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This article focuses on quality improvement in medical care in the U.S. Efforts, particularly in the last decade or so that have been undertaken to improve the quality and efficiency of medical care. Changing the behavior of physicians and other health care workers is at the crux of many of these efforts. Clinical pathways and computerized decision support have been advocated to improve adherence with practice guidelines, and Quality Improvement Organizations have been charged with assisting providers in quality improvement activities. Clinical pathways, structured care plans that note the essential elements of care by hospital day, have been widely used by hospitals in efforts to improve the quality and efficiency of care.

The article discusses various reports published within the issue, including one by John Olson, James Belohlav, Lori Cook and Julie Hays on common quality improvement programs among hospitals, one by Anita Tucker, Sara Singer, Jennifer Hayes and Alyson Falwell on hospital work systems, and one by Vivian Valdmanis, Michael Rosko and Ryan Mutter on inefficiency in hospitals.

Objective. This paper analyzes whether the rise in managed care during the 1990s caused the increase in hospital concentration. Data Sources. We assemble data from the American Hospital Association, InterStudy and government censuses from 1990 to 2000. Study Design. We employ linear regression analyses on long differenced data to estimate the impact of managed care penetration on hospital consolidation. Instrumental variable analogs of these regressions are also analyzed to control for potential endogeneity. Data Collection. All data are from secondary sources merged at the level of the Health Care Services Area. Principle Findings. In 1990, the mean population-weighted hospital Herfindahl–Hirschman index (HHI) in a Health Services Area was .19. By 2000, the HHI had risen to .26. Most of this increase in hospital concentration is due to hospital consolidation. Over the same time frame HMO penetration increased three fold. However, our regression analysis strongly implies that the rise of managed care did not cause the hospital consolidation wave. This finding is robust to a number of different specifications. [ABSTRACT FROM AUTHOR]

The article presents the authors' response to the comments made on their study "Anesthesia provider model, hospital resources, and maternal outcomes." According to the authors, they have addressed the general approach to dealing with the limitations to observational studies. They also said that the criticism regarding selection bias may have occurred due to high-risk patients referred from certified registered nurse-anesthetist (CRNA) model hospitals.

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]

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]

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 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]

Objective To analyze the impact of hospital mergers on treatment intensity and health outcomes. Data Hospital inpatient data from California for 1990 through 2006, encompassing 40 mergers. Study Design I used a geographic-based IV approach to determine the effect of a zip code's exposure to a merger. The merged facility's market share represents exposure, instrumented with combined premerge shares. Additional specifications include Herfindahl Index ( HHI), instrumented with predicted change in HHI. Results The primary specification results indicate that merger completion is associated with a 3.7 percent increase in the utilization of bypass surgery and angioplasty and a 1.7 percent increase in inpatient mortality above averages in 2000 for the average zip code. Isolating the competition mechanism mutes the treatment intensity result slightly, but it more than doubles the merger exposure effect on inpatient mortality to a 3.9 percent increase. The competition mechanism is associated with a sizeable increase in number of procedures. Conclusions Unlike previous studies, this analysis finds that hospital mergers are associated with increased treatment intensity and higher inpatient mortality rates among heart disease patients. Access to additional outcome measures such as 30-day mortality and readmission rates might shed additional light on whether the relationship between these outcomes is causal. [ABSTRACT FROM AUTHOR]

Objective. To test whether nonprofit, for-profit, or government hospital ownership affects medical service provision in rural hospital markets, either directly or through the spillover effects of ownership mix. Data Sources/Study Setting. Data are from the American Hospital Association, U.S. Census, CMS Healthcare Cost Report Information System and Prospective Payment System Minimum Data File, and primary data collection for geographic coordinates. The sample includes all nonfederal, general medical, and surgical hospitals located outside of metropolitan statistical areas and within the continental United States from 1988 to 2005. Study Design. We estimate multivariate regression models to examine the effects of (1) hospital ownership and (2) hospital ownership mix within rural hospital markets on profitable versus unprofitable medical service offerings. Principal Findings. Rural nonprofit hospitals are more likely than for-profit hospitals to offer unprofitable services, many of which are underprovided services. Nonprofits respond less than for-profits to changes in service profitability. Nonprofits with more for-profit competitors offer more profitable services and fewer unprofitable services than those with fewer for-profit competitors. Conclusions. Rural hospital ownership affects medical service provision at the hospital and market levels. Nonprofit hospital regulation should reflect both the direct and spillover effects of ownership. [ABSTRACT FROM AUTHOR]

Objective. To examine the relationship between organizational leadership for patient safety and five types of learning from patient safety events (PSEs). Study Setting. Forty-nine general acute care hospitals in Ontario, Canada. Study Design. A nonexperimental design using cross-sectional surveys of hospital patient safety officers (PSOs) and patient care managers (PCMs). PSOs provided data on organization-level learning from (a) minor events, (b) moderate events, (c) major near misses, (d) major event analysis, and (e) major event dissemination/communication. PCMs provided data on organizational leadership (formal and informal) for patient safety. Extraction Methods. Hospitals were the unit of analysis. Seemingly unrelated regression was used to examine the influence of formal and informal leadership for safety on the five types of learning from PSEs. The interaction between leadership and hospital size was also examined. Principal Findings. Formal organizational leadership for patient safety is an important predictor of learning from minor, moderate, and major near-miss events, and major event dissemination. This relationship is significantly stronger for small hospitals (<100 beds). Conclusions. We find support for the relationship between patient safety leadership and patient safety behaviors such as learning from safety events. Formal leadership support for safety is of particular importance in small organizations where the economic burden of safety programs is disproportionately large and formal leadership is closer to the front lines. [ABSTRACT FROM AUTHOR]

Objective. To provide a method for any hospital to evaluate patient mortality using a hierarchical risk-adjustment equation derived from a reference sample. Data Source. American College of Surgeons National Trauma Data Bank (NTDB). Study Design. Hierarchical logistic regression models predicting mortality were estimated from NTDB data. Risk-adjusted hospital effects obtained directly from models using standard software were compared with approximations derived from a summary equation and data from each individual hospital. Principal Findings. Theoretical approximations were similar to results using standard software. Conclusions. To allow independent verification, agencies using reference databases for hospital mortality “report cards” should publish their risk-adjustment equations. Similar hospitals not in the reference database may also use the published equations along with the approximations described to evaluate their own outcomes using their own data. [ABSTRACT FROM AUTHOR]

Objective. To evaluate the need for survey mode adjustments to hospital care evaluations by discharged inpatients and develop the appropriate adjustments. Data Source. A total of 7,555 respondents from a 2006 national random sample of 45 hospitals who completed the CAHPS® Hospital (HCAHPS [Hospital Consumer Assessments of Healthcare Providers and Systems]) Survey. Study Design/Data Collection/Extraction Methods. We estimated mode effects in linear models that predicted each HCAHPS outcome from hospital-fixed effects and patient-mix adjustors. Principal Findings. Patients randomized to the telephone and active interactive voice response (IVR) modes provided more positive evaluations than patients randomized to mail and mixed (mail with telephone follow-up) modes, with some effects equivalent to more than 30 percentile points in hospital rankings. Mode effects are consistent across hospitals and are generally larger than total patient-mix effects. Patient-mix adjustment accounts for any nonresponse bias that could have been addressed through weighting. Conclusions. Valid comparisons of hospital performance require that reported hospital scores be adjusted for survey mode and patient mix. [ABSTRACT FROM AUTHOR]

Objective. To develop and explore the characteristics of a novel “nearest neighbor” methodology for creating peer groups for health care facilities. Data Sources. Data were obtained from the Department of Veterans Affairs (VA) databases. Statistical Methods and Findings. Peer groups are developed by first calculating the multidimensional Euclidean distance between each of 133 VA medical centers based on 16 facility characteristics. Each medical center then serves as the center for its own peer group, and the nearest neighbor facilities in terms of Euclidean distance comprise the peer facilities. We explore the attributes and characteristics of the nearest neighbor peer groupings. In addition, we construct standard cluster analysis-derived peer groups and compare the characteristics of groupings from the two methodologies. Conclusions. The novel peer group methodology presented here results in groups where each medical center is at the center of its own peer group. Possible advantages over other peer group methodologies are that facilities are never on the “edge” of a group and group size—and thus group dispersion—is determined by the researcher. Peer groups with these characteristics may be more appealing to some researchers and administrators than standard cluster analysis and may thus strengthen organizational buy-in for financial and quality comparisons. [ABSTRACT FROM AUTHOR]

Context. The Centers for Medicare and Medicaid Services will introduce the reporting of patient surveys in 2008. The Consumer Assessment of Health Care Providers and Systems (CAHPS®) Hospital Survey contains 18 questions about hospital care. Internal consistency reliability of the discharge information scale is relatively low and some important domains of care are not represented. Objective. To determine whether adding questions increases the reliability and validity of the survey. Data Sources and Study Setting. Surveys of patients at 181 hospitals participating in the California Hospitals Assessment and Reporting Taskforce (CHART), an initiative for voluntary public reporting of hospital performance in California. Study Design. CHART added nine questions to the CAHPS Hospital Survey; two to improve reliability of the discharge information domain, five to create a coordination of care domain, and two relating to interpreter services. Data Collection. Surveys were sent to randomly selected patients from each CHART hospital. Principal Findings. A total of 40,172 surveys were included. Adding the new discharge information questions improved the internal consistency reliability from 0.45 to 0.72 and the hospital-level reliability from 0.75 to 0.81. New coordination of care composites had good internal consistency reliabilities ranging from 0.58 to 0.70 and hospital-level reliabilities ranging from 0.84 to 0.87. The new coordination of care composites were more closely correlated with overall hospital ratings and willingness to recommend than six of the seven original domains. Conclusions. The additional discharge information questions and the new coordination of care questions significantly improved the psychometric properties of the CAHPS Hospital Survey. [ABSTRACT FROM AUTHOR]

Objective. To evaluate the impact of rigorous WalkRounds on frontline caregiver assessments of safety climate, and to clarify the steps and implementation of rigorous WalkRounds. Data Sources/Study Setting. Primary outcome variables were baseline and post WalkRounds safety climate scores from the Safety Attitudes Questionnaire (SAQ). Secondary outcomes were safety issues elicited through WalkRounds. Study period was August 2002 to April 2005; seven hospitals in Massachusetts agreed to participate; and the project was implemented in all patient care areas. Study Design. Prospective study of the impact of rigorously applied WalkRounds on frontline caregivers assessments of safety climate in their patient care area. WalkRounds were conducted weekly and according to the seven-step WalkRounds Guide. The SAQ was administered at baseline and approximately 18 months post-WalkRounds implementation to all caregivers in patient care areas. Results. Two of seven hospitals complied with the rigorous WalkRounds approach; hospital A was an academic teaching center and hospital B a community teaching hospital. Of 21 patient care areas, SAQ surveys were received from 62 percent of respondents at baseline and 60 percent post WalkRounds. At baseline, 10 of 21 care areas (48 percent) had safety climate scores below 60 percent, whereas post-WalkRounds three care areas (14 percent) had safety climate scores below 60 percent without improving by 10 points or more. Safety climate scale scores in hospital A were 62 percent at baseline and 77 percent post-WalkRounds ( t=2.67, p=.03), and in hospital B were 46 percent at baseline and 56 percent post WalkRounds ( t=2.06, p=.06). Main safety issues by category were equipment/facility (A [26 percent] and B [33 percent]) and communication (A [24 percent] and B [18 percent]). Conclusions. WalkRounds implementation requires significant organizational will; sustainability requires outstanding project management and leadership engagement. In the patient care areas that rigorously implemented WalkRounds, frontline caregiver assessments of patient safety increased. SAQ results such as safety climate scores facilitate the triage of quality improvement efforts, and provide consensus assessments of frontline caregivers that identify themes for improvement. [ABSTRACT FROM AUTHOR]

Objective. To assess the impact of employing a variety of controls for hospital quality and patient burden of illness on the mean estimated inefficiency and relative ranking of hospitals generated by stochastic frontier analysis (SFA). Study Setting. This study included urban U.S. hospitals in 20 states operating in 2001. Data Design/Data Collection. We took hospital data for 1,290 hospitals from the American Hospital Association Annual Survey and the Medicare Cost Reports. We employed a variety of controls for hospital quality and patient burden of illness. Among the variables we used were a subset of the quality indicators generated from the application of the Patient Safety Indicator and Inpatient Quality Indicator modules of the Agency for Healthcare Research and Quality, Quality Indicator software to the Healthcare Cost and Utilization Project (HCUP), State Inpatient Databases. Measures of a component of patient burden of illness came from the application of the Comorbidity Software to HCUP data. Data Analysis. We used SFA to estimate hospital cost-inefficiency. We tested key assumptions of the SFA model with likelihood ratio tests. Principal Findings. The measures produced by the Comorbidity Software appear to account for variations in patient burden of illness that had previously been masquerading as inefficiency. Outcome measures of quality can provide useful insight into a hospital's operations but may have little impact on estimated inefficiency once controls for structural quality and patient burden of illness have been employed. Conclusions. Choices about controlling for quality and patient burden of illness can have a nontrivial impact on mean estimated hospital inefficiency and the relative ranking of hospitals generated by SFA. [ABSTRACT FROM AUTHOR]

Objective. To use an advance in data envelopment analysis (DEA) called congestion analysis to assess the trade-offs between quality and efficiency in U.S. hospitals. Study Setting. Urban U.S. hospitals in 34 states operating in 2004. Study Design and Data Collection. Input and output data from 1,377 urban hospitals were taken from the American Hospital Association Annual Survey and the Medicare Cost Reports. Nurse-sensitive measures of quality came from the application of the Patient Safety Indicator (PSI) module of the Agency for Healthcare Research and Quality (AHRQ) Quality Indicator software to State Inpatient Databases (SID) provided by the Healthcare Cost and Utilization Project (HCUP). Data Analysis. In the first step of the study, hospitals' relative output-based efficiency was determined in order to obtain a measure of congestion (i.e., the productivity loss due to the occurrence of patient safety events). The outputs were adjusted to account for this productivity loss, and a second DEA was performed to obtain input slack values. Differences in slack values between unadjusted and adjusted outputs were used to measure either relative inefficiency or a need for quality improvement. Principal Findings. Overall, the hospitals in our sample could increase the total amount of outputs produced by an average of 26 percent by eliminating inefficiency. About 3 percent of this inefficiency can be attributed to congestion. Analysis of subsamples showed that teaching hospitals experienced no congestion loss. We found that quality of care could be improved by increasing the number of labor inputs in low-quality hospitals, whereas high-quality hospitals tended to have slack on personnel. Conclusions. Results suggest that reallocation of resources could increase the relative quality among hospitals in our sample. Further, higher quality in some dimensions of care need not be achieved as a result of higher costs or through reduced access to health care. [ABSTRACT FROM AUTHOR]

Background. Access to high quality medical care is an important determinant of health outcomes, but the quality of care is difficult to determine. Objective. To apply the PRIDIT methodology to determine an aggregate relative measure of hospital quality using individual process measures. Design. Retrospective analysis of Medicare hospital data using the PRIDIT methodology. Subjects. Four-thousand-two-hundred-seventeen acute care and critical access hospitals that report data to CMS' Hospital Compare database. Measures. Twenty quality measures reported in four categories: heart attack care, heart failure care, pneumonia care, and surgical infection prevention and five structural measures of hospital type. Results. Relative hospital quality is tightly distributed, with outliers of both very high and very low quality. The best indicators of hospital quality are patients given assessment of left ventricular function for heart failure and patients given β-blocker at arrival and patients given β-blocker at discharge for heart attack. Additionally, teaching status is an important indicator of higher quality of care. Conclusions. PRIDIT allows us to rank hospitals with respect to quality of care using process measures and demographic attributes of the hospitals. This method is an alternative to the use of clinical outcome measures in measuring hospital quality. Hospital quality measures should take into account the differential value of different quality indicators, including hospital “demographic” variables. [ABSTRACT FROM AUTHOR]

Objective. To examine the effect of a weekend hospitalization on the timing and incidence of intensive cardiac procedures, and on subsequent expenditures, mortality and readmission rates for Medicare patients hospitalized with acute myocardial infarction (AMI). Data Sources. The primary data are longitudinal, administrative claims for 922,074 elderly, nonrural, fee-for-service Medicare beneficiaries hospitalized with AMI from 1989 to 1998. Annual patient-level cohorts provide information on ex ante health status, procedure use, expenditures, and health outcomes. Study Design. The patient is the primary unit of analysis. I use ordinary least squares regression to estimate the effect of weekend hospitalization on rates of cardiac catheterization, angioplasty, and bypass surgery (in various time periods subsequent to the initial hospitalization), 1-year expenditures and rates of adverse health outcomes in various periods following the AMI admission. Principal Findings. Weekend AMI patients are significantly less likely to receive immediate intensive cardiac procedures, and experience significantly higher rates of adverse health outcomes. Weekend admission leads to a 3.47 percentage point reduction in catheterization at 1 day, a 1.52 point reduction in angioplasty, and a 0.35 point reduction in by-pass surgery ( p<.001 in all cases). The primary effect is delayed treatment, as weekend–weekday procedure differentials narrow over time from the initial hospitalization. Weekend patients experience a 0.38 percentage point ( p<.001) increase in 1-year mortality and a 0.20 point ( p<.001) increase in 1-year readmission with congestive heart failure. Conclusions. Weekend hospitalization leads to delayed provision of intensive procedures and elevated 1-year mortality for elderly AMI patients. The existence of measurable differences in treatments raises questions regarding the efficacy of a single input regulation (e.g., mandated nurse staffing ratios) in enhancing the quality of weekend care. My results suggest that targeted financial incentives might be a more cost-effective policy response than broad regulation aimed at improving quality. [ABSTRACT FROM AUTHOR]

Objective. To examine how the financial pressures resulting from the Balanced Budget Act (BBA) of 1997 interacted with private sector pressures to affect indigent care provision. Data Sources/Study Setting. American Hospital Association Annual Survey, Area Resource File, InterStudy Health Maintenance Organization files, Current Population Survey, and Bureau of Primary Health Care data. Study Design. We distinguished core and voluntary safety net hospitals in our analysis. Core safety net hospitals provide a large share of uncompensated care in their markets and have large indigent care patient mix. Voluntary safety net hospitals provide substantial indigent care but less so than core hospitals. We examined the effect of financial pressure in the initial year of the 1997 BBA on uncompensated care for three hospital groups. Data for 1996–2000 were analyzed using approaches that control for hospital and market heterogeneity. Data Collection/Extraction Methods. All urban U.S. general acute care hospitals with complete data for at least 2 years between 1996 and 2000, which totaled 1,693 institutions. Principal Findings. Core safety net hospitals reduced their uncompensated care in response to Medicaid financial pressure. Voluntary safety net hospitals also responded in this way but only when faced with the combined forces of Medicaid and private sector payment pressures. Nonsafety net hospitals did not exhibit similar responses. Conclusions. Our results are consistent with theories of hospital behavior when institutions face reductions in payment. They raise concern given continuing state budget crises plus the focus of recent federal deficit reduction legislation intended to cut Medicaid expenditures. [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]

Objective. To examine the predictors of unit and item nonresponse, the magnitude of nonresponse bias, and the need for nonresponse weights in the Consumer Assessment of Health Care Providers and Systems (CAHPS®) Hospital Survey. Methods. A common set of 11 administrative variables (41 degrees of freedom) was used to predict unit nonresponse and the rate of item nonresponse in multivariate models. Descriptive statistics were used to examine the impact of nonresponse on CAHPS Hospital Survey ratings and reports. Results. Unit nonresponse was highest for younger patients and patients other than non-Hispanic whites ( p<.001); item nonresponse increased steadily with age ( p<.001). Fourteen of 20 reports of ratings of care had significant ( p<.05) but small negative correlations with nonresponse weights (median −0.06; maximum −0.09). Nonresponse weights do not improve overall precision below sample sizes of 300–1,000, and are unlikely to improve the precision of hospital comparisons. In some contexts, case-mix adjustment eliminates most observed nonresponse bias. Conclusions. Nonresponse weights should not be used for between-hospital comparisons of the CAHPS Hospital Survey, but may make small contributions to overall estimates or demographic comparisons, especially in the absence of case-mix adjustment. [ABSTRACT FROM AUTHOR]

Objective. To describe the developmental process for the CAHPS® Hospital Survey. Study Design. A pilot was conducted in three states with 19,720 hospital discharges. Methods of Analysis. A rigorous, multi-step process was used to develop the CAHPS Hospital Survey. It included a public call for measures, multiple Federal Register notices soliciting public input, a review of the relevant literature, meetings with hospitals, consumers and survey vendors, cognitive interviews with consumer, a large-scale pilot test in three states and consumer testing and numerous small-scale field tests. Findings. The current version of the CAHPS Hospital Survey has survey items in seven domains, two overall ratings of the hospital and five items used for adjusting for the mix of patients across hospitals and for analytical purposes. Conclusions. The CAHPS Hospital Survey is a core set of questions that can be administered as a stand-alone questionnaire or combined with a broader set of hospital specific items. [ABSTRACT FROM AUTHOR]

Objectives: To develop a model for case-mix adjustment of Consumer Assessment of Healthcare Providers and Systems (CAHPS®) Hospital survey responses, and to assess the impact of adjustment on comparisons of hospital quality. Data Sources: Survey of 19,720 patients discharged from 132 hospitals. Methods: We analyzed CAHPS Hospital survey data to assess the extent to which patient characteristics predict patient ratings (“predictive power”) and the heterogeneity of the characteristics across hospitals. We combined the measures to estimate the impact of each predictor (“impact factor”) and selected high impact variables for adjusting ratings from the CAHPS Hospital survey. Principle Findings: The most important case-mix variables are: hospital service (surgery, obstetric, medical), age, race (non-Hispanic black), education, general health status (GHS), speaking Spanish at home, having a circulatory disorder, and interactions of each of these variables with service. Adjustment for GHS and education affected scores in each of the three services, while age and being non-Hispanic black had important impacts for those receiving surgery or medical services. Circulatory disorder, Spanish language, and Hispanic affected scores for those treated on surgery, obstetrics, and medical services, respectively. Of the 20 medical conditions we tested, only circulatory problems had an important impact within any of the services. Results were consistent for the overall ratings of nurse, doctor, and hospital. Although the overall impact of case-mix adjustment is modest, the rankings of some hospitals may be substantially affected. Conclusions: Case-mix adjustment has a small impact on hospital ratings, but can lead to important reductions in the bias in comparisons between hospitals. [ABSTRACT FROM AUTHOR]

Objective. To estimate the effect of survey mode (mail versus telephone) on reports and ratings of hospital care. Data Sources/Study Setting. The total sample included 20,826 patients discharged from a group of 24 distinct hospitals in three states (Arizona, Maryland, New York). We collected CAHPS® data in 2003 by mail and telephone from 9,504 patients, of whom 39 percent responded by telephone and 61 percent by mail. Study Design. We estimated mode effects in an observational design, using both propensity score blocking and (ordered) logistic regression on covariates. We used variables derived from administrative data (either included as covariates in the regression function or used in estimating the propensity score) grouped in three categories: individual characteristics, characteristics of the stay and hospital, and survey administration variables. Data Collection/Extraction Methods. We mailed a 66-item questionnaire to everyone in the sample and followed up by telephone with those who did not respond. Principal Findings. We found significant ( p<.01) mode effects for 13 of the 21 questions examined in this study. The maximum magnitude of the survey mode effect was an 11 percentage-point difference in the probability of a “yes” response to one of the survey questions. Telephone respondents were more likely to rate care positively and health status negatively, compared with mail respondents. Standard regression-based case-mix adjustment captured much of the mode effects detected by propensity score techniques in this application. Conclusions. Telephone mode increases the propensity for more favorable evaluations of care for more than half of the items examined. This suggests that mode of administration should be standardized or carefully adjusted for. Alternatively, further item development may minimize the sensitivity of items to mode of data collection. [ABSTRACT FROM AUTHOR]

To assess the capacity of Thai public hospitals to proportionately expand services to both the poor and the nonpoor. This is accomplished by measuring the production of services provided to poor, relative to nonpoor, patients and the plant capacity of individual public hospitals to care for the patient load.Thai public hospitals operating in 1999, following the economic crisis when public hospitals were required to treat all patients irrespective of ability to pay.Input and output data for 68 hospitals were collected using databases and questionnaire surveys. A distinction was made between inpatient and outpatient services to both poor and nonpoor patients and the data were assessed statistically.Congestion and capacity indices to measure poor/nonpoor service trade-offs and capacity utilization were estimated. The analysis was undertaken by data envelopment analysis (DEA), a nonparametric linear programming approach used to derive efficiency and productivity estimates.Increases in the amount of services provided to poor patients did not reduce the amount of services to nonpoor patients. Overall, hospitals are producing services relatively close to their capacity given fixed inputs. Possible increases in capacity utilization amounted to 5 percent of capacity.Results suggest that some increased public hospital care can be accomplished by reallocation of resources to less highly utilized hospitals, given the budgetary constraints. However, further expansion and increase in access to health services will require plant investments. The study illustrates how DEA methodologies can be used in planning health services in data constrained settings. [ABSTRACT FROM AUTHOR]

To determine whether managed mental health care for Medicaid enrollees in King County, Washington, has led to indirect cost-shifting to substitute treatments, such as jails and state mental hospitals that are free goods to providers. Complete service records for 47,300 adults who used at least one of the following systems from 1993 to 1998: King County jail system, Medicaid, or the King County mental health system. Data were also obtained from the Washington State Hospital System. A quasi-experimental analysis that compares the difference in outcomes between the pre- and post-managed care periods for Medicaid enrollees compared to non-Medicaid enrollees. The outcomes—jail costs, state hospital costs, and county outpatient mental health costs—were estimated with two-part difference-in-differences models. The regressions control for person-level fixed effects on up to 66 months of data per person. Administrative data were collected from the jail, Medicaid, and mental health systems, then merged and cleaned. Additional data on costs were obtained in interviews. There is a striking increase in the probability of jail use for persons on Medicaid following the introduction of managed care. There was a significant decrease in expenditures in the county mental health system for outpatient care. Managed care led to indirect cost-shifting, probably through poor access to services, which may have led to an increased probability of jail detention. [ABSTRACT FROM AUTHOR]

The study related community social capital to the level of community accountability and provision of community-oriented services in U.S. community hospitals. The sample included 1,383 community hospitals that participated in the 1997 American Hospital Association's (AHA) Hospital Annual and Governance Surveys. (1) The 1997 AHA Annual Hospital Survey, (2) the 1997 AHA Hospital Governance Survey, (3) the DDB Needham Market Facts Survey, (4) the 1996 County Election Data File, and (5) the 1998 Area Resource File. The study used a mix of longitudinal and cross-sectional data. We identified two distinct indicators of social capital—community participation and voting participation. Community accountability in hospitals was unrelated to either indicator. Hospitals' provision of community-oriented health services was negatively associated with community participation but unrelated with voting participation. The interaction between voting participation and community representation on hospital governance was positively associated with community accountability and provision of community-oriented health services. Neither community participation nor voting participation was sufficient to influence hospital behavior. The positive finding associated with the interaction between voting participation and community representation on hospital governance underscored the importance of an active political culture in influencing hospital behavior, without which the installation of community representatives on hospital governance might be more symbolic than actually serving the health concerns of community residents. [ABSTRACT FROM AUTHOR]

To determine the relationship between hospital membership in systems and the treatments, expenditures, and outcomes of patients. The Medicare Provider Analysis and Review dataset, for data on Medicare patients admitted to general medical-surgical hospitals between 1985 and 1998 with a diagnosis of acute myocardial infarction (AMI); the American Hospital Association Annual Survey, for data on hospitals. A multivariate regression analysis. An observation is a fee-for-service Medicare AMI patient admitted to a study hospital. Dependent variables include patient transfers, catheterizations, angioplasties or bypass surgeries, 90-day mortality, and Medicare expenditures. Independent variables include system participation, other admission hospital and patient traits, and hospital and year fixed effects. The five-part system definition incorporates the size and location of the index admission hospital and the size and distance of its partners. While the effects of multihospital system membership on patients are in general limited, patients initially admitted to small rural system hospitals that have big partners within 100 miles experience lower mortality rates than patients initially admitted to independent hospitals. Regression results show that to the extent system hospital patients experience differences in treatments and outcomes relative to patients of independent hospitals, these differences remain even after controlling for the admission hospital's capacity to provide cardiac services. Multihospital system participation may affect AMI patient treatment and outcomes through factors other than cardiac service offerings. Additional investigation into the nature of these factors is warranted. [ABSTRACT FROM AUTHOR]

To determine the relationship between hospital–physician affiliations and the treatments, expenditures, and outcomes of patients. Sources include the Medicare Provider Analysis and Review dataset, the Annual Survey, and the Area Resource File (ARF). A multivariate regression analysis of the relationship between hospital–physician affiliations (such as physician–hospital organizations [PHOs] or salaried employment) and the treatment of Medicare patients with a diagnosis of acute myocardial infarction admitted to general medical-surgical hospitals between 1994 and 1998. Dependent variables include whether the patient received a catheterization or angioplasty or bypass surgery; whether a patient was readmitted, or died within 90 days of initial admission; and expenditures. Independent variables include patient, admission hospital, and market characteristics, as well as hospital and year fixed effects. The integrated salary model form of hospital–physician affiliation is associated with slightly higher procedure rates, and higher patient expenditures. At the same time, there is little evidence that hospital–physician affiliations in the aggregate have had any measurable impact on patient treatment or outcomes. The limited effect of hospital–physician affiliations on patient outcomes is consistent with previous research showing that affiliations have not much changed the nature of health care delivery. However, the finding that the integrated salary model is associated with higher treatment intensity suggests that affiliations may have had some impact on patients, and could have more in the future. [ABSTRACT FROM AUTHOR]

To examine the effects of health maintenance organization (HMO) penetration on preventable hospitalizations. Hospital inpatient discharge abstracts for 932 urban counties in 22 states from the Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID), hospital data from American Hospital Association (AHA) annual survey, and population characteristics and health care capacity data from Health Resources and Services Administration (HRSA) Area Resource File (ARF) for 1998. Preventable hospitalizations due to 14 ambulatory care sensitive conditions were identified using the Agency for Healthcare Research and Quality (AHRQ) Prevention Quality Indicators. Multiple regressions were used to determine the association between preventable hospitalizations and HMO penetration while controlling for demographic and socioeconomic characteristics and health care capacity of the counties. A 10 percent increase in HMO penetration was associated with a 3.8 percent decrease in preventable hospitalizations (95 percent confidence interval, 2.0 percent–5.6 percent). Advanced age, female gender, poor health, poverty, more hospital beds, and fewer primary care physicians per capita were significantly associated with more preventable hospitalizations. Our study suggests that HMO penetration has significant effects in reducing preventable hospitalizations due to some ambulatory care sensitive conditions. [ABSTRACT FROM AUTHOR]

To describe how hospitals' negotiating leverage with managed care plans changed from 1996 to 2001 and to identify factors that explain any changes. Primary semistructured interviews, and secondary qualitative (e.g., newspaper articles) and quantitative (i.e., InterStudy, American Hospital Association) data. The Community Tracking Study site visits to a nationally representative sample of 12 communities with more than 200,000 people. These 12 markets have been studied since 1996 using a variety of primary and secondary data sources. Semistructured interviews were conducted with a purposive sample of individuals from hospitals, health plans, and knowledgeable market observers. Secondary quantitative data on the 12 markets was also obtained. Our findings suggest that many hospitals' negotiating leverage significantly increased after years of decline. Today, many hospitals are viewed as having the greatest leverage in local markets. Changes in three areas—the policy and purchasing context, managed care plan market, and hospital market—appear to explain why hospitals' leverage increased, particularly over the last two years (2000­2001). Hospitals' increasednegotiatingleveragecontributedto higher payment rates, which in turn are likely to increase managed care plan premiums. This trend raises challenging issues for policymakers, purchasers, plans, and consumers. [ABSTRACT FROM AUTHOR]

The article focuses on the effects of competition on health care outcomes. Studying competition in health care in a meaningful way, is far easier made a goal than a reality. While there has been a substantial amount of research conducted on the possible effects of competition among hospitals in market areas on prices, research on effects of hospital competition on quality of care is considerably more limited. For many researchers, policy analysts, managed care and other industry representatives, and federal and state officials, several issues affect the feasibility of conducting informative studies of competition.

Objective. To examine changes in hospital use in British Columbia during a decade of capacity reductions. Data Sources/Study Setting. The data used are all separation records for British Columbia hospitals for the years 1969, 1978, 1985/86, 1993/94, and 1995/96. Separation records include acute care, rehabilitation, extended care, and surgical day care hospital encounters in British Columbia that were concluded during the years of interest. Study Design. Analyses were based on per capita use of services for five-year age groups of the population to ages 90+; the emphasis was on looking at changes in the use of specific types of hospital services over the 26 years of study, with a particular focus on the most recent decade. Data Collection/Extraction Methods. Data were extracted from hospital separations files owned by the British Columbia Ministry of Health and housed at the Centre for Health Services and Policy Research. All separation records for the years of interest were included in the study. Principal Findings. Acute care use continued to fall over the last decade. The rate of decline increased during the last time period of study and affected seniors to the same degree as younger patients. At the same time, use of extended care decreased, compared to steady increases in earlier years. The result was that by 1995/9() nearly 40 percent of inpatient days were used by people who died in hospital, compared to 9 percent in 1969, These people, however, still represent a small proportion of separations. Conclusions. The "bed blocker" problem common to many hospital systems appears to have been largely alleviated in British Columbia over the decade 1985-95. The concurrent decrease in extended care use, however, makes it difficult to say where and how these people are now being cared for. Care for the dying has become a bigger issue for hospitals, but whether this is because of heroic interventions at the end of life is not clear. A "top down," capacity-driven management approach to hospital use in British Columbia has produced effects that may seem familiar to those involved in more "bottom-up" managed care approaches in the United States. [ABSTRACT FROM AUTHOR]

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.