Leadership Perceptions and Organizational Reality: Barriers to Evidence-Based Emergency Department Misuse Reduction in Community Health Systems

Leadership Perceptions and Organizational Reality: Barriers to Evidence-Based Emergency Department Misuse Reduction in Community Health Systems | 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 Leadership Perceptions and Organizational Reality: Barriers to Evidence-Based Emergency Department Misuse Reduction in Community Health Systems Yumiko Lee, Michael Petros, Steve Seweryn, Yuka Asada, David Presser This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7881194/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 Emergency department (ED) misuse remains a persistent problem for healthcare organizations. How leaders perceive the causes and solutions to this issue strongly shapes their strategic responses. This study explores the alignment between senior leadership's perceptions and evidence about ED misuse reduction at a community health system serving a diverse, safety-net population. Methods A cross-sectional exploratory survey of senior managers (N = 47) at a mid-sized community health system in Southern California, was conducted between June 13–27, 2025. This exploratory analysis identified potential areas of perception-data divergence that warrant further investigation in larger samples with sufficient statistical power. Results Three areas of difference emerged between leadership perspectives and operational data. First, leaders identified mental health and substance abuse as primary drivers of ED misuse; however, institutional data show these issues accounted for only 8.3% of all ED visits between 2022 and 2024. Second, while 73.2% of survey respondents reported Human Resources (human factor) influence priority-setting, 55% of leaders cited an insufficient workforce as a barrier to establishing effective priorities. Third, although 55.2% of leaders reported confidence in their ability to discontinue ineffective programs, only one-third reported frequent success with de-implementation. These differences were accompanied by modest ratings in organizational data use capabilities. Conclusions While leadership perceptions diverged from institutional data, these differences may reflect resource intensity considerations not captured in utilization statistics alone. A mid-sized community health system could benefit from enhanced data analytics and formal processes to routinely check leadership perceptions before committing resources to priority initiatives. Emergency Service Hospital Community Health Planning Organization & Administration Health Care System Leadership Evidence-Based Medicine/Method Figures Figure 1 Figure 2 Figure 3 Background Use of emergency departments for medical issues that could be treated elsewhere—often called ED misuse—continues to challenge healthcare systems nationwide ( 1 , 2 ). In the United States, these avoidable visits contribute significantly to rising costs, overcrowded emergency rooms, delays for critically ill patients, and strain on limited resources. While much research has focused on the clinical and patient factors influencing these visits, less work has examined how healthcare leaders' perceptions shape their choices to address this problem ( 3 , 4 ). Healthcare leadership today involves complicated priority-setting that balances clinical goals, financial realities, and operational capacity ( 5 ). Yet the assumptions leaders make during this process do not always reflect administrative data, a challenge that is especially difficult for leaders in organizations with tight resources ( 6 ). If leaders’ assumptions differ from trends observed in institutional data, there is a possibility that strategic decisions may not dully realize their intended outcomes ( 7 ). Community health systems face particular hurdles in this area. As safety-net providers, they serve diverse, often vulnerable populations, many uninsured or underinsured, and frequently act as primary care contact points. Patients in these groups encounter barriers—like cost, lack of primary care relationships, or inconvenient clinic hours—that make alternatives to the emergency department harder to access ( 8 , 9 ). Despite the strategic importance of leadership perspectives, few studies have systematically investigated how these assumptions align with real-world data in community healthcare settings. This study aims to fill this gap by comparing what senior leaders assume about the causes and solutions for ED misuse with empirical evidence from a community system's data. It focuses on assumptions about drivers, organizational readiness, and capabilities to phase out ineffective programs. Key questions included: How do healthcare leaders perceive ED misuse causes and their organization's capacity to intervene? How do these perceptions match with actual evidence? What organizational factors contribute to any gaps uncovered? Methods Study Design and Setting An exploratory cross-sectional survey was conducted among senior managers at a mid-sized non-profit medical center serving diverse community health system serving a diverse safety-net community region. This exploratory design aimed to identify potential patterns of perception-reality divergence for hypothesis generation in future confirmatory studies. The study received approval from the Institutional Review Board as exempt status and also from the University of Illinois Chicago (Protocol # STUDY2025-0618). The survey was conducted between June 13-27, 2025. Senior managers (N=93) with strategic decision-making and priority-setting authority were identified through a purposive sampling approach. Inclusion criteria required participants to hold positions with direct involvement in organizational priority-setting and resource allocation decisions. This included department directors, service line leaders, vice presidents, and executive team members. Forty-seven senior managers completed the survey, yielding a 50.5% response rate. Gap analysis was conducted by comparing leadership perceptions with institutional data, described next. Data Collection Survey Instrument A structured survey instrument was developed based on evidence-based management literature (20) and reviewed by three healthcare administration faculty members. The survey assessed leadership perceptions across multiple disciplines as summarized in Table 1. Comparative Data Sources Survey findings were compared with multiple empirical data sources: 2025 Community Health Needs Assessment (CHNA): Assessment conducted in collaboration with community stakeholders California Health Interview Survey (CHIS): 2021-2022 State Benchmarking Data: California Health and Human Services Agency opioid-related ED visit statistics Institutional ED Utilization: Internal data on visit patterns and diagnostic categories from EMR 2022 – 2024. Insert Table 1 about here Analysis Data Analysis Population-based Approach: The analytical framework employed a population-based survey approach (10) examining leadership assumptions across the entire senior management cohort (N=47). This approach ensured comprehensive coverage of organizational decision-makers and enabled identification of systematic patterns in assumption formation across functional domains. Biostatistical Methods: Quantitative data were analyzed using Microsoft Excel® with descriptive statistics appropriate for categorical and ordinal variables. Frequencies were calculated for all categorical responses, including organizational commitment levels, perceived ED misuse drivers, and priority-setting challenges. For Likert-scale organizational effectiveness ratings, distributions were computed. Organizational effectiveness ratings were dichotomized into meaningful analytical categories: upper effectiveness (ratings 4-5 on five-point scale, representing "Good" to "Excellent" performance) and lower effectiveness (ratings 1-3, representing "Very Poor" to "Fair" performance). This categorization facilitated comparison across the four effectiveness domains (technical, human, financial, data aspects) and enabled identification of relative organizational strengths and limitations. Categorical associations were evaluated using Fisher's exact test, chosen over chi-square analysis due to expected cell frequencies below 5 in several cross-tabulations (17,18). Two-tailed tests were employed with significance set at α = 0.05 (19). This approach is recommended for small sample contingency table analysis where traditional chi-square assumptions may be violated. All statistical analyses were performed using Microsoft Excel®. Mixed-Methods Integration: Qualitative responses from open-ended survey questions were analyzed by applying a deductive thematic analysis process grounded in evidence-based management theoretical frameworks (20). The integration of quantitative survey data with qualitative thematic findings followed a convergent parallel design, where both data types were analyzed independently and subsequently integrated to provide comprehensive understanding of perceptions and institutional patterns. Perception – Data Reality Gap Analysis: The core analytical approach involved systematic comparative analysis examining alignment between leadership perceptions (survey responses) and data from multiple organizational and community data sources. Substantial differences were identified, suggesting opportunities for further exploration and learning rather than clear contradictions. Qualitative Contradictions: Systematic variations were noted between leadership assumptions and available empirical evidence from institutional data sources, community health assessments, or benchmark comparisons were identified. Give a list of which variables were compared here. Validation and data triangulation were employed to enhance validity through systematic comparison of survey findings with multiple independent data sources (11). These included 2022 California Health Interview Survey population data, institutional electronic health record utilization patterns, Community Health Needs Assessment findings shown in Table 2, and state-level benchmarking statistics. This multi-source approach strengthened confidence in identified perception-reality gaps by confirming patterns across different data collection methods and timeframes. Insert Table 2 Results Participant Characteristics This exploratory analysis examined perception-data alignment patterns among senior leadership. Given the sample size (N=47) and exploratory nature of the study, findings should be interpreted as hypothesis-generating observations that warrant further investigation in larger samples with sufficient statistical power. Forty-seven of the invited senior managers completed the survey (response rate 50.5%) as shown in Table 3. Participants represented diverse organizational functions including clinical departments (42.6%, n=20), administrative services (31.9%, n=15), support services (17.0%, n=8), and executive leadership (8.5%, n=4). Average tenure in current positions was 6.3 years (range 1-18 years), with 72.3% (n=34) holding master's degrees or higher. Insert Table 3 Organizational Commitment and Leadership Consensus Senior managers demonstrated strong consensus regarding organizational commitment to addressing ED misuse. When asked if ED misuse reduction represented an organizational priority, 74.5% (n=35) indicated "Yes" and only 6.4% (n=3) responded "No." An additional 19.1% (n=9) responded "Uncertain," suggesting some ambiguity in strategic communication. This substantial majority endorsement indicates broad leadership alignment on the strategic importance of ED misuse reduction. Comparing Leadership Perceptions and Data Leadership Perceptions Leadership perceptions about ED misuse drivers as responded through survey were heavily influenced by the organization's Community Health Needs Assessment, which prioritized behavioral health services expansion as a primary strategy for addressing ED utilization patterns in the past several years. Key informants’ qualitative responses included: - " Mental health patients have nowhere else to go ”; “ drug-seeking behavior and psychiatric crises drive most inappropriate visits "; and" we need more behavioral health resources to address the root causes. ” Figure 1 illustrates the contrast between these leadership perceptions and empirical data. Insert Figure 1 While leadership focused on certain causes of ED misuse, institutional data indicated additional patterns worth consideration. Analysis of electronic medical records from January 2022 through December 2024 showed that mental health and substance abuse visits comprised 8.3% of all ED encounters (25,705 visits out of 309,701 total visits). Despite representing a minority of overall ED utilization, leadership identified behavioral health as the primary strategic target for ED misuse reduction initiatives. Although care for mental health and substance use cases is resource-intensive, addressing their impact on resources is beyond the scope of this study. The data indicated that leadership focus on behavioral health interventions may not substantially impact the majority of ED utilization patterns driving organizational concerns about misuse. Difference between Perceptions and Data: Human Resource Adequacy Leadership Perceptions Senior leadership operated under the assumption that sufficient human resources existed to address ED misuse reduction initiatives without substantial organizational restructuring. Strategic planning documents reflect confidence in implementing new programs while maintaining existing operations. Survey data revealed significant human resource constraints that contradicted leadership assumptions (Figure 2). First, human resource capacity emerged as the predominant challenge in organizational priority-setting, identified by 55.3% of respondents (n=26) as their primary difficulty. This substantially exceeded other challenge categories: technical aspects at 23.4% (n=11), financial aspects at 12.8% (n=6), and data aspects at 8.5% (n=4). Second, organizational effectiveness ratings showed human resource needs (human aspects) (59.6% rating "good" or "excellent") scored lower than technical aspects (70.2%), indicating recognized limitations in human resource capacity. Finally, analysis revealed that regulatory compliance activities consumed 36.2% (n=17) of available management attention, substantially limiting discretionary capacity for new initiatives. The contrast between assumed adequacy and 55.3% of leaders identifying human resources as their primary challenge represents a fundamental misalignment in capacity assessment. This difference highlights the importance of ongoing assessment of workforce capacity during strategic planning. Insert Figure 2 Alignment of Perception and Data: De-implementation Capability The strategic planning process was based on the perception that the organization could discontinue certain existing programs and reallocate those resources to support new initiatives focused on reducing ED misuse. Planning documents reflected confidence in organizational change management capabilities. Survey responses revealed limited success with program discontinuation efforts: De-implementation Success Rates: Always successful (>80% success rate): 6.4% (n=3) Often successful (60-80% success rate): 27.7% (n=13) Sometimes successful (40-60% success rate): 42.6% (n=20) Rarely successful (20-40% success rate): 19.1% (n=9) Never successful (<20% success rate): 4.3% (n=2) Only 34.1% (n=16) reported frequent success (Always/Often categories), while 65.9% (n=31) reported limited success (Sometimes/Rarely/Never categories). Emotional Responses to Priority-Setting: Leaders identified predominant emotional states when addressing ED misuse priorities: Difficult: 36.2% (n=17) Reluctant: 25.5% (n=12) Optimistic: 19.1% (n=9) Confident: 12.8% (n=6) Frustrated: 6.4% (n=3) The concentration of challenging emotions (61.7% difficult/reluctant combined) suggests substantial barriers to change implementation. Priority-Setting Influences: Analysis of factors driving priority-setting decisions revealed: Regulatory requirements: 36.2% (n=17) Clinical outcomes: 29.8% (n=14) Financial considerations: 21.3% (n=10) Community needs: 12.8% (n=6) The assumption of de-implementation capability contrasts sharply with only 34.1% of leaders reporting frequent success with program discontinuation. This 31.9 percentage point gap between assumed and demonstrated capability suggests systematic overestimation of organizational change management capacity. Organizational Effectiveness Patterns Analysis of organizational effectiveness ratings across four domains revealed varying perceived capabilities (Figure 3): Upper Effectiveness Categories (Ratings 4-5): Technical aspects: 70.2% (n=33) Human aspects: 59.6% (n=28) Financial aspects: 53.2% (n=25) Data aspects: 51.0% (n=24) The lowest effectiveness ratings in data utilization (51.0%) may contribute to the persistence of assumption-reality gaps, as limited analytical capabilities prevent systematic validation of leadership assumptions against empirical evidence. Examination of the relationship between perceived organizational effectiveness and de-implementation success revealed no clear patterns. Leaders reporting high effectiveness in technical and human domains showed similar de-implementation success rates (75.0% for high effectiveness vs. 63.6% for lower effectiveness domains), suggesting that perceived capabilities do not reliably predict actual performance outcomes (Table 4). Examination of the relationship between perceived organizational effectiveness and de-implementation success revealed no clear patterns. Fisher's exact test showed no statistically significant association between perceived organizational effectiveness levels and de-implementation success rates (p = 0.743, two-tailed) (17,18). Leaders reporting high effectiveness in technical and human domains showed similar de-implementation success rates (75.0% for high effectiveness vs. 63.6% for lower effectiveness domains), suggesting that perceived capabilities do not reliably predict actual performance outcomes. This finding supports the broader pattern of misalignment between leadership assumptions and organizational realities documented throughout the study. Discussion This exploratory analysis identified three potential areas of perception-reality divergence that may warrant further investigation. While the observed patterns suggest possible systematic differences between leadership assumptions and empirical evidence, the exploratory nature and sample size limitations require cautious interpretation of these preliminary findings. Mental Health Perception: Implications and Context The focus on mental health and substance abuse primarily drives ED misuse may influence resource allocation approaches. Leadership perceptions identified behavioral health as the primary intervention target, despite institutional data showing these conditions account for only 8.3% of ED visits. This finding aligns with previous research suggesting that perceived versus actual drivers of healthcare utilization may differ substantially due to availability heuristic and confirmation bias ( 12 , 13 ). The community-level data from CHNA provides additional context. While substantial unmet mental health needs exist (50.5% are unable to receive needed services), these needs do not translate proportionally to ED utilization from the Institutional data review. This suggests that barriers to mental health care access may be resolved through community-based interventions rather than ED-focused strategies. The below-average opioid-related ED visits (34 vs. 58.7 per 100,000) further challenge assumptions about substance abuse as a primary driver. This focus may be influenced by high-visibility cases, where psychiatric emergencies are particularly memorable. Additionally, community stakeholder input during needs assessment processes may emphasize behavioral health concerns that, while legitimate, may not reflect actual ED utilization patterns. Human Resource Adequacy: Strategic Planning Implications The perception of human resource adequacy, contradicted by empirical evidence showing 55.3% of leaders identify human aspects as their primary challenge, has profound implications for implementation planning. This gap suggests that strategic initiatives may be designed with unrealistic expectations about available workforce capacity, potentially leading to implementation failures and staff burnout, including staff outside of direct ED clinical roles (i.e., security, admission clerks, cleaning staff, etc.). The finding that regulatory requirements consume 36.2% of management attention provides crucial context for understanding resource constraints. In community health systems serving safety net populations, regulatory compliance demands may be particularly intensive due to complex payer mix, quality reporting requirements, and accreditation standards. This regulatory burden limits discretionary capacity for new initiatives, making accurate capacity assessment essential for successful program implementation. The lower effectiveness ratings for human aspects (59.6%) compared to technical aspects (70.2%) suggest that leadership recognizes human resource limitations but may not fully integrate this recognition into strategic planning processes. The gap between awareness and action may reflect cultural dynamics within the organization or a general inclination toward optimistic planning. De-implementation Capability: Change Management Realities Unsuccessful de-implementation capability, contradicted by limited success rates (only 34.1% frequent success), represents perhaps the most significant barrier to effective resource reallocation. Healthcare organizations typically operate with established programs that have developed stakeholder support, operational integration, and cultural acceptance ( 14 ). The necessity to discontinue these programs requires sophisticated change management capabilities that may exceed organizational capacity. This gap may have implications that go beyond resource allocation. Challenges in de-implementation may contribute to resistance or hesitation regarding future initiatives. The predominant emotional responses of difficulty and reluctance (61.7% combined) may reflect past experiences with unsuccessful change efforts. Data Utilization as a Contributing Factor The lowest perceived effectiveness in data aspects (51.0%) suggests a potential contributing factor to the assumption-reality gaps observed in this study. When analytical capabilities are limited, organizations may find it challenging to systematically validate assumptions against empirical evidence, which can lead to decision-making processes that draw more heavily on intuition, past experience, or external pressures rather than comprehensive data analysis ( 15 ). This observation may be particularly relevant for community health systems, which often operate with different analytical infrastructure compared to larger health systems or academic medical centers. The findings suggest that strengthening data analytics capabilities—including both technological tools and analytical expertise—could support more evidence-informed decision-making processes. Although this study does not establish causation, the observed pattern of data use and gaps between planning and practice indicates that strengthening analytical capacity could contribute to broader organizational initiatives to align strategy with operational realities. Emotional and Psychological Barriers The predominant emotional responses of difficulty and reluctance indicate that psychological factors may influence leadership's ability to revise foundational perceptions when presented with contradictory evidence. These emotional responses could reflect the natural discomfort that occurs when new information challenges established organizational beliefs and practices ( 16 ). Addressing these challenges may benefit from organizational development approaches that recognize the emotional aspects of strategic decision-making while offering structured processes to help leaders examine and potentially update their assumptions over time. Implications for Practice Community health system leaders may consider paying more attention to the empirical data that compares strategic premises with empirical evidence before major resource allocation decisions. This might include: Pre-decision Data Review: Increase ability to understand available data sources before strategic planning sessions and allocation of human resources Assumption Documentation: Explicit identification and documentation of underlying assumptions in strategic plans Evidence-Based Checkpoints: Regular review of assumptions against emerging evidence during implementation Cultural Change (fundamental behavioral change) Initiatives: Programs to promote data-driven decision-making culture The development of enhanced data analytics capabilities represents a foundational investment that may improve decision-making across multiple organizational domains. However, technology investments alone may be insufficient without corresponding changes in organizational culture and decision-making processes. Study Limitations Several limitations should be considered when interpreting these findings. First, this study examines a single mid-size non-profit community integrated health care delivery system in a metropolitan area, limiting generalizability to other healthcare settings including academic medical centers, large health systems, or for-profit hospitals. The organizational culture, resource constraints, and operational challenges at a mid-sized community health system in Southern California may not be representative of other community health systems. Second, cross-sectional design prevents assessment of assumption evolution over time or the effectiveness of interventions designed to improve assumption-evidence alignment. Longitudinal research would provide valuable insights into how assumptions change in response to evidence and organizational learning processes. Third, the study relies on self-reported perceptions of organizational effectiveness, which may be subject to social desirability bias or limited self-awareness. Objective measures of organizational performance might provide different perspectives on effectiveness patterns. Fourth, the comparative data sources, while comprehensive, may not capture all relevant dimensions of ED utilization patterns. Additional data sources, including detailed clinical reviews of ED visits categorized as "misuse," might provide more nuanced understanding of utilization drivers. Finally, the study does not examine the effectiveness of assumption-based versus evidence-based strategies, which would require controlled intervention studies or natural experiments comparing different approaches. The exploratory nature of this single-site study limits generalizability and prevents definitive conclusions about perception-reality alignment patterns. The sample size (N = 47) was insufficient for robust statistical testing of associations, and findings should be interpreted as preliminary observations requiring validation in larger, multi-site investigations. Future Research Directions Future research should examine assumption-reality alignment across multiple healthcare organizations to assess the generalizability of these findings. Comparative studies across different organizational types (community hospitals, academic medical centers, integrated health systems) would provide insights into how organizational characteristics influence assumption formation and persistence. Longitudinal research examining how assumptions evolve in response to evidence and organizational interventions would provide valuable insights for developing effective change strategies. Intervention studies testing structured approaches to assumption validation and evidence-based decision-making would contribute to practical knowledge for healthcare leaders. Additionally, research examining the relationship between assumption-reality alignment and organizational performance outcomes would provide empirical support for the importance of evidence-based management practices in healthcare settings. Conclusions Findings from this study indicate differences between leadership assumptions and empirical evidence regarding ED misuse reduction in a community health system, with potentially significant implications for strategic effectiveness and resource allocation. The three critical gaps identified—assumptions about mental health drivers, human resource adequacy, and de-implementation capability—represent common challenges that may extend beyond the study setting. These assumptions persisted alongside evidence suggesting alternative interpretations. The findings highlight the importance of evidence-based decision-making processes that systematically compare strategic assumptions with empirical evidence before major resource commitments. For healthcare leaders addressing complex operational challenges such as ED misuse reduction, success may depend more on accurate situational assessment and realistic capacity planning than on intervention strategies based on unvalidated assumptions. Emotional responses observed in this study may inform future organizational development efforts focused on supporting leaders through change. Community health systems seeking to improve ED misuse reduction effectiveness should consider investing in data utilization capabilities, evidence-based decision-making processes, and organizational development approaches that promote assumption validation. These investments may be particularly important for safety net providers serving diverse, vulnerable populations where ED misuse patterns may differ from assumptions based on general healthcare literature or other organizational settings. The findings contribute to growing literature on evidence-based management in healthcare settings and provide practical insights for community health system leaders seeking to improve strategic decision-making processes. As healthcare organizations face increasing pressure to demonstrate value and optimize resource utilization, the ability to align strategic assumptions with empirical evidence becomes increasingly critical for sustainable operations and improved patient outcomes. This exploratory analysis identified potential areas of perception-reality divergence that warrant further investigation in larger samples with sufficient statistical power. Future confirmatory studies should examine these preliminary patterns across multiple healthcare organizations to determine their generalizability and statistical significance. The findings provide a foundation for developing hypotheses about leadership perception-evidence alignment that can be tested in more robust research designs. Declarations Ethics Approval and Consent to Participate This study was approved by the Institutional Review Board as an exempt quality assurance project. The study was also approved by the Institutional Review Board (Protocol # STUDY2025-0618). All participants provided informed consent prior to survey completion. This study is part of a doctoral dissertation project conducted in the Doctor of Public Health (DrPH) program. No norm or standard used (N/A) Consent for Publication Not applicable. Competing Interests The authors declare no competing interests. Funding This research received no external funding. Author Contribution YL conceived the study, designed the methodology, collected and analyzed data, and drafted the manuscript. All authors read and approved the final manuscript. Acknowledgments The authors thank the senior management team at partnering institutions for their participation in this study and their commitment to evidence-based organizational improvement. Data Availability The datasets used and analyzed during the current study are available from the corresponding author on reasonable request, subject to institutional data sharing policies and participant privacy protections. References Fingar KR, Barrett ML, Elixhauser A, Stocks C, Steiner CA. Trends in Potentially Preventable Inpatient Hospital Admissions and Emergency Department Visits. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville (MD): Agency for Healthcare Research and Quality; 2015. Parkinson B, Meacock R, Checkland K, Sutton M. Unseen patterns of preventable emergency care: Emergency department visits for ambulatory care sensitive conditions. J Health Serv Res Policy. 2022;27(3):232–41. Uscher-Pines L, Pines J, Kellermann A, Gillen E, Mehrotra A. Deciding to visit the emergency department for non-urgent conditions: a systematic review of the literature. Am J Manag Care. 2013;19(1):47–59. Kraaijvanger N, Van Leeuwen H, Rijpsma D, Edwards M. 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Evidence-based management. Harv Bus Rev. 2006;84(1):62–74. Tables Table 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table14.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. 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14:41:31","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":81505,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7881194/v1/7dd78607edad29d95d8da2c5.html"},{"id":97171668,"identity":"eacfd93a-200b-4453-8a19-53412e3c8218","added_by":"auto","created_at":"2025-12-01 14:41:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11443,"visible":true,"origin":"","legend":"\u003cp\u003eLeadership Assumptions vs. Empirical Data on ED Misuse Drivers\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7881194/v1/002a4c91f44bf3901bcbe2ce.png"},{"id":97171669,"identity":"1a971201-7bb1-4e8c-bcaf-d669d5b2077c","added_by":"auto","created_at":"2025-12-01 14:41:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87826,"visible":true,"origin":"","legend":"\u003cp\u003ePrimary Organizational Priority-Setting Challenges\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7881194/v1/1725bd8f3092565e4dc6d1c8.png"},{"id":97250134,"identity":"a01ea5c3-bc3f-4fc9-9b8a-8d536d2bae30","added_by":"auto","created_at":"2025-12-02 13:13:59","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":313847,"visible":true,"origin":"","legend":"\u003cp\u003eLikert-Scale Organizational Effectiveness\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7881194/v1/8332bd025d55d2a9a01d81d7.jpeg"},{"id":98741393,"identity":"eb09a191-2431-4933-9c5c-6c9c26a4cc29","added_by":"auto","created_at":"2025-12-22 07:40:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1299295,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7881194/v1/9075141b-a79e-4515-9701-01c99ee1744f.pdf"},{"id":97248786,"identity":"65df93bc-d004-4485-935f-5b9e9a2de985","added_by":"auto","created_at":"2025-12-02 13:07:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":303592,"visible":true,"origin":"","legend":"","description":"","filename":"Table14.docx","url":"https://assets-eu.researchsquare.com/files/rs-7881194/v1/70b69d58edac1651b18aff09.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Leadership Perceptions and Organizational Reality: Barriers to Evidence-Based Emergency Department Misuse Reduction in Community Health Systems","fulltext":[{"header":"Background","content":"\u003cp\u003eUse of emergency departments for medical issues that could be treated elsewhere\u0026mdash;often called ED misuse\u0026mdash;continues to challenge healthcare systems nationwide (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In the United States, these avoidable visits contribute significantly to rising costs, overcrowded emergency rooms, delays for critically ill patients, and strain on limited resources. While much research has focused on the clinical and patient factors influencing these visits, less work has examined how healthcare leaders' perceptions shape their choices to address this problem (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHealthcare leadership today involves complicated priority-setting that balances clinical goals, financial realities, and operational capacity (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Yet the assumptions leaders make during this process do not always reflect administrative data, a challenge that is especially difficult for leaders in organizations with tight resources (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). If leaders\u0026rsquo; assumptions differ from trends observed in institutional data, there is a possibility that strategic decisions may not dully realize their intended outcomes (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCommunity health systems face particular hurdles in this area. As safety-net providers, they serve diverse, often vulnerable populations, many uninsured or underinsured, and frequently act as primary care contact points. Patients in these groups encounter barriers\u0026mdash;like cost, lack of primary care relationships, or inconvenient clinic hours\u0026mdash;that make alternatives to the emergency department harder to access (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Despite the strategic importance of leadership perspectives, few studies have systematically investigated how these assumptions align with real-world data in community healthcare settings.\u003c/p\u003e\u003cp\u003eThis study aims to fill this gap by comparing what senior leaders assume about the causes and solutions for ED misuse with empirical evidence from a community system's data. It focuses on assumptions about drivers, organizational readiness, and capabilities to phase out ineffective programs. Key questions included:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHow do healthcare leaders perceive ED misuse causes and their organization's capacity to intervene?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eHow do these perceptions match with actual evidence?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat organizational factors contribute to any gaps uncovered?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn exploratory cross-sectional survey was conducted among senior managers at a mid-sized non-profit medical center serving diverse community health system serving a diverse safety-net community region. This exploratory design aimed to identify potential patterns of perception-reality divergence for hypothesis generation in future confirmatory studies.\u003c/p\u003e\n\u003cp\u003eThe study received approval from the Institutional Review Board as exempt status and also from the University of Illinois Chicago (Protocol # STUDY2025-0618). The survey was conducted between June 13-27, 2025.\u003c/p\u003e\n\u003cp\u003eSenior managers (N=93) with strategic decision-making and priority-setting authority were identified through a purposive sampling approach. Inclusion criteria required participants to hold positions with direct involvement in organizational priority-setting and resource allocation decisions. This included department directors, service line leaders, vice presidents, and executive team members. Forty-seven senior managers completed the survey, yielding a 50.5% response rate. Gap analysis was conducted by comparing leadership perceptions with institutional data, described next.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSurvey Instrument\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA structured survey instrument was developed based on evidence-based management literature (20) and reviewed by three healthcare administration faculty members. The survey assessed leadership perceptions across multiple disciplines as summarized in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparative Data Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvey findings were compared with multiple empirical data sources:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e2025 Community Health Needs Assessment (CHNA): Assessment conducted in collaboration with community stakeholders\u003c/li\u003e\n \u003cli\u003eCalifornia Health Interview Survey (CHIS): 2021-2022\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eState Benchmarking Data: California Health and Human Services Agency opioid-related ED visit statistics\u003c/li\u003e\n \u003cli\u003eInstitutional ED Utilization: Internal data on visit patterns and diagnostic categories from EMR 2022 \u0026ndash; 2024.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eInsert Table 1 about here\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation-based Approach:\u003c/strong\u003e The analytical framework employed a population-based survey approach (10) examining leadership assumptions across the entire senior management cohort (N=47). This approach ensured comprehensive coverage of organizational decision-makers and enabled identification of systematic patterns in assumption formation across functional domains.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiostatistical Methods:\u003c/strong\u003e Quantitative data were analyzed using Microsoft Excel\u0026reg; with descriptive statistics appropriate for categorical and ordinal variables. Frequencies were calculated for all categorical responses, including organizational commitment levels, perceived ED misuse drivers, and priority-setting challenges. For Likert-scale organizational effectiveness ratings, distributions were computed.\u003c/p\u003e\n\u003cp\u003eOrganizational effectiveness ratings were dichotomized into meaningful analytical categories: upper effectiveness (ratings 4-5 on five-point scale, representing \u0026quot;Good\u0026quot; to \u0026quot;Excellent\u0026quot; performance) and lower effectiveness (ratings 1-3, representing \u0026quot;Very Poor\u0026quot; to \u0026quot;Fair\u0026quot; performance). This categorization facilitated comparison across the four effectiveness domains (technical, human, financial, data aspects) and enabled identification of relative organizational strengths and limitations.\u003c/p\u003e\n\u003cp\u003eCategorical associations were evaluated using Fisher\u0026apos;s exact test, chosen over chi-square analysis due to expected cell frequencies below 5 in several cross-tabulations (17,18). Two-tailed tests were employed with significance set at \u0026alpha; = 0.05 (19). This approach is recommended for small sample contingency table analysis where traditional chi-square assumptions may be violated. All statistical analyses were performed using Microsoft Excel\u0026reg;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMixed-Methods Integration:\u003c/strong\u003e Qualitative responses from open-ended survey questions were analyzed by applying a deductive thematic analysis process grounded in evidence-based management theoretical frameworks (20). The integration of quantitative survey data with qualitative thematic findings followed a convergent parallel design, where both data types were analyzed independently and subsequently integrated to provide comprehensive understanding of perceptions and institutional patterns.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerception \u0026ndash; Data Reality Gap Analysis:\u003c/strong\u003e The core analytical approach involved systematic comparative analysis examining alignment between leadership perceptions (survey responses) and data from multiple organizational and community data sources. Substantial differences were identified, suggesting opportunities for further exploration and learning rather than clear contradictions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQualitative Contradictions: Systematic variations were noted between leadership assumptions and available empirical evidence from institutional data sources, community health assessments, or benchmark comparisons were identified. Give a list of which variables were compared here.\u003c/p\u003e\n\u003cp\u003eValidation and data triangulation were employed to enhance validity through systematic comparison of survey findings with multiple independent data sources (11). These included 2022 California Health Interview Survey population data, institutional electronic health record utilization patterns, Community Health Needs Assessment findings shown in Table 2, and state-level benchmarking statistics. This multi-source approach strengthened confidence in identified perception-reality gaps by confirming patterns across different data collection methods and timeframes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Table 2\u003c/strong\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis exploratory analysis examined perception-data alignment patterns among senior leadership. Given the sample size (N=47) and exploratory nature of the study, findings should be interpreted as hypothesis-generating observations that warrant further investigation in larger samples with sufficient statistical power.\u003c/p\u003e\n\u003cp\u003eForty-seven of the invited senior managers completed the survey (response rate 50.5%) as shown in Table 3. Participants represented diverse organizational functions including clinical departments (42.6%, n=20), administrative services (31.9%, n=15), support services (17.0%, n=8), and executive leadership (8.5%, n=4). Average tenure in current positions was 6.3 years (range 1-18 years), with 72.3% (n=34) holding master\u0026apos;s degrees or higher.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInsert Table 3\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOrganizational Commitment and Leadership Consensus\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSenior managers demonstrated strong consensus regarding organizational commitment to addressing ED misuse. When asked if ED misuse reduction represented an organizational priority, 74.5% (n=35) indicated \u0026quot;Yes\u0026quot; and only 6.4% (n=3) responded \u0026quot;No.\u0026quot; An additional 19.1% (n=9) responded \u0026quot;Uncertain,\u0026quot; suggesting some ambiguity in strategic communication. This substantial majority endorsement indicates broad leadership alignment on the strategic importance of ED misuse reduction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparing Leadership Perceptions and Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLeadership Perceptions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLeadership perceptions about ED misuse drivers as responded through survey were heavily influenced by the organization\u0026apos;s Community Health Needs Assessment, which prioritized behavioral health services expansion as a primary strategy for addressing ED utilization patterns in the past several years. Key informants\u0026rsquo; qualitative responses included: - \u0026quot;\u003cem\u003eMental health patients have nowhere else to go\u003c/em\u003e\u0026rdquo;; \u0026ldquo;\u003cem\u003edrug-seeking behavior and psychiatric crises drive most inappropriate visits\u003c/em\u003e\u0026quot;; and\u0026quot; \u003cem\u003ewe need more behavioral health resources to address the root causes.\u003c/em\u003e\u0026rdquo; Figure 1 illustrates the contrast between these leadership perceptions and empirical data.\u003c/p\u003e\n\u003cp\u003eInsert Figure 1\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile leadership focused on certain causes of ED misuse, institutional data indicated additional patterns worth consideration. Analysis of electronic medical records from January 2022 through December 2024 showed that mental health and substance abuse visits comprised 8.3% of all ED encounters (25,705 visits out of 309,701 total visits). Despite representing a minority of overall ED utilization, leadership identified behavioral health as the primary strategic target for ED misuse reduction initiatives. Although care for mental health and substance use cases is resource-intensive, addressing their impact on resources is beyond the scope of this study. The data indicated that leadership focus on behavioral health interventions may not substantially impact the majority of ED utilization patterns driving organizational concerns about misuse.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifference between Perceptions and Data: Human Resource Adequacy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLeadership Perceptions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSenior leadership operated under the assumption that sufficient human resources existed to address ED misuse reduction initiatives without substantial organizational restructuring. Strategic planning documents reflect confidence in implementing new programs while maintaining existing operations.\u003c/p\u003e\n\u003cp\u003eSurvey data revealed significant human resource constraints that contradicted leadership assumptions (Figure 2). First, human resource capacity emerged as the predominant challenge in organizational priority-setting, identified by 55.3% of respondents (n=26) as their primary difficulty. This substantially exceeded other challenge categories: technical aspects at 23.4% (n=11), financial aspects at 12.8% (n=6), and data aspects at 8.5% (n=4). Second, organizational effectiveness ratings showed human resource needs (human aspects) (59.6% rating \u0026quot;good\u0026quot; or \u0026quot;excellent\u0026quot;) scored lower than technical aspects (70.2%), indicating recognized limitations in human resource capacity.\u003c/p\u003e\n\u003cp\u003eFinally, analysis revealed that regulatory compliance activities consumed 36.2% (n=17) of available management attention, substantially limiting discretionary capacity for new initiatives.\u003c/p\u003e\n\u003cp\u003eThe contrast between assumed adequacy and 55.3% of leaders identifying human resources as their primary challenge represents a fundamental misalignment in capacity assessment. This difference highlights the importance of ongoing assessment of workforce capacity during strategic planning.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInsert Figure 2\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAlignment of Perception and Data: De-implementation Capability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe strategic planning process was based on the perception that the organization could discontinue certain existing programs and reallocate those resources to support new initiatives focused on reducing ED misuse. Planning documents reflected confidence in organizational change management capabilities.\u003c/p\u003e\n\u003cp\u003eSurvey responses revealed limited success with program discontinuation efforts:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDe-implementation Success Rates:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eAlways successful (\u0026gt;80% success rate): 6.4% (n=3)\u003c/li\u003e\n \u003cli\u003eOften successful (60-80% success rate): 27.7% (n=13)\u003c/li\u003e\n \u003cli\u003eSometimes successful (40-60% success rate): 42.6% (n=20)\u003c/li\u003e\n \u003cli\u003eRarely successful (20-40% success rate): 19.1% (n=9)\u003c/li\u003e\n \u003cli\u003eNever successful (\u0026lt;20% success rate): 4.3% (n=2)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOnly 34.1% (n=16) reported frequent success (Always/Often categories), while 65.9% (n=31) reported limited success (Sometimes/Rarely/Never categories).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmotional Responses to Priority-Setting:\u003c/strong\u003e Leaders identified predominant emotional states when addressing ED misuse priorities:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eDifficult: 36.2% (n=17)\u003c/li\u003e\n \u003cli\u003eReluctant: 25.5% (n=12)\u003c/li\u003e\n \u003cli\u003eOptimistic: 19.1% (n=9)\u003c/li\u003e\n \u003cli\u003eConfident: 12.8% (n=6)\u003c/li\u003e\n \u003cli\u003eFrustrated: 6.4% (n=3)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe concentration of challenging emotions (61.7% difficult/reluctant combined) suggests substantial barriers to change implementation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePriority-Setting Influences:\u003c/strong\u003e Analysis of factors driving priority-setting decisions revealed:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eRegulatory requirements: 36.2% (n=17)\u003c/li\u003e\n \u003cli\u003eClinical outcomes: 29.8% (n=14)\u003c/li\u003e\n \u003cli\u003eFinancial considerations: 21.3% (n=10)\u003c/li\u003e\n \u003cli\u003eCommunity needs: 12.8% (n=6)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe assumption of de-implementation capability contrasts sharply with only 34.1% of leaders reporting frequent success with program discontinuation. This 31.9 percentage point gap between assumed and demonstrated capability suggests systematic overestimation of organizational change management capacity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOrganizational Effectiveness Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of organizational effectiveness ratings across four domains revealed varying perceived capabilities (Figure 3):\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUpper Effectiveness Categories (Ratings 4-5):\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eTechnical aspects: 70.2% (n=33)\u003c/li\u003e\n \u003cli\u003eHuman aspects: 59.6% (n=28)\u003c/li\u003e\n \u003cli\u003eFinancial aspects: 53.2% (n=25)\u003c/li\u003e\n \u003cli\u003eData aspects: 51.0% (n=24)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe lowest effectiveness ratings in data utilization (51.0%) may contribute to the persistence of assumption-reality gaps, as limited analytical capabilities prevent systematic validation of leadership assumptions against empirical evidence.\u003c/p\u003e\n\u003cp\u003eExamination of the relationship between perceived organizational effectiveness and de-implementation success revealed no clear patterns. Leaders reporting high effectiveness in technical and human domains showed similar de-implementation success rates (75.0% for high effectiveness vs. 63.6% for lower effectiveness domains), suggesting that perceived capabilities do not reliably predict actual performance outcomes (Table 4).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExamination of the relationship between perceived organizational effectiveness and de-implementation success revealed no clear patterns. Fisher\u0026apos;s exact test showed no statistically significant association between perceived organizational effectiveness levels and de-implementation success rates (p = 0.743, two-tailed) (17,18). Leaders reporting high effectiveness in technical and human domains showed similar de-implementation success rates (75.0% for high effectiveness vs. 63.6% for lower effectiveness domains), suggesting that perceived capabilities do not reliably predict actual performance outcomes.\u003c/p\u003e\n\u003cp\u003eThis finding supports the broader pattern of misalignment between leadership assumptions and organizational realities documented throughout the study.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis exploratory analysis identified three potential areas of perception-reality divergence that may warrant further investigation. While the observed patterns suggest possible systematic differences between leadership assumptions and empirical evidence, the exploratory nature and sample size limitations require cautious interpretation of these preliminary findings.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003eMental Health Perception: Implications and Context\u003c/h2\u003e\u003cp\u003eThe focus on mental health and substance abuse primarily drives ED misuse may influence resource allocation approaches. Leadership perceptions identified behavioral health as the primary intervention target, despite institutional data showing these conditions account for only 8.3% of ED visits. This finding aligns with previous research suggesting that perceived versus actual drivers of healthcare utilization may differ substantially due to availability heuristic and confirmation bias (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The community-level data from CHNA provides additional context. While substantial unmet mental health needs exist (50.5% are unable to receive needed services), these needs do not translate proportionally to ED utilization from the Institutional data review. This suggests that barriers to mental health care access may be resolved through community-based interventions rather than ED-focused strategies. The below-average opioid-related ED visits (34 vs. 58.7 per 100,000) further challenge assumptions about substance abuse as a primary driver.\u003c/p\u003e\u003cp\u003eThis focus may be influenced by high-visibility cases, where psychiatric emergencies are particularly memorable. Additionally, community stakeholder input during needs assessment processes may emphasize behavioral health concerns that, while legitimate, may not reflect actual ED utilization patterns.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eHuman Resource Adequacy: Strategic Planning Implications\u003c/h2\u003e\u003cp\u003eThe perception of human resource adequacy, contradicted by empirical evidence showing 55.3% of leaders identify human aspects as their primary challenge, has profound implications for implementation planning. This gap suggests that strategic initiatives may be designed with unrealistic expectations about available workforce capacity, potentially leading to implementation failures and staff burnout, including staff outside of direct ED clinical roles (i.e., security, admission clerks, cleaning staff, etc.).\u003c/p\u003e\u003cp\u003eThe finding that regulatory requirements consume 36.2% of management attention provides crucial context for understanding resource constraints. In community health systems serving safety net populations, regulatory compliance demands may be particularly intensive due to complex payer mix, quality reporting requirements, and accreditation standards. This regulatory burden limits discretionary capacity for new initiatives, making accurate capacity assessment essential for successful program implementation.\u003c/p\u003e\u003cp\u003eThe lower effectiveness ratings for human aspects (59.6%) compared to technical aspects (70.2%) suggest that leadership recognizes human resource limitations but may not fully integrate this recognition into strategic planning processes. The gap between awareness and action may reflect cultural dynamics within the organization or a general inclination toward optimistic planning.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eDe-implementation Capability: Change Management Realities\u003c/h2\u003e\u003cp\u003eUnsuccessful de-implementation capability, contradicted by limited success rates (only 34.1% frequent success), represents perhaps the most significant barrier to effective resource reallocation. Healthcare organizations typically operate with established programs that have developed stakeholder support, operational integration, and cultural acceptance (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The necessity to discontinue these programs requires sophisticated change management capabilities that may exceed organizational capacity.\u003c/p\u003e\u003cp\u003eThis gap may have implications that go beyond resource allocation. Challenges in de-implementation may contribute to resistance or hesitation regarding future initiatives. The predominant emotional responses of difficulty and reluctance (61.7% combined) may reflect past experiences with unsuccessful change efforts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eData Utilization as a Contributing Factor\u003c/h2\u003e\u003cp\u003eThe lowest perceived effectiveness in data aspects (51.0%) suggests a potential contributing factor to the assumption-reality gaps observed in this study. When analytical capabilities are limited, organizations may find it challenging to systematically validate assumptions against empirical evidence, which can lead to decision-making processes that draw more heavily on intuition, past experience, or external pressures rather than comprehensive data analysis (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis observation may be particularly relevant for community health systems, which often operate with different analytical infrastructure compared to larger health systems or academic medical centers. The findings suggest that strengthening data analytics capabilities\u0026mdash;including both technological tools and analytical expertise\u0026mdash;could support more evidence-informed decision-making processes.\u003c/p\u003e\u003cp\u003eAlthough this study does not establish causation, the observed pattern of data use and gaps between planning and practice indicates that strengthening analytical capacity could contribute to broader organizational initiatives to align strategy with operational realities.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section3\"\u003e\u003ch2\u003eEmotional and Psychological Barriers\u003c/h2\u003e\u003cp\u003eThe predominant emotional responses of difficulty and reluctance indicate that psychological factors may influence leadership's ability to revise foundational perceptions when presented with contradictory evidence. These emotional responses could reflect the natural discomfort that occurs when new information challenges established organizational beliefs and practices (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAddressing these challenges may benefit from organizational development approaches that recognize the emotional aspects of strategic decision-making while offering structured processes to help leaders examine and potentially update their assumptions over time.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eImplications for Practice\u003c/h2\u003e\u003cp\u003eCommunity health system leaders may consider paying more attention to the empirical data that compares strategic premises with empirical evidence before major resource allocation decisions. This might include:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePre-decision Data Review: Increase ability to understand available data sources before strategic planning sessions and allocation of human resources\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAssumption Documentation: Explicit identification and documentation of underlying assumptions in strategic plans\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEvidence-Based Checkpoints: Regular review of assumptions against emerging evidence during implementation\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eCultural Change (fundamental behavioral change) Initiatives: Programs to promote data-driven decision-making culture\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe development of enhanced data analytics capabilities represents a foundational investment that may improve decision-making across multiple organizational domains. However, technology investments alone may be insufficient without corresponding changes in organizational culture and decision-making processes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003eStudy Limitations\u003c/h2\u003e\u003cp\u003eSeveral limitations should be considered when interpreting these findings. First, this study examines a single mid-size non-profit community integrated health care delivery system in a metropolitan area, limiting generalizability to other healthcare settings including academic medical centers, large health systems, or for-profit hospitals. The organizational culture, resource constraints, and operational challenges at a mid-sized community health system in Southern California may not be representative of other community health systems.\u003c/p\u003e\u003cp\u003eSecond, cross-sectional design prevents assessment of assumption evolution over time or the effectiveness of interventions designed to improve assumption-evidence alignment. Longitudinal research would provide valuable insights into how assumptions change in response to evidence and organizational learning processes.\u003c/p\u003e\u003cp\u003eThird, the study relies on self-reported perceptions of organizational effectiveness, which may be subject to social desirability bias or limited self-awareness. Objective measures of organizational performance might provide different perspectives on effectiveness patterns.\u003c/p\u003e\u003cp\u003eFourth, the comparative data sources, while comprehensive, may not capture all relevant dimensions of ED utilization patterns. Additional data sources, including detailed clinical reviews of ED visits categorized as \"misuse,\" might provide more nuanced understanding of utilization drivers.\u003c/p\u003e\u003cp\u003eFinally, the study does not examine the effectiveness of assumption-based versus evidence-based strategies, which would require controlled intervention studies or natural experiments comparing different approaches.\u003c/p\u003e\u003cp\u003eThe exploratory nature of this single-site study limits generalizability and prevents definitive conclusions about perception-reality alignment patterns. The sample size (N\u0026thinsp;=\u0026thinsp;47) was insufficient for robust statistical testing of associations, and findings should be interpreted as preliminary observations requiring validation in larger, multi-site investigations.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFuture Research Directions\u003c/h3\u003e\n\u003cp\u003eFuture research should examine assumption-reality alignment across multiple healthcare organizations to assess the generalizability of these findings. Comparative studies across different organizational types (community hospitals, academic medical centers, integrated health systems) would provide insights into how organizational characteristics influence assumption formation and persistence.\u003c/p\u003e\u003cp\u003eLongitudinal research examining how assumptions evolve in response to evidence and organizational interventions would provide valuable insights for developing effective change strategies. Intervention studies testing structured approaches to assumption validation and evidence-based decision-making would contribute to practical knowledge for healthcare leaders.\u003c/p\u003e\u003cp\u003eAdditionally, research examining the relationship between assumption-reality alignment and organizational performance outcomes would provide empirical support for the importance of evidence-based management practices in healthcare settings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eFindings from this study indicate differences between leadership assumptions and empirical evidence regarding ED misuse reduction in a community health system, with potentially significant implications for strategic effectiveness and resource allocation. The three critical gaps identified\u0026mdash;assumptions about mental health drivers, human resource adequacy, and de-implementation capability\u0026mdash;represent common challenges that may extend beyond the study setting.\u003c/p\u003e\u003cp\u003eThese assumptions persisted alongside evidence suggesting alternative interpretations. The findings highlight the importance of evidence-based decision-making processes that systematically compare strategic assumptions with empirical evidence before major resource commitments.\u003c/p\u003e\u003cp\u003eFor healthcare leaders addressing complex operational challenges such as ED misuse reduction, success may depend more on accurate situational assessment and realistic capacity planning than on intervention strategies based on unvalidated assumptions. Emotional responses observed in this study may inform future organizational development efforts focused on supporting leaders through change.\u003c/p\u003e\u003cp\u003eCommunity health systems seeking to improve ED misuse reduction effectiveness should consider investing in data utilization capabilities, evidence-based decision-making processes, and organizational development approaches that promote assumption validation. These investments may be particularly important for safety net providers serving diverse, vulnerable populations where ED misuse patterns may differ from assumptions based on general healthcare literature or other organizational settings.\u003c/p\u003e\u003cp\u003eThe findings contribute to growing literature on evidence-based management in healthcare settings and provide practical insights for community health system leaders seeking to improve strategic decision-making processes. As healthcare organizations face increasing pressure to demonstrate value and optimize resource utilization, the ability to align strategic assumptions with empirical evidence becomes increasingly critical for sustainable operations and improved patient outcomes.\u003c/p\u003e\u003cp\u003eThis exploratory analysis identified potential areas of perception-reality divergence that warrant further investigation in larger samples with sufficient statistical power. Future confirmatory studies should examine these preliminary patterns across multiple healthcare organizations to determine their generalizability and statistical significance. The findings provide a foundation for developing hypotheses about leadership perception-evidence alignment that can be tested in more robust research designs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003cp\u003eThis study was approved by the Institutional Review Board as an exempt quality assurance project. The study was also approved by the Institutional Review Board (Protocol # STUDY2025-0618). All participants provided informed consent prior to survey completion. This study is part of a doctoral dissertation project conducted in the Doctor of Public Health (DrPH) program. No norm or standard used (N/A)\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research received no external funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYL conceived the study, designed the methodology, collected and analyzed data, and drafted the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e\u003cp\u003eThe authors thank the senior management team at partnering institutions for their participation in this study and their commitment to evidence-based organizational improvement.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request, subject to institutional data sharing policies and participant privacy protections.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFingar KR, Barrett ML, Elixhauser A, Stocks C, Steiner CA. Trends in Potentially Preventable Inpatient Hospital Admissions and Emergency Department Visits. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Rockville (MD): Agency for Healthcare Research and Quality; 2015.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eParkinson B, Meacock R, Checkland K, Sutton M. Unseen patterns of preventable emergency care: Emergency department visits for ambulatory care sensitive conditions. J Health Serv Res Policy. 2022;27(3):232\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUscher-Pines L, Pines J, Kellermann A, Gillen E, Mehrotra A. Deciding to visit the emergency department for non-urgent conditions: a systematic review of the literature. Am J Manag Care. 2013;19(1):47\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKraaijvanger N, Van Leeuwen H, Rijpsma D, Edwards M. Motives for self-referral to the emergency department: a systematic review of the literature. BMC Health Serv Res. 2016;16(1):685.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMorgan SR, Chang AM, Alqatari M, Pines JM. Non-emergency department interventions to reduce ED utilization: a systematic review. Acad Emerg Med. 2013;20(10):969\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDonaldson C, Mitton C. Priority Setting Toolkit: Guide to the Use of Economics in Healthcare Decision Making. Oxford: Blackwell Publishing; 2008.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKovner AR, Rundall TG. Evidence-based management reconsidered. Front Health Serv Manage. 2006;22(3):3\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eInstitute of Medicine. America's Health Care Safety Net: Intact but Endangered. Washington, DC: National Academies; 2000.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGindi RM, Cohen RA, Kirzinger WK. Emergency room use among adults aged 18\u0026ndash;64: Early release of estimates from the. National Health Interview Survey. National Center for Health Statistics; 2012.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFowler FJ. Survey Research Methods. 5th ed. Thousand Oaks, CA: SAGE; 2014.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKruse CS, Goswamy R, Raval Y, Marawi S. Leveraging big data in population health management. Big Data Analytics. 2016;1(1):1\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTversky A, Kahneman D, Availability. A heuristic for judging frequency and probability. Cogn Psychol. 1973;5(2):207\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNickerson RS. Confirmation bias: A ubiquitous phenomenon in many guises. Rev Gen Psychol. 1998;2(2):175\u0026ndash;220.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNorton WE, Chambers DA, Kramer BS. Conceptualizing de-implementation in cancer care delivery. J Clin Oncol. 2019;37(2):93\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDavenport TH, Harris JG. Competing on Analytics: The New Science of Winning. Boston: Harvard Business Review; 2007.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFestinger L. A Theory of Cognitive Dissonance. Stanford: Stanford University Press; 1957.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAgresti A. Categorical Data Analysis. 3rd ed. Hoboken, NJ: Wiley; 2013.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFisher RA. On the interpretation of χ\u0026sup2; from contingency tables, and the calculation of P. J Roy Stat Soc. 1922;85(1):87\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLydersen S, Fagerland MW, Laake P. Recommended tests for association in 2\u0026times;2 tables. Stat Med. 2009;28(7):1159\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePfeffer J, Sutton RI. Evidence-based management. Harv Bus Rev. 2006;84(1):62\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 4 are available in the Supplementary Files section.\u003c/p\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":"Emergency Service, Hospital, Community Health Planning, Organization \u0026 Administration, Health Care System, Leadership, Evidence-Based Medicine/Method","lastPublishedDoi":"10.21203/rs.3.rs-7881194/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7881194/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eEmergency department (ED) misuse remains a persistent problem for healthcare organizations. How leaders perceive the causes and solutions to this issue strongly shapes their strategic responses. This study explores the alignment between senior leadership's perceptions and evidence about ED misuse reduction at a community health system serving a diverse, safety-net population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional exploratory survey of senior managers (N\u0026thinsp;=\u0026thinsp;47) at a mid-sized community health system in Southern California, was conducted between June 13\u0026ndash;27, 2025. This exploratory analysis identified potential areas of perception-data divergence that warrant further investigation in larger samples with sufficient statistical power.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThree areas of difference emerged between leadership perspectives and operational data. First, leaders identified mental health and substance abuse as primary drivers of ED misuse; however, institutional data show these issues accounted for only 8.3% of all ED visits between 2022 and 2024. Second, while 73.2% of survey respondents reported Human Resources (human factor) influence priority-setting, 55% of leaders cited an insufficient workforce as a barrier to establishing effective priorities. Third, although 55.2% of leaders reported confidence in their ability to discontinue ineffective programs, only one-third reported frequent success with de-implementation. These differences were accompanied by modest ratings in organizational data use capabilities.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eWhile leadership perceptions diverged from institutional data, these differences may reflect resource intensity considerations not captured in utilization statistics alone. A mid-sized community health system could benefit from enhanced data analytics and formal processes to routinely check leadership perceptions before committing resources to priority initiatives.\u003c/p\u003e","manuscriptTitle":"Leadership Perceptions and Organizational Reality: Barriers to Evidence-Based Emergency Department Misuse Reduction in Community Health Systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 14:41:26","doi":"10.21203/rs.3.rs-7881194/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":"f0b05efc-eba9-4ae0-a868-0293a23f36d2","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T07:39:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 14:41:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7881194","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7881194","identity":"rs-7881194","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}