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.

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.

Associations Between Practice Characteristics and Demonstration of Stage 1 Meaningful Use for the Electronic Health Record Incentive Program.

BACKGROUND: The Medicare and Medicaid meaningful use (MU) incentive programs promote adoption and "meaningful use" of certified electronic health records among hospitals and eligible providers in outpatient settings, with a goal of improving the quality of patient care. Despite the potential importance of MU for providers and patients, little is currently known about the practice characteristics that facilitate providers' demonstration of MU. This study examined whether selected practice characteristics were associated with providers' meeting Stage 1 MU objectives at the end of 1 year in a single large North Carolina integrated delivery system. METHODS: Our retrospective database analysis included all 702 eligible providers from 54 ambulatory care practices on the main campus of the University of North Carolina Health Care System. We assessed associations between providers' ability to meet Stage 1 MU objectives as of December 2012 and the following practice characteristics: practice specialty, size, and mix of Medicare- and Medicaid-eligible providers. RESULTS: The following practice characteristics were associated with providers' ability to meet MU objectives: primary care practices as compared to specialty practices (odds ratio [OR] = 2.49; 95% CI, 1.11-5.62), small practices as compared to medium-sized practices (OR = 0.29; 95% CI, 0.09-0.89), and the presence of only Medicare-eligible providers in the practice as compared to the presence of only Medicaid-eligible providers (OR = 6.48; 95% Cl, 1.08-38.97). LIMITATIONS: Because our sample was drawn from a single integrated delivery system, results may not be generalizable to all ambulatory practice settings. CONCLUSIONS: This study suggests that larger practices, primary care practices, and practices comprised of Medicare-eligible providers may be better able to meet MU objectives. Further research is needed to evaluate strategies that account for practice characteristics and other contextual factors in the MU implementation process.

Rural Hospital Mergers and Acquisitions: Which Hospitals Are Being Acquired and How Are They Performing Afterward?.

The number of stand-alone rural hospitals has been shrinking as larger health systems target these hospitals for mergers and acquisitions (M and As). However, little research has focused specifically on rural hospital M and A transactions. Using data from Irving Levin Associates' Healthcare M and A Report and Medicare Cost Reports from 2005 to 2012, we examined two research questions: (1) What were the characteristics of rural hospitals that merged or were acquired, and (2) were there changes in rural hospital financial performance, staffing, or services after an M and A transaction? We used logistic regression to identify factors predictive of merger, and we used multiple regression to examine various hospital measures after an M or A. Study results showed that hospitals with weaker financial performance but lower staffing levels and staffing costs were more likely to merge or be acquired. Statistically weak evidence suggested that operating margins declined after the merger; stronger evidence suggested reductions in salary expense. There was no statistically significant evidence of changes to the number of full-time equivalent (FTE) employees, the service lines that were included in the study, capital expenditures, or the amount of debt financing among the hospitals that merged or were acquired. M and A may not result in a rapid influx of capital, a relief of debt burden, or an improvement in bottom-line profitability. However, M and A may be a viable option for maintaining the hospital and the access to care it provides.

Stage 1 of the meaningful use incentive program for electronic health records: a study of readiness for change in ambulatory practice settings in one integrated delivery system.

BACKGROUND: Meaningful Use (MU) provides financial incentives for electronic health record (EHR) implementation. EHR implementation holds promise for improving healthcare delivery, but also requires substantial changes for providers and staff. Establishing readiness for these changes may be important for realizing potential EHR benefits. Our study assesses whether provider/staff perceptions about the appropriateness of MU and their departments' ability to support MU-related changes are associated with their reported readiness for MU-related changes. METHODS: We surveyed providers and staff representing 47 ambulatory practices within an integrated delivery system. We assessed whether respondent's role and practice-setting type (primary versus specialty care) were associated with reported readiness for MU (i.e., willingness to change practice behavior and ability to document actions for MU) and hypothesized predictors of readiness (i.e., perceived appropriateness of MU and department support for MU). We then assessed associations between reported readiness and the hypothesized predictors of readiness. RESULTS: In total, 400 providers/staff responded (response rate approximately 25%). Individuals working in specialty settings were more likely to report that MU will divert attention from other patient-care priorities (12.6% vs. 4.4%, p = 0.019), as compared to those in primary-care settings. As compared to advanced-practice providers and nursing staff, physicians were less likely to have strong confidence in their department's ability to solve MU implementation problems (28.4% vs. 47.1% vs. 42.6%, p = 0.023) and to report strong willingness to change their work practices for MU (57.9% vs. 83.3% vs. 82.0%, p < 0.001). Finally, provider/staff perceptions about whether MU aligns with departmental goals (OR = 3.99, 95% confidence interval (CI) = 2.13 to 7.48); MU will divert attention from other patient-care priorities (OR = 2.26, 95% CI = 1.26 to 4.06); their department will support MU-related change efforts (OR = 3.99, 95% CI = 2.13 to 7.48); and their department will be able to solve MU implementation problems (OR = 2.26, 95% CI = 1.26 to 4.06) were associated with their willingness to change practice behavior for MU. CONCLUSIONS: Organizational leaders should gauge provider/staff perceptions about appropriateness and management support of MU-related change, as these perceptions might be related to subsequent implementation.

Facing the recession: how did safety-net hospitals fare financially compared with their peers?

OBJECTIVE: To examine the effect of the recession on the financial performance of safety-net versus non-safety-net hospitals. DATA SOURCES/STUDY SETTING: Agency for Healthcare Research and Quality Hospital Cost and Utilization Project State Inpatient Databases, Medicare Cost Reports, American Hospital Association Annual Survey, InterStudy, and Area Health Resource File. STUDY DESIGN: Retrospective, longitudinal panel of hospitals, 2007-2011. Safety-net hospitals were identified using percentage of patients who were Medicaid or uninsured. Generalized estimating equations were used to estimate average effects of the recession on hospital operating and total margins, revenues and expenses in each year, 2008-2011, comparing safety-net with non-safety-net hospitals. DATA COLLECTION/EXTRACTION METHODS: 1,453 urban, nonfederal, general acute hospitals in 32 states with complete data. PRINCIPAL FINDINGS: Safety-net hospitals, as identified in 2007, had lower operating and total margins. The gap in operating margin between safety-net and non-safety-net hospitals was sustained throughout the recession; however, total margin was more negatively affected for non-safety-net hospitals in 2008. Higher percentages of Medicaid and uninsured patients were associated with lower revenue in private hospitals in all years, and lower revenue and expenses in public hospitals in 2011. CONCLUSIONS: Safety-net hospitals may not be disproportionately vulnerable to macro-economic fluctuations, but their significantly lower margins leave less financial cushion to weather sustained financial pressure.

Costs and benefits of transforming primary care practices: a qualitative study of North Carolina's Improving Performance in Practice.

Primary care organizations must transform care delivery to realize the Institute for Healthcare Improvement's Triple Aim of better healthcare, better health, and lower healthcare costs. However, few studies have considered the financial implications for primary care practices engaged in transformation. In this qualitative, comparative case study, we examine the practice-level personnel and nonpersonnel costs and the benefits involved in transformational change among 12 primary care practices participating in North Carolina's Improving Performance in Practice (IPIP) program. We found average annual opportunity costs of $21,550 ($6,659 per full-time equivalent provider) for maintaining core IPIP activities (e.g., data management, form development and maintenance, meeting attendance). This average represents the cost of a 50% full-time equivalent registered nurse or licensed practical nurse. Practices were able to limit transformation costs by scheduling meetings during relatively slow patient care periods and by leveraging resources such as the assistance of IPIP practice coaches. Still, the costs of practice transformation were not trivial and would have been much higher in the absence of these efforts. Benefits of transformation included opportunities for enhanced revenue through reimbursement incentives and practice growth, improved efficiency and care quality, and maintenance of certification. Given the potentially high costs for some practices, policy makers may need to consider reimbursement and other strategies to help primary care practices manage the costs of practice redesign.

Assessing organizational capacity for achieving meaningful use of electronic health records.

BACKGROUND: Health care institutions are scrambling to manage the complex organizational change required for achieving meaningful use (MU) of electronic health records (EHR). Assessing baseline organizational capacity for the change can be a useful step toward effective planning and resource allocation. PURPOSE: The aim of this article is to describe an adaptable method and tool for assessing organizational capacity for achieving MU of EHR. Data on organizational capacity (people, processes, and technology resources) and barriers are presented from outpatient clinics within one integrated health care delivery system; thus, the focus is on MU requirements for eligible professionals, not eligible hospitals. METHODS: We conducted 109 interviews with representatives from 46 outpatient clinics. FINDINGS: Most clinics had core elements of the people domain of capacity in place. However, the process domain was problematic for many clinics, specifically, capturing problem lists as structured data and having standard processes for maintaining the problem list in the EHR. Also, nearly half of all clinics did not have methods for tracking compliance with their existing processes. Finally, most clinics maintained clinical information in multiple systems, not just the EHR. The most common perceived barriers to MU for eligible professionals included EHR functionality, changes to workflows, increased workload, and resistance to change. PRACTICE IMPLICATIONS: Organizational capacity assessments provide a broad institutional perspective and an in-depth clinic-level perspective useful for making resource decisions and tailoring strategies to support the MU change effort for eligible professionals.