable, we used staffing levels of nurses and MSWs affiliated with the discharge planning department per 100 hospital beds in the hospital. In Japan, the discharge planning fee in medical reimbursement was established in 2008 [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e4.5 Covariates\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eWe used ward-, hospital-, and regional-level characteristics from literature reviews as covariates [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n\u003ch2\u003e4.5.1 Ward-level characteristics\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eTo represent the ward-level characteristics, the following variables were employed: type of ward (special function, acute care wards with 7:1 and 10:1 patient-to-nurse ratios, respectively), the annual number of inpatient admissions, number of beds, number of full-time equivalent nursing staff, presence or absence of full-time rehabilitation staff, percentage by admission status (planned, unplanned, emergency), percentage by pre-admission location (home, inpatient transfer, patient transfer, long-term facilities, others), percentage by destination (home, inpatient transfer, patient transfer, long-term care facilities, others), in-hospital mortality rate, presence or absence of discharge planning fee (none, fee Type 1, fee Type 2), and percentage of inpatients meeting the criteria of the Severity of a Patient\u0026rsquo;s Condition and Extent of a Patient\u0026rsquo;s Need for Medical/Nursing Care tool. This assessment tool involves checking the presence or absence of items in three categories and scoring accordingly: Item A primarily focuses on monitoring and treatment, Item B on patients' conditions, and Item C on medical conditions, with eight, seven, and seven items, respectively (for more details, see [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]). To ensure the appropriate allocation of healthcare resources and nurse staffing, the Severity of a Patient\u0026rsquo;s Condition and the Extent of a Patient\u0026rsquo;s Need for Medical/Nursing Care tool has been a requirement for acute care wards to receive medical reimbursement since 2008 [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. In acute care wards, nursing staff must record the nursing care needs on a 24-hour basis. Since individual patient records were not available, the following variables were utilised as approximations: the total number of annual general anaesthesia surgeries, the total number of annual cancer treatments (chemotherapy and radiation therapy), and the total number of annual rehabilitations. Because of the nature of the data, the numbers ranging from 0 to 9 were represented using symbolic notation and, therefore, were replaced with an average value of 4.5.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n\u003ch2\u003e4.5.2 Hospital-level characteristics\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThe characteristics of hospitals were represented by the following variables: type of ownership (national government, public medical institutions, social insurance bodies, private, others), implementation of the Diagnosis Procedure Combination payment system, adoption of special functions, establishment of regional medical care support, establishment of home health clinic support, adoption of emergency medical service, number of beds, number of physicians per 100 hospital beds, number of nursing staff per hospital 100 hospital beds, and number of rehabilitation staff per hospital 100 hospital beds. Nursing staff comprises registered and licensed practical nurses, excluding nursing assistants, while rehabilitation staff comprises physical, occupational, and speech-language therapists.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n\u003ch2\u003e4.5.3 Regional-level characteristics\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThe regional-level factors were adopted on the basis of secondary medical area (SMA) units. In Japan, an SMA is a regional unit defined by the Medical Care Act, which is a system that provides general inpatient care while considering geographical conditions, infrastructure, and other social factors [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. An SMA is commonly employed to investigate the healthcare resources within a region [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. As of 2021, 334 SMAs had been established for all 47 prefectures [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. The variables\u0026mdash;number of community care beds per 10,000 people aged 65 years or older, number of rehabilitation beds per 10,000 people aged 65 years or older, and number of long-term care beds per 10,000 people aged 65 years or older\u0026mdash;were originally derived from reports of medical functions of hospital beds [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. Subsequently, we calculated the number of beds by obtaining the population aged 65 years or older from the 2021 Resident Basic Registry [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. The number of home healthcare support clinics was retrieved from publicly available data in the 2021 datasets [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. For in-home service agencies, publicly disclosed data for long-term care information in 2021 was utilised [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. The in-home service agencies variable was created by aggregating the number of service providers for care managers, home-visit care, home-visit night care, 24-hour home-visit service, home-visit rehabilitation, home-visit bathing, and home-visit nursing care.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003ch2\u003e4.6 Statistical analysis\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eWe excluded all cases with missing data in the selected covariates. We described the mean with standard deviation (SD) or the medians with interquartile ranges (IQR) for the numeric variables and the percentages for the categorical variables of the characteristics of the eligible samples. We depicted boxplots by ward functions to illustrate the distribution of the average length of stay at each ward and the staffing levels in the discharge planning department at the hospital level. To compare the average length of stay among the three wards\u0026rsquo; functions, analysis of variance and student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test were conducted. To compare the staffing levels at the discharge planning department among the three wards' functions, the Kruskal\u0026ndash;Wallis test and Mann\u0026ndash;Whitney U test were performed. To investigate the relationship between the staffing level in the discharge planning department and the average length of hospital stay in the ward, we performed a two-level (level 1 being the ward and level 2 being the hospital) multilevel regression analysis with random intercept stratified by the ward functions, since our data were nested within hospitals. The residual analysis using a null model and adding the regional-level as the third level suggested no significant difference in residual between level 2 and level 3; therefore, we proceeded with a two-level multilevel analysis. The covariates employed in the models were selected based on a univariate analysis that potentially correlated with outcome, identification in previous studies [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e], and clinical significance. To test for multicollinearity, variance inflation factors were computed for each independent variable. \u003cem\u003eP\u003c/em\u003e-values below .05 were considered significant. To check whether the results would change the model\u0026rsquo;s results, sensitivity analyses were conducted by segregating the number of nurses and MSWs in the discharge planning department. All analyses used Stata version 16.1 (Stata Corp. College Station, TX, USA).\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"5. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003ch2\u003e5.1 Characteristics of study samples\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eWe selected 5,580 acute care wards in 1,101 hospitals in 260 SMAs out of 28,030 wards in 7,019 hospitals in 334 SMAs nationwide as of 2021 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Of these, there were 1,017 wards in 70 designated special function hospitals, 3,828 general acute care wards with a 7:1 patient-to-nurse ratio in 596 hospitals, and 735 general acute care wards with a 10:1 patient-to-nurse ratio in 435 hospitals. The characteristics of wards, hospitals, and SMAs are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The median (IQR) number of beds and the annual number of inpatients per ward were approximately 46 (40\u0026ndash;50) beds and 13,070 (10,445\u0026ndash;14,997) inpatients, respectively\u0026mdash;almost the same among the three ward types. The percentage of inpatients who met two or more criteria in Item A and three or more criteria in Item B in the Severity of a Patient\u0026rsquo;s Condition and Extent of a Patient's Need for Medical/Nursing Care tool was highest in acute care wards with a 7:1 patient-to-nurse ration, at 19.0%. The admission rate from long-term care facilities was 0.2% (IQR 0.1\u0026ndash;0.7) for special function wards, 1.8% (IQR 0.7\u0026ndash;3.7) for acute care wards with a 7:1 patient-to-nurse ratio, and 5.3% (IQR 2.0\u0026ndash;11.9) for acute care wards with a 10:1 patient-to-nurse ratio. Regarding the discharge destination by ward type, the discharge rate to home was 83.3% (IQR 71.8\u0026ndash;90.4) for special function wards, 78.0% (IQR 66.6\u0026ndash;86.3) for acute care wards with a 7:1 patient-to-nurse ratio, and 75.5% (IQR 61.1\u0026ndash;85.7) for acute care wards with a 10:1 patient-to-nurse ratio. The discharge rate to long-term care facilities was 0.3% (IQR 0.1\u0026ndash;0.7) for special function wards, 1.9% (IQR 0.8\u0026ndash;3.5) for acute care wards with a 7:1 patient-to-nurse ratio, and 6.2% (IQR 2.5\u0026ndash;12.9) for acute care wards with a 10:1 patient-to-nurse ratio. Regarding discharge planning fees, it was calculated in all wards only for special function beds. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The means (SDs) number of hospital beds were 757 (184.62) for designated special function hospitals, 345 (96.93) for acute care hospitals with a 7:1 patient-to-nurse ratio, and 101 (79.33) for acute care hospitals with a 10:1 patient-to-nurse ratio. For type of establishment, national or public medical institution hospitals accounted for the largest proportion of hospitals with special function wards (68.6%, 48 hospitals). Private hospitals had the highest proportions of acute wards with a 7:1 (41.9%, 250 hospitals) and 10:1 (68.7%, 299 hospitals) patient-to-nurse ratios. Special function hospitals were located in areas with a greater number of SMAs than were wards with acute care wards with 7:1 or 10:1 patient-to-nurse ratios.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCharacteristics of study participants\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSpecial function wards\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAcute care wards with a 7:1 patient-to-nurse ratio\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eAcute care wards with a 10:1 patient-to-nurse ratio\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5,580)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,017)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3,828)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;735)\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\u003eVariables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003en\u003c/em\u003e/median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%/IQR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003en\u003c/em\u003e/median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%/IQR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003en\u003c/em\u003e/median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%/IQR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003en\u003c/em\u003e/median\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%/IQR\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnnual total number of inpatient admissions in the ward\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13,070\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10,445\u0026ndash;14,997\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13,033\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11,059\u0026ndash;14,726\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13,205\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10,514\u0026ndash;15,155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12,394\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9,684\u0026ndash;14,572\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of beds per ward\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40\u0026ndash;50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41\u0026ndash;50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41\u0026ndash;50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e40\u0026ndash;52\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFull-time equivalent nursing staff per ward\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28\u0026ndash;94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u0026ndash;33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u0026ndash;33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u0026ndash;26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePresence or absence of full-time rehabilitation staff assigned to the ward (\u003cem\u003en\u003c/em\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5119.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e91.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1008.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3567.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e544.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e461.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e261.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e191.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercentage of admissions considering annual total number of inpatients by route\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePlanned admissions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.5\u0026ndash;80.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74.9\u0026ndash;89.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e52.5\u0026ndash;77.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e45.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27.6\u0026ndash;61.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eUnplanned admissions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.1\u0026ndash;20.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.9\u0026ndash;13.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.9\u0026ndash;18.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.2\u0026ndash;45.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmergency admissions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.8\u0026ndash;29.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.1\u0026ndash;13.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.8\u0026ndash;31.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.0\u0026ndash;37.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercentage of admissions considering annual total number of inpatients by pre-admission location\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdmissions from home\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69.0\u0026ndash;88.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e86.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77.0\u0026ndash;93.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68.0\u0026ndash;86.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.3\u0026ndash;89.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInpatient transfers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.3\u0026ndash;22.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.0\u0026ndash;18.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.8\u0026ndash;24.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u0026ndash;8.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTransfers from other hospitals\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.0\u0026ndash;3.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8\u0026ndash;2.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9\u0026ndash;2.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.9\u0026ndash;8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdmissions from long-term facilities\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u0026ndash;3.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1\u0026ndash;0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7\u0026ndash;3.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.0\u0026ndash;11.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther admissions\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u0026ndash;0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u0026ndash;0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u0026ndash;0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u0026ndash;0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercentage of discharges considering annual total number of inpatient discharges\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDischarges to home\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.9\u0026ndash;87.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.8\u0026ndash;90.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66.6\u0026ndash;86.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61.1\u0026ndash;85.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eInpatient transfers\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.1\u0026ndash;16.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.7\u0026ndash;18.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.8\u0026ndash;16.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u0026ndash;13.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTransfers to other hospitals\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.1\u0026ndash;10.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.1\u0026ndash;7.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.4\u0026ndash;11.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.8\u0026ndash;10.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDischarges to long-term facilities\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u0026ndash;3.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1\u0026ndash;0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8\u0026ndash;3.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.5\u0026ndash;12.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther discharges\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u0026ndash;0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u0026ndash;0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u0026ndash;0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u0026ndash;0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercentage of in-hospital mortality\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8\u0026ndash;3.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u0026ndash;1.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.0\u0026ndash;3.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.8\u0026ndash;7.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePercentage of patients meeting the criteria in the Severity of a Patient\u0026rsquo;s Condition and Extent of a Patient\u0026rsquo;s Need for Medical/Nursing Care Tool among the annual total number of patients in the ward\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTwo or more criteria in Item A and three or more criteria in Item B\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.5\u0026ndash;23.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.1\u0026ndash;20.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u0026ndash;23.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.1\u0026ndash;22.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThree or more criteria in Item C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.4\u0026ndash;21.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.5\u0026ndash;22.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.8\u0026ndash;22.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u0026ndash;10.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnnual total number of general anesthesia surgeries\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.5\u0026ndash;247.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.5\u0026ndash;295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.0\u0026ndash;259.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u0026ndash;101\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnnual total number of cancer treatments (chemotherapy and radiation therapy)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.5\u0026ndash;205.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e142.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54\u0026ndash;338.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.5\u0026ndash;197.75\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.5\u0026ndash;63.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnnual total number of rehabilitations\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e327.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98\u0026ndash;590\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e228.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.0\u0026ndash;411.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e369.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e107.0\u0026ndash;636.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e322.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78\u0026ndash;552.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePresence or absence of a discharge planning fee\u003csup\u003e\u0026sect;\u003c/sup\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e288\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e209\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFee Type 1 (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e923\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e322\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e326\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e275\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFee Type 2 (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4369\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e695\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3423\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e251\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eType of ownership*\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNational government (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1010\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e526\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e428\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePublic medical institutions (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2152\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1854\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e181\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSocial insurance bodies (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e125\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrivate (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e374\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1256\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e471\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e191\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e165\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital adopted the DPC payment system (\u003cem\u003en\u003c/em\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e696\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e151\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e545\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4884\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3677\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e190\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.9\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital adopted special functions (\u003cem\u003en\u003c/em\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4563\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3828\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e735\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital adopted regional medical care support (\u003cem\u003en\u003c/em\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2581\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e964\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e94.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e931\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e686\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2999\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2897\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital adopted home medical care support (\u003cem\u003en\u003c/em\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4774\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e85.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3245\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e84.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e528\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e806\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e583\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e207\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital adopted emergency medical services (\u003cem\u003en\u003c/em\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\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3184\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e257\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2202\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e725\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e98.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2396\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e760\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1626\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePopulation (100,000 people)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.2\u0026ndash;14.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.1\u0026ndash;14.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.3\u0026ndash;15.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.1\u0026ndash;11.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProportion of individuals aged 65 years or older\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.8\u0026ndash;30.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.5\u0026ndash;29.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24.8\u0026ndash;30.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.3\u0026ndash;32.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of community care beds per 10,000 people aged 65 years or older\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3\u0026ndash;0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3\u0026ndash;0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3\u0026ndash;0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3\u0026ndash;0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of rehabilitation beds per 10,000 people aged 65 years or older\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u0026ndash;0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u0026ndash;0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u0026ndash;0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.4\u0026ndash;0.6\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of long-term care beds per 10,000 people aged 65 years or older\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e254.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e220.0\u0026ndash;291.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e232.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e203.1\u0026ndash;271.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e255.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e220.5\u0026ndash;291.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e272.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e230.0\u0026ndash;313.7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of home health care support clinics per 10,000 people aged 65 years or older\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.6\u0026ndash;6.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.4\u0026ndash;7.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.6\u0026ndash;5.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.5\u0026ndash;5.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNumber of in-home service agencies per 10,000 people aged 65 years or older\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80.0\u0026ndash;183.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e153.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83.6\u0026ndash;198.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e121.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e65.9\u0026ndash;183.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e117.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e81.2\u0026ndash;183.2\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\"\u003eDPC: diagnosis procedure combination; IQR: interquartile range\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003e\u0026sect;\u003c/sup\u003e With discharge planning fee Type 1, at least one dedicated discharge planning staff member must be assigned to each of the two wards. Additional interventions include identifying patients with difficulty in discharge, supporting patients' and caregivers\u0026rsquo; decision-making, holding conferences with other necessary professionals, and initiating a discharge support plan within seven days of patient admission. Discharge planning fee Type 2 has no staffing standards, but the same steps as those for fee Type 1 must be performed as soon as possible.\u003c/p\u003e\n\u003cp\u003e* The detailed classification of types of ownership is as follows. National government: Ministry of Health, Labour and Welfare, National Hospital Organization, National University Corporation, National Institute of Occupational Safety and Health, National Research Center for Advanced and Specialized Medical Care, and Japan Community Health Care Organizations. Public medical institutions: prefectures, municipalities, local incorporated administrative agencies, Japanese Red Cross, Saiseikai Imperial Gift Foundation, Hokkaido Social Service Association, National Welfare Federation, and Federation of National Health Insurance Organizations. Social insurance bodies: health insurance societies and their federations, mutual aid associations and their federations, and national health insurance societies. Private: medical corporations, public interest corporations, private university corporations, social welfare corporations, medical co-ops, and companies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003ch2\u003e5.2 Average length of stay\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThe average length of stay for all 5,580 wards was 12.5 days (SD 5.26). When stratified by ward type, the average length of stay was 12.5 days (SD 4.65) for special function wards, 11.5 days (SD 4.36) for acute care wards with a 7:1 patient-to-nurse ratio, and 18.0 days (SD 7.01) for acute care wards with a 10:1 patient-to-nurse ratio. The length of stay for acute care wards with a 10:1 patient-to-nurse ratio was significantly longer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) compared with that for other wards (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n\u003ch2\u003e5.3 Staffing level in the discharge planning department\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThe median number of nurses and MSWs per 100 hospital beds in the discharge planning department across all 1,101 hospitals was 2.9 (IQR 2.0\u0026ndash;4.0). When stratified by ward type, median values were 2.1 (IQR 1.6\u0026ndash;2.7) per 100 hospital beds for special function wards, 2.7 (IQR 2.0\u0026ndash;3.7) per 100 hospital beds for acute care wards with a 7:1 patient-to-nurse ratio, and 3.5 (IQR 2.3\u0026ndash;5) per 100 hospital beds for acute care wards with a 10:1 patient-to-nurse ratio. The number of nurses and MSWs in acute care wards with a 10:1 patient-to-nurse ratio was significantly higher (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The proportion of nurses in the discharge planning department was 56.1% for hospitals with special function wards and 50% for hospitals with acute care wards with a 7:1 and 10:1 patient-to-nurse ratio, respectively. When considering the individual counts of nurses and MSWs per 100 hospital beds, there was a trend for higher staffing numbers for both nurses and MSWs in hospitals with acute care wards with a 10:1 patient-to-nurse ratio, followed by acute care wards with a 7:1 patient-to-nurse ratio and special function wards (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n\u003ch2\u003e5.4 Multilevel linear regression analysis for the length of stay and the discharge planning department structure\u003c/h2\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eStratified analysis by ward type, while adjusting for covariates, revealed that an increase in one nurse or MSW per 100 hospital beds impacted the length of stay as follows: in hospitals with special function wards, coefficient (coef.) = -0.32, 95% confidence interval (CI) = -0.80 to 0.15, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.18; in hospitals with acute care wards with a 7:1 patient-to-nurse ratio, coef. = -0.19, 95% CI = -0.33 to -0.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001; and in hospitals with acute care wards with a 10:1 patient-to-nurse ratio, coef. = -0.12, 95% CI = -0.38 to 0.13, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.33 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Sensitivity analysis separately analysing the number of nurses and MSWs showed that an increase of one nurse per 100 hospital beds was significantly associated with the average length of stay in acute care wards with a 7:1 patient-to-nurse ratio (coef. = -0.21, 95% CI = -0.38 to -0.04, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02), whereas an increase of one MSW per 100 hospital beds was non-significantly associated with the average length of stay in any ward model (Appendix 2\u0026ndash;4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eResults of random intercept multilevel linear regression analysis for length of stay\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eModel 1\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eModel 2\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eModel 3\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\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eSpecial function wards\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eAcute wards with a 7:1 patient-to-nurse ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eAcute wards with a 10:1 patient-to-nurse ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,017)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3,828)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;735)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCoef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCoef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCoef.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal number of nurses and MSWs in the discharge planning sector per 100 hospital beds\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.33\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal number of nurses in the discharge planning sector per 100 hospital beds\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal number of MSWs in the discharge planning sector per 100 hospital beds\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e.95\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"13\"\u003eMSWs: medical social workers; Coef.: coefficient; 95% CI: 95% confidence interval\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eModel 1: Multiple regression analysis was conducted using the following adjusting variables: discharge planning nurse ratio in the discharge planning sector; planned admissions in the annual total number of inpatients by route; admissions from home in the annual total by pre-admission location; transfers from other hospitals in the annual total pre-admission location; admissions from long-term facilities in the annual total pre-admission location; discharges to home in the annual total discharges; transfers to other hospitals in the annual total discharges; discharges to long-term facilities in the annual total discharges; patients meeting two or more criteria in item A and three or more criteria in item B in the Severity of a Patient\u0026rsquo;s Condition and Extent of a Patient\u0026rsquo;s Need for Medical/Nursing Care tool; the annual total number of general anesthesia surgeries; types of ownership (national government, public medical institutions, privates, other); hospital-adopted regional and medical care support and emergency medical service; number of beds per hospital; physicians, nursing staff, and rehabilitation staff per 100 beds; discharge planning fee (none and Type 2); population (100,000 people); proportion of individuals aged 65 years or older; number of community care beds, rehabilitation beds, and long-term beds per 10,000 people aged 65 years or older; and number of home health care support clinics and in-home service agencies per 10,000 people aged 65 years or older.\u003c/p\u003e\n\u003cp\u003eModels 2 and 3: All types of ownership, discharge planning fee Type 1, and hospital-adopted diagnosis procedure combination were added to the adjusting variables in Model 1.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"6. Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eA higher number of nurses and MSWs per 100 hospital beds in the discharge planning department, especially higher nurse staffing, was associated with a lower length of stay in acute care wards with a 7:1 patient-to-nurse ratio, but with no significant associations in the special function wards and acute care wards with a 10:1 patient-to-nurse ratio.\u003c/p\u003e\u003cp\u003ePrevious studies have examined the effect of discharge planning on the decrease in the length of hospital stay and showed mixed results: discharge planning interventions conducted by a multidisciplinary team including clinical nurse specialists [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and one systematic review [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] reported no difference or prolonged length of stay. Other randomised controlled trials or comparative studies targeting specific settings, such as exacerbated chronic obstructive pulmonary disease [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], elderly patients [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and general medical unit interventions [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] revealed a significant reduction in the length of stay. Our findings support the potential contribution of higher staffing levels in the discharge planning department, particularly in terms of nurse staffing, in reducing the length of stay at the ward level in acute care wards with a 7:1 patient-to-nurse ratio. Although this study could not determine the mechanism owing to the study design, this result might be explained by two reasons: strengthening the activities for discharge support for patients and fostering a culture of discharge planning throughout the hospital. Regarding the former, in acute care hospitals in Japan, the specific interventions conducted by discharge planning nurses include screening patients who face challenges with discharge, formulating discharge support plans, collecting information from relevant local professionals during hospitalisation, supporting decision-making for patients and their families, and coordinating pre-discharge conferences with visiting physicians and home care nurses [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Additionally, some discharge planning nurses provide follow-up services such as home visiting care after patients are discharged if needed [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Thus, more staffing per inpatient in the discharge planning department improved the process and might contribute to shortening the average length of stay as a result. Regarding the second reason, hospitals in which discharge planning nurses are well-staffed, there is likely capacity to expand the scope of activities beyond those mentioned earlier, such as providing education and training on discharge planning for ward nurses [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] and other professionals. Consequently, by increasing cross-organisational activities, discharge planning nurses had a spillover effect in fostering a culture of discharge planning throughout the hospital, which may have influenced the positive outcomes of this study. This finding reinforces the need for appropriate staffing levels in discharge planning departments tailored to hospital functions.\u003c/p\u003e\u003cp\u003e Noteworthy, this study showed the association between higher staffing in the discharge planning department and the shorter average length of stay only in acute care wards with a 7:1 patient-to-nurse ratio, but not in special function hospitals or hospitals with a 10:1 patient-to-nurse ratio. This may be influenced by the difference in hospital types and policies for differentiation of bed functions. As for special function hospitals, because of the nature of providing advanced medical care in a national or large university hospital with a substantial number of beds, special function hospitals may prioritise improving treatment outcomes rather than shortening hospital days [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Additionally, as many patients with complex medical conditions are referred from across the country, the hospital itself is not tied to a specific region. Therefore, since the transition to the alternative phase of care often starts from scratch, reliance on post-discharge local resources, rather than the number of staff in the discharge planning department, may have a significant impact. In terms of the length of stay criteria set for each ward under the medical fee system, wards with a 7:1 patient-to-nurse ratio for the length of stay calculation typically have a period of 18 days; this is much shorter than the criteria for special function hospitals (within 26\u0026ndash;28 days) and acute care wards with a 10:1 patient-to-nurse ratio (21 days). The revision of the medical fee schedule in 2024 is expected to further shorten the length of stay to within 16 days. This pressure to reduce the average length of stay limit on reimbursement may make staff in discharge planning departments place even more importance on reducing the length of stay as an outcome of their activities.\u003c/p\u003e\u003cp\u003eContrastingly, in acute care wards with a patient-to-nurse ratio of 10:1, there may be patients with relatively mild conditions, or continue their hospitalisation owing to reasons related to the absence of caregivers or other social factors, despite not needing hospitalisation from a medical standpoint. This situation\u0026mdash;'social admission\u0026rsquo; [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u0026mdash;could potentially contribute to an extended length of stay. Staff in discharge planning departments in acute wards with a 10:1 patient-to-nurse ratio may be more concerned with securing a discharge destination and consuming more time for coordination with caregivers and staff at the next care setting than with getting patients discharged quickly.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Strengths and limitations\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe major strength of this study is its focus on acute care hospitals nationwide in Japan, making it highly generalisable within the country. However, due to differences in systems and policies among countries, it is challenging to directly adapt them to overseas contexts.\u003c/p\u003e\u003cp\u003eThis study has two main limitations. First, the data were collected during the COVID-19 pandemic, resulting in potential deviations from the figures seen in typical years. Second, in the association between the number of nurses and MSWs per 100 hospital beds and length of stay in acute care wards with a 7:1 patient-to-nurse ratio, the coefficient was extremely small (-0.19). However, even though it may be slight, for patients with prolonged hospitalisations for non-medical reasons, in hospitals experiencing patient overcrowding owing to bed shortage (although bed occupancy rates were not examined in this study), and in countries such as Japan where policies for reducing the length of stay will continue in the case of acute care wards, a coefficient of -0.19 is considered clinically significant.\u003c/p\u003e\u003cp\u003eTo increase the accuracy of evaluating the effectiveness of discharge planning, it is necessary to collect more detailed information on structural aspects such as the organisational positioning of the discharge planning department, the years of experience of nurses and MSWs, the number of cases they handle, the presence of a certified nurse or certified nurse specialist qualifications, and information on other tasks besides discharge planning. Furthermore, combining this information with actual specialised interventions and patient characteristics would help clarify the mechanism of discharge planning.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study examined the association between staffing in the discharge planning department at the hospital level and the average length of stay in acute care wards using nationwide data. Higher staffing in the discharge planning department, particularly of nurses in acute care wards with a 7:1 patient-to-nurse ratio, potentially contributed to the reduction in the length of stay. Further research by obtaining more detailed information on the structure of the discharge planning department and comparing this information with combined patient data is needed.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMSW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emedical social worker\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esecondary medical area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003estandard deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile ranges\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 \u003cp\u003eThis study did not use individual data. All data were obtained from open source and are available on the website. This study adhered to the principles of the Declaration of Helsinki and the study protocol was approved by the Tokyo Medical and Dental University Ethics Review Board on April 17, 2024 (No. C2024-02).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAM designed the study, acquired the data, conducted the statistical analyses, and drafted and revised the manuscript. NM and MK supervised the study and statistical analyses and revised the manuscript. All authors agreed to be accountable for all aspects of the work and gave final approval of the manuscript to be published.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eNot Applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analysed during the current study are publicly available in the Ministry of Health, Labour and Welfare (http://www.mhlw.go.jp/index.html, in Japanese), Ministry of Internal Affairs and Communications (http://www.soumu.go.jp/, in Japanese), and Japanses government repository (http://www.e-stat.go.jp/en). The detailed data are listed in the references.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eColeman EA. Falling through the cracks: challenges and opportunities for improving transitional care for persons with continuous complex care needs. 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BMJ Open. 2019;9(2):e021851. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2018-021851\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2018-021851\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell JC, Ikegami N. Long-term care insurance comes to Japan. Health Aff. 2000;19(3):26\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1377/hlthaff.19.3.26\u003c/span\u003e\u003cspan address=\"10.1377/hlthaff.19.3.26\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"discharge planning, health service research, acute care, quantitative approach, length of stay","lastPublishedDoi":"10.21203/rs.3.rs-4302724/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4302724/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe transition from hospital to the next care setting is when care fragmentations are likely to occur, making discharge planning essential; however, the relationship between discharge planning and length of stay is unclear. This study aimed to investigate the association between staffing levels, particularly the number of nurses and medical social workers in the discharge planning department, and the average length of stay at the ward level in acute care hospitals in Japan.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eApplying a cross-sectional approach, we used nationwide administrative hospital- and ward-level data from the fiscal year 2021. A total of 5,580 acute care wards in 1,101 hospitals across 206 secondary medical areas were included. A two-level multilevel regression analysis with random intercept stratified by three types of acute care ward functions was performed by adjusting ward, hospital, and regional characteristics.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003e A total of 1,017 wards in 70 designated special function hospitals, 3,828 general acute care wards with a 7:1 patient-to-nurse ratio in 596 hospitals, and 735 general acute care wards with a 10:1 patient-to-nurse ratio in 435 hospitals were included in the final analysis. The average length of stay was 12.5 days, 11.5 days, and 18.0 days, respectively. There was a significant association between the total number of nurses and medical social workers per 100 hospital beds in acute care wards with a 7:1 patient-to-nurse ratio, but not in special function wards or in acute care wards with a 10:1 patient-to-nurse ratio. Sensitivity analysis that separately analysed the number of nurses and medical social workers showed that the number of nurses per 100 hospital beds was associated with the average length of stay in acute care wards with a 7:1 patient-to-nurse ratio. Medical social workers per 100 hospital beds showed no association in any ward model.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA greater number of nurses and medical social workers per 100 hospital beds in the discharge planning department, especially greater nurse staffing, was associated with short lengths of stay in acute care wards with a 7:1 patient-to-nurse ratio.\u003c/p\u003e","manuscriptTitle":"Staffing level in the discharge planning department and average length of stay in acute care wards: A cross-sectional study using a nationwide hospital- and ward-level data in Japan","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-30 19:35:52","doi":"10.21203/rs.3.rs-4302724/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":"e5b13c06-e239-4059-91b2-4150022abede","owner":[],"postedDate":"April 30th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-18T13:39:17+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-30 19:35:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4302724","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4302724","identity":"rs-4302724","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}