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Objective: To describe the cost of integrating social needs activities into a health care program that works toward health equity by addressing socioeconomic barriers. Data Sources/Study Setting: Costs for a heart failure health care program based in a safety‐net hospital were reported by program staff for the program year May 2018–April 2019. Additional data sources included hospital records, invoices, and staff survey. Study Design: We conducted a retrospective, cross‐sectional, case study of a program that includes health education, outpatient care, financial counseling and free medication; transportation and home services for those most in need; and connections to other social services. Program costs were summarized overall and for mutually exclusive categories: health care program (fixed and variable) and social needs activities. Data Collection: Program cost data were collected using a activity‐based, micro‐costing approach. In addition, we conducted a survey that was completed by key staff to understand time allocation. Principal Findings: Program costs were approximately $1.33 million, and the annual per patient cost was $1455. Thirty percent of the program costs was for social needs activities: 18% for 30‐day supply of medications and addressing socioeconomic barriers to medication adherence, 18% for mobile health services (outpatient home visits), 53% for navigating services through a financial counselor and community health worker, and 12% for transportation to visits and addressing transportation barriers. Most of the program costs were for personnel: 92% of the health care program fixed, 95% of the health care program variable, and 78% of social needs activities. Discussion: Historically, social and health care services are funded by different systems and have not been integrated. We estimate the cost of implementing social needs activities into a health care program. This work can inform implementation for hospitals attempting to address social determinants of health and social needs in their patient population. [ABSTRACT FROM AUTHOR]

Objective: To develop outcome measures that are more sensitive than current measures for evaluating primary or transitional care after hospitalizations, emergency department (ED) visits, or observation stays.Data Sources: Medicare claims data from January 1, 2015, to October 31, 2017, for 1 261 707 Medicare fee-for-service beneficiaries served by (a) primary care practices participating in Track 1 of the Comprehensive Primary Care Plus (CPC+) initiative, and (b) their matched comparison practices.Study Design: Given the poor statistical power in many studies to detect effects on readmissions, we developed two novel claims-based measures of unplanned acute care (UAC) following an index acute care event. The first measure assesses the proportion of hospitalizations followed by an unplanned readmission, ED visit, or observation stay within 30 days of discharge; the second assesses the proportion of ED visits and observation stays followed by a hospitalization, ED visit, or observation stay within 30 days. We calculate minimum detectable effects (MDEs) for both measures and for a conventional measure of 30-day unplanned readmissions, using CPC+ data.Principal Findings: Repeat UAC events are common among Medicare beneficiaries served by the CPC+ practices. In 2017, 22% of discharges and 21% of ED visits and observation stays had a UAC event within 30 days. Readmissions were the most common UAC event following discharge, whereas ED visits were most common following index ED visits or observation stays. MDEs are 25%-40% lower for the new measures than for the standard 30-day readmissions measure, indicating better statistical power to detect impacts of primary or transitional care interventions.Conclusions: This study introduces two new claims-based measures to assess quality of care during a patient's vulnerable period following acute care. The new measures complement existing measures, covering a broader range of UAC events than the standard 30-day readmissions measure, and yielding greater statistical power. [ABSTRACT FROM AUTHOR]

Objective: We aim to assess whether system providers perform better than nonsystem providers under an alternative payment model that incentivizes high-quality, cost-efficient care. We posit that the payment environment and the incentives it provides can affect the relative performance of vertically integrated health systems. To examine this potential influence, we compare system and nonsystem hospitals participating in Medicare's Comprehensive Care for Joint Replacement (CJR) model.Data Sources: We used hospital cost and quality data from the Centers for Medicare & Medicaid Services linked to data from the Agency for Healthcare Research and Quality's Compendium of US Health Systems and hospital characteristics from secondary sources. The data include 706 hospitals in 67 metropolitan areas.Study Design: We estimated regressions that compared system and nonsystem hospitals' 2017 cost and quality performance providing lower joint replacements among hospitals required to participate in CJR.Principal Findings: Among CJR hospitals, system hospitals that provided comprehensive services in their local market had 5.8 percent ($1612) lower episode costs (P = .01) than nonsystem hospitals. System hospitals that did not provide such services had 3.5 percent ($967) lower episode costs (P = .14). Quality differences between system hospitals and nonsystem hospitals were mostly small and statistically insignificant.Conclusions: When operating under alternative payment model incentives, vertical integration may enable hospitals to lower costs with similar quality scores. [ABSTRACT FROM AUTHOR]

Objective: To develop an automated, reproducible method for delineating hospital service areas (HSAs).Data Sources/setting: Discharge data from all Swiss acute care hospitals for the years 2013 to 2016.Study Design: We derived HSAs and hospital referral regions for Switzerland using a newly developed flow-based, automated, objective, and reproducible method using all discharge data. We compared our method to the classical, partially subjective approach used to delineate the Swiss Health Care Atlas by delineating four sets of intervention-specific HSAs.Principal Findings: Based on 4 105 885 discharges, the fully automated method delineated 63 HSAs. Comparison with existing HSAs reveals good overlap and comparable measures of health utilization between the methods and shows that in the Swiss setting, our method outperforms a cluster-based approach to defining HSAs. While the classical method potentially takes an entire day to delineate the regions, our method took approximately 10 minutes.Conclusions: Hospital service areas are used to analyze differences in use of health care that may indicate underuse and overuse. Our new, fully automated, objective, and reproducible method provides a useful tool for hospital services researchers that will enable them to delineate and update patient-flow-based HSAs. [ABSTRACT FROM AUTHOR]

Objective: To quantify the impact of two approaches (directed and query-based) to health information exchange (HIE) on potentially avoidable use of health care services.Data Sources/study Setting: Data on ambulatory care providers' adoption of HIE were merged with Medicare fee-for-service claims from 2008 to 2014. Providers were from 13 counties in New York served by the Rochester Regional Health Information Organization (RHIO).Study Design: Linear regression models with provider and year fixed effects were used to estimate changes in the probability of utilization outcomes for Medicare beneficiaries attributed to providers adopting directed and/or query-based HIE compared with beneficiaries attributed to providers who had not adopted HIE.Data Collection: Providers' HIE adoption status was determined through Rochester RHIO registration records. RHIO and claims data were linked via National Provider Identifiers.Principal Findings: Query-based HIE adoption was associated with a 0.2 percentage point reduction in the probability of an ambulatory care sensitive hospitalization and a 1.1 percentage point decrease in the likelihood of an unplanned readmission. Directed HIE adoption was not associated with any outcome.Conclusions: The Centers for Medicare & Medicaid Services' (CMS) EHR certification criteria includes requirements for directed HIE, but not query-based HIE. Pending further research, certification criteria should place equal weight on facilitating query-based and directed exchange. [ABSTRACT FROM AUTHOR]

Objective: To characterize the nature and degree of hospitals' efforts to collaborate with skilled nursing facilities (SNFs) and associated patient outcomes.Data Sources/study Setting: Qualitative data were collected through 138 interviews with staff in 16 hospitals and 25 SNFs in eight markets across the United States in 2015. Quantitative data include Medicare claims data for the 290,603 patients discharged from those 16 hospitals between 2008 and 2015.Study Design/data Collection: Semi-structured interviews with hospital and SNF staff were coded and used to classify hospitals' collaboration efforts with SNFs into high versus low collaboration hospitals, and risk-adjusted, claims-based hospital readmission rates from SNF were compared.Principal Findings: Hospital collaboration efforts were defined as establishing SNF partners, transition management initiatives, and hospital staff visits to SNFs. High collaboration hospitals were more likely to send patients to SNFs (as opposed to home, home with home health, or other PAC settings), sent a higher share of patients to high quality SNFs, and had fewer hospital readmissions from SNF sooner than did low collaboration hospitals.Conclusions: Although collaboration with SNF requires significant administrative and clinical time investment, it is associated with positive patient outcomes. [ABSTRACT FROM AUTHOR]

Objectives: To explore effects of disease prevalence adjustment on ambulatory care-sensitive hospitalization (ACSH) rates used for quality comparisons.Data Sources/study Setting: County-level hospital administrative data on adults discharged from German hospitals in 2011 and prevalence estimates based on administrative ambulatory diagnosis data were used.Study Design: A retrospective cross-sectional study using in- and outpatient secondary data was performed.Data Collection: Hospitalization data for hypertension, diabetes, heart failure, chronic obstructive pulmonary disease, and asthma were obtained from the German Diagnosis Related Groups (DRG) database. Prevalence estimates were obtained from the German Central Research Institute of Ambulatory Health Care.Principal Findings: Crude hospitalization rates varied substantially across counties (coefficients of variation [CV] 28-37 percent across conditions); this variation was reduced by prevalence adjustment (CV 21-28 percent). Prevalence explained 40-50 percent of the observed variation (r = 0.65-0.70) in ACSH rates for all conditions except asthma (r = 0.07). Between 30 percent and 38 percent of areas moved into or outside condition-specific control limits with prevalence adjustment.Conclusions: Unadjusted ACSH rates should be used with caution for high-stakes public reporting as differences in prevalence may have a marked impact. Prevalence adjustment should be considered in models analyzing ACSH. [ABSTRACT FROM AUTHOR]

Objective: Little attention has been given to psychological factors as correlates of health care use, which could be an important key to manage it. We analyzed the association of psychological factors with health care use.Data Sources: Primary data were obtained from three follow-ups (2002, 2008, and 2011) of a large population-based study with participants aged 40+.Study Design: Using a longitudinal observational study, we analyzed the psychological factors of negative and positive affect (affective well-being), life satisfaction (cognitive well-being), self-efficacy, loneliness, self-esteem, optimism, and flexible goal adjustment using fixed-effects regressions.Data Collection: The participants provided data on health care use (visits to general practitioners [GPs] and specialists as well as hospitalization) and psychological factors via self-administered questionnaires and personal interviews (7,116 observations). The sample was drawn using national probability sampling.Principal Findings: Controlling for self-rated health, chronic diseases and sociodemographics, increases in affective well-being, and optimism decreased health care use of GPs, specialists, and hospital treatment. Increases in cognitive well-being decreased health care use of GPs and specialists. Increases in self-efficacy decreased hospitalization.Conclusions: The study underlines the influence of psychological factors on health care use. Thus, whenever possible, future studies of health care use should include psychological factors, and efforts to reduce health care use might focus on such factors. [ABSTRACT FROM AUTHOR]

Objective: To assess the change in efficiency and total factor productivity (TFP) of the local public hospitals in Japan after the local public hospital reform launched in late 2007, which was aimed at improving the financial capability and operational efficiency of hospitals.Data Sources and Data Collection: Secondary data were collected from the Ministry of Internal Affairs and Communications on 213 eligible medium-sized hospitals, each operating 100-400 beds from FY2006 to FY2011.Study Design: The improved slacks-based measure nonoriented data envelopment analysis models (Quasi-Max SBM nonoriented DEA models) were used to estimate dynamic efficiency score and Malmquist Index.Principal Findings: The dynamic efficiency measure indicated an efficiency gain in the first several years of the reform and then was followed by a decrease. Malmquist Index analysis showed a significant decline in the TFP between 2006 and 2011. The financial improvement of medium-sized hospitals was not associated with enhancement of efficiency. Hospital efficiency was not significantly different among ownership structure and law-application system groups, but it was significantly affected by hospital location.Conclusions: The results indicate a need for region-tailored health care policies and for a more comprehensive reform to overcome the systemic constraints that might contribute to the decline of the TFP. [ABSTRACT FROM AUTHOR]

Objective: To examine the causal effect of a hospital's experience with treating hip fractures (volume) on patient treatment outcomes.Data Sources: We use a full sample of administrative data from German hospitals for 2007. The data provide detailed information on patients and hospitals. We also reference the hospitals' addresses and the zip codes of patients' place of residence.Study Design: We apply an instrumental variable approach to address endogeneity concerns due to reverse causality and unobserved patient heterogeneity. As instruments for case volume, we use the number of potential patients and number of other hospitals in the region surrounding each hospital.Principal Findings: Our results indicate that after applying an instrumental variables (IV) regression of volume on outcome, volume significantly increases quality.Conclusions: We provide evidence for the practice-makes-perfect hypothesis by showing that volume is a driving factor for quality. [ABSTRACT FROM AUTHOR]

Objective: To corroborate anecdotal evidence with systematic evidence of a lower threshold for admission among for-profit hospitals.Data Sources: The study used Florida emergency department and hospital discharge datasets for 2012 to 2014. The treatment variable of interest was for-profit-designated trauma center status. The dependent variable indicated whether a patient with mild-to-moderate injuries was admitted after presenting as a trauma alert and then discharged to home. A separate analysis was conducted of discharges that had a 1-day length of stay.Study Design: Generalized estimation equations with logistic distribution models were used to control for the confounding influences and developed for four groups of patients: ICISS = 1 (no probability of mortality), ICISS ≥ 0.99, ICISS ≥ 0.95, and ICISS ≥ 0.85 (zero to 15 percent probability of mortality, which includes all mild and moderate injury patients).Principal Findings: For the ICISS = 1 and ICISS ≥ 0.99 models, the centers' for-profit status was the most important predictor. In the ICISS ≥ 0.95 and ICISS ≥ 0.85 models, injury type played a more important role, but for-profit status remained important. For patients with a 1-day stay, for-profit status was associated with an even higher probability of hospitalization.Conclusions: Considerable differences exist between for-profit and not-for-profit trauma centers concerning hospitalization among the study population, which may be explained by supplier-induced demand. [ABSTRACT FROM AUTHOR]

Objective: To develop an automated, data-driven, and scale-flexible method to delineate hospital service areas (HSAs) and hospital referral regions (HRRs) that are up-to-date, representative of all patients, and have the optimal localization of hospital visits.Data Sources: The 2011 state inpatient database in Florida from the Healthcare Cost and Utilization Project.Study Design: A network optimization method was used to redefine HSAs and HRRs by maximizing patient-to-hospital flows within each HSA/HRR while minimizing flows between them. We first constructed as many HSAs/HRRs as existing Dartmouth units in Florida, and then compared the two by various metrics. Next, we sought to derive the optimal numbers and configurations of HSAs/HRRs that best reflect the modularity of hospitalization patterns in Florida.Principal Findings: The HSAs/HRRs by our method are favored over the Dartmouth units in balance of region size and market structure, shape, and most important, local hospitalization.Conclusions: The new method is automated, scale-flexible, and effective in capturing the natural structure of the health care system. It has great potential for applications in delineating other health care service areas or in larger geographic regions. [ABSTRACT FROM AUTHOR]

Objective: To assess whether provider organizations exhibit distinct profiles of low-value service provision.Data Sources: 2007-2011 Medicare fee-for-service claims and enrollment data.Study Design: Use of 31 services that provide minimal clinical benefit was measured for 4,039,733 beneficiaries served by 3,137 provider organizations. Variation across organizations, persistence within organizations over time, and correlations in use of different types of low-value services within organizations were estimated via multilevel modeling, with adjustment for beneficiary sociodemographic and clinical characteristics.Principal Findings: Organizations provided 45.6 low-value services per 100 beneficiaries on average, with considerable variation across organizations (90th/10th percentile ratio, 1.78; 95 percent CI, 1.72-1.84), including substantial between-organization variation within hospital referral regions (90th/10th percentile ratio, 1.66; 95 percent CI, 1.60-1.71). Low-value service use within organizations was highly correlated over time (r, 0.98; 95 percent CI, 0.97-0.99) and positively correlated between 13 of 15 pairs of service categories (average r, 0.26; 95 percent CI, 0.24-0.28), with the greatest correlation between low-value imaging and low-value cardiovascular testing and procedures (r, 0.54).Conclusions: Use of low-value services in provider organizations exhibited substantial variation, high persistence, and modest consistency across service types. These findings are consistent with organizations shaping the practice patterns of affiliated physicians. [ABSTRACT FROM AUTHOR]

Objective: To improve the predictions provided by Medicare's Hospital Compare (HC) to facilitate better informed decisions regarding hospital choice by the public.Data Sources/setting: Medicare claims on all patients admitted for Acute Myocardial Infarction between 2009 through 2011.Study Design: Cohort analysis using a Bayesian approach, comparing the present assumptions of HC (using a constant mean and constant variance for all hospital random effects), versus an expanded model that allows for the inclusion of hospital characteristics to permit the data to determine whether they vary with attributes of hospitals, such as volume, capabilities, and staffing. Hospital predictions are then created using directly standardized estimates to facilitate comparisons between hospitals.Data Collection/extraction Methods: Medicare fee-for-service claims.Principal Findings: Our model that included hospital characteristics produces very different predictions from the current HC model, with higher predicted mortality rates at hospitals with lower volume and worse characteristics. Using Chicago as an example, the expanded model would advise patients against seeking treatment at the smallest hospitals with worse technology and staffing.Conclusion: To aid patients when selecting between hospitals, the Centers for Medicare and Medicaid Services (CMS) should improve the HC model by permitting its predictions to vary systematically with hospital attributes such as volume, capabilities, and staffing. [ABSTRACT FROM AUTHOR]

Objective: To examine the relationship between Magnet recognition, an indicator of nursing excellence, and patients' experience with their hospitalization reported in the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey.Data Sources: This secondary analysis includes cross-sectional data from the 2010 HCAHPS survey, the American Hospital Association, and the American Nurses Credentialing Center.Study Design: We conducted a retrospective observational study.Data Collection: Using common hospital identifiers, we created a matched set of 212 Magnet hospitals and 212 non-Magnet hospitals.Principal Findings: Patients in Magnet hospitals gave their hospitals higher overall ratings, were more likely to recommend their hospital, and reported more positive care experiences with nurse communication.Conclusions: Magnet recognition is associated with better patient care experiences, which may positively enhance reimbursement for hospitals. [ABSTRACT FROM AUTHOR]

Objectives: To estimate the effects of medical home support on the use of clinical services and Medicaid expenditures.Data Source: Medicaid claims.Study Design: A difference-in-differences model where changes in utilization and expenditures of the intervention group are compared to changes in the nonintervention group.Extraction Methods: Using Medicaid claims from October 2010 through September 2013, service use and expenditures are measured for 12 months before and 21 months after implementation. Changes for four health status groups are examined separately.Principal Findings: The introduction of community-based support was associated with a small reduction in use and no statistically significant overall effect on expenditures. However, among those with chronic and/or mental health conditions, there were modest, statistically significant increases in use of and expenditures for a range of ambulatory and inpatient health care services, while service use for those without these conditions declined. Emergency department use increased for all groups.Conclusions: Community-based support for medical home practices is associated with a shift in the service mix provided to higher cost, more vulnerable subgroups in Medicaid. Such systems are unlikely to be associated with significant overall cost savings, at least in the short term, but may have other benefits. [ABSTRACT FROM AUTHOR]

As health care continues to digitize information, concerns of data privacy and cybersecurity also grow. It is very likely many of the health care organizations in the control group implemented increased cybersecurity measures during the study period as part of a natural cybersecurity maturation process to reduce risks of future breaches or disruptive cyberattacks. We fully agree with the authors that cyberattacks on critical hospital infrastructure are likely to pose a bigger threat to patient safety than security controls implemented after a breach of protected health information. [Extracted from the article]

The author assesses inpatient service continuity based on referral patterns between hospitals and skilled nursing facilities. The author acknowledges that the assessment process may suffer from selection bias, but asserts that unmeasured factors have been discussed in the limitations section of the report. He also states that he agrees with comments that associations are not enough to inform health care delivery and policy and mixed-method studies are needed to better understand continuity.