Breaking the Cycle: Identifying Drivers of 30-Day Readmissions in Rural Medicare Populations

Breaking the Cycle: Identifying Drivers of 30-Day Readmissions in Rural Medicare Populations | 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 Breaking the Cycle: Identifying Drivers of 30-Day Readmissions in Rural Medicare Populations Sheryll Go This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6866236/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 Reporting Method: This manuscript adheres to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist from the EQUATOR Network. Background: Hospital readmissions within 30 days are key indicators of care quality and are penalized under CMS policy. Bandura’s self-efficacy theory and recent global research emphasize the role of modifiable behavioral and system-level factors in preventing readmissions, particularly in rural contexts. Objective: To identify common contributors to 30-day readmissions among Medicare patients with Acute Myocardial Infarction (AMI), Heart Failure (HF), and Pneumonia (PNA). Methods: This retrospective descriptive study reviewed electronic records of 30 Medicare patients aged 65 and older readmitted within 30 days between January and June 2023 at a rural Southern California hospital. Data were extracted from an Epic-based EHR using a structured audit tool capturing demographics, comorbidities, and readmission diagnoses. Results: Among AMI patients, all had hypertension, obesity, and smoking history; 40% were non-compliant with medications. HF patients had 100% medication non-compliance; 90% were obese or smokers. PNA patients showed high rates of swallowing dysfunction (100%), low ADL scores (90%), and smoking (60%). Overall, obesity (95%), smoking (83%), and hypertension (90%) were the most frequent contributors. Conclusion: Preventable lifestyle and management-related factors were prevalent in early readmissions. Findings support the role of nurse-led transitional care strategies such as tailored discharge education and follow-up. Relevance to Clinical Practice: Recognizing key readmission drivers enables nurses to enhance discharge planning, patient teaching, and care coordination for at-risk rural populations. No Patient or Public Contribution: This retrospective chart review did not include direct patient or public involvement. Nursing hospital readmission Medicare rural hospital AMI heart failure pneumonia discharge planning quality improvement What does this paper contribute to the wider global clinical community? • Identifies preventable, lifestyle-related drivers of 30-day readmissions • Demonstrates how nurses use EHR data to target discharge interventions • Supports nurse-led strategies for reducing global readmission rates Introduction Hospital readmissions within 30 days represent a significant challenge in healthcare quality, care coordination, and reimbursement. The Centers for Medicare & Medicaid Services (CMS) Hospital Readmissions Reduction Program (HRRP), part of the Affordable Care Act, continues to financially penalize hospitals with excessive readmissions for conditions including acute myocardial infarction (AMI), heart failure (HF), and pneumonia (PNA) (Centers for Medicare & Medicaid Services, 2023). While national readmission rates have modestly improved, disparities persist, especially in rural hospitals with limited resources and fragmented transitional care systems (Bailey et al., 2021 ; Joynt Maddox & Rai, 2022 ). Recent studies emphasize that successful readmission prevention requires coordinated discharge planning, social determinants of health (SDOH) assessment, and structured follow-up interventions (Medicare Payment Advisory Commission, 2022 ). Transitions of care programs, such as nurse-led telephone follow-up and home visits, have demonstrated effectiveness in reducing readmissions across diverse populations (Shi, Zhu, & White, 2020 ). Bandura’s theory of self-efficacy further supports the need for proactive education and skill development among patients, especially when lifestyle modifications or medication adherence are critical to preventing relapse (Bandura, 1997 ). Self-efficacy influences confidence in following through with health behaviors and is increasingly incorporated into patient education and transitional care planning. Internationally, studies from Australia and Canada highlight similar predictors of readmission, including social isolation and limited health literacy, especially among elderly populations in remote or resource-limited communities (Harrison et al., 2021 ; Sutherland & Crump, 2020 ). These findings align with patterns observed in U.S. rural hospitals. This study aimed to identify the most common factors associated with 30-day readmissions among Medicare patients with AMI, HF, and PNA diagnoses at a small Southern California hospital. By analyzing retrospective chart data, we sought to inform targeted interventions and strengthen transitional care planning in rural healthcare settings. Materials and Methods This retrospective, descriptive study was approved by the Institutional Review Board at the author’s affiliated institution. Additional site-level approval was granted by the leadership of the participating hospital. Data were extracted from the hospital’s Epic-based electronic medical records for Medicare patients aged 65 and older who were readmitted between January and June 2023 with primary diagnoses of AMI, HF, or PNA. A structured audit tool was developed to capture demographics, medical history, functional indicators (e.g., ADLs), medication compliance, and readmission-related variables. No identifiable patient data were used, and all data were handled in compliance with HIPAA and institutional data security policies. All participants were de-identified and no direct contact was made; thus, individual consent was not required. The IRB deemed this study exempt due to its use of de-identified secondary data. Descriptive statistics were used to identify common readmission factors across the three diagnostic categories. Results were presented using frequency distributions, percentages, and summary tables. Results Among AMI patients, 100% had hypertension, obesity, and smoking history, while 40% were non-compliant with medications. For HF patients, 100% showed non-compliance with home medications; 90% were obese or smokers. PNA patients had high rates of swallowing dysfunction (100%), low ADL scores (90%), and smoking (60%). Overall, obesity (95%), smoking (83%), and hypertension (90%) were the most prevalent readmission factors. Discussion The findings of this study align with contemporary evidence highlighting chronic disease, behavioral factors, and care transition breakdowns as key contributors to 30-day readmissions (Joynt Maddox & Rai, 2022 ; Medicare Payment Advisory Commission, 2022 ). The high prevalence of obesity, smoking, and uncontrolled hypertension emphasizes the need for lifestyle modification and chronic disease management as part of discharge planning. Particularly notable is the 100% rate of medication non-compliance among heart failure patients—an outcome consistent with national data indicating medication-related problems are a leading cause of avoidable readmissions (Shi, Zhu, & White, 2020 ). Nursing-led interventions, such as structured medication reconciliation and follow-up calls, can improve adherence and reduce preventable readmissions (Bailey et al., 2021 ). International studies reinforce that health system fragmentation and lack of patient empowerment are global challenges. Sutherland and Crump ( 2020 ) emphasize the importance of continuity of care and communication strategies post-discharge in reducing rural readmissions. Additionally, quality improvement (QI) models such as nurse navigator programs and nurse-led post-discharge phone calls have been associated with reduced readmissions by ensuring continuity and bridging communication gaps (Stall et al., 2023 ). These approaches align well with the findings of this study and suggest practical, scalable interventions. Swallowing dysfunction and low ADL scores among pneumonia patients reinforce the importance of functional and nutritional assessments prior to discharge. CMS and AHRQ guidelines now recommend incorporating SDOH and functional risk screening in transitional care pathways (Centers for Medicare & Medicaid Services, 2023). These results support integrating a multidisciplinary team approach, improved discharge education, and enhanced follow-up practices tailored to high-risk diagnoses and populations, particularly in resource-limited rural environments. Conclusions High-risk factors for early readmission include preventable lifestyle and management-related elements. This study reinforces the value of nurse-led transitional care interventions, including discharge education and follow-up, while drawing on international and theoretical frameworks to advocate for patient empowerment and system-based change. Results support structured interventions focused on chronic disease management, medication adherence, and functional support—particularly relevant to critical and rural care settings. Relevance to Clinical Practice This study highlights the central role that bedside and transitional care nurses play in identifying and mitigating risk factors for hospital readmissions. Conditions such as heart failure, pneumonia, and AMI require ongoing assessment of medication adherence, functional status, and patient education, particularly at discharge. By recognizing patterns of preventable readmissions in a rural hospital setting, nurses can take proactive steps to improve discharge teaching, reinforce chronic disease management strategies, and collaborate more closely with interdisciplinary teams. This supports evidence-informed clinical decision-making and better outcomes across care transitions. Declarations Conflict of Interest The author declares no conflict of interest. Ethical Approval This study was reviewed and approved by the Institutional Review Board (IRB) at the author's affiliated institution. Additional permission was obtained from the participating hospital. All data were de-identified; therefore, participant consent was waived in accordance with IRB and institutional policies. This manuscript includes a full statement of ethical compliance as required. References Bailey, M. K., Weiss, A. J., Barrett, M. L., & Jiang, J. H. (2021). Characteristics of 30-day all-cause hospital readmissions. HCUP Statistical Brief #248 . Agency for Healthcare Research and Quality. Https://www.hcup-us.ahrq.gov/reports/statbriefs/sb248-Hospital-Readmissions-2018.jsp Bandura, A. (1997). Self-efficacy: The exercise of control . W.H. Freeman. Centers for Medicare & Medicaid Services. (2023). Hospital Readmissions Reduction Program (HRRP) . Https://www.cms.gov/medicare/medicare-fee-for-service-payment/acuteinpatientpps/readmissions-reduction-program Harrison, J. D., Miller, T., & Lu, J. (2021). Predictors of hospital readmissions among elderly patients in remote communities: An Australian perspective. BMC Geriatrics , 21(1), 112. Https://doi.org/10.1186/s12877-021-02099-w Joynt Maddox, K. E., & Rai, D. (2022). Hospital readmissions: Improving quality and safety during transitions of care. JAMA , 327(14), 1323–1324. Https://doi.org/10.1001/jama.2022.2933 Medicare Payment Advisory Commission. (2022). Report to the Congress: Medicare and the health care delivery system . Https://www.medpac.gov Shi, C., Zhu, H., & White, J. (2020). Impact of transitional care interventions on hospital readmissions: A meta-analysis. Health Services Research , 55(5), 842–854. Https://doi.org/10.1111/1475-6773.13284 Stall, N. M., Sinha, S. K., & Brown, E. E. (2023). Reducing hospital readmissions through nurse-led transitional care: Results from a Canadian quality improvement initiative. BMJ Open Quality , 12(2), e002083. Https://doi.org/10.1136/bmjoq-2022-002083 Sutherland, K., & Crump, R. T. (2020). Avoidable hospital readmissions: A review of interventions and outcomes in rural populations. Healthcare Policy , 15(4), 38–51. Https://doi.org/10.12927/hcpol.2020.26233 Tables Tables 1 to 3 are available in the Supplementary Files section Additional Declarations The authors declare no competing interests. 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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-6866236","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":469443096,"identity":"5824fb1d-7d90-4491-906c-b874b176783e","order_by":0,"name":"Sheryll Go","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDACCRBRYAEkmA+AmAlAbECEFgMQyZZAshYeA+K0yM9uPvaZB6hF3r3n48efbXV5DOzN2yTwaTG4cyx5NkiL4Zmzm6V52w4XM/AcK8OvRSLHmBmsZUbuNmbGtgOJDRI5Zni1yM/I/wzVkvOMEeiwxAb5N/i1MNzIYQZrkZfIYWPgbWMG2sKDX4vBjTRjxjkGEjwGPMeMpXnOHS5m40krtsDvsOTHDG8qbOTk25sffvxRVpfHz3544w28DgMCJh5QpByA8tgIKQcBxh8g6xqIUToKRsEoGAUjEgAAXmo/pXFKAhEAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0008-1072-9862","institution":"California State University San Bernardino","correspondingAuthor":true,"prefix":"","firstName":"Sheryll","middleName":"","lastName":"Go","suffix":""}],"badges":[],"createdAt":"2025-06-10 21:21:25","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6866236/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6866236/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84444230,"identity":"93ecc067-e3b3-4d8d-85ca-0630baf0401f","added_by":"auto","created_at":"2025-06-12 04:56:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":345387,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6866236/v1/0e7290b7-1126-46d9-95a7-60ed76c8da6e.pdf"},{"id":84442897,"identity":"85d621a9-2968-40f4-a6d7-163ed183e1e8","added_by":"auto","created_at":"2025-06-12 04:32:35","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":558722,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6866236/v1/090e1c53ead35e59c4f36357.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eBreaking the Cycle: Identifying Drivers of 30-Day Readmissions in Rural Medicare Populations\u003c/p\u003e","fulltext":[{"header":"What does this paper contribute to the wider global clinical community?","content":"\u003cp\u003e• Identifies preventable, lifestyle-related drivers of 30-day readmissions\u003cbr\u003e\u0026nbsp;• Demonstrates how nurses use EHR data to target discharge interventions\u003cbr\u003e\u0026nbsp;• Supports nurse-led strategies for reducing global readmission rates\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eHospital readmissions within 30 days represent a significant challenge in healthcare quality, care coordination, and reimbursement. The Centers for Medicare \u0026amp; Medicaid Services (CMS) Hospital Readmissions Reduction Program (HRRP), part of the Affordable Care Act, continues to financially penalize hospitals with excessive readmissions for conditions including acute myocardial infarction (AMI), heart failure (HF), and pneumonia (PNA) (Centers for Medicare \u0026amp; Medicaid Services, 2023). While national readmission rates have modestly improved, disparities persist, especially in rural hospitals with limited resources and fragmented transitional care systems (Bailey et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Joynt Maddox \u0026amp; Rai, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent studies emphasize that successful readmission prevention requires coordinated discharge planning, social determinants of health (SDOH) assessment, and structured follow-up interventions (Medicare Payment Advisory Commission, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Transitions of care programs, such as nurse-led telephone follow-up and home visits, have demonstrated effectiveness in reducing readmissions across diverse populations (Shi, Zhu, \u0026amp; White, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBandura\u0026rsquo;s theory of self-efficacy further supports the need for proactive education and skill development among patients, especially when lifestyle modifications or medication adherence are critical to preventing relapse (Bandura, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Self-efficacy influences confidence in following through with health behaviors and is increasingly incorporated into patient education and transitional care planning.\u003c/p\u003e \u003cp\u003eInternationally, studies from Australia and Canada highlight similar predictors of readmission, including social isolation and limited health literacy, especially among elderly populations in remote or resource-limited communities (Harrison et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sutherland \u0026amp; Crump, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These findings align with patterns observed in U.S. rural hospitals.\u003c/p\u003e \u003cp\u003eThis study aimed to identify the most common factors associated with 30-day readmissions among Medicare patients with AMI, HF, and PNA diagnoses at a small Southern California hospital. By analyzing retrospective chart data, we sought to inform targeted interventions and strengthen transitional care planning in rural healthcare settings.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e This retrospective, descriptive study was approved by the Institutional Review Board at the author\u0026rsquo;s affiliated institution. Additional site-level approval was granted by the leadership of the participating hospital. Data were extracted from the hospital\u0026rsquo;s Epic-based electronic medical records for Medicare patients aged 65 and older who were readmitted between January and June 2023 with primary diagnoses of AMI, HF, or PNA.\u003c/p\u003e \u003cp\u003e A structured audit tool was developed to capture demographics, medical history, functional indicators (e.g., ADLs), medication compliance, and readmission-related variables. No identifiable patient data were used, and all data were handled in compliance with HIPAA and institutional data security policies.\u003c/p\u003e \u003cp\u003eAll participants were de-identified and no direct contact was made; thus, individual consent was not required. The IRB deemed this study exempt due to its use of de-identified secondary data.\u003c/p\u003e \u003cp\u003eDescriptive statistics were used to identify common readmission factors across the three diagnostic categories. Results were presented using frequency distributions, percentages, and summary tables.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAmong AMI patients, 100% had hypertension, obesity, and smoking history, while 40% were non-compliant with medications. For HF patients, 100% showed non-compliance with home medications; 90% were obese or smokers. PNA patients had high rates of swallowing dysfunction (100%), low ADL scores (90%), and smoking (60%). Overall, obesity (95%), smoking (83%), and hypertension (90%) were the most prevalent readmission factors.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings of this study align with contemporary evidence highlighting chronic disease, behavioral factors, and care transition breakdowns as key contributors to 30-day readmissions (Joynt Maddox \u0026amp; Rai, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Medicare Payment Advisory Commission, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The high prevalence of obesity, smoking, and uncontrolled hypertension emphasizes the need for lifestyle modification and chronic disease management as part of discharge planning.\u003c/p\u003e \u003cp\u003eParticularly notable is the 100% rate of medication non-compliance among heart failure patients\u0026mdash;an outcome consistent with national data indicating medication-related problems are a leading cause of avoidable readmissions (Shi, Zhu, \u0026amp; White, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nursing-led interventions, such as structured medication reconciliation and follow-up calls, can improve adherence and reduce preventable readmissions (Bailey et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInternational studies reinforce that health system fragmentation and lack of patient empowerment are global challenges. Sutherland and Crump (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) emphasize the importance of continuity of care and communication strategies post-discharge in reducing rural readmissions.\u003c/p\u003e \u003cp\u003eAdditionally, quality improvement (QI) models such as nurse navigator programs and nurse-led post-discharge phone calls have been associated with reduced readmissions by ensuring continuity and bridging communication gaps (Stall et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These approaches align well with the findings of this study and suggest practical, scalable interventions.\u003c/p\u003e \u003cp\u003eSwallowing dysfunction and low ADL scores among pneumonia patients reinforce the importance of functional and nutritional assessments prior to discharge. CMS and AHRQ guidelines now recommend incorporating SDOH and functional risk screening in transitional care pathways (Centers for Medicare \u0026amp; Medicaid Services, 2023).\u003c/p\u003e \u003cp\u003eThese results support integrating a multidisciplinary team approach, improved discharge education, and enhanced follow-up practices tailored to high-risk diagnoses and populations, particularly in resource-limited rural environments.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eHigh-risk factors for early readmission include preventable lifestyle and management-related elements. This study reinforces the value of nurse-led transitional care interventions, including discharge education and follow-up, while drawing on international and theoretical frameworks to advocate for patient empowerment and system-based change. Results support structured interventions focused on chronic disease management, medication adherence, and functional support—particularly relevant to critical and rural care settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelevance to Clinical Practice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study highlights the central role that bedside and transitional care nurses play in identifying and mitigating risk factors for hospital readmissions. Conditions such as heart failure, pneumonia, and AMI require ongoing assessment of medication adherence, functional status, and patient education, particularly at discharge.\u003c/p\u003e\n\u003cp\u003eBy recognizing patterns of preventable readmissions in a rural hospital setting, nurses can take proactive steps to improve discharge teaching, reinforce chronic disease management strategies, and collaborate more closely with interdisciplinary teams. This supports evidence-informed clinical decision-making and better outcomes across care transitions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Institutional Review Board (IRB) at the author's affiliated institution. Additional permission was obtained from the participating hospital. All data were de-identified; therefore, participant consent was waived in accordance with IRB and institutional policies. This manuscript includes a full statement of ethical compliance as required.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBailey, M. K., Weiss, A. J., Barrett, M. L., \u0026amp; Jiang, J. H. (2021). Characteristics of 30-day all-cause hospital readmissions. \u003cem\u003eHCUP Statistical Brief #248\u003c/em\u003e. Agency for Healthcare Research and Quality. Https://www.hcup-us.ahrq.gov/reports/statbriefs/sb248-Hospital-Readmissions-2018.jsp\u003c/li\u003e\n \u003cli\u003eBandura, A. (1997). \u003cem\u003eSelf-efficacy: The exercise of control\u003c/em\u003e. W.H. Freeman.\u003c/li\u003e\n \u003cli\u003eCenters for Medicare \u0026amp; Medicaid Services. (2023). \u003cem\u003eHospital Readmissions Reduction Program (HRRP)\u003c/em\u003e. Https://www.cms.gov/medicare/medicare-fee-for-service-payment/acuteinpatientpps/readmissions-reduction-program\u003c/li\u003e\n \u003cli\u003eHarrison, J. D., Miller, T., \u0026amp; Lu, J. (2021). Predictors of hospital readmissions among elderly patients in remote communities: An Australian perspective. \u003cem\u003eBMC Geriatrics\u003c/em\u003e, 21(1), 112. Https://doi.org/10.1186/s12877-021-02099-w\u003c/li\u003e\n \u003cli\u003eJoynt Maddox, K. E., \u0026amp; Rai, D. (2022). Hospital readmissions: Improving quality and safety during transitions of care. \u003cem\u003eJAMA\u003c/em\u003e, 327(14), 1323\u0026ndash;1324. Https://doi.org/10.1001/jama.2022.2933\u003c/li\u003e\n \u003cli\u003eMedicare Payment Advisory Commission. (2022). \u003cem\u003eReport to the Congress: Medicare and the health care delivery system\u003c/em\u003e. Https://www.medpac.gov\u003c/li\u003e\n \u003cli\u003eShi, C., Zhu, H., \u0026amp; White, J. (2020). Impact of transitional care interventions on hospital readmissions: A meta-analysis. \u003cem\u003eHealth Services Research\u003c/em\u003e, 55(5), 842\u0026ndash;854. Https://doi.org/10.1111/1475-6773.13284\u003c/li\u003e\n \u003cli\u003eStall, N. M., Sinha, S. K., \u0026amp; Brown, E. E. (2023). Reducing hospital readmissions through nurse-led transitional care: Results from a Canadian quality improvement initiative. \u003cem\u003eBMJ Open Quality\u003c/em\u003e, 12(2), e002083. Https://doi.org/10.1136/bmjoq-2022-002083\u003c/li\u003e\n \u003cli\u003eSutherland, K., \u0026amp; Crump, R. T. (2020). Avoidable hospital readmissions: A review of interventions and outcomes in rural populations. \u003cem\u003eHealthcare Policy\u003c/em\u003e, 15(4), 38\u0026ndash;51. Https://doi.org/10.12927/hcpol.2020.26233\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 3 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"California State University, San Bernardino","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":"hospital readmission, Medicare, rural hospital, AMI, heart failure, pneumonia, discharge planning, quality improvement","lastPublishedDoi":"10.21203/rs.3.rs-6866236/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6866236/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eReporting Method:\u003c/strong\u003eThis manuscript adheres to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist from the EQUATOR Network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Hospital readmissions within 30 days are key indicators of care quality and are penalized under CMS policy. Bandura’s self-efficacy theory and recent global research emphasize the role of modifiable behavioral and system-level factors in preventing readmissions, particularly in rural contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To identify common contributors to 30-day readmissions among Medicare patients with Acute Myocardial Infarction (AMI), Heart Failure (HF), and Pneumonia (PNA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This retrospective descriptive study reviewed electronic records of 30 Medicare patients aged 65 and older readmitted within 30 days between January and June 2023 at a rural Southern California hospital. Data were extracted from an Epic-based EHR using a structured audit tool capturing demographics, comorbidities, and readmission diagnoses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Among AMI patients, all had hypertension, obesity, and smoking history; 40% were non-compliant with medications. HF patients had 100% medication non-compliance; 90% were obese or smokers. PNA patients showed high rates of swallowing dysfunction (100%), low ADL scores (90%), and smoking (60%). Overall, obesity (95%), smoking (83%), and hypertension (90%) were the most frequent contributors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Preventable lifestyle and management-related factors were prevalent in early readmissions. Findings support the role of nurse-led transitional care strategies such as tailored discharge education and follow-up.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelevance to Clinical Practice:\u003c/strong\u003e Recognizing key readmission drivers enables nurses to enhance discharge planning, patient teaching, and care coordination for at-risk rural populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNo Patient or Public Contribution:\u003c/strong\u003e This retrospective chart review did not include direct patient or public involvement.\u003c/p\u003e","manuscriptTitle":"Breaking the Cycle: Identifying Drivers of 30-Day Readmissions in Rural Medicare Populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 04:32:31","doi":"10.21203/rs.3.rs-6866236/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":"69a36905-741b-47e5-a3c2-2f99bf6737ad","owner":[],"postedDate":"June 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49844601,"name":"Nursing"}],"tags":[],"updatedAt":"2025-06-12T04:32:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-12 04:32:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6866236","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6866236","identity":"rs-6866236","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}