Exploring the Business Case for Magnet Status: Comparison of Medicare Spending per Beneficiary and Average Cost per Discharge Among North Carolina Hospitals based on Geographic Location, Bed Size, Time of Trend Measures, and Healthcare System Association Status By Pamela Dawn Peele December 2025 Director of Dissertation: Dr. Annette Greer Major Department: Public Health ABSTRACT Research Problem and Objectives Rising healthcare costs are a significant concern for individuals, healthcare providers, and our nation. The vitality of North Carolina (NC) hospitals is impacted by the organizations’ human and fiscal resources. Magnet designation is a human resource strategy utilized by some hospitals to recruit and retain their nursing workforce. This study investigated whether Magnet designation among NC hospitals may also support fiscal strategy, contributing to lower Medicare Spending per Beneficiary, and if differences exist in cost per discharge between Magnet- designated hospitals and non-Magnet-designated hospitals. Additional factors considered included hospital location, bed size, time of trend measures, and healthcare system association status. Methods Independent variables included in this quantitative study included Magnet designation, hospital location (rural/urban and NC Medicaid Managed Care Region), bed size, Disproportionate Share Hospital percentage, system status, county tier, and county tier economic rank. Outcome variables were Medicare Spending per Beneficiary (MSPB) and average cost per discharge among North Carolina hospitals. Using secondary data from 2016 to 2021, four quantitative research questions were analyzed using linear mixed model methodology and included 76 NC hospitals. Results Magnet designation does not support lower Medicare Spending per Beneficiary among NC hospitals. The mean MSPB was 0.972 versus 0.941 (p<.001) for Magnet and non-Magnet NC hospitals, respectively, over 2016-2021. Analysis revealed no significant effect of Magnet status on the average cost per discharge (p = 0.221) over the period from 2016 to 2021. However, the average cost per discharge was significantly higher among Magnet hospitals compared to non-Magnet hospitals when 2016-2019 and 2020-2021 were considered independently. Magnet hospitals’ mean average cost per discharge was $10,604 compared to $7,943 (p<.001) for non-Magnet hospitals in 2016-2019. Similarly, Magnet hospitals’ mean average cost per discharge was $15,725 compared to $8,717 (p<.001) for non-Magnet hospitals in 2020-2021. Other independent variables associated with MSPB and average cost per discharge included bed size and system status, with lower MSPB and lower average cost per discharge observed among independent NC hospitals and those with fewer than 100 beds. Additionally, the DSH% among NC hospitals had an effect on MSPB and average cost per discharge, and the association between DSH% differed between Magnet and non-Magnet NC hospitals. Conclusion Magnet designation does not support a fiscal strategy to lower MSPB and cost per discharge among NC hospitals. However, through consideration of additional independent variables, hospital bed size and whether the hospital is independent or affiliated were found to be associated with lower MSPB and lower costs per discharge, offering additional insight into the drivers of healthcare costs. The findings related to DSH% in relation to MSPB and average cost per discharge warrant further study to fully understand this complex interaction. The methodology and findings of this study provide a framework to further evaluate differences in MSPB and average cost per discharge among N.C. hospitals using additional independent variables. Exploring the Business Case for Magnet Status: Comparison of Medicare Spending per Beneficiary and Average Cost per Discharge Among North Carolina Hospitals based on Geographic Location, Bed Size, Time of Trend Measures, and Healthcare System Association Status A Dissertation Presented to the Faculty of the Department of Public Health East Carolina University In Partial Fulfillment of the Requirements for the Degree Doctor of Public Health By Pamela Dawn Peele December 2025 Director of Dissertation: Annette Greer, PhD, MSN, RN, FNAP Dissertation Committee Members: Ruth Little, EdD, MPH Xiangming Fang, PhD Ray Hylock, PhD ©Pamela D. Peele, 2025 Dedication This dissertation is dedicated to the people who have encouraged me throughout my education. Especially to my husband, Charles Peele, thank you for your patience and unwavering support. ACKNOWLEDGEMENTS I want to express my sincere gratitude to my dissertation committee chair, Dr. Annette Greer, and committee members, Dr. Xiangming Fang, Dr. Ray Hylock, and Dr. Ruth Little. I am thankful for their invaluable support, guidance, and constructive feedback throughout this project. I would like to extend my gratitude to the Department of Public Health and East Carolina University for the resources and environment that enabled me to complete my research. A special thank you to my family and friends who motivated me through the challenges and provided ample distractions to help me reset and move forward. TABLE OF CONTENTS TITLE .............................................................................................................................................. i COPYRIGHT .................................................................................................................................. ii DEDICATION ............................................................................................................................... iii ACKNOWLEDGEMENTS ........................................................................................................... iv LIST OF TABLES ......................................................................................................................... ix LIST OF ABBREVIATIONS ........................................................................................................ xi CHAPTER 1: INTRODUCTION ................................................................................................... 1 Background ......................................................................................................................... 5 Significance to Public Health .............................................................................................. 9 Purpose of the Study ......................................................................................................... 13 Research Questions ........................................................................................................... 14 Hypotheses ........................................................................................................................ 15 Hypothesis 1.......................................................................................................... 15 Hypothesis 2.......................................................................................................... 15 Hypothesis 3: ........................................................................................................ 16 Hypothesis 4: ........................................................................................................ 16 Chapter 1 Summary .......................................................................................................... 17 Overview of the Dissertation Paper .................................................................................. 17 Chapter 2: Review of the Literature ...................................................................... 18 Chapter 3: Methodology and Theoretical Framework .......................................... 18 Chapter 4: Results ................................................................................................. 18 Chapter 5: Summary, Conclusions, and Implications ........................................... 19 References ............................................................................................................. 19 Appendices ............................................................................................................ 19 Definitions......................................................................................................................... 19 CHAPTER 2: REVIEW OF THE LITERATURE ....................................................................... 23 Elements of Magnet .......................................................................................................... 23 Nursing and Magnet .......................................................................................................... 26 Quality and Magnet........................................................................................................... 32 Business Case for Magnet ................................................................................................. 39 Literature Gaps.................................................................................................................. 41 Chapter 2 Summary .......................................................................................................... 42 CHAPTER 3: METHODOLOGY ................................................................................................ 43 Theoretical Framework ..................................................................................................... 43 Methodology and Research Design .................................................................................. 45 Data Collection: .................................................................................................... 46 Data Analysis: ....................................................................................................... 55 Chapter 3 Summary .......................................................................................................... 56 CHAPTER 4: RESULTS .............................................................................................................. 57 Descriptive Statistics ......................................................................................................... 57 Research Questions / Hypotheses ..................................................................................... 61 Research question 1 and Hypothesis 1.................................................................. 61 Research question 2 and Hypothesis 2.................................................................. 62 Research question 3 and Hypothesis 3.................................................................. 64 Research question 4 and Hypothesis 4.................................................................. 69 Multiple regression - MSPB ................................................................................. 75 Multiple regression – average cost per discharge ................................................. 77 Chapter 4 Summary .......................................................................................................... 78 CHAPTER 5: SUMMARY, CONCLUSIONS, AND IMPLICATIONS ..................................... 80 Discussion and Interpretation of Findings ........................................................................ 81 Magnet designation and MSPB ............................................................................ 81 Magnet designation and average cost per discharge ............................................. 82 MSPB and other independent variables ................................................................ 83 Cost per discharge and other independent variables ............................................. 84 Strengths of the Study ....................................................................................................... 85 NC healthcare........................................................................................................ 85 Magnet designation ............................................................................................... 86 Hospital and community characteristics ............................................................... 86 COVID-19............................................................................................................. 86 Limitations of the study .................................................................................................... 87 Conclusion ........................................................................................................................ 88 Recommendations for public health practice .................................................................... 89 Summary and Opportunities for Further Research ........................................................... 91 Opportunities for further research ......................................................................... 91 REFERENCES ............................................................................................................................. 94 APPENDIX A - IRB REVIEW .................................................................................................. 105 APPENDIX B - COMPETENCIES............................................................................................ 109 LIST OF TABLES 1: Measure of Magnet financial benefit with financial impacts ................................................... 39 2: Magnet-designated organizations ............................................................................................. 47 3: Total NC facilities 2016-2021 .................................................................................................. 48 4: Excluded facilities..................................................................................................................... 49 5: Included organizations .............................................................................................................. 50 6: NC Medicaid Managed Care Regions and Counties ................................................................ 53 7: Descriptive Statistics................................................................................................................. 59 8: Correlations ............................................................................................................................... 61 9: Mean MSPB for Magnet and Non-Magnet hospitals ............................................................... 62 10: Mean Average Cost per discharge for Magnet and Non-Magnet hospitals ............................ 63 11: MSPB association with independent variables ....................................................................... 65 12: Mean MSPB of significant independent variables ................................................................. 66 13: Interactions of independent variables with Magnet status on MSPB ..................................... 67 14: MSPB over time with interactions between Magnet status and system status ....................... 68 15: Average cost per discharge association with independent variables ...................................... 70 16: Average cost per discharge of significant independent variables ........................................... 71 17: Interactions of independent variables with Magnet status on average cost per discharge ..... 72 18: Average cost per discharge with interactions between Magnet status and bed size ............... 73 19: Interaction between Magnet status and system status on average cost per discharge ............ 73 20: Average cost per discharge when there is Magnet interaction with the Medicaid region ...... 74 21: Magnet status significance in final MSPB models ................................................................. 76 22: Magnet status significance in final average cost per discharge models ................................. 77 23: Mean MSPB and average cost per discharge with Magnet status pre and post COVID-19... 87 LIST OF ABBREVIATIONS AAN American Academy of Nursing ACO Accountable Care Organization AMI Acute Myocardial Infarction ANCC American Nurses Credentialing Center CABG Coronary Artery Bypass Grafting CAUTI Catheter-Associated Urinary Tract Infection CDI Clostridium difficile Infection CLABSI Central Line-Associated Bloodstream Infection CMS Centers for Medicare and Medicaid Services CNO Chief Nursing Officer COPD Chronic Obstructive Pulmonary Disease COVID-19 Coronavirus Disease 2019 DHHS Department of Services DSH Disproportionate Share Hospital FY Fiscal Year HAC Hospital-Acquired Conditions HAI Hospital-Acquired Infection HCRIS Healthcare Cost Reporting Information System HCUP Healthcare Cost and Utilization Project HRRP Hospital Readmissions Reduction Program IHI Institute for Healthcare Improvement IPPS Inpatient Prospective Payment System IRB Institutional Review Board MA Medicare Advantage MISSCARE Missed Nursing Care MRSA Methicillin-resistant Staphylococcus aureus MS-DRG Medicare Severity Diagnostic-Related Group MSPB Medicare Spending per Beneficiary MSSP Medicare Shared Savings Program NC North Carolina NCDHHS North Carolina Department of Health and Human Services NDNQI National Database of Nursing Quality Indicators NIS Nationwide Inpatient Sample NJHA New Jersey Hospital Association NQF National Quality Forum NSRN National Survey of Registered Nurses OIG Office of Inspector General P4P Pay-for-Performance PSI Patient Safety Indicators RN Registered Nurse SIR Standardized Infection Ration SPSS Statistical Package for the Social Sciences SSI Surgical Site Infection TPS Total Performance Score UMCIRB University and Medical Center Institutional Review Board US United States VBP Value-Based Purchasing Chapter 1: Introduction The cost of healthcare in the United States is substantial, totaling 17.6% of the gross domestic product in 2023. Healthcare costs continue to rise in the US. While the years between 2013 and 2022 saw a steady increase of around 4.5% each year (except for 2020, due to the COVID-19 pandemic), from 2022 to 2023, national healthcare expenditures increased by a staggering 7.5% from $4.5 trillion to $4.9 trillion (Centers for Medicare & Medicaid Services [CMS], 2024j). In 2023, Medicaid spending and enrollment growth slowed while Medicare and private health insurance spending grew at an accelerated rate compared to 2022. Growth in private health insurance was primarily through increased Marketplace enrollments in 2023, driven by enhanced subsidies made available by the American Rescue Plan Act of 2021 and renewed through the Inflation Reduction Act of 2022. Ultimately, 2.7 million more individuals enrolled in private health insurance in 2023. Spending for hospital costs increased to $1.5 trillion or 10.4%, the largest single-year growth since 1990, driven by increased enrollments, hospital discharges, and Medicare outpatient services (CMS, n.d.-b). As healthcare costs continue to increase, the federal government and the Institute for Healthcare Improvement (IHI) are focused on the Quadruple Aim, a strategy to optimize the performance of the healthcare system through reducing costs, improving population health and patient experience, and reducing burnout among healthcare workers (Arnetz et al., 2020). The Quadruple Aim was further refined in 2021 to include health equity, recognizing that improvements in patient outcomes and costs should incorporate an understanding of social determinants of health (Itchhaporia, 2021). 2 The IHI introduced the Triple Aim, a predecessor to the Quadruple Aim, in 2008. This strategy included improved population health, improved patient experience, and reduced per- capita costs (Berwick, 2008). The passage of the Affordable Care Act on March 23, 2010, brought about several reforms aimed at reducing growth in healthcare spending while maintaining or improving the quality of care and health outcomes for fee-for-service Medicare beneficiaries. Most notable policy changes included the Shared Savings Accountable Care Organization program and the Hospital Readmissions Reduction Program (HRRP) (Sood et al., 2021). Additionally, the Centers for Medicare and Medicaid Services (CMS) initiated Value- Based Purchasing (VBP) programs in 2012-2013, which tied reimbursement to quality by comparing individual hospital performance to that of all hospitals, including consideration of improvements made year over year (Kim et al., 2022). The Medicare Shared Savings Program (MSSP) is one such VBP model. A key characteristic of the hospital VBP program is the provision for extra payments to hospitals that meet or exceed performance thresholds, as well as payment penalties for hospitals that underperform. Under the hospital VPB program, hospitals focus on the process of care, clinical outcomes, safety, and patient experience to avoid reductions in reimbursement (Kim et al., 2022). Financial penalties imposed through VBP prompt organizations to assess the quality of care provided. Additional cost-containment healthcare policy changes and payment reform, such as cost-sharing, case management interventions, prior authorization requirements, and utilization review, provide further incentives for organizations to minimize costs without compromising quality. Coordination of care specifically has been shown to contain costs and positively affect the quality of care (Stadhouders et al., 2019). 3 At the state level, there is great potential to slow healthcare spending growth and improve patient outcomes. States operate large Medicaid healthcare programs and legislate system-wide solutions tailored to the state’s unique healthcare and budgetary needs (Crook et al., 2020). According to 2023 data, North Carolina ranked 32nd in overall health and 40th in the rate of uninsured individuals. Approximately 12% of adults in the state avoided care due to cost. Additionally, North Carolina is ranked 44th in the nation in Public Health Funding (dollars per person) (United Health Foundation, 2023). Despite these challenges, North Carolina’s initiatives are centered on efforts to ensure all North Carolinians have opportunities for better health and equitable well-being. The state is addressing social drivers of health through coordinated care, aiming to improve quality and outcomes, which in turn results in lower total medical costs (Dzau et al., 2020). North Carolina’s shift to value-based care aligns with reform components across VBP models nationally. As care coordination and quality improve, the cost of care and the cost of providing care may be impacted, as measured by the Medicare Spending Per Beneficiary (MSPB) and the average cost per discharged patient. Improvement in quality of care can be achieved through the Hospital Magnet designation, which reflects superior nurse work environments and improved patient safety and outcomes (Lasater et al., 2019). This research specifically examines the MSPB and average cost per discharged patient in North Carolina hospitals, comparing hospitals with Magnet status to those without Magnet status. MSPB is a rate that compares Medicare spending at a specific hospital for an episode of care to the average spending at all U.S. hospitals. The episode of care includes Part A and Part B payments to the hospital and healthcare providers over the period of 3 days before, during, and 30 days after a patient’s inpatient hospital stay. The hospital’s Medicare spending is compared to the national median hospital spending, accounting 4 for factors such as patient age and health status to provide risk adjustment, as well as geographic payment differences to provide payment standardization (Data.CMS.gov, 2024). The average cost per discharge reflects the expenses incurred by hospitals when providing services to patients. This cost to the organization is calculated using total inpatient operating charges, total inpatient discharges, and the cost-to-charge ratio reported from the Hospital Cost Reports (CMS- 2552-2010), as collected by the Healthcare Cost Reporting Information System (HCRIS) annually (CMS, 2024h). This study uses MSPB to capture the relative cost of care to the healthcare system through Medicare payments for services, while the average cost per discharge captures the financial impact on the hospitals providing the care. Hospitals that are Magnet- designated foster a culture of innovation and evidence-based clinical practice, aligning with the Quadruple Aim strategy to improve care and lower costs. Costs can be measured by MSPB across healthcare organizations to determine if Magnet-designated hospitals have lower MSPB compared to non-Magnet-designated hospitals in North Carolina. Additionally, Magnet designation commits an organization to meet the program's requirements through investments in non-operating costs. Higher average cost per discharge may be evident in Magnet-designated hospitals compared to non-Magnet-designated hospitals in North Carolina. Magnet is closely aligned with the CMS Quadruple Aim strategy to improve healthcare and lower healthcare costs. The purpose of this research is to determine if MSPB and average cost per discharge are impacted by hospital Magnet status among North Carolina hospitals. Chapter 1 provides information about the history and purpose of hospital Magnet status, as well as the relationship between Magnet status and an organization's financial performance. Chapter 1 also outlines the significance of the study to public health practice, provides definitions, presents research questions, and formulates hypotheses. 5 Background High-quality health care and nursing excellence are characteristics generally attributed to hospitals with Magnet designation. Hospitals with Magnet status, as designated by the American Nurses Credentialing Center (ANCC), exhibit positive work environments that promote nursing practice. As a result, patients experience better health outcomes and nurses experience greater job satisfaction (Rodriguez-Garcia et al., 2020). The concept of Magnet hospital status began during the 1980s nursing shortage, when the American Academy of Nursing (AAN) conducted a national study to understand why some hospitals were able to recruit and retain nurses while others were not. Successful hospitals were found to share three common elements: administration, professional practice, and professional development. The ANCC established the Magnet program in the 1990s based on these fundamental characteristics to acknowledge excellence in hospital nursing services (Wai Chi Tai & Bame, 2017). Currently, the program consists of five model components: transformational leadership, structural empowerment, exemplary professional practice, new knowledge, innovation and improvements, and empirical quality results. These components are demonstrated through nursing-related outcomes, patient outcomes, and organizational outcomes (Rodriguez- Garcia et al., 2020). According to work by Rodriguez-Garcia (2020), there are positive outcomes for both nurses and patients in Magnet-designated hospitals. Nurses in Magnet-designated facilities experience higher job satisfaction, lower burnout, and reduced intent to leave their jobs. Nurses report better work environments with better nurse-patient ratios and staffing levels. The culture in Magnet hospitals has a preventive effect on hostile behaviors between professionals, such as bullying. Hospitals report lower nurse turnover rates and lower hospital mortality rates. The 6 improved nursing work environment supports improved patient outcomes. Patients experience fewer falls and greater satisfaction with their care. Lower rates of central line-associated bloodstream infections and methicillin-resistant Staphylococcus aureus bloodstream infections are reported in Magnet facilities compared to non-Magnet facilities. Patients score Magnet hospitals higher on patient experience surveys, indicating high levels of satisfaction in the areas of nurse communication, pain management, and health-related information. With lower mortality rates and reduced lengths of stay in Magnet hospitals, organizations see decreased costs and better performance on Centers for Medicare and Medicaid Services’ (CMS) Hospital Value- Based Purchasing (VBP) Program measures compared to non-Magnet hospitals. Total performance, clinical processes, and patient experience measures are significantly better among Magnet hospitals, contributing to overall lower organizational costs (Rodriguez-Garcia et al., 2020). The Hospital VBP program is structured by CMS to adjust payments to hospitals based on the quality of the care they provide. Through the Inpatient Prospective Payment System (IPPS), hospital quality performance is linked to cost measures, rewarding hospitals that deliver high-quality care. To remain cost-neutral, the program withholds a percentage of Medicare payments each year and uses the reductions to finance value-based incentive payments. Based on the performance of hospitals, CMS applies either a reduction or an incentive adjustment factor to hospitals’ base Medicare severity diagnosis-related group (MS-DRG) reimbursements for the fiscal year aligned with the performance period (CMS, 2024d). While the hospital VBP program is designed to reduce healthcare costs and improve quality, it has taken time to incorporate MSPB as one of the program's specific domains. The VBP program began with fiscal year (FY) payment adjustments in 2013, and it remains a key CMS strategy to reform healthcare delivery 7 and payment for services. Each year, CMS evaluates the hospital VBP program domain weightings and categories. In FY 2013, two domains were used to calculate a Total Performance Score (TPS) for each hospital. The FY 2013 domains were clinical process of care and patient experience, with each weighted 70 percent and 30 percent, respectively (CMS, 2011). In FY 2014, the outcome domain was added to the VBP program, which included three mortality measures. Domain weighting in FY 2014 was 45% for clinical process of care, 30% for patient experience, and 25% for outcomes (CMS, 2012). For the first time, in FY 2015, CMS added the efficiency domain, which is entirely comprised of the MSPB. The efficiency domain calculates the total cost to Medicare for hospitals’ episodes of care delivery. Domain weighting for FY 2015 was 20% for clinical process of care, 30% for patient experience, 30% for outcomes, and 20% for efficiency (CMS, 2014). In FY 2017, CMS modified the hospital VBP program to add a safety domain with the resulting domain weightings as follows: outcomes/process 30%, patient experience 25%, safety 20%, and efficiency 25% (CMS, 2015). By FY 2018, CMS equally weighted the four domains at 25% and included clinical care, patient experience, safety, and efficiency. The patient experience domain consisted of eight measures of satisfaction determined through patient surveys. Seven measures comprised the safety domain, capturing infection rates for targeted conditions and early elective delivery. The clinical care domain included three measures of mortality rates for acute myocardial infarction, heart failure, and pneumonia. Only one measure, MSPB, was included for the efficiency domain (Brown, n.d.). Over the years between FY 2018 and FY 2023, the hospital VBP program domains and weights have remained consistent with the FY 2018 program. The VBP program evaluates organizations on the process of care, clinical outcomes, safety, and patient experience to calculate a total performance score (Kim et al., 2022). Dierkes 8 (2021) outlines the VBP program and its potential financial impacts on hospitals, as follows. The VPB measures are a component of the CMS Pay-for-Performance (P4P) programs, which collectively encourage hospitals to improve health care with a focus on quality and patient outcomes. All hospitals paid under the inpatient prospective payment system must participate in the P4P program. The three areas of measure are Hospital Readmission Reduction Program (HRRP), Hospital-Acquired Conditions (HAC), and Hospital VBP. Under the P4P model, hospitals compete on metrics within each of the three areas of measure and have the potential to lose up to 6% of total hospital CMS reimbursement based on performance. Each year, hospitals with excess unplanned readmissions for select patient populations may experience a reduction of up to 3% in CMS reimbursement under HRRP. Similarly, hospitals with poor performance related to healthcare-associated infections can expect another 1% reduction in CMS reimbursement under HAC. CMS automatically withholds 2% of reimbursements through the hospital VBP program and redistributes funds based on performance across the four VBP domains. The financial impact on organizations can be significant due to penalties applied when metrics fall short of expected performance. Hospitals with better nursing resources, such as lower patient-to-nurse ratios, lower readmission rates, and fewer hospital-acquired conditions, perform better on P4P metrics and avoid financial penalties. While Magnet status does not appear to impact penalties imposed under HRRP and HAC, some literature suggests that Magnet hospitals are more likely to receive a bonus under VBP, which may be substantial enough to offset the financial penalties of HRRP and HAC (Dierkes et al., 2021). Given the superior performance of Magnet hospitals on VBP measures, it is essential to determine if a positive financial impact on MSPB is evident in Magnet hospitals and whether the cost per discharge in Magnet organizations is affected. Hospital Magnet status touches the patient/caregiver domain, 9 safety domain, and clinical care domain through the characteristics inherent to the Magnet program as described. This study focuses on the efficiency domain as measured by MSPB and the average cost per discharge to evaluate the potential relationship to Magnet and non-Magnet organizations in North Carolina. Significance to Public Health This quantitative evaluation project is designed to address a serious public health issue, the cost of healthcare, by determining whether Magnet designation supports reduced MSPB without a higher cost per discharge for organizations providing healthcare services. The cost of healthcare is considered on multiple levels, including the individual, the organization, and the population. As health care costs continue to rise, individuals face difficulties affording medical care and medications, even among those with health insurance. KFF (Amin et al., 2024) reported several findings with cause for concern. Out-of-pocket spending per person was $677 in 1970 (adjusted for inflation) and $1,425 per person in 2022. Out-of-pocket costs include the money individuals spend on copays, deductibles, and care that is not covered by insurance. Monthly health insurance premium costs are not included in out-of- pocket expenses. High health care costs are particularly concerning for individuals with poor overall health and those with lower incomes. A lack of affordable healthcare can be a barrier to care, leading individuals to delay or forgo important medical care. Receiving care can result in significant medical debt and financial insecurity. In the U.S., roughly half of adults have difficulty affording health care, especially those with lower incomes, those who are uninsured, and those in fair or poor health. Inadequate health insurance is a significant barrier to affording healthcare for people under age 65. Among insured individuals, four in ten are concerned about their ability to pay their monthly premiums and deductibles. Six in ten uninsured adults have 10 delayed or omitted getting needed healthcare in the last 12 months due to cost, with cost-related barriers more commonly occurring among people with lower incomes, poor health, or who are uninsured. Hispanic individuals report higher rates of cost-related access to care barriers, and Black individuals are more likely to have medical debt. Disabled adults are much more likely to have significant medical debt and a higher burden of medical costs (Amin et al., 2024). At the individual level, out-of-pocket healthcare costs and medical debt contribute to financial vulnerability, leading some adults to make the difficult decision to postpone or omit needed medical care, which further contributes to individuals’ poor health status. It is important to recognize the individual-level challenges faced by patients when evaluating MSPB and cost per discharge. The impact of rising healthcare costs on individuals, especially the uninsured, is substantial. At the organizational level, employers also face this issue through the spending paid toward their employees’ health insurance premiums; however, most U.S. health spending results from hospital and physician care, as well as prescription drugs (Amin et al., 2024). Hospitals accounted for 30.4% of all health spending in 2022, while physicians accounted for 19.8%. Between 2020 and 2022, retail prescription drug costs grew by 7.6%, more than double the annual average growth rate from 2010 to 2020 (Amin et al., 2024). Despite this level of spending, roughly half of hospitals ended 2022 with an operating loss, reporting a decline of 28.3% in days cash on hand (American Hospital Association [AHA], 2024). The vitality of hospitals and health systems is impacted by the organizations’ human and fiscal resources. According to the American Hospital Association (AHA, 2024), between 2021 and 2023, U.S. inflation grew by 12.4%, more than twice the rate of Medicare reimbursement for inpatient care (5.2%), leaving hospitals unable to cover the increasing costs of labor, drugs, and 11 supplies. Hospitals’ labor costs account for roughly 60% of hospital expenses, including the high costs of contracted staff necessary to fill labor gaps and maintain services. Critical labor shortages due to growing burnout among clinicians add to the labor expense as hospitals experience high turnover rates, increased recruiting investments, and agency fees related to staffing with contracted employees (AHA, 2024). The AHA also noted that the rapid growth of drug expenses for hospitals and health systems has placed significant cost pressures on organizations. Hospitals’ spending on drug expenses totaled $115 billion in 2023 as drug companies imposed price increases and introduced new drugs at record-high prices. The average price increase between 2022 and 2023 was 15.2%, compared to a 12.4% increase in inflation over the same period. The median annual list price for a new drug was $300,000 (AHA, 2024). With an aging population and higher patient acuity, more clinically complex patients required care, many of whom were covered by Medicare and Medicaid. Payments by Medicare covered 82 cents for every dollar that hospitals spent caring for patients in 2022. Hospital spending goes beyond direct patient care services to include significant administrative expenses necessary to counteract practices by some commercial health insurers, including Medicare Advantage (MA) and Medicaid managed care plans. In 2023, commercial health insurance premiums increased twice as fast as hospital prices, and denials by commercial MA plans rose by 55.7% with 75% of those denials subsequently overturned, according to data released by The Office of Inspector General (OIG) following review of MA plan practices (AHA, 2024). Hospitals are not reimbursed for costs incurred due to activities they must undertake to challenge denied claims. The OIG findings also revealed that approximately 20% of the MA denials met Medicare coverage rules for payment without denial. When MA plans paid hospitals, the payment was less than 90% of the Medicare rate. Similarly, denials by 12 commercial payers increased by 20.2% in 2023, and the time taken to pay hospital claims increased by 19.7%. These practices by MA and commercial insurance plans result in billions of dollars in lost revenue for hospitals and health systems, creating an unsustainable and growing administrative expense (AHA, 2024). When considering average cost per discharge, the organizational factors described above are important. Added organizational costs for labor, drugs, and supplies, coupled with reduced reimbursement due to denied claims, cannot be overlooked when evaluating MSPB and the average cost per discharge among N.C. hospitals. Much like the challenges faced by individuals and hospitals, rising inflation and healthcare spending have had community- and population-level impacts, especially among older populations and those with significant health needs, according to Amin et al. (2024). A large share (56%) of annual health spending in 2021 was attributed to people aged 55 and over, although they comprised only 31% of the population. More concerning, individuals reporting fair or poor health status accounted for 29% of the total health spending but comprised only 10% of the population. The 5% of the population with the highest health spending had an average annual health expenditure of $71,100 and accounted for half of the total health spending in 2021. Although high health costs are particularly difficult for those in poor health and lower incomes, challenges to affording healthcare exist across the U.S., even among individuals with private health insurance. Despite reductions in uninsured rates through Medicaid expansion and enrollment, as well as subsidies available for the Affordable Care Act Marketplace, affordability remains a challenge across the U.S. At the population level, many individuals do not have sufficient savings to afford deductibles or out-of-pocket maximum amounts (Amin et al., 2024). The recent evolution of cost controls by States may offer some promise. Considering Magnet 13 status among N.C. hospitals may offer insight to support state initiatives to lower MSPB and improve costs impacting hospitals across the state. In North Carolina, the Medicaid Accountable Care Organization (ACO) structure mirrors the Medicare Shared Savings Program (MSSP) (Crook et al., 2020). North Carolina is focused on improving healthcare delivery and addressing the underlying economic and social drivers of health. Shifting the state’s Medicaid system to managed care, while addressing homelessness, lack of transportation, hunger, and other socioeconomic factors contributing to poor health, began in earnest with Mandy Cohen’s appointment as North Carolina’s Secretary of Health and Human Services in 2017. Under her leadership, the agency focused on value achieved through health care spending and systematically embedding food, housing, transportation, and jobs into the larger concept of the health system (Kenen, 2019). Additionally, the N.C. Department of Health and Human Services (NCDHHS) has aligned financial incentives to encourage insurers and health systems to incorporate nonmedical health drivers into care delivery. North Carolina has rapidly adopted VBP models and established VBP arrangements through Medicare accountable care organizations since 2017. The resulting VBP environment focuses on the total cost of care and health outcomes (Wortman et al., 2020). This environment also aligns with characteristics demonstrated among Magnet-designated hospitals. Overall, improved patient outcomes have been noted for patients treated in Magnet hospitals (Friese et al., 2015). This study goes further and considers the cost of care among N.C. hospitals relative to their Magnet status. Purpose of the Study At the macro level, this study brings together an evaluation of the goals of VBP programs aimed at lowering costs without compromising care outcomes. Medicare cost and quality of care 14 have a bidirectional relationship, with higher quality of care yielding lower Medicare spending (Cook et al., 2021). While all hospitals subject to CMS payment rules are impacted by the VBP program measures, hospitals have a choice regarding the mechanisms they use to optimize their operations, improve quality, and achieve greater efficiency in delivering services. With hospitals facing staggering costs for labor, supplies, and drugs, becoming a Magnet-designated hospital may offer a strategy to align with VBP program efficiency goals and improve operations. The purpose of this study is to determine whether Magnet designation among North Carolina hospitals contributes to lower MSPB and if costs per discharge among Magnet-designated N.C. hospitals are higher, lower, or similar compared to non-Magnet-designated N.C. hospitals. Across N.C., hospitals serve unique populations and vary in size, local resources, and affiliations. This study considers hospital location, bed size, system status (independent versus associated with a multi-hospital health system), Disproportionate Share Hospital (DSH) percentage, county tier, and county economic distress rank for Magnet and non-Magnet N.C. hospitals relative to MSPB and costs per discharge. Research Questions The following research questions were the focus of this qualitative study, which utilized secondary data. 1. Do N.C. hospitals with Magnet status report lower MSPB than N.C. non-Magnet hospitals? 2. Do N.C. hospitals with Magnet status report higher, lower, or similar average cost per discharge compared to N.C. non-Magnet hospitals? 3. Is MSPB associated with location (rural/urban and N.C. Managed Care Region), bed size, system status (independent versus associated with a multi-hospital health system), DSH 15 percentage, county tier, or county economic distress rank among N.C. hospitals with Magnet designation and without Magnet designation? 4. Is the average cost per discharge associated with location (rural/urban and N.C. Medicaid Managed Care Region), bed size, system status (independent versus associated with a multi-hospital health system), DSH percentage, county tier, or county economic distress rank among N.C. hospitals with Magnet designation and without Magnet designation? Hypotheses Hypothesis 1 N.C. hospitals with Magnet designation have lower MSPB than N.C. hospitals without Magnet designation. As hospitals that are Magnet-designated adopt a culture of innovation and evidence- based clinical practice, positive effects on MSPB (i.e., lower MSPB) are expected to occur. For example, using evidence-based clinical practice not only reduces the likelihood of errors due to practice variation but also improves quality and patient outcomes. With improved outcomes should come lower MSPB. Hypothesis 2 N.C. hospitals with Magnet designation have a higher average cost per discharge than N.C. hospitals without Magnet designation. The decision to become Magnet-designated commits an organization to meet the program's requirements, investing the necessary resources to achieve the culture described above. It is hypothesized that cost per discharge would increase, driven by higher non-operating 16 costs (i.e., general and administrative costs, nursing administration costs) in a Magnet-designated hospital. Hypothesis 3 MSPB is associated with location (rural/urban and N.C. Medicaid Managed Care Region), bed size, system status, DSH percentage, county tier, and county economic distress rank among N.C. hospitals with Magnet designation and without Magnet designation. The MSPB rate is risk-adjusted for factors such as patient age and health status. MSPB is also adjusted for geographic payment differences to provide payment standardization. However, MSPB compares each hospital to the national median hospital MSPB. N.C. ranks 32nd in the nation in overall health (United Health Foundation, 2023). This status, compared to other states, is well below the median. It is hypothesized that the MSPB is determined by hospital characteristics and the characteristics of the community/population served by the hospitals. Hospital characteristics include Magnet designation, location, bed size, system status, and DSH percentage. Community/population characteristics include county tier and county economic distress rank. Hypothesis 4 The average cost per discharge is associated with location (rural/urban and N.C. Medicaid Managed Care Region), bed size, system status, DSH percentage, county tier, and county economic distress rank among N.C. hospitals with and without Magnet designation. Hospitals’ costs are reflected as the average cost per discharge, which captures two elements: total costs and total discharges. It is hypothesized that the expenses incurred to provide services vary based on the following hospital and community/population characteristics: Magnet 17 status, location, bed size, system status, DSH percentage, county tier, and county economic distress rank. These attributes impact hospitals’ average cost per discharge. Chapter 1 Summary The cost of healthcare in the United States is a significant public health issue, accounting for 17.6% of the gross domestic product in 2023 and projected to continue rising (CMS, 2024j). In response, the federal government has implemented strategies to optimize the performance of the healthcare system by reducing costs, improving population health and patient experience, and mitigating burnout among healthcare workers (Arnetz et al., 2020). Similarly, North Carolina’s leadership has developed initiatives to ensure all North Carolinians have opportunities for better health and equitable well-being. To lower total medical costs, the state is focused on coordinated care that addresses social drivers of health to improve quality and outcomes (Dzau et al., 2020). The Hospital Magnet designation reflects superior work environments for nurses and improved patient outcomes, aligning with national and state initiatives (Lasater et al., 2019). This quantitative evaluation project examines the cost of healthcare by determining whether Magnet designation supports reduced MSPB without higher costs per discharge among N.C. hospitals. This research study also examines whether MSPB and average cost per discharge are influenced by location, bed size, system status, DSH percentage, county tier, or county economic distress rank among N.C. hospitals with and without Magnet designation. It is hypothesized that N.C. Magnet hospitals have lower MSPB and higher average cost per discharge. The hospital and community characteristics listed above also determine MSPB and average cost per discharge. Overview of the Dissertation Paper The remainder of the dissertation paper follows the outline below. 18 Chapter 2: Review of the Literature Chapter 2 includes a literature review that describes the current state of Magnet-related research. Specific review of published literature reflects organizational outcomes, patient outcomes, hospital financial performance, and hospital VBP performance. Additionally, an overview and fundamental elements of the Magnet program are provided for context. Gaps in the literature that this research aims to address are identified. Chapter 3: Methodology and Theoretical Framework Chapter 3 describes Hotelling’s model as the theoretical framework guiding this study. The research methodology and design encompass the sample, research procedures, and data analysis plan. Data collection includes Medicare Spending per Beneficiary and average cost per discharge for N.C. hospitals from 2016 to 2021 as the dependent variables. Data for the independent variables include magnet status, hospital location, bed size, DSH percentage, system status, county tier, and county tier economic rank over the same timeframe for N.C. hospitals. Chapter 3 includes the identification of data sources for variables. Chapter 4: Results Chapter 4 provides results for each of the four research questions posed by this study. Results reflect an analysis of the complete data set covering the period from 2016 to 2021, as well as individual results for the years 2016-2019 and 2020-2021. Descriptive statistics for the dependent and independent variables are included. The findings of the hypotheses tested are presented both narratively and in data tables. 19 Chapter 5: Summary, Conclusions, and Implications The conclusions of the analysis are presented in Chapter 5, along with an interpretation of the results and implications for public health policy and practice. Findings are evaluated against previous related research, drawing distinctions where necessary. The study's strengths are presented, followed by its limitations and opportunities for further research. References Supporting literature and published research cited in the paper are included in the References section. Appendices Supplemental documentation is presented in the Appendices, including UMCIRB correspondence that notes this research does not involve human subjects and therefore does not require IRB approval. Definitions • Affordable Care Act (ACA) – Comprehensive health care reform law enacted 3/2010. Three primary goals: make health insurance affordable, expand Medicaid, and lower the cost of health care (U.S. Department of Health and Human Services [HHS], 2022). • Average cost per discharge – Calculation of Total costs divided by the Total discharges from the Hospital Cost Reports (CMS-2552-2010) as collected by the Healthcare Cost Reporting Information System (HCRIS) annually (CMS, 2024h). • Centers for Medicare and Medicaid Services (CMS) – Federal agency provides health coverage to more than 160 million people through Medicare, Medicaid, the Children's 20 Health Insurance Program, and the Health Insurance Marketplace. Goals are to improve health care quality, equity, and outcomes (Centers for Medicare & Medicaid Services [CMS], n.d.-a). • County economic distress rank – All North Carolina counties (n=100) are ranked annually according to economic well-being by the North Carolina Department of Commerce using the tier ranking factors (average unemployment rate, median household income, percentage growth in population, and adjusted property tax base per capita). Each county is ranked on each variable, and the county rank sum is used to determine the overall economic distress rank (1 is most distressed; 100 is least distressed) (North Carolina Department of Commerce, 2024a). • County tier – Using the County economic distress rankings, all North Carolina counties (n=100) are assigned to a development tier designation as mandated by state statute (§143B-437.08) with Tier designation of Tier One (most distressed), Tier Two, or Tier Three (least distressed). State statute requires the assignment of 40 counties to Tier One, 40 counties to Tier Two, and 20 counties to Tier Three. Adjustment factors for small population sizes and poverty rates were eliminated beginning in 2019 by the 2018 Appropriations Act (S.L.2018-5, Section 15.2. (a)) (North Carolina Department of Commerce, 2024b). • Disproportionate Share Hospitals (DSH) percentage – sum of the percentage of Medicare inpatient days attributable to patients eligible for both Medicare Part A and Supplemental Security Income (SSI), and the percentage of total inpatient days attributable to patients eligible for Medicaid but not Medicare Part A. DSH percentage calculates care provided to a disproportionate number of low-income Medicare patients. Hospitals get additional 21 Medicare payments to cover the costs of providing care to them if thresholds are met (CMS, 2024g). • Hospital-Acquired Conditions (HAC) – selection of 14 categories of conditions that are not present on admission but occur during a hospital stay. Examples include foreign objects retained after surgery, air embolism, pressure ulcers, catheter-associated urinary tract infections, surgical site infections, and deep vein thrombosis (CMS, 2024b). • Hospital Readmissions Reduction Program (HRRP) – a Medicare value-based purchasing program that reduces payments to hospitals for avoidable readmissions, targeting the following conditions: acute myocardial infarction, chronic obstructive pulmonary disease, heart failure, pneumonia, coronary artery bypass graft, and elective total hip/total knee (CMS, 2024e). • Inpatient Prospective Payment System (IPPS) – Medicare payment for hospital inpatient costs at predetermined, specific rates for each hospital discharge. Discharges are classified according to a list of Medicare Severity Diagnosis-Related Groups (MS-DRGs) (CMS, 2024f). • Institute for Healthcare Improvement (IHI) – not-for-profit health care improvement organization focused on evidence-based quality improvement in health care (Institute for Healthcare Improvement, n.d.) • Medicaid Accountable Care Organization (ACO) – A provider payment arrangement in which a group of health care providers agrees to take responsibility for the quality and cost of care for a defined patient population. Medicaid ACOs are state-specific (Rosenthal et al., 2023). 22 • Medicare severity diagnosis-related group (MS-DRG) – a classification system of inpatient discharges used to calculate payment for inpatient hospital services. IPPS payments are adjusted based on principal diagnosis, up to 24 additional diagnoses, and up to 25 procedures performed during the stay (CMS, 2024i). • Medicare Shared Savings Program (MSSP) – an Accountable Care Organization that agrees to be held accountable for the quality, cost, and experience of care of an assigned Medicare fee-for-service (FFS) beneficiary population (CMS, 2024a). • Medicare Spending per Beneficiary (MSPB) – a rate indicating whether Medicare spends more, less, or about the same for an episode of care. The hospital-specific comparison is made to a national hospital median. Episode costs include Medicare Part A and Part B payments 3 days prior to, during, and 30 days following a patient's inpatient stay (Data.CMS.gov, 2024). • N.C. Medicaid Managed Care Regions – North Carolina established six regions across the state to facilitate the transition to Medicaid Managed Care beginning in 2019. Transition to Medicaid Managed Care is phased over time, by region (North Carolina Department of Health and Human Services [NCDHHS], 2019). • Pay-for-Performance (P4P) – CMS payment incentive program with payment based on patient outcomes reflecting care that is safe, effective, centered on the patient’s needs, timely, efficient, and equitable (CMS/ORDI/MDPG, 2005) • Value-Based Purchasing (VBP) – healthcare provider payments linked to improved cost and quality of care provided. The goal is to reduce inappropriate care and financially reward the best-performing providers (HealthCare.gov, n.d). Chapter 2: Review of the Literature The association between the Hospital Magnet designation and various attributes and outcomes has been well-documented in the literature. Generally, Magnet hospitals tend to demonstrate above-average nurse satisfaction and high-quality patient care. There are, however, some conflicting results, indicating that Magnet designation may not drive outcomes in specific contexts. Additionally, literature examining the possible link between Magnet designation and MSPB or average cost per discharge is lacking. The following literature review describes the state of research involving Magnet designation related to organizational outcomes, patient outcomes, financial performance, and hospital VBP performance. Gaps in the literature are identified, and the intent to address them through this research is described. An overview of the Magnet program is provided for background, including the steps to achieve Magnet designation and the fundamental elements of Magnet status. Elements of Magnet In 1981, the Governing Council of the American Academy of Nursing (American Academy of Nursing, n.d.) established a Task Force on Nursing Practice in Hospitals to understand the reasons some hospitals were able to attract and retain highly qualified nursing staff while others were not. At the time, hospital nursing shortages posed a serious concern across the U.S. The Task Force was charged with identifying the important variables and combinations of variables in hospitals that create a magnetism that attracts and retains nurses, additionally considering models of hospital nursing practice that include professional and personal satisfaction sufficient to recruit and retain qualified nurses. The work of the Task Force is considered the seminal study of Magnet hospitals (McClure et al., 1983). The identification of hospitals for the study began with the selection of a national sample through nominations by the 24 Fellows of the American Academy of Nursing across eight regions of the U.S. The Task Force evaluated and ranked each hospital based on its recruitment and retention records. Ultimately, a total of 41 hospitals made up the sample, with 78.0% being private, nonprofit institutions affiliated with educational nursing programs. These sample hospitals, which are represented as magnet hospitals, reported that 85% or more of their registered nurse positions were filled annually. Staff nurses and nursing directors were interviewed to understand the organizational attributes that made the hospitals good places for nurses to work (McClure et al., 1983). Through the work of the Task Force, three elements of magnetism emerged across all institutions, regardless of size and region: administration, professional practice, and professional development. Administration included management style, quality of leadership, organizational structure, staffing, and personnel policies. Professional practice included quality of patient care, teaching, and nursing image. Professional development encompassed orientation, continuing education, formal training, and career development. While a combination of elements created a positive practice environment that supported recruitment and retention of nursing staff members in magnet hospitals, the Task Force concluded that the quality of administration and organizational leadership ultimately distinguished magnet hospitals from other organizations (McClure et al., 1983). Based on the work of the Task Force, the American Nurses Credentialing Center (ANCC) developed a voluntary recognition program to formally credential Magnet organizations in 1990. The first Magnet hospital was credentialled four years later, in 1994 (Kelly et al., 2011). According to the most recent listing by ANCC, there are 601 Magnet hospitals in the U.S., representing 9.8% of all U.S. hospitals (American Nurses Credentialing Center [ANCC], n.d.-c). Organization eligibility requirements include the following criteria: 25 • The organization must exist within a healthcare organization. • Nursing leadership includes a Chief Nursing Officer (CNO) who is responsible for nursing practice across the organization. • Educational criteria for the CNO, Nurse Managers, and Nurse Leaders must be met, with a master’s degree required for CNO and a baccalaureate or graduate degree in nursing required for Nurse Managers and Nurse Leaders. • Nursing Administration Scope and Standards of Practice must be implemented. • Policies and procedures that encourage and permit confidential expression of concern regarding the professional practice environment must be in place. • Compliance with all federal laws and regulations is required. • Nurse-sensitive quality data must be collected at the unit level and benchmarked to support research and quality improvement. (ANCC, n.d.-b) Becoming a Magnet hospital takes an average of 4.25 years to complete (Russell, 2010). The process includes organizational evaluation of eligibility, followed by completion of the Magnet application. Through the application process, organizations typically complete additional activities, such as conducting a gap analysis and providing education to the nursing workforce via webinars made available by ANCC. The development and implementation of shared governance processes and a quality infrastructure for data capture are also essential elements of the Magnet program, which often require attention and resources not already present prior to initiating the Magnet application process (Drenkard, 2022). Costs associated with becoming a Magnet organization can include not only the application fee, but also expenses for document submission, site visit, and travel fees, as well as 26 additional personnel such as a Magnet Program Coordinator (Drenkard, 2022). The Magnet Fee structure varies based on the number of licensed beds, starting at $30,823 for the Appraisal Fee for 1-399 licensed beds and reaching $72,213 for organizations with 950 licensed beds. Each additional bed over 950 adds $70 (ANCC, 2021). The estimated cost for an organization to complete the Magnet process, from beginning the application to achieving designation, is $250,000, with some variability based on bed size and existing organizational resources (Drenkard, 2022). Nursing and Magnet As previously discussed, the original 1981 study by the Task Force focused on nursing recruitment and retention across U.S. hospitals. It is therefore essential to evaluate the literature for significant contributions related to nursing and Magnet designation in recent years. The ANCC program began in 1990 as the Magnet Nursing Services Recognition Program and was later renamed Magnet Recognition Program in 2002 (ANCC, n.d.-a). Prior to 2002, there were limited publications related to Magnet-designated hospitals (Yang et al., 2021). A notable publication by Aiken et al. (2000) compared seven ANCC Magnet hospitals to thirteen of the original magnet hospitals studied in 1981, based on nurse satisfaction and nursing assessments of quality of care. The study’s purpose was to determine if ANCC Magnet hospitals’ nurse practice environments were consistent with the attributes of the original magnet hospitals. Results noted differences between the two groups in nurses’ education and training, nurse staffing, clinical practice environment, and job satisfaction, with better measures among the ANCC Magnet hospitals compared to the original Magnet hospitals in all categories. Additionally, this study aimed to provide support for the notion that the ANCC Magnet designation corresponds to hospitals with high-quality nursing care (Aiken et al., 2000). Aiken et al. (2000) used this study 27 to highlight the benefits of the ANCC Magnet designation as well as the slow adoption across the U.S., with only 16 Magnet-designated hospitals at the time of publication in 2000. Annual publications related to Magnet increased slowly in the mid-2000s, followed by a steep climb and sustained increase in annual studies after 2009 (Yang et al., 2021). By 2010, IHI had introduced the Triple Aim as a strategy to improve patient care and reduce healthcare costs (Berwick, 2008). Additionally, the Affordable Care Act became law on March 23, 2010, bringing policy reforms with a focus on quality of care and health outcomes (HHS, 2022). The number of Magnet hospitals grew to 230 in 2006 and to 386 by 2011, representing roughly 7% of U.S. hospitals at that time (Hess et al., 2011). Publications began reporting examination of potential differences between nursing outcomes in Magnet hospitals versus non-Magnet hospitals, with varying results. A 2010 publication by Trinkoff et al. (2010), which used 2004 data from the Nurses Worklife and Health Study, reported no differences in working conditions among nurses in Magnet hospitals, specifically regarding work schedules, job demands, or practice environments. Following the publication of the findings by Trinkoff et al. (2010), Kelly et al. (2011) conducted a study of hospitals in four states from 2006 to 2007 through an extensive survey of nurses working in Magnet and non-Magnet organizations. Over 26,000 nurses in 567 acute care hospitals across California, Florida, Pennsylvania, and New Jersey provided data regarding the nurse work environment. Work environment, as defined by the 31-item Practice Environment Scale of the Nursing Workforce Index, was found to be significantly better in Magnet hospitals. Additionally, nurses in Magnet hospitals indicated less work dissatisfaction and were less likely to have high levels of burnout (Kelly et al., 2011). 28 In another large study, Hess et al., (2011) used data gathered via the 2010 National Survey of Registered Nurses (NSRN) to compare perceptions of career satisfaction, professional relationships, opportunities to influence decisions in the workplace, the nursing shortage, and work environment among nurses working in Magnet hospitals, non-Magnet hospitals, and hospitals in the process of pursuing Magnet designation. Hess et al. (2011) found that nurses in Magnet hospitals reported increased opportunities to influence and participate in shared governance, stronger relationships with Advanced Practice Nurses and faculty, and more employer-sponsored continuing education. There was no statistically significant difference in nurses’ opportunities to influence decisions about patient care. These results supported the conclusion that while Magnet designation has a positive influence on nurses, a positive professional environment can also be derived from the Magnet program or other professional organizational forces. The professional practice environment, a cornerstone feature of the Magnet program, has been examined from various angles. Kalisch et al. (2012) took an interesting approach, comparing missed nursing care among nurses working in Magnet-designated hospitals with that of nurses working in non-Magnet hospitals. The publication by Kalisch et al. (2012) evaluated data from eleven hospitals across the West and Midwest regions of the US. The nursing staff’s perception of missed care and reasons care was missing were captured using the MISSCARE Survey tool. Nurses were asked the frequency with which elements of nursing care were missed on their units, and the reasons for the missed care. Although it was a small study with only four Magnet hospitals compared to seven non-Magnet hospitals, nurses practicing in Magnet hospitals reported significantly less overall missed nursing care compared to those in non- Magnet hospitals participating in the survey. This study further concluded that the culture of 29 Magnet hospitals and patient units within those hospitals accounted for positive staff outcomes, operational efficiency, and work environments that are supportive of safety, quality, teamwork, and participation in decision-making (Kalisch & Lee, 2012). Similarly, Budin et al. (2013) found nurses working in Magnet hospitals reported superior work environments. This study included 1,407 RNs surveyed via mail across the US using the Manderino and Banton Verbal Abuse Scale and focused on verbal abuse from nurse colleagues among early career nurses. The Budin et al. (2013) study reinforced previous findings that nurses employed in Magnet hospitals have a more positive work environment and are less likely to exhibit and tolerate verbal abuse by their peers due to the professional practice environment present in Magnet facilities. While these studies varied in their approaches to evaluate the professional practice environment, the attributes of the Magnet program seem to provide a beneficial framework in those organizations. Although some publications support Magnet designation as an indicator of superior nurse work environments, other data is mixed. Brickner et al. (2023) published a recent study that concluded that individual and professional values contribute to nursing practice, without regard to the Magnet status of the organization. The study, conducted in 2022, included survey responses from 825 direct-care nurses and nurse managers using the Practice Environment Scale of the Nursing Work Index to measure job satisfaction. Hospitals represented by the individuals included nine Magnet hospitals, three non-Magnet hospitals, and seven hospitals in the process of becoming Magnet hospitals. With this study, Brickner et al. (2023) provided a necessary perspective that the individual values of nursing staff are important to consider, regardless of the type of organization. With nurse recruitment and retention as one of the primary drivers for the 1981 Task Force, publications have attempted to confirm the connection between a superior nursing work 30 environment and Magnet designation. A superior nurse work environment established in Magnet hospitals is often measured using survey tools, including some that capture nurses’ intent to stay in their current job for the next 12 months. Lacey et al. (2007) used data obtained via the Individual Workload Perception Scale, a 32-item survey developed between 2001 and 2003 to assess the degree to which hospital infrastructures supported professional nursing practice. In the study, Lacey et al. (2007) analyzed specific subscales of data, including manager, peer, and unit support, workload, nurse satisfaction, and intent to stay for nurses participating in the survey between 2003 and 2005. The team evaluated the results based on Magnet status, finding that mean scores were consistently higher for Magnet hospitals compared to non-Magnet hospitals or those in the process of becoming a Magnet hospital. Based on this data from the early 2000s, nurses working in Magnet hospitals reported an improved work environment for nursing professionals compared to those in non-Magnet hospitals, and their intent to remain in their jobs for the next year was higher. Similarly, using the Practice Environment Scale of the Nursing Work Index, Kutney-Lee et al. (2015) reported a 16% decline in the percentage of nurses with an intention to leave in Magnet-emerging hospitals, compared to only a 9% decline in non-Magnet hospitals, over a seven-year timeframe spanning 1999-2006. This study included 136 Pennsylvania hospitals and noted there were no Magnet hospitals in Pennsylvania in 1999, but 13 Magnet hospitals by 2006. For this reason, emerging Magnet hospitals were compared to non- Magnet hospitals. Although using slightly different approaches, both Lacey et al. (2007) and Kutney-Lee et al. (2015) confirmed the positive association between the work environment and nurse retention among Magnet and emerging Magnet hospitals, as evidenced by nurse survey results. 31 Nurse retention has also been evaluated using turnover data gathered through the National Database of Nursing Quality Indicators (NDNQI), a data repository for nursing-related indicators representing hospitals across the US and internationally (Press Ganey, n.d.). Park et al. (2016) utilized 2013 NDNQI data to assess nursing turnover among Magnet and non-Magnet hospitals, including an examination of reasons for separation. The study included 497 US hospitals, representing 2,958 nursing units. Park et al. (2016) found that nursing turnover rates were higher among non-Magnet hospitals (17%) compared to Magnet hospitals (14%). Work environment factors, including staffing levels, workload, and work schedule, contributed to the higher rate of turnover, as indicated by unit-level NDNQI data analyzed in this study. While capturing intent to stay in a position through nurse surveys is beneficial, using turnover data provides a necessary measure for the level of nursing retention success between Magnet and non-Magnet hospitals. Based on a review of the literature, in most instances, a superior nursing environment supports nursing recruitment and retention, aligning with the attributes of Magnet hospitals. Hospitals with Magnet designation by ANCC have committed to providing a positive work environment for nursing practice and have demonstrated satisfaction of the eligibility requirements (ANCC, n.d.-b). The five essential components of the program are: transformational leadership, exemplary professional practice, empirical quality results, new knowledge innovation and improvements, and structural empowerment (Rodriguez-Garcia et al., 2020). By succeeding in the five essential components, empirical outcomes that demonstrate improved patient care may also be realized. Stimpfel et al. (2014) reported that the professional practice environment of Magnet hospitals is a key driver for quality of care. A considerable 32 amount of literature exists regarding empirical outcomes related to the quality of care in Magnet hospitals. Quality and Magnet One of the essential foundational components of the Magnet program, empirical quality results, has been studied extensively. The 2005 Deficit Reduction Act (Congress.gov, 2006) required the Secretary of the Department of Health and Human Services (DHHS) to identify high-cost, high-volume hospital-acquired conditions that could reasonably be prevented. By October 2008, CMS mandated (CMS, 2019) that no reimbursement would be provided for eight hospital-acquired conditions, including patient falls, in an effort to align hospital reimbursement with quality of care (Inouye et al., 2009). With these changes in reimbursement and the enactment of the Affordable Care Act following close behind in 2010, researchers began observing patient outcomes across Magnet and non-Magnet hospitals to evaluate potential quality benefits among Magnet facilities. With high-quality nurse work environments in Magnet hospitals, researchers considered whether organizations might avoid pay-for-performance penalties imposed through various federal programs, such as the Hospital Readmission Reduction Program (HRRP), the Hospital-Acquired Conditions (HAC) Reduction Program, and the VPB program. Boylan et al. (2019) evaluated the performance of Magnet and non-Magnet hospitals in the HRRP, HAC Reduction Program, and VBP program using Hospital Compare data from 3,190 hospitals from 2012 to 2015. A cohort of matched Magnet and non-Magnet hospitals was established to account for differences in hospital size, location, and teaching status. Data between Magnet and non-Magnet hospitals in the cohort revealed no difference in VBP penalties received by the facilities; however, HRRP penalties and HAC penalties were more common at Magnet hospitals. A similar study by Dierkes et al. (2021) used CMS data sets from 33 2015 to 2017 to evaluate VBP penalties among 331 matched Magnet and non-Magnet hospitals. Findings from this study illustrated similar HAC penalties received by Magnet and non-Magnet hospitals. However, more Magnet hospitals were penalized under the HRRP compared to non- Magnet hospitals, and fewer Magnet hospitals were penalized under VBP compared to non- Magnet hospitals. Rather than considering VBP penalties, Lasater et al. (2016) used 2015 CMS data to evaluate the VBP performance of 253 matched Magnet and non-Magnet hospitals. The 2015 VBP measure was represented by a Total Performance Score, which included four weighted domains: Clinical Processes (20%), Patient Experience (30%, Outcomes (30%), and Efficiency (20%). Among the matched hospital data, Lasater et al. (2016) found that Magnet hospitals performed better than non-Magnet hospitals in Total Performance Score, Clinical Processes, and Patient Experience. Outcomes and Efficiency domains demonstrated no difference between Magnet and non-Magnet matched hospitals. Studies by Boylan et al. (2019), Dierkes et al. (2021), and Lasater et al. (2016) demonstrate the complexity of quality performance comparisons between Magnet and non-Magnet hospitals. While VBP penalties and performance were the same or better among Magnet hospitals, performance in other aspects of the VBP Program was not clearly superior in Magnet hospitals. A further review of the literature regarding Magnet status in relation to the HAC Reduction Program, HRRP, and mortality is presented below. Quality measures of the HAC Reduction Program (CMS, 2024c) include rates of central line-associated bloodstream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), and eight patient safety indicators identified by the Agency for Healthcare Research and Quality. Patient Safety for Selected Indicators (PSI-90) as a composite score. Pressure ulcers and post-surgical complications are reflected in the PSI-90 score (Barnes et al., 2016). These 34 patient outcomes have been studied in Magnet and non-Magnet hospitals with mixed results. In a publication by Goode et al. (2011), Magnet hospitals had better outcomes compared to non- Magnet hospitals for pressure ulcers, but worse outcomes for infections and sepsis. Lower staffing levels among Magnet hospitals in the study sample were considered a contributing factor. Mills et al. (2013) also evaluated pressure ulcers among Magnet and non-Magnet hospitals in a large study using the Healthcare Cost and Utilization Project (HCUP) and Nationwide Inpatient Sample (NIS) database of the Agency for Healthcare Research and Quality spanning 2001 – 2005. Eighty matched Magnet hospitals were compared to 80 non-Magnet hospitals for risk-adjusted pressure ulcer rates and failure-to-rescue rates. No differences existed between Magnet and non-Magnet hospitals in these two measures. The nurse work environment at the unit level may help explain the mixed findings regarding the management of patients to prevent pressure ulcers. Ma et al. (2015) utilized 2013 NDNQI data, which represented 373 hospitals across the US, to estimate the effects of unit work environment on hospital-acquired pressure ulcers in both Magnet and non-Magnet hospitals. The study found that pressure ulcers were significantly less likely to occur on nursing units in Magnet hospitals, noting that the nursing unit work environment has the most impact on patient outcomes. The HAC Reduction Program’s focus on hospital-associated infections can result in financial penalties for hospitals. One would expect superior performance among Magnet hospitals compared to non-Magnet hospitals to avoid such penalties. In a study published by Hamadi, Bokar et al. (2021), the global measures of the HAC Reduction Program were evaluated using CMS HAC Reduction Program data from 2014 to 2016. The data included both the PSI-90 scores and the Hospital Acquired Infection (HAI) composite. The HAI composite included infection rates for CLABSI, CAUTI, surgical site infections (SSIs), Methicillin-resistant 35 Staphylococcus aureus (MRSA) bacteremia, and Clostridium difficile infection (CDI) collected through the HAC Reduction Program. A total of 2,984 hospitals (390 Magnet, 2594 non- Magnet) were scored to calculate the hospitals’ overall performance. Hamadi, Bokar et al. (2021) found that while Magnet hospitals performed better than non-Magnet hospitals on the PSI-90 measure, there was no difference in the Hospital Acquired Infection (HAI) composite scores between Magnet and non-Magnet hospitals. Additionally, total HAC scores were not significantly different, although there was variability within the HAI components. Magnet hospitals demonstrated better performance regarding MRSA infection rates, but worse with CAUTI and SSI. Studies have also considered the components of healthcare-acquired infections (HAI). Barnes et al. (2016) evaluated 2013 Medicare.gov Hospital Compare data from 291 matched Magnet hospitals and 291 non-Magnet hospitals to determine the relative rates of CLABSI. In this study, Magnet hospitals were more likely to have CLABSI rates that were lower than the average, compared to CLABSI rates of non-Magnet hospitals. However, it remained unclear whether Magnet designation resulted in better CLABSI rates. Similarly, Tai et al. (2023) evaluated 467 acute care hospitals using 2021 Leapfrog Hospital Safety Survey data, specifically scores for HAI relative to Magnet status. Although the CLABSI rate among Magnet hospitals was found to be lower compared to non-Magnet hospitals, infection rates for CAUTI, surgical site infection (post-colon surgery), MRSA infection, and CDI were no different between Magnet and non-Magnet hospitals. Conversely, in an earlier study, Pakyz et al. (2021) found that among hospitals participating in the 2013 Leapfrog Safety program, Magnet hospitals had fewer MRSA infections than predicted and more CDIs than predicted, as measured by Standardized Infection Ratios (SIR), as reported through Hospital Compare. The SIR is a calculated value obtained by 36 dividing the number of laboratory-identified infections by a predicted number of infections adjusted for infection type and baseline data determined by the CDC’s National Healthcare Safety Network (NHSN). The SIR provides standardization of infection rates and is useful in comparisons. This extensive study included 2266 US hospitals evaluated for CDI and 1701 for MRSA. The variability in HAC Reduction Program metrics relative to Magnet status, as documented in the literature, is notable. The presence of processes to improve quality in any hospital may help explain the lack of systematically lower infection rates among Magnet hospitals compared to non-Magnet hospitals (Tai et al., 2023). Hospital re-admissions and mortality quality measures are captured by the HRRP program and the VBP Mortality Program, respectively. The HRRP program focuses on specific conditions, including readmissions following coronary artery bypass grafting (CABG), chronic obstructive pulmonary disease (COPD), total hip and total knee arthroplasty, and stroke. The Mortality Program measures 30-day risk-adjusted mortality for the same conditions. Poor performance in these measures can lead to financial penalties from CMS, prompting studies of Magnet hospitals to investigate the link between improved performance in these areas and the characteristics of Magnet facilities. McHugh et al. (2013) analyzed mortality data from 2006 to 2007 for hospitals in California, Florida, Pennsylvania, and New Jersey, including 56 Magnet and 508 non-Magnet facilities. This early study found that surgical patients had 14% lower odds of 30-day mortality in Magnet hospitals compared to non-Magnet hospitals. Similarly, Hamadi et al. (2023) examined readmission rates and mortality rates for 3,877 hospitals between 2013 and 2016, including 355 Magnet hospitals. The study found that Magnet hospitals performed better for acute myocardial infarction (AMI), CABG, heart failure, and pneumonia mortality measures compared to non-Magnet hospitals. However, readmission rates for heart failure, pneumonia, 37 and COPD were the same between Magnet and non-Magnet hospitals. For readmissions related to CABG, stroke, and AMI, Magnet hospitals performed worse than non-Magnet hospitals. A later study by Hamadi, Martinez et al. (2021) reached similar conclusions, following the evaluation of HRRP and VBP data from 2017 to 2019. This second study, which considered both hospital and community factors, found no differences in readmission rates between Magnet and non-Magnet hospitals when socioeconomic factors were taken into account. The 30-day mortality rate was better among Magnet hospitals for pneumonia, COPD, and heart failure. However, AMI mortality rates were no different between Magnet and non-Magnet hospitals when socioeconomic and community considerations were included. With these mixed results, Hamadi, Martinez et al. (2021) concluded that hospitals may be taking steps to improve readmissions to minimize financial penalties, regardless of Magnet designation. The literature presents mixed results for mortality and readmission measures related to hospital Magnet designation. According to the research presented above, mortality rates were generally better among Magnet hospitals compared to non-Magnet hospitals, with some variation based on disease or condition. Readmission rates, however, were the same or worse in Magnet hospitals compared to non-Magnet hospitals. The potential benefits of Magnet designation on the quality of care delivered, as evaluated through the outcomes described above and tied to the VBP, HRRP, and HAC Reduction Program, are not the only considerations in the literature. Jayawardhana et al. (2011) examined the adoption of the 30 National Quality Forum (NQF) Safe Practices by Magnet hospitals compared to non-Magnet hospitals, using Leapfrog data from 2004 to 2006. NQF, a private, nonprofit organization, has developed a national strategy for healthcare quality based on validated practices that reduce the risk of patient harm with a focus on processes, environments, 38 and systems. Leapfrog collects data on the adoption of NQF Safe Practices through a survey of participating hospitals. Among the hospitals studied, Magnet hospitals were more likely to adopt NQF Safe Practices. Although adoption of the NQF Safe Practices is not a direct CMS measure of quality, the use of validated practices that promote patient safety can impact CMS quality measures. For example, patient falls are a standard measure of staff efforts to keep patients safe when hospitalized. The fall rate is measured as falls/1000 patient days and is reportable to CMS. This measure can be captured using the NDNQI survey, with patient fall defined by CMS as “an unintentional change in position coming to rest on the ground, floor, or onto the next lower surface” (CMS, n.d.-c). Lake et al. (2010) utilized 2004 NDNQI data, which represented 5,388 nursing units across 636 hospitals, including 108 Magnet-designated and 528 non-Magnet- designated hospitals, to examine the relationship between patient falls and Magnet designation. This study specifically examined nursing staffing relative to patient falls in Magnet and non- Magnet hospitals. Lake et al. (2010) reported a 5% lower fall rate in Magnet hospitals compared to non-Magnet hospitals. It is also important to note that Lake et al. (2010) found that while the Magnet hospitals in the sample had RN staffing levels higher than those in non-Magnet hospitals, the effect of Magnet designation on fall rates was independent of RN staffing levels, as determined through multivariate regression analysis. Measuring quality through metrics such as patient fall rates can demonstrate a patient care benefit to hospitals with Magnet designation. While delivering quality patient care is an important priority for hospitals, achieving Magnet designation requires an investment on their part, leading them to consider the business case for undertaking and completing the process to become a Magnet hospital. 39 Business Case for Magnet The business case for Magnet designation combines the benefits of the nursing professional practice environment, retention, and recruitment seen in Magnet hospitals, the potential VBP results among Magnet hospitals to avoid financial penalties, and financial analysis. Table 1 provides a summary of estimated financial impacts associated with Magnet designation. Table 1 Measure of Magnet financial benefit with financial impacts Measure Financial impact Prevented patient falls $35,365 direct costs/fall (Dykes, et al., 2023) Prevented pressure injuries $10,708 / patient (Padula & Delarmente, 2019) Prevented CAUTI $9,808 / CAUTI (Kelly, Ai, Jung, & Yu, 2024) Prevented CLABSI $43,975 / CLABSI (Sentiff, et al., 2023) Nurse turnover avoided $61,110 / nurse (NSI Nursing Solutions, Inc, 2025) Increased inpatient net income $127.05 / per discharge (Jayawardhana et al., 2014) Reduced readmission rate penalty 0.64% reduction avoided (Lasater et al., 2016) Aside from the literature that relates nursing and quality outcomes to Magnet designation, there are limited publications that measure the financial performance of Magnet versus non-Magnet hospitals. Tuazon (2007) published the first study to examine the financial health of Magnet hospitals. This small study used a convenience sample of New Jersey Hospital Association (NJHA) facilities that submitted financial data to NJHA in 2002 and 2003. After matching Magnet and non-Magnet hospitals, the resulting sample size consisted of 16 hospitals 40 (eight Magnet and eight non-Magnet). Tuazon (2007) found higher mean scores for operating margin, total margin, and return on equity among Magnet hospitals compared to non-Magnet hospitals. Among Magnet hospitals, operating margin means were positively correlated with return on total assets means and return on equity means. In non-Magnet hospitals, operating margin means were positively correlated with total margin means and total assets means. Although this was a limited study, it provided evidence of higher profitability ratios among Magnet hospitals compared to non-Magnet hospitals. A later study by Jayawardhana et al. (2014) offered the first evaluation of inpatient costs and inpatient revenue relative to Magnet status. Using cost data obtained via the CMS Hospital Cost Reporting Information System (HCRIS) for 1998 – 2006, a total of 2,682 hospitals were included in the analysis (141 Magnet, 2541 non-Magnet). Magnet hospitals were found to have increased inpatient costs by 2.46% and increased net inpatient revenue of 3.89% compared to non-Magnet hospitals. The calculated net increase per discharge in this study was $127.05 for Magnet hospitals. Although each of these studies measured financial performance using different criteria, both demonstrated support for the Magnet business case. However, a publication by Karim et al. (2018) found that Magnet status did not have an effect on hospital market share or reimbursement. The study included HCRIS data from 2000 to 2010 for 1,155 hospitals (231 Magnet, 924 non-Magnet) located in urban areas of the U.S. Because market share is influenced by factors such as referring physicians, insurers, and managed care arrangements, the hospital's Magnet status may have a reduced impact on market share. Similarly, reimbursement is largely by Government payers using prospective payment systems. Only the relatively small percentage of commercial payers with negotiated reimbursement rates could provide variability in payments, which is still insufficient to see a benefit in Magnet hospitals compared to non-Magnet hospitals (Karim et al., 41 2018). While operating and total margins may be higher, and net inpatient revenue increased in Magnet hospitals compared to non-Magnet hospitals, reimbursement was not superior. These combined findings may indicate financial operating advantages in Magnet hospitals in a payment environment with largely fixed reimbursement. Literature Gaps The literature review demonstrates publications related to Magnet designation are most often related to organizational and patient outcomes, with many evaluated against VBP measures. Comparisons in the literature are made between Magnet-designated hospitals and those that are not Magnet-designated, rather than with organizations that transition to Magnet- designated status. Since the Magnet program focuses on excellence in nursing through administration, professional practice, and professional development, it is not surprising that the literature supports superior nursing outcomes in Magnet hospitals compared to non-Magnet hospitals. The connection between Magnet and patient outcomes, as evaluated through various VBP measures, is less clear. While hospital performance on VBP measures impacts organizations financially, costs are typically captured as avoided costs or avoided penalties. Although evidence suggests that Magnet designation offers financial benefits in operational measures, while reimbursement remains consistent, there is very little direct analysis comparing the financial performance of Magnet hospitals to that of non-Magnet hospitals in the literature. This study evaluates costs by using HCRIS to obtain the cost per discharge among North Carolina Magnet and non-Magnet hospitals. Additionally, analysis of Magnet status relative to the Efficiency measure of the VBP program, which is entirely comprised of MSPB, is absent from the literature. This study aims to investigate whether hospitals with Magnet designation have lower MSPB, a VBP component that has not been previously evaluated in this context. 42 Chapter 2 Summary The state of published research involving Magnet status includes evaluation of organizational outcomes, especially those related to the nursing staff’s professional practice environment. Overall, the literature reflects superior nurse recruitment and retention among Magnet organizations. Other organizational outcomes are captured in the literature, reflecting performance in the quality measures mandated through VBP. Although results are mixed regarding Magnet status and VBP elements, much work has been done in this area, except for the Efficiency domain. Additionally, there has been limited evaluation of financial performance published. This study aims to supplement existing research and fill noted gaps by analyzing MSPB and cost per discharge among Magnet and non-Magnet hospitals. Chapter 3: Methodology