Non-operating revenue is an important source of funding for rural hospitals, especially those that are government-owned.

PURPOSE: Non-operating revenue (NOR), derived from investments, contributions, government appropriations, and medical space rentals, can contribute to financial stability of hospitals by offsetting operating losses and improving profitability. NOR might benefit rural hospitals that often face intense financial pressures. However, little is known about how much rural hospitals rely on NOR and if certain organizational characteristics are associated with differences in NOR. METHODS: Healthcare Cost Report Information System data from 2011 to 2019 were used to analyze sources of revenue among Critical Access Hospitals (CAHs) and Rural Prospective Payment System (R-PPS) hospitals through descriptive statistics and regression models. Reliance on NOR was measured by the percentage of total revenue from non-operating sources. FINDINGS: Results indicate that both CAHs and R-PPS hospitals rely on NOR; however, CAHs have a higher percentage of total revenue derived from non-operating sources (3.2%) as compared to R-PPS hospitals (1.9%) (p < 0.001). Government-owned hospitals have significantly higher reliance on NOR than other ownership types. System affiliation also influences reliance on NOR. Lastly, results suggest that NOR may play a role in improving overall profit margins. CONCLUSIONS: As rural hospitals disproportionately face challenges related to declining profitability and the risk for closure, they may rely on NOR to continue to strengthen financial performance and provide health care to their communities. However, NOR is not guaranteed, and reliance on NOR further reiterates the value of stable, adequate reimbursement to guard against fluctuations in NOR.

The Association of Readmission Reduction Activities with Primary Care Practice Readmission Rates.

BACKGROUND: A great deal of research has focused on how hospitals influence readmission rates. While hospitals play a vital role in reducing readmissions, a significant portion of the work also falls to primary care practices. Despite this critical role of primary care, little empirical evidence has shown what primary care characteristics or activities are associated with reductions in hospital admissions. OBJECTIVE: To examine the relationship between practices' readmission reduction activities and their readmission rates. DESIGN, SETTING, AND PARTICIPANTS: A retrospective study of 1,788 practices who responded to the National Survey of Healthcare Organizations and Systems (fielded 2017-2018) and 415,663 hospital admissions for Medicare beneficiaries attributed to those practices from 2016 100% Medicare claims data. We constructed mixed-effects logistic regression models to estimate practice-level readmission rates and a linear regression model to evaluate the association between practices' readmission rates with their number of readmission reduction activities. INTERVENTIONS: Standardized composite score, ranging from 0 to 1, representing the number of a practice's readmission reduction capabilities. The composite score was composed of 12 unique capabilities identified in the literature as being significantly associated with lower readmission rates (e.g., presence of care manager, medication reconciliation, shared-decision making, etc.). MAIN OUTCOMES AND MEASURES: Practices' readmission rates for attributed Medicare beneficiaries. KEY RESULTS: Routinely engaging in more readmission reduction activities was significantly associated (P < .05) with lower readmission rates. On average, practices experienced a 0.05 percentage point decrease in readmission rates for each additional activity. Average risk-standardized readmission rates for practices performing 10 or more of the 12 activities in our composite measure were a full percentage point lower than risk-standardized readmission rates for practices engaging in none of the activities. CONCLUSIONS: Primary care practices that engaged in more readmission reduction activities had lower readmission rates. These findings add to the growing body of evidence suggesting that engaging in multiple activities, rather than any single activity, is associated with decreased readmissions.

The Direct Clinic-Level Cost of the Implementation and Use of a Protocol to Assess and Address Social Needs in Diverse Community Health Center Primary Care Clinical Settings.

PURPOSE: Social determinants of health, including food insecurity, housing instability, social isolation, and unemployment are important drivers of health outcomes and utilization. To inform implementation of social needs screening and response protocols, there is a need to identify the associated costs in routine primary care encounters. METHODS: We interviewed key stakeholders in four diverse community health centers that had adopted a widely used social needs screening and response protocol. We evaluated costs using an activity-based costing tool across both the initial implementation phase and ongoing maintenance phase. RESULTS: Clinic costs were associated with workforce development, planning, and electronic health record integration. These initial implementation costs varied by site ($6,644-$49,087). On a per-patient basis, ongoing maintenance costs ranged from $9.76 to $47.98. CONCLUSION: Our findings can aid in designing reimbursement mechanisms tied to social needs screening and response to accelerate translational efforts and promote health equity.

Evaluating the association of social needs assessment data with cardiometabolic health status in a federally qualified community health center patient population.

BACKGROUND: Health systems are increasingly using standardized social needs screening and response protocols including the Protocol for Responding to and Assessing Patients' Risks, Assets, and Experiences (PRAPARE) to improve population health and equity; despite established relationships between the social determinants of health and health outcomes, little is known about the associations between standardized social needs assessment information and patients' clinical condition. METHODS: In this cross-sectional study, we examined the relationship between social needs screening assessment data and measures of cardiometabolic clinical health from electronic health records data using two modelling approaches: a backward stepwise logistic regression and a least absolute selection and shrinkage operation (LASSO) logistic regression. Primary outcomes were dichotomized cardiometabolic measures related to obesity, hypertension, and atherosclerotic cardiovascular disease (ASCVD) 10-year risk. Nested models were built to evaluate the utility of social needs assessment data from PRAPARE for risk prediction, stratification, and population health management. RESULTS: Social needs related to lack of housing, unemployment, stress, access to medicine or health care, and inability to afford phone service were consistently associated with cardiometabolic risk across models. Model fit, as measured by the c-statistic, was poor for predicting obesity (logistic = 0.586; LASSO = 0.587), moderate for stage 1 hypertension (logistic = 0.703; LASSO = 0.688), and high for borderline ASCVD risk (logistic = 0.954; LASSO = 0.950). CONCLUSIONS: Associations between social needs assessment data and clinical outcomes vary by cardiometabolic condition. Social needs assessment data may be useful for prospectively identifying patients at heightened cardiometabolic risk; however, there are limits to the utility of social needs data for improving predictive performance.

For Rural Hospitals That Merged, Inpatient Charges Decreased and Outpatient Charges Increased: A Pre-/Post-Comparison of Rural Hospitals That Merged and Rural Hospitals That Did Not Merge Between 2005 and 2015.

PURPOSE: To determine whether inpatient and outpatient charges changed at rural hospitals after a merger. METHODS: Hospital mergers were derived from proprietary Irving Levin Associates data through manual review and validation. Hospital-level characteristics were derived from HCRIS, CMS Impact File Hospital Inpatient Prospective Payment System, Hospital MSA file, AHRF, and US Census data. A difference-in-differences approach was used to determine whether inpatient and outpatient charges changed at rural hospitals after a merger. The comparison group, rural hospitals that did not merge at any point during the sample period, was weighted using inverse probability of treatment weights. Key outcome measures were total inpatient and total outpatient charges (logged). FINDINGS: Hospitals that merged billed 17.73% more inpatient charges and 12.66% more outpatient charges at baseline compared to hospitals that did not merge. Our results indicate that merging was associated with a 3.04% decrease in inpatient charges (P < .001) and a 1.07% increase in outpatient charges (P = .082). Merging was also associated with a 4.38% decrease in total revenue, a 3.58% decrease in net patient revenue, and no change in total inpatient discharges or average daily census. CONCLUSIONS & IMPLICATIONS: Merging was strongly associated with a decrease in inpatient charges and somewhat associated with an increase in outpatient charges for rural hospitals. Future work could build upon this work to determine whether acquirers reduce or eliminate certain services at rural hospitals after a merger, and ultimately how changes in service delivery could impact patients in those rural communities.

Capital Expenditures Increased at Rural Hospitals That Merged Between 2012 and 2015.

EXECUTIVE SUMMARY: The number of rural hospital mergers has increased substantially in recent years. A commonly reported reason for merging is to increase access to capital. However, no empirical evidence exists to show whether capital expenditures increased at rural hospitals after a merger. We used a difference-in-differences approach to determine whether total capital expenditures changed at rural hospitals after a merger. The comparison group (rural hospitals that did not merge during the 2012 through 2015 study period) was weighted using inverse probability of treatment weights. The key outcome measure was logged total capital expenditures.Merging resulted in a 26% increase in capital expenditures and also was associated with a significant improvement in plant age. The postmerger improvement in plant age may have been partially attributable to merger-related accounting changes and partially attributable to increased capital expenses, possibly on long-term asset renovations and replacement.These findings suggest that through mergers, rural hospital board members and executives who have accepted or are considering a merger may improve a hospital's ability to increase capital expenditures. Further, increased capital investments in rural hospitals may be an important signal to the community that the acquirer intends to keep the rural hospital open and continue providing some volume and level of services within the community. Future research should determine how capital is spent after a merger.

Rural Hospital Mergers Increased Between 2005 and 2016-What Did Those Hospitals Look Like?

The objective of this study is to determine whether key hospital-level financial and market characteristics are associated with whether rural hospitals merge. Hospital merger status was derived from proprietary Irving Levin Associates data for 2005 through 2016 and hospital-level characteristics from HCRIS, CMS Impact File Hospital Inpatient Prospective Payment System, Hospital MSA file, AHRF, and U.S. Census data for 2004 through 2016. A discrete-time hazard analysis using generalized estimating equations was used to determine whether factors were associated with merging between 2005 and 2016. Factors included measures of profitability, operational efficiency, capital structure, utilization, and market competitiveness. Between 2005 and 2016, 11% (n = 326) of rural hospitals were involved in at least one merger. Rural hospital mergers have increased in recent years, with more than two-thirds (n = 261) occurring after 2011. The types of rural hospitals that merged during the sample period differed from nonmerged rural hospitals. Rural hospitals with higher odds of merging were less profitable, for-profit, larger, and were less likely to be able to cover current debt. Additional factors associated with higher odds of merging were reporting older plant age, not providing obstetrics, being closer to the nearest large hospital, and not being in the West region. By quantifying the hazard of characteristics associated with whether rural hospitals merged between 2005 and 2016, these findings suggest it is possible to determine leading indicators of rural mergers. This work may serve as a foundation for future research to determine the impact of mergers on rural hospitals.

The Effect of the Magnet Recognition Signal on Hospital Financial Performance.

EXECUTIVE SUMMARY: The objective of this study was to investigate the effect of the Magnet Recognition (MR) signal on hospital financial performance. MR is a quality designation granted by the American Nurses Credentialing Center (ANCC). Growing evidence shows that MR hospitals are associated with various interrelated positive outcomes that have been theorized to affect hospital financial performance.In this study, which covered the period from 2000 to 2010, we applied a pre-post research design using a longitudinal, unbalanced panel of MR hospitals and hospitals that had never received MR designation located in urban areas in the United States. We obtained data for this analysis from Medicare's Hospital Cost Report Information System, the American Hospital Association Annual Survey Database, the Health Resources & Services Administration's Area Resource File, and the ANCC website. Propensity score matching was used to construct the final study sample. We then applied a difference-in-difference model with hospital fixed effects to the matched hospital sample to test the effect of the MR signal, while controlling for both hospital and market characteristics.According to signaling theory, signals aim to reduce the imbalance of information between two parties, such as patients and providers. The MR signal was found to have a significant positive effect on hospital financial performance. These findings support claims in the literature that the nonfinancial benefits resulting from MR lead to improved financial performance. In the current healthcare environment in which reimbursement is increasingly tied to delivery of quality care, healthcare executives may be encouraged to pursue MR to help hospitals maintain their financial viability while improving quality of care.

Telestroke Adoption Among Community Hospitals in North Carolina: A Cross-Sectional Study.

OBJECTIVE: This study identifies community and hospital characteristics associated with adoption of telestroke among acute care hospitals in North Carolina (NC). METHODS: Our sample included 107 hospitals located in NC. Our analytic dataset included variables from the American Hospital Association (AHA) annual survey, AHA Health IT supplement, Healthcare Cost Report Information System, and Centers for Disease Control and Prevention's WONDER online database. We supplemented our secondary sources with data on telestroke adoption and market-level variables developed for NC. We used the Consolidated Framework for Implementation Research and previous telehealth studies to guide selection of variables. We conducted a multivariate logistic regression to determine associations with telestroke adoption. RESULTS: Proportion of discharges that are Medicare (odds ratio [OR] = 1.93, P < .04) and total operating margin (OR = 2.89, P = .00) were positively associated with telestroke adoption. Critical access hospital status was positively associated with telestroke adoption, although not at P < .05 (OR = 5.61, P = .07). Distance to the nearest hospital with a telestroke program (OR = .91, P = .01) and volume of emergency department visits (OR = .98, P < .05) were both negatively associated with telestroke adoption. CONCLUSIONS: Our study is novel in its focus on telestroke adoption and use of variables not included in previous telehealth analyses. Our findings suggest some hospitals have neither the financial resources nor the ability to pool resources for acquiring needed technology, and differences in adoption may result in geographic inequities in access to telestroke services.

Contextual factors that influence quality improvement implementation in primary care: The role of organizations, teams, and individuals.

BACKGROUND: Recent emphasis on value-based health care has highlighted the importance of quality improvement (QI) in primary care settings. QI efforts, which require providers and staff to work in cross-functional teams, may be implemented with varying levels of success, with implementation being affected by factors at the organizational, teamwork, and individual levels. PURPOSE: The purpose of our study was to (a) identify contextual factors (organizational, teamwork, and individual) that affect implementation effectiveness of QI interventions in primary care settings and (b) compare perspectives about these factors across roles (health care administrators, physician and nonphysician clinicians, and administrative staff). METHODS/APPROACH: We conducted semistructured interviews with 24 health care administrators, physician and nonphysician primary care providers, and administrative staff representing 10 primary care practices affiliated with one integrated delivery system. RESULTS: Participants across all roles identified similar organizational- and team-level factors that influence QI implementation including organizational capacity to take on new initiatives (e.g., time availability of physicians), technical capability for QI (e.g., data analysis skills), and team climate (e.g., how well staff work together). There was greater variation in terms of individual-level factors, particularly perceived meaning and purpose of QI. Perceptions about value of QI ranged from positive impacts on patient care and practice competitiveness to decreased efficiency and distractions from patient care, but differences did not appear attributable to role. CONCLUSIONS: Successful QI implementation requires effective collaboration within cross-functional teams. Additional research is needed to assess how best to employ implementation strategies that promote cross-understanding of QI among team members and, ultimately, effective implementation of QI programs. PRACTICE IMPLICATIONS: Health care managers in primary care settings should strive to create a strong teamwork climate, reinforced by opportunities for staff in various roles to discuss QI as a collective.

High-Tech Versus High-Touch: Components of Hospital Costs Vary Widely.

EXECUTIVE SUMMARY: The recent release by the Centers for Medicare & Medicaid Services of hospital charge and payment data to the public has renewed a national dialogue on hospital costs and prices. However, to better understand the driving force of hospital pricing and to develop strategies for controlling expenditures, it is important to understand the underlying costs of providing hospital services. We use Medicare Provider and Analysis Review inpatient claims data and Medicare cost report data for fiscal years 2008 and 2012 to examine variations in the contribution of "high-tech" resources (i.e., technology/medical device-intensive resources) versus "high-touch" resources (i.e., labor-intensive resources) to the total costs of providing two common services, as well as assess how these costs have changed over time. We found that high-tech inputs accounted for a greater proportion of the total costs of surgical service, whereas medical service costs were primarily attributable to high-touch inputs. Although the total costs of services did not change significantly over time, the distribution of high-tech, high-touch, and other costs for each service varied considerably across hospitals. Understanding resource inputs and the varying contribution of these inputs by clinical condition is an important first step in developing effective cost control strategies.

Medicaid Expansion Affects Rural And Urban Hospitals Differently.

Rural hospitals differ from urban hospitals in many ways. For example, rural hospitals are more reliant on public payers and have lower operating margins. In addition, enrollment in the health insurance Marketplaces of the Affordable Care Act (ACA) has varied across rural and urban areas. This study employed a difference-in-differences approach to evaluate the average effect of Medicaid expansion in 2014 on payer mix and profitability for urban and rural hospitals, controlling for secular trends. For both types of hospitals, we found that Medicaid expansion was associated with increases in Medicaid-covered discharges. However, the increases in Medicaid revenue were greater among rural hospitals than urban hospitals, and the decrease in the proportion of costs for uncompensated care were greater among urban hospitals than rural hospitals. This preliminary analysis of the early effects of Medicaid expansion suggests that its financial impacts may be different for hospitals in urban and rural locations.

Measuring Mortality Performance: How Did Safety-Net Hospitals Compare With Other Hospitals?

BACKGROUND: Safety-net hospitals (SNHs) tend to be weaker in financial condition than other hospitals, leading to a concern about how the quality of care at these hospitals would compare to other hospitals. OBJECTIVES: To assess mortality performance of SNHs using all-payer databases and measures for a broad range of conditions and procedures. DESIGN: Longitudinal analysis of hospitals from 2006 through 2011 with data from the Healthcare Cost and Utilization Project State Inpatient Databases, the American Hospital Association Annual Survey, and the Area Health Resources File. SUBJECTS: A total of 1891 urban, nonfederal, general acute hospitals from 31 states. METHODS: SNHs were identified by the percentage of Medicaid and uninsured patients. Hospital mortality performance was measured by 2 composites covering 6 common medical conditions and 4 surgical procedures with risk adjustment for patient characteristics. Differences in each composite between SNHs and non-SNHs were estimated through generalized estimating equations to control for hospital factors and community resources. RESULTS: Inpatient mortality rates declined over time for all hospitals. Small differences in risk-adjusted mortality rates between SNHs and non-SNHs were found only among teaching hospitals. After controlling for hospital factors, these differences were substantially reduced and remained significant only for surgical mortality rates. The small gap in surgical mortality rates diminished in later years. CONCLUSIONS: SNHs appeared to perform equally well as other hospitals in medical and surgical mortality measures. Policymakers should continue to monitor the quality of care at SNHs and ensure that it would not decline under the current value-based purchasing program.

Quality improvement teams, super-users, and nurse champions: a recipe for meaningful use?

OBJECTIVE: This study assessed whether having an electronic health record (EHR) super-user, nurse champion for meaningful use (MU), and quality improvement (QI) team leading MU implementation is positively associated with MU Stage 1 demonstration. METHODS: Data on MU demonstration of 596 providers in 37 ambulatory care clinics came from the clinical data warehouse and administrative systems of UNC Health Care. We surveyed the 37 clinics about champions, super-users, and QI teams. We used generalized estimating equation methods with an independence working correlation matrix to account for clustering within clinics and to weight contributions from each clinic according to clinic size. RESULTS: Having a QI team lead MU implementation was significantly associated with MU demonstration (odds ratio, OR = 3.57, 95% CI, 1.83-6.96, P < .001, Table 2). Having neither a nurse champion nor an EHR super-user was significant. CONCLUSION: Our findings support the alignment of MU with QI efforts by having the QI team lead MU implementation.

The Cost to Successfully Apply for Level 3 Medical Home Recognition.

BACKGROUND: The National Committee for Quality Assurance patient-centered medical home recognition program provides practices an opportunity to implement medical home activities. Understanding the costs to apply for recognition may enable practices to plan their work. METHODS: Practice coaches identified 5 exemplar practices (3 pediatric and 2 family medicine practices) that received level 3 recognition. This analysis focuses on 4 that received recognition in 2011. Clinical, informatics, and administrative staff participated in 2- to 3-hour interviews. We determined the time required to develop, implement, and maintain required activities. We categorized costs as (1) nonpersonnel, (2) developmental, (3) those used to implement activities, (4) those used to maintain activities, (5) those to document the work, and (6) consultant costs. Only incremental costs were included and are presented as costs per full-time equivalent (pFTE) provider. RESULTS: Practice size ranged from 2.5 to 10.5 pFTE providers, and payer mixes ranged from 7% to 43% Medicaid. There was variation in the distribution of costs by activity by practice, but the costs to apply were remarkably similar ($11,453-15,977 pFTE provider). CONCLUSION: The costs to apply for 2011 recognition were noteworthy. Work to enhance care coordination and close loops were highly valued. Financial incentives were key motivators. Future efforts to minimize the burden of low-value activities could benefit practices.

The strategic case for establishing public-private partnerships in cancer care.

BACKGROUND: In 2007, the National Cancer Institute (NCI) launched the NCI Community Cancer Centers Program (NCCCP) as a public-private partnership with community hospitals with a goal of advancing cancer care and research. In order to leverage federal dollars in a time of limited resources, matching funds from each participating hospital were required. The purpose of this paper is to examine hospitals' level of and rationale for co-investment in this partnership, and whether there is an association between hospitals' co-investment and achievement of strategic goals. METHODS: Analysis using a comparative case study and micro-cost data was conducted as part of a comprehensive evaluation of the NCCCP pilot to determine the level of co-investment made in support of NCI's goals. In-person or telephone interviews with key informants were conducted at 10 participating hospital and system sites during the first and final years of implementation. Micro-cost data were collected annually from each site from 2007 to 2010. Self-reported data from each awardee are presented on patient volume and physician counts, while secondary data are used to examine the local Medicare market share. RESULTS: The rationale expressed by interviewees for participation in a public-private partnership with NCI included expectations of increased market share, higher patient volumes, and enhanced opportunities for cancer physician recruitment as a result of affiliation with the NCI. On average, hospitals invested resources into the NCCCP at a level exceeding $3 for every $1 of federal funds. Six sites experienced a statistically significant change in their Medicare market share. Cancer patient volume increased by as much as one-third from Year 1 to Year 3 for eight of the sites. Nine sites reported an increase in key cancer physician recruitment. CONCLUSIONS: Demonstrated investments in cancer care and research were associated with increases in cancer patient volume and perhaps in recruitment of key cancer physicians, but not in increased Medicare market share. Although the results reflect a small sample of hospitals, findings suggest that hospital executives believe there to be a strategic case for a public-private partnership as demonstrated through the NCCCP, which leveraged federal funds to support mutual goals for advancing cancer care and research.

Uncompensated Care Burden May Mean Financial Vulnerability For Rural Hospitals In States That Did Not Expand Medicaid.

The implementation of the Affordable Care Act has led to a large decrease in the number of uninsured people. Yet uncompensated care will still occur, particularly in states where eligibility for Medicaid is not expanded. We compared rural hospitals in Medicaid expansion and nonexpansion states in terms of the amount of uncompensated care they provided and their profitability and market characteristics in 2013. We found that rural hospitals in expansion states provided more dollars of uncompensated care than those in nonexpansion states and that the difference was at least partly driven by greater uncompensated costs associated with public programs such as Medicaid. We found higher dollar values of unrecoverable debt and charity care among non-critical access rural hospitals in nonexpansion states than among those in expansion states. Compared to hospitals in expansion states, those in nonexpansion states provided greater amounts of uncompensated care as a percentage of revenues and appeared to be more financially vulnerable; thus, these hospitals may be more likely to experience financial pressure or losses. Policy makers need to formulate strategies for maintaining access to care for rural populations residing in nonexpansion states.

The business case for pediatric asthma quality improvement in low-income populations: examining a provider-based pay-for-reporting intervention.

OBJECTIVE: To measure the return on investment (ROI) for a pediatric asthma pay-for-reporting intervention initiated by a Medicaid managed care plan in New York State. DESIGN: Practice-level, randomized prospective evaluation. SETTING: Twenty-five primary care practices providing care to children enrolled in the Monroe Plan for Medical Care (the Monroe Plan). PARTICIPANTS: Practices were randomized to either treatment (13 practices, 11 participated) or control (12 practices). INTERVENTION: For each of its eligible members assigned to a treatment group practice, the Monroe plan paid a low monthly incentive fee to the practice. To receive the incentive, treatment group practices were required to conduct, and report to the Monroe Plan, the results of chart audits on eligible members. Chart audits were conducted by practices every 6 months. After each chart audit, the Monroe Plan provided performance feedback to each practice comparing its adherence to asthma care guidelines with averages from all other treatment group practices. Control practices continued with usual care. MAIN OUTCOME MEASURES: Intervention implementation and operating costs and per member, per month claims costs. ROI was measured by net present value (discounted cash flow analysis). RESULTS: The ROI to the Monroe Plan was negative, primarily due to high intervention costs and lack of reductions in spending on emergency department and hospital utilization for children in treatment relative to control practices. CONCLUSIONS: A pay-for-reporting, chart audit intervention is unlikely to achieve the meaningful reductions in utilization of high-cost services that would be necessary to produce a financial ROI in 2.5 years.