Summers_638_Formatted Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 131 Community Characteristics and Readmissions: Hospitals in Jeopardy Michele Summers, PhD, RN-BC, FNP-C 1 Serdar Atav, PhD 2 1 Clinical Associate Professor, Decker College of Nursing and Health Sciences, Binghamton University, msummers@binghamton.edu 2 Professor, Decker College of Nursing and Health Sciences, Binghamton University, atav@binghamton.edu Abstract Objective: The purpose was to identify community characteristics that contribute to reductions in readmission rates and reimbursement penalties for hospital systems in upstate New York. Methods: Hospitals in upstate NY were selected (N = 94). Using an ex post facto design and the ecological model, community characteristics of hospital systems were analyzed and coded. Independent t-tests, ANOVA, and Pearson Correlation tests were conducted. Results: Characteristics correlated with reduced hospital readmission rates and reimbursement penalties included hospitals (1) with critical access status; (2) located in counties with a better county health rank; and (3) located in a primary care shortage area that utilized house calls. Discussion: Implications include supporting policies that increase access to services, improve formulas for reimbursement, and encourage innovation in care delivery models. Future research efforts should focus on house calls in primary care shortage areas. Keywords: readmission rates, ecological model, house calls, community health Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 132 Community Characteristics and Readmissions: Hospitals in Jeopardy Mandates of the Patient Protection and Affordable Care Act (ACA) require monitoring the quality of care received in hospitals by tracking readmissions. Since the passing of this federal law, mandates allow for penalties in the form of reduced hospital reimbursement when Medicare patients are readmitted within a 30-day period (Jweinat, 2010; U.S. Department of Health and Human Services [USDHHS], 2010). Nearly 75% of hospitals nationwide were penalized for poor readmission rates between July 2010 and June 2013, and in New York State (NYS), 80% of hospitals lost revenue because of readmission rates. Well after the rollout of the ACA in 2016, NYS did even worse with 93% of hospitals penalized by Medicare (Rau, 2014, 2017). NYS ranked 2nd worst (49th) for healthcare costs, and worst (50th) in the nation for readmission rates. NYS exceeded one billion dollars in direct costs for heart failure and pneumonia (New York State Department of Health [NYSDOH], n.d. a; NYSDOH, n.d. b; New York State Health Foundation, 2014). These reduced payments threaten hospital viability. Rural hospitals have more vulnerability to closure due to reduced revenues. The Hill-Burton Act of 1946 funded the construction of many hospitals in rural areas. Within the past five decades many of these hospitals closed as a result of the U.S. Congress Prospective Payment System for Medicare, which capped payments to hospitals by a fixed amount based on diagnoses (Wishner et al., 2016). In 1997, the Clinton administration attempted to address the problem of hospital closures with the Medicare Rural Hospital Flexibility Program, providing financial relief for rural hospitals with less than 25 beds. However, during the recession of 2008-2009, 42 rural hospitals across the country closed (Wishner et al., 2016). More alarming was the closure of another 72 rural hospitals between 2010 and 2016, further jeopardizing patient access to care. Most of those closures Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 133 occurred after the implementation of the ACA (Kaufman et al., 2016; Wishner et al., 2016). Hospitals are major employers and sources of income for numerous businesses; therefore, hospitals that are weak financially disrupt community well-being (American Hospital Association, 2017). Reduced hospital revenue as a result of high readmission rates threatens nurse staffing. It is well documented that low nurse staffing is related to poor patient health and safety outcomes (Agency for Healthcare Research and Quality, 2004; American Nurses Association, 2014). High readmission rates contribute to a vicious cycle of poor staffing, poor quality of care, and lower reimbursement. Since nurses constitute the largest sector of the health care workforce and are responsible for the majority of care given in hospitals (American Association of Colleges of Nursing, 2019), a reduction in this human resource by hospital organizations has not only quality of care implications but economic as well. It is within the scope of nursing practice and shared governance to develop and implement evidence-based solutions that can yield cost savings and improve health outcomes (Lundmark, 2008). Rationale In response to the mandates of the ACA, hospitals across the country have created initiatives to enhance their quality of care to reduce readmission rates, improve health outcomes and maximize reimbursement (Linden & Butterworth, 2014). Little is known, comparatively or cumulatively, about how effective these programs are in bringing improvement to health status and reducing readmission rates in NYS. It would benefit healthcare providers working in policy, communities, and organizations to identify community characteristics and readmission reduction initiatives that are successful at improving health status and reducing the burden of disease. It is imperative to identify cost-effective and worthwhile strategies (Cohen et al., 2016). Strategies can also assist communities to reach two aims of Healthy People 2030 developed for older adults, Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 134 which are to “improve the health and well-being for older adults,” and to reduce pneumonia related hospitalizations among the elderly (USDHHS, 2020, Older Adults section). Healthy People 2030 began in 1980, and provides a unifying framework for communities and organizations working on population health in the U.S. Purpose and Goal The purpose of this study was to identify community characteristics and organizational initiatives that were correlated with lower reimbursement penalties and lower hospital readmission rates for heart failure and pneumonia. As previously stated, the cost of treating heart failure and pneumonia continue to strain the NYS budget. This article specifically addresses community characteristics. Organizational initiatives are discussed in a separate paper (Summers & Atav, 2020). The study’s purpose is driven by the overarching goal of improving health quality for the elderly. By building on community characteristics and implementing organizational initiatives that are correlated with reduced readmissions or loss of revenue, improved health quality for the elderly is achievable. Framework and Review of the Literature Ecological Model This study used the ecological model as a framework and guide for the literature review. For the purposes of this article, policy and community levels of the ecological model (McLeroy, et al., 1988) are discussed. Stanhope and Lancaster (2020) describe the five levels of the ecological model as individual, interpersonal, organizational, community, and public policy. The Institute of Medicine has long supported the ecological perspective and its importance in addressing determinants of population health, acknowledging the interchange and impact among various levels (2002). The study specifically applied the ecological model (see Figure 1) to examine the Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 135 impact that the levels of policy and community have on hospital quality, namely readmissions, and to identify components that are correlated to lower reimbursement rates and penalties. Figure 1 The Ecological Model (Summers, 2018) Policy Level Factors The ACA and Centers for Medicare and Medicaid Services (CMS) regulations represent the policy level because these entities govern quality measures, and they create algorithms for calculating the payment adjustment factor which ultimately determines payment (Centers for Medicare and Medicaid Services [CMS], 2020). The federal ACA authorized the Secretary of Health and Human Services to identify measures of quality that include avoidable hospital readmissions and sets guidelines for reimbursement reductions. Beginning with the Deficit Reduction Act of 2005, the Secretary was mandated to include hospital outcomes such as readmission rates in the Hospital Inpatient Quality Reporting program. These legislated outcome measures were expanded with the ACA in 2010 and are now included in reports generated by the Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 136 Agency for Healthcare Research and Quality (AHRQ) and the Hospital Consumer Assessment of Healthcare Providers and Systems (CMS, 2020). For the purpose of this study, the outcome for the ecological model was hospital quality (see Figure 1) and was represented using (1) hospital 30-day all cause readmission rates, (2) heart failure related readmission rates (RRHF), and (3) pneumonia related readmission rates (RRPN), as reported by the NYS Department of Health. The fourth outcome examined was the payment adjustment factor (PAF), which is the reimbursement penalty imposed on hospitals. The PAF is calculated using a base hospital operating payment for specific diagnostic related groups, and the local wage index of the hospital’s region. A PAF of one is desirable. In the first year of the ACA, the PAF penalty was capped at 1%, and has incrementally increased to a 3% reduction in reimbursement as of 2016 (CMS, 2020; Hoffman, 2017). Since the PAF is calculated using heart failure and pneumonia related data, two additional interval outcomes were included in this study: the excess readmission ratios for heart failure and pneumonia (ERRHF and ERRPN, respectively). These two ratios are risk-adjusted measures that compare actual readmission rates to that of expected rates for that hospital based on national rates and hospital and community characteristics. A higher ratio is equated to poorer than expected readmissions related to heart failure or pneumonia (CMS, 2020). Community Level Factors Community health workers provide care to individuals, families, and groups, with an aim to improve population health. One of the strongest correlations to life expectancy among race, age, and sex, is education and socioeconomic status (Olshansky et al., 2012). Socio-economic status has a direct influence on life expectancy, and when the distribution of social supports in a community is increased, health status can be improved (Marmot et al., 2008; Wilkinson & Marmot, Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 137 2003). Community level variables addressed in this study included characteristics of (a) rural status, (b) critical access hospital status, (c) primary care provider shortage status and (d) county rank for health factors. Rural Status. The Office of Management and Budget (OMB) classification for a core based statistical area (CBSA) was utilized. The OMB classification uses three CBSAs based on a county geographic unit. The metropolitan area consists of a core urban population of 50,000 or more. The micropolitan area has populations from 10,000 to 50,000. Non-metropolitan areas contain a population of less than 10,000, which for this study is termed rural (Rural Health Information Hub, 2020). Critical Access Hospital Status. Another community level factor examined was whether the hospital was classified by CMS as a critical access hospital (CAH). To qualify as a CAH, the hospital must have (1) less than 25 acute care hospital beds, (2) be located greater than 35 miles from another hospital, (3) have an average length of stay of less than 96 hours, and (4) provide emergency care 24 hours daily and seven days a week (Rural Health Information Hub, 2019). CAHs have provided needed access to healthcare services in otherwise remote geographic areas. CAHs are granted more leeway than non-CAHs in terms of financial reimbursement. Organizations that oversee the administration of CAHs often experience an excessive financial burden (Bigby, 2014; Reiter et al., 2013). Baernholdt et al. (2014) found no difference between hospitals with a critical access designation and those without the designation when measuring nurse-rated quality of care and community perceptions. Gaps in the literature exist for studies comparing readmission and reimbursement outcomes between CAHs and non-CAH entities. Primary Care Shortage Area Status. The Health Resources and Services Administration designates Health Professional Shortage Areas for regions with shortages of primary care Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 138 providers, dentists, and mental health providers (Health Resources and Services Administration, 2020). Primary care provider shortages were examined because advanced practice nurses (APNs) serve in the role of a primary care provider. Primary care providers are a patient’s first contact with the health care system. A lack of providers contributes to decreased access to care and potentially a deterioration of health status. A shortage also creates an environment for over- utilization of emergency rooms and increased hospital admissions (Commonwealth Fund, 2013). County Health Rank. The Health Factor Rank, developed by the University of Wisconsin Population Health Institute, is based on categories that quantify aspects of social determinants contributing to population health including elements of health behaviors, clinical care, as well as social, economic and physical environmental features (University of Wisconsin Population Health Institute, 2016). A county with a score of one is better than the other counties in that state. Consequently, it was expected that hospitals located in metropolitan communities with a better county health rank and adequate availability of primary care providers would have lower readmission rates and less reimbursement penalties. Organizational Level Factors The authors have discussed in a separate paper the variables at the organizational level of the ecological model. Those variables include whether a hospital utilized any hospital readmission reduction programs, as well as the level of nurse competency involved in a hospital’s interdisciplinary discharge planning team. Hospital programs that contributed significantly to reductions in readmission rates and payment penalties for hospital systems in upstate New York included an organizational collaboration or utilization of (1) certified home health agencies, (2) telehealth, (3) house calls, (4) APNs on interdisciplinary discharge teams, and (5) the utilization of more than one type of hospital readmission reduction initiative (Summers & Atav, 2020). Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 139 Methods Design and Sample This study incorporated a non-experimental, ex post facto design which is considered another method of determining causal relationships (Lord, 1973). Ex post facto designs may be categorized as quasi-experimental. In this design, there is neither random assignment nor manipulation. The research is conducted after events have occurred without any intervention by the researcher (Ex Post Facto Research Design, 2021). The university’s Institutional Review Board (IRB) granted an exempt status since no human subjects or populations were under investigation (IRB protocol #3891-16). This study included 94 hospitals located in 53 upstate NY counties. Hospitals in nine counties with close proximity to the New York City metropolis were excluded in order to fairly represent rural hospitals. Hospitals excluded were in the following nine counties: Bronx, Kings, New York, Queens, Richmond, Rockland, Westchester, Nassau, and Suffolk. Five hospitals were excluded due to: (a) closure (n = 1); (b) the facility was either strictly an urgent care or emergency department (n = 3); or (c) the facility was under the organization and data collection of a Pennsylvania hospital system (n = 1). Six counties in NYS did not have any hospitals. Rural status was limited to and based on the core-based statistical area as defined by the OMB. Data were collected from public domain sources including CMS, the AHRQ, the U.S. Census, the Rural Health Information Hub, NYSDOH, the University of Wisconsin Population Health Institute, and specific hospital websites. Scope and Limitations Information was accessed through various government, non-government, and hospital public domain websites. Data were collected at the hospital and county levels for 94 hospitals in Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 140 53 counties, then categorized following the ecological model as a framework. This study’s scope is applicable to upstate NY hospitals, and based on the assumption that data sources were correctly collected, analyzed, and reported. One limitation inherent in a quasi-experimental ex post facto design is that it is a weaker level of evidence for establishing causal relationships when compared to a randomized control trial (Lord, 1973). Outcome Measures The outcome of the study was hospital quality. To measure quality, the following variables were used to operationalize hospital quality (see Table 1): • Hospital readmission rates • Readmission rates for heart failure • Readmission rates for pneumonia • Payment adjustment factor • Excess readmission ratio for heart failure • Excess readmission ratio for pneumonia Table 1 Dependent Variable Measurement and Source Dependent (Outcome) Variable Name Measure Sources Hospital Readmission Rate Interval 1. NYSDOH: Health Profiles- Hospitals 2. Medicare: Hospital Compare Dataset Readmission Rate for Heart Failure Interval 1. NYSDOH: Health Profiles- Hospitals 2. Medicare: Hospital Compare Dataset Readmission Rate for Pneumonia Interval 1. NYSDOH: Health Profiles- Hospitals 2. Medicare: Hospital Compare Dataset Payment Adjustment Factor Interval CMS: Readmission Reduction Program Excess Readmission Ratio for Heart Failure Interval CMS: Readmission Reduction Program Excess Readmission Ratio for Pneumonia Interval CMS: Readmission Reduction Program Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 141 Predictor Qualities This study examined several community factors that could possibly impact the outcome measures. 1. Was the hospital located in a rural area? The three categories of location included metropolitan, micropolitan, or rural. 2. Was the hospital a critical access hospital or not? 3. Was the hospital located in a primary care shortage area or not? 4. What was the hospital’s county health rank? The range for this variable was 1 to 62. Data Analyses Analyses were conducted using IBM’s Statistical Package for the Social Sciences (SPSS) version 22, facilitating the completion of statistical tests. Descriptive statistics were conducted providing a summary of the data and how often the community characteristic occurred among hospitals in upstate NY. Bivariate analyses included independent t-tests to compare the means of outcome variables, and to determine if there was a significant difference in readmission rates and reimbursement penalties for hospitals that were (1) categorized as a critical access hospital and (2) located in a primary care shortage area. For the independent variable of rural status with three groups, the parametric analysis of variance (ANOVA) was utilized providing analysis for differences among the groups’ means in both within-group variance and between-group variance. Games-Howell post hoc analyses were chosen to determine which groups were statistically different. Pearson correlations were also conducted for the county health rank with each of the readmission outcomes. This parametric test is particularly useful in determining if a correlational relationship exists, how strong that relationship is, and if that relationship is direct or inverse (Plichta & Kelvin, 2013). Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 142 Results Several significant findings are discussed in this section. Community characteristics addressed in this study included (1) rural status, (2) critical access hospital status, (3) primary care provider shortage status, and (4) county health rank. Organizational level factors and the type of hospital readmission reduction program employed by the hospital are discussed elsewhere in detail (Summers & Atav, 2020). Since no individuals were studied, and the subjects under consideration were hospitals, the interpersonal and intrapersonal levels of the ecological model were not specifically addressed in this study. Of the community characteristics studied, none were associated with significant differences in (1) readmission rates, (2) readmission rates related to heart failure, (3) excess readmission ratios for heart failure, or (4) excess readmission ratios for pneumonia. The hospitals with critical access status, however, had lower readmission rates related to pneumonia compared to non-critical access hospitals. Reimbursement penalties based on the payment adjustment factors were lower for hospitals located in counties with a better county health rank and located in primary care shortage areas utilizing house calls. Descriptive statistics were calculated for 94 hospitals in upstate NY (see Table 2): Table 2 Descriptive Statistics and Frequencies for Hospitals in Upstate NY: Community Variables Characteristics Number (%) of Hospitals (N = 94) Mean (SD) [Min. –Max. Range] Community Predictor Variables County Health Rank 94 (100%) 32.72 (15.55) [2-61] Metropolitan 63 (67%) Micropolitan 21 (22.3%) Rural 10 (10.6%) Primary Care Provider Shortage 58 (61.7%) Critical Access Hospital 12 (12.8%) Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 143 Rural Status A one-way, between subjects ANOVA was conducted to compare the effect of rural status on readmission rates and reimbursement penalty rates in upstate NY hospitals. There was not a significant effect. Results, however, indicated that hospitals located in micropolitan settings trended towards worse outcomes compared to hospitals located in either metropolitan or rural settings, (see Table 3). These findings could be explained by a greater source of revenue and economic support in metropolitan areas. Likewise, rural areas may receive federal government financial support. It could be argued that micropolitan communities have an economic disadvantage and include many older industrial cities and towns with an aging population and a diminishing workforce (Sisson, 2018). Table 3 ANOVA for Outcomes According to Rural Status Outcome Metro Mean SD n Micro Mean SD n Rural Mean SD n F p RR 15.612 .8166 56 15.845 .7131 20 15.720 .7563 5 .657 .521 RRHF 22.282 1.5567 56 22.570 1.5991 20 21.960 1.1014 5 .410 .665 RRPN 17.679 1.5984 56 17.330 1.2978 20 17.140 1.2361 5 .595 .554 PAF .9950 .0054 56 .9926 .0045 20 .9929 .0048 5 1.702 .189 ERRHF 1.0240 .0687 56 1.0514 .0849 20 1.0216 .0608 5 1.098 .339 ERRPN 1.0340 .1174 56 1.0159 .0669 20 1.0007 .0791 5 .389 .679 Note. RR= readmission rates; RRHF=Readmission rates related to heart failure; RRPN= Readmission rates related to pneumonia; PAF= Payment adjustment factor; ERRHF= Excess readmission ratio for heart failure; ERRPN= Excess readmission ratio for pneumonia Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 144 Critical Access Hospital Status Those hospitals classified as CAHs had lower readmission rates related to pneumonia than non-CAHs, t (33) = -.171, p = .021 (see Table 4). Hospitals classified as a CAH did not have the same data collection and analysis methods as non-CAHs. Therefore, independent sample t-tests were only conducted for the three readmission rates. There is limited literature available that examines various readmission rates of CAHs, but these findings do not agree with a previous study stating that CAH status does not make a difference in readmission rates (Lichtman et al, 2012). This research adds new knowledge regarding pneumonia related admissions and CAHs. Table 4 Independent Sample t-tests Results for the Predictor Critical Access Hospital Status Critical Access Hospital Non-Critical Access Hospital Outcome Mean SD n Mean SD n p t df RR 15.45 .3475 11 15.67 .7833 82 .103 -.919 33 RRHF 21.65 1.1396 11 22.35 1.5317 82 .064 .562 33 RRPN 16.87 .8211 11 17.57 1.4975 82 .021* -.171 33 Note. RR= readmission rates; RRHF=Readmission rates related to heart failure; RRPN= Readmission rates related to pneumonia. p < .05* Primary Care Shortage Area Status Those hospitals located in communities with a shortage of primary care providers had similar readmission rates and reimbursement penalties (no significant difference) compared to hospitals located in non-shortage areas (see Table 5). This could be due, in part, to the size of the sample. This adds to the body of literature that Medicare hospital readmission and reimbursement Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 145 rates have not been specifically studied in the context of shortage areas; however, it is known that a shortage of providers, among other factors, is predictive of Medicaid readmissions (Jiang et al, 2016). Table 5 Independent Sample t-tests Results for the Predictor Primary Care Shortage Area Shortage Area Non- Shortage Area Outcome Mean SD n Mean SD n p t df RR 15.71 .7453 46 15.63 .8464 35 .645 .436 79 RRHF 22.57 1.5641 46 22.02 1.4596 35 .087 1.633 79 RRPN 17.51 1.4432 46 17.63 1.5907 35 .745 -.359 79 PAF .9938 .0049 46 .9949 .0056 35 .395 -.941 79 ERRHF 1.0422 .0751 46 1.0152 .0673 35 .091 1.673 79 ERRPN 1.0178 .0639 46 1.0402 .1418 35 .418 -.950 79 Note. RR= readmission rates; RRHF=Readmission rates related to heart failure; RRPN= Readmission rates related to pneumonia; PAF= Payment adjustment factor; ERRHF= Excess readmission ratio for heart failure; ERRPN= Excess readmission ratio for pneumonia. Hospital Readmission Reduction Programs and Controlling for Primary Care Shortage Area Status Additionally, each hospital readmission reduction program’s effect on hospital readmission rates and reimbursement penalties was examined using independent sample t-tests, while controlling for primary care shortage area. Those hospitals located in primary care shortage areas utilizing house calls had lower reimbursement penalties compared to those hospitals not utilizing house calls, t (44) = 1.848, p = .018 (see Table 6). Hospitals in primary care shortage areas utilizing Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 146 other hospital readmission reduction programs had similar readmission rates and reimbursement penalties compared to those hospitals not utilizing the programs. Table 6 Independent t-tests Results for the Predictor Variable (House Call) in Primary Care Shortage Areas House Call No House Call Outcome Mean SD n Mean SD n p t df RR 15.61 .6875 8 15.73 .7640 38 .673 -.398 44 RRHF 22.41 1.1115 8 22.61 1.6537 38 .671 -.318 44 RRPN 16.88 .8730 8 17.64 1.5231 38 .056 -1.365 44 PAF .9967 .0030 8 .9932 .0051 38 .018* 1.848 44 ERRHF 1.0415 .0836 8 1.0424 .0745 38 .972 -.029 44 ERRPN 1.0004 .0752 8 1.0215 .0618 38 .477 -.847 44 Note. RR= readmission rates; RRHF=Readmission rates related to heart failure; RRPN= Readmission rates related to pneumonia; PAF= Payment adjustment factor; ERRHF= Excess readmission ratio for heart failure; ERRPN= Excess readmission ratio for pneumonia p < .05* County Health Rank The Pearson correlation indicated that those hospitals located in communities with a poorer (higher number) county health rank had a negative correlation with the PAF (r (79) = -.230, p = .039) (see Table 7), meaning that hospitals located in counties with a worse health rank experienced higher reimbursement penalties. County health rank is based on social-economic elements, the environment, certain health behaviors, and the availability and quality of clinical care (University of Wisconsin Population Health Institute, 2016). These findings add new Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 147 knowledge to the body of literature since no known previous studies have specifically examined county health rank with reimbursement penalties. Table 7 Pearson Product Correlation for Community Health Rankings on Readmission Outcomes RR RRHF RRPN PAF ERRHF ERRPN County Health Rank -.068 .067 -.043 -.230* .086 -.068 p < .05* Discussion In light of the significant findings, critical access hospitals play an important part in managing pneumonia. Hospitals located in a county with a better county health rank have less reimbursement penalties assessed against them. Additionally, hospital organizations located in primary care shortage areas that utilize either physician or APNs to conduct house calls benefit financially. They experience lower reimbursement penalties when compared to hospitals in non- shortage areas that utilize house calls. Rural Status This study demonstrated that hospitals located in micropolitan areas had worse outcomes than hospitals located in metropolitan or rural settings. Critical access hospitals have different reimbursement formulas. Unlike most hospitals that are under a prospective payment reimbursement, critical access hospitals’ reimbursements are cost based (Reiter et al., 2013). These formulas may provide an advantage over micropolitan hospitals. Metropolitan hospitals may have the advantage of rich resources in specialists, technology, and a stronger economic base. Laws and regulations that govern healthcare and healthcare financing in all settings should be reevaluated. Furthermore, expanding the study to include other states may solidify this finding. Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 148 Critical Access Hospital Status Critical access hospitals had lower readmission rates related to pneumonia compared to the rates in non-critical access hospitals. This unexpected finding could be related to the number of hospitals in the study. These favorable findings could be related to the financial leeway and incentives granted to them (Bigby, 2014; Reiter et al., 2013). Why readmission rates for pneumonia were lower, and why readmission rates for heart failure were not lower in critical access hospitals compared to non-critical access hospitals could be explained by the acute nature of pneumonia. Patients may have a better chance of resolving the condition without a subsequent hospital readmission compared to the chronic disease of heart failure which frequently entails multiple readmissions (Goroll & Mulley, 2021). This finding supports the need to implement policies that will help sustain these small rural hospitals. Primary Care Shortage Status Hospitals located in communities with a shortage of primary care providers had neither better nor worse readmission rates and reimbursement penalties compared to hospitals located in non-shortage areas. Communities in non-primary care shortage areas typically have better outcomes (Commonwealth Fund, 2013). However, when each hospital readmission reduction program was examined while controlling for whether hospitals were located in primary care shortage areas, hospitals that utilized house calls had lower reimbursement penalties. Organizations located in primary care shortage areas should consider expanding their services to include the use of house calls. County Health Rank As expected, hospitals located in counties with a better county health rank had lower reimbursement penalties. There is a need for supportive funding for county policies and programs Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 149 that address specific measures used in the calculation of county health rank. Nevertheless, this is an indication of a reciprocal relationship between healthier communities and better hospitals. Recommendations Policy As previously stated, it is important to promote policies that will add value to the county health rank, and that will financially support successful hospital programs, thus strengthening communities and organizations. Although there is much heated debate over reforming the ACA, there is no lack of support for continuing efforts to improve both reimbursement and hospital readmission reduction schemes proposed by the ACA (Network for Regional Healthcare Improvement, 2015; Richie et al., 2014). There are several federal bills under consideration in Congress that have the potential to enhance population health, and also lend support to the findings of this research. The Independence-at-Home Act moves this model of care delivery from a demonstration status to permanent status. This legislation could ensure that the delivery of health care within the context of patient homes would continue on a larger scale, through funding and expanding Medicare benefits (U.S. Senate 1202, 2019). Another federal bill that directly impacts reimbursement penalties has been introduced calling for reform of the methodology by which reimbursement penalties are calculated, namely the payment adjustment factor. Sponsors of the bill state that the calculations do not take into account the chronic illness burden nor the severity of the illness, and thus reform of the reimbursement methodology would lessen the financial strain on the nation’s hospitals (U.S. House of Representatives 3611, 2017). Similarly, the Save Rural Hospitals Act called for reform in reimbursement calculations for rural hospitals and CAHs, because current methodologies were not halting the rapid rate of these hospital closures. This Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 150 legislation would eliminate hospital payment reductions for both Medicare and Medicaid in rural hospitals (U.S. House of Representatives 2957, 2017), and ultimately contribute to the strength of rural communities. State policy recommendations include a bill to revise public health law in New York. NY Senate Bill 1805 proposes funding that would support the development and implementation of collaborative service plans among hospitals, home care, practitioners, and emergency medical services, which in turn, builds community health (NY Senate 1805, 2020). Other models of care for communities need to be developed. Primary prevention strategies must be a priority focus for policymakers and communities such as the recently passed expansion of Medicare payment for home health services ordered by a nurse practitioner, clinical nurse specialist, certified nurse- midwife, or physician assistant (American Association of Nurse Practitioners, 2020). Future Research Considering the legislative proposals and study findings, future research efforts should be directed towards examining care delivery models that expand the practice of house calls and the role of the advanced nurse practitioner. Conducting research in primary care shortage areas on the use of house calls is of particular interest and could prove financially beneficial to hospital organization serving those areas. The house call shows promise in reducing pneumonia related readmissions, and in reducing reimbursement penalties especially in primary care shortage areas. Expanding research to explore levels of the ecological model not included in this study, namely interpersonal and intrapersonal levels, could be included in these future research efforts. Practice There are numerous and well documented theories and models that believe nursing care is what makes a difference in patient outcomes. The nurse-patient relationship has been a vital factor Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 151 in patients reaching optimal health outcomes (Bell et al., 2009). When nurse practitioners grow their practice from a nursing theoretical basis advances in nursing theory can be achieved in both organizational and community settings (Ackerman et al, 2010). This approach to nursing care is in contrast to the predominant medical framework that is foundational to most hospital administrations (Scott, 1982). Nurse practitioner competencies include using evidence to shape practice environments, solving practice problems, and ultimately improving outcomes (Hamric & Tracy, 2019). Organizations should employ nurse practitioners to either conduct house calls for each primary care office, or redesign the workflow to allow providers the time to follow-up with house calls when appropriate for their patients In conclusion, this study supports efforts to revise reimbursement formulas to increase fiscal sustainability of hospital systems. It also supports community level workforce development for house calls in primary care shortage areas. With awareness of various community characteristics that affect hospital quality, APNs can promote and implement initiatives correlated with reduced readmissions and loss of revenue. In doing so, health quality for the elderly is achievable. Compliance with Ethical Standards Declaration of Conflicting Interest: The authors declare that there is no conflict of interest, financial or otherwise, and have obtained exempt status from Binghamton University’s Institutional Review Board. References Ackerman, M., Mick, D., & Witzel, P. (2010). Creating an organizational model to support advanced practice. Journal of Nursing Administration, 40(2), 63-68. https://doi.org/ 10.1097/NNA.0b013e3181cb9f71 Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 152 Agency for Healthcare Research and Quality. (2004). Hospital nurse staffing and quality of care: Research in action, (14). Retrieved from https://archive.ahrq.gov/research/findings/ factsheets/services/nursestaffing/nursestaff.html American Association of Colleges of Nursing (2019). Nursing fact sheet. Retrieved from https://www.aacnnursing.org/News-Information/Fact-Sheets/Nursing-Shortage American Association of Nurse Practitioners. (2020). Coronavirus Aid, Relief, and Economic Security Act (CARES Act) Summary Document March 27, 2020. Retrieved from https://storage.aanp.org/www/documents/advocacy/CARES-Act-Health-Provision- Legislative-Summary.pdf American Hospital Association. (2017). Hospitals are economic anchors in their communities. Retrieved from https://www.aha.org/system/files/content/17/17econcontribution.pdf American Nurses Association. (2014). Key findings from research studies on safe RN staffing: Safe staffing literature review. Retrieved from https://patientsafestaffing.org/wp- content/uploads/2018/07/ANA.-Research.-UPDATED-STAFFING-LIT-REVIEW.- 2013.-04.07.2015.pdf Baernholdt, M., Keim-Malpass, J., Hinton, I., Yan, G., & Bratt, M./ (2014). A comparison of quality of care in critical access hospitals and other rural hospitals. Online Journal of Rural Nursing and Health Care, 14, 3(2), 3-31. https://doi.org/10.14574/ojrnhc.v14i2.328 Bell, J., Moules, N., & Wright, L. (2009). Therapeutic letters and the family nursing unit: A legacy of advanced nursing practice. Journal of Family Nursing, 15(1), 6–30. https://doi.org/10.1177/1074840709331865 Bigby, J. (2014). Examine critical access hospital payment policies within the context of Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 153 integrated systems. JAMA Internal Medicine, 174(1), 144-145. https://doi.org/10.1001/ jamainternmed.2013.11469 Centers for Medicare and Medicaid Services. (2020). Hospital readmissions reduction program (HRRP). Retrieved from https://www.cms.gov/Medicare/Medicare-Fee-for-Service- Payment/AcuteInpatientPPS/Readmissions-Reduction-Program Cohen, J., Crane, T., Greenspan, E., & Whitlock, R. (2016). Promise to repeal the ACA Poses threat to MACRA implementation. Mintz Levin: Health Law and Policy Matters November 21, 2016. Retrieved from https://www.healthlawpolicymatters.com/2016/11/21/promise- to-repeal-the-aca-poses-threat-to-macra-implementation/ Commonwealth Fund. (2013). Primary care: Our first line of defense. The Commonwealth Fund: Health reform and You. Retrieved from http://www.commonwealthfund.org/publications/ health-reform-and-you/primary-care-our-first-line-of-defense Ex Post Facto Research Design. (2020, February 17). In Alleydog.com's online glossary. Retrieved from: https://www.alleydog.com/glossary/definition-cit.php?term=Ex+Post+ Facto+Research+Design Goroll, A. & Mulley, A. (2021). Primary care medicine: Office evaluation and management of the adult patient (8th ed.). Wolters Kluwer. Hamric, A. & Tracy, M. (2019). A Definition of Advanced Practice Nursing. In M. Tracy & E. O’Grady (eds.), Advanced practice nursing: An integrative approach. (6th ed., pp. 62-79). Elsevier. Health Resources and Services Administration. (2020). What s shortage designation? Retrieved from https://bhw.hrsa.gov/shortage-designation/hpsas Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 154 Hoffman, J. (2017). Overview of CMS readmissions penalties for 2016. Retrieved from http://www.besler.com/2016-readmissions-penalties/ Institute of Medicine. (2002). The future of the public’s health in the 21st century. National Academies Press. Jiang, H., Boutwell, A., Maxwell, J., Bourgoin, A., Regenstein, M., & Andres, E. (2016). Understanding patient, provider, and system factors related to Medicaid readmissions. Joint Commission Journal on Quality and Patient Safety, 42(3), 115-121. https://doi.org/10.1016/S1553-7250(16)42014-3 Jweinat, J. (2010). Hospital readmissions under the spotlight. Journal of Healthcare Management, 55(4), 252-264. Kaufman, B., Thomas, S., Randolph, R., Perry, J., Thompson, K., Holmes, G., & Pink, G. (2016). The rising rate of rural hospital closures. Journal of Rural Health, 32(1), 35-43. https://doi.org/10.1111/jrh.12128 Lichtman, J., Leifheit-Limson, E., Jones, S. Wang, Y., & Goldstein, L. (2012). 30-day risk- standardized mortality and readmission rates after ischemic stroke in critical access hospitals. Stroke, 43, 2741-2747. https://doi.org/10.1161/STROKEAHA.112.665646 Linden, A. & Butterworth, S. (2014). A comprehensive hospital-based intervention to reduce readmissions for chronically ill patients: A randomized control trial. The American Journal of Managed Care, 20(10), 783-792. Lord, H. (1973). Ex post facto studies as a research method: Special report 7320. Bureau of Education for the Handicapped. Lundmark V. (2008). Magnet environments for professional nursing practice. In R. Hughes Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 155 (Editor), Patient safety and quality: An evidence-based handbook for nurses. (Chapter 46). Agency for Healthcare Research and Quality. https://www.ncbi.nlm. nih.gov/books/NBK2667/ Marmot, M., Friel, S., Houweling, T., & Taylor, S. (2008). Closing the gap in a generation: Health equity through action on the social determinants of health. The Lancet, 372(9650), 1661-1669. https://doi.org/10.1016/S0140-6736(08)61690-6 McLeroy, K., Bibeau, D., Steckler, A., & Glanz, K. (1988). An ecological perspective on health promotion programs. Health Education Quarterly, 15(4), 351-377. https://doi.org/10.1177/109019818801500401 Network for Regional Healthcare Improvement. (2015). The building blocks of successful payment reform: Designing payment systems that support higher value health care. Retrieved from http://www.chqpr.org/downloads/BuildingBlocksofSuccessfulPaymentReform.pdf New York Senate 1805, General Assembly. (2020). Authorizes collaborative programs for community paramedicine services as part of the hospital-home care-physician collaboration program. New York State Department of Health. (n.d. a). A plan to transform the empire state’ Medicaid program: Better care, better health, lower costs. Retrieved from http://www.health.ny.gov/health_care/medicaid/redesign/docs/mrtfinalreport.pdf New York State Department of Health. (n.d. b). The burden of cardiovascular disease in New York: Mortality, prevalence, risk factors, costs, and selected populations. Retrieved from https://www.health.ny.gov/diseases/cardiovascular/heart_disease/docs/burden_of_cvd_in _nys.pdf Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 156 New York State Health Foundation. (2014). Health care costs and spending in New York State. Retrieved from https://nyshealthfoundation.org/wp-content/uploads/2017/12/health-care- costs-in-NYS-chart-book.pdf Olshansky, S., Antonucci, T., Berkman, L., Binstock, R., Boersch-Supan, A., Cacioppo, J., Carnes, B., Carstensen, L., Fried, L., Goldman, D., Jackson, J., Kohli, M. Rother, Y., & Rowe, J. (2012). Differences in life expectancy due to race and educational differences are widening, and many may not catch up. Health Affairs, 31(8), 1803-1813. https://doi.org/10.1377/hlthaff.2011.0746 Plichta, S. & Kelvin, E. (2013). Munro’s statistical methods for health care research. Wolters Kluwer. Rau, J. (2014). Medicare fines 2,610 hospitals in third round of readmission penalties. Kaiser Health News, October 2, 2014. Retrieved from http://khn.org/news/medicare- readmissions-penalties-2015/ Rau, J. (2017). Under Trump, hospitals face same penalties embraced by Obama. Kaiser Health News, August 3, 2017. Retrieved from https://khn.org/news/under-trump-hospitals-face- same-penalties-embraced-by-obama/ Reiter, K., Holmes, G., & Broyles, I. (2013). Medicare, swing beds, and critical access hospitals. Medical Care and Research Review, 70(2), 206-217. https://doi.org/10.1177/1077 558712463247 Richie, A., Marbury, D., Verdon, D., Mazzolini, C., & Boyles, S. (2014). Shifting reimbursement models: the risks and rewards for primary care. Medical Economics. April 8, 2014. Retrieved from https://www.medicaleconomics.com/view/shifting-reimbursement- models-risks-and-rewards-primary-care Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 157 Rural Health Information Hub. (2019). Critical access hospitals. Retrieved from https://www.rural healthinfo.org/topics/critical-access-hospitals#how-many Rural Health Information Hub. (2020). Am I rural: Tool. Retrieved from https://www.rural healthinfo.org/am-i-rural Scott, W. (1982). Managing professional work: Three models of control for health organizations. Health Services Research, 17(3), 213-240. Sisson, P. (2018). Why ‘micropolitan’ cities may be the key to rural resurgence. Retrieved from https://archive.curbed.com/2018/10/30/18042760/job-business-rural-economic- micropolitan Stanhope, M. & Lancaster, J. (2020). Foundations for population health in community/public health nursing. (5th ed.). Elsevier. Summers, M. L. (2018). Hospital readmission reduction programs in upstate New York: Correlates, best practices and nursing contributions. Hospital readmission reduction programs in upstate New York: Correlates, best practices & nursing contributions, (Publication 12781) [Doctoral dissertation, Binghamton University]. ProQuest Dissertations. Summers, M. & Atav, S. (2020). Reducing hospital readmissions in upstate New York: Teasing out the effective programs. Journal of Professional Case Management, 25, (1), 26-36. U.S. Department of Health and Human Services (2010). The patient protection and affordable care act. Retrieved from http://www.hhs.gov/healthcare/about-the-law/read-the-law/ index.html U.S. Department of Health and Human Services (2020, February 28). Healthy people: Older Online Journal of Rural Nursing and Health Care, 21(1) https://doi.org/10.14574/ojrnhc.v21i1.638 158 adults. Retrieved from https://health.gov/healthypeople/objectives-and-data/browse- objectives/older-adults U.S. Department of Health and Human Services (2017b). Healthy people: Heart disease and stroke. Retrieved from https://www.healthypeople.gov/2020/topics-objectives/topic/heart- disease-and-stroke U.S. House of Representatives 2957, 115th Congress (2017). Save Rural Hospitals Act. U.S. House of Representatives 3611, 115th Congress (2017). Healthcare Outcomes Act of 2017. U.S. Senate 1202, 116th Congress (2019). Independence at Home Act of 2019. University of Wisconsin Population Health Institute. (2016). County health rankings and roadmaps. Retrieved from www.countyhealthrankings.org Wilkinson, R. & Marmot, M. (2003). Social determinants of health: The solid facts (2nd ed.). World Health Organization. Wishner, J., Solleveld, P., Rudowitz, R., Paradise, J., & Antonisse, L. (2016). A look at rural hospital closures and implications for access to care: Three case studies. Kaiser Family Foundation: Medicaid. Retrieved from http://kff.org/report-section/a-look-at-rural- hospital-closures-and-implications-for-access-to-care-three-case-studies-issue-brief/