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Objective: To improve the performance of International Classification of Disease (ICD) code rule-based algorithms for identifying low acuity Emergency Department (ED) visits by using machine learning methods and additional covariates., Data Sources: We used secondary data on ED visits from the National Hospital Ambulatory Medical Survey (NHAMCS), from 2016 to 2020., Study Design: We established baseline performance metrics with seven published algorithms consisting of International Classification of Disease, Tenth Revision codes used to identify low acuity ED visits. We then trained logistic regression, random forest, and gradient boosting (XGBoost) models to predict low acuity ED visits. Each model was trained on five different covariate sets of demographic and clinical data. Model performance was compared using a separate validation dataset. The primary performance metric was the probability that a visit identified by an algorithm as low acuity did not experience significant testing, treatment, or disposition (positive predictive value, PPV). Subgroup analyses assessed model performance across age, sex, and race/ethnicity., Data Collection: We used 2016-2019 NHAMCS data as the training set and 2020 NHAMCS data for validation., Principal Findings: The training and validation data consisted of 53,074 and 9542 observations, respectively. Among seven rule-based algorithms, the highest-performing had a PPV of 0.35 (95% CI [0.33, 0.36]). All model-based algorithms outperformed existing algorithms, with the least effective-random forest using only age and sex-improving PPV by 26% (up to 0.44; 95% CI [0.40, 0.48]). Logistic regression and XGBoost trained on all variables improved PPV by 83% (to 0.64; 95% CI [0.62, 0.66]). Multivariable models also demonstrated higher PPV across all three demographic subgroups., Conclusions: Machine learning models substantially outperform existing algorithms based on ICD codes in predicting low acuity ED visits. Variations in model performance across demographic groups highlight the need for further research to ensure their applicability and fairness across diverse populations., (© 2024 The Authors. Health Services Research published by Wiley Periodicals LLC on behalf of Health Research and Educational Trust.)

Objective: To assess whether an online interactive report designed to facilitate interpretation of patients' narrative feedback produces change in ambulatory staff learning, behavior at the individual staff and practice level, and patient experience survey scores., Data Sources and Setting: We studied 22 ambulatory practice sites within an academic medical center using three primary data sources: 333 staff surveys; 20 in-depth interviews with practice leaders and staff; and 9551 modified CG-CAHPS patient experience surveys augmented by open-ended narrative elicitation questions., Study Design: We conducted a cluster quasi-experimental study, comparing 12 intervention and 10 control sites. At control sites, narratives were delivered free-form to site administrators via email; at intervention sites, narratives were delivered online with interactive tools for interpretation, accompanied by user training. We assessed control-versus-intervention site differences in learning, behavior, and patient experience scores., Data Collection: Staff surveys and interviews were completed at intervention and control sites, 9 months after intervention launch. Patient surveys were collected beginning 4 months pre-launch through 9 months post-launch. We used control-versus-intervention and difference-in-difference analyses for survey data and thematic analysis for interview data., Principal Findings: Interviews suggested that the interface facilitated narrative interpretation and use for improvement. Staff survey analyses indicated enhanced learning from narratives at intervention sites (29% over control sites' mean of 3.19 out of 5 across eight domains, p < 0.001) and greater behavior change at staff and practice levels (31% and 21% over control sites' means of 3.35 and 3.39, p < 0.001, respectively). Patient experience scores for interactions with office staff and wait time information increased significantly at intervention sites, compared to control sites (3.7% and 8.2%, respectively); however, provider listening scores declined 3.3%., Conclusions: Patient narratives presented through structured feedback reporting methods can catalyze positive changes in staff learning, promote behavior change, and increase patient experience scores in domains of non-clinical interaction., (© 2023 Health Research and Educational Trust.)

Objective: To summarize the predictors and outcomes of empathy by health care personnel, methods used to study their empathy, and the effectiveness of interventions targeting their empathy, in order to advance understanding of the role of empathy in health care and facilitate additional research aimed at increasing positive patient care experiences and outcomes., Data Source: We searched MEDLINE, MEDLINE In-Process, PsycInfo, and Business Source Complete to identify empirical studies of empathy involving health care personnel in English-language publications up until April 20, 2021, covering the first five decades of research on empathy in health care (1971-2021)., Study Design: We performed a systematic review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines., Data Collection/extraction Methods: Title and abstract screening for study eligibility was followed by full-text screening of relevant citations to extract study information (e.g., study design, sample size, empathy measure used, empathy assessor, intervention type if applicable, other variables evaluated, results, and significance). We classified study predictors and outcomes into categories, calculated descriptive statistics, and produced tables to summarize findings., Principal Findings: Of the 2270 articles screened, 455 reporting on 470 analyses satisfied the inclusion criteria. We found that most studies have been survey-based, cross-sectional examinations; greater empathy is associated with better clinical outcomes and patient care experiences; and empathy predictors are many and fall into five categories (provider demographics, provider characteristics, provider behavior during interactions, target characteristics, and organizational context). Of the 128 intervention studies, 103 (80%) found a positive and significant effect. With four exceptions, interventions were educational programs focused on individual clinicians or trainees. No organizational-level interventions (e.g., empathy-specific processes or roles) were identified., Conclusions: Empirical research provides evidence of the importance of empathy to health care outcomes and identifies multiple changeable predictors of empathy. Training can improve individuals' empathy; organizational-level interventions for systematic improvement are lacking., (© 2022 Health Research and Educational Trust.)

Objective: To construct a new measure of end-of-life (EoL) spending-the elevated EoL spending-and examine its associations with measures of quality of care and patient and physician preferences in comparison with the commonly used total Medicare EoL spending measures., Data Sources and Study Setting: Medicare claims data for a 20% random sample of Medicare fee-for-service (FFS) patients, from the health care quality data for 2015-2016, from the Hospital Compare and the Medicare Geographic Variation public use file, and survey data about patient and physician preferences., Study Design: We constructed the elevated EoL spending measure as the differential monthly spending between decedents and survivors with the same one-year mortality risk, where the risk was predicted using machine learning models. We then examined the associations of the hospital referral region (HRR)-level elevated EoL spending with various health care quality measures and with the survey-elicited patient and provider preferences. We also examined analogous associations for monthly total EoL spending on decedents., Data Extraction Methods: Medicare FFS patients who were continuously enrolled in Medicare Parts A & B in 2015 and were alive as of January 1, 2016., Principal Findings: We found a large variation in the elevated EoL spending across HRRs in the United States. There was no evidence of an association between HRR-level elevated EoL spending and established health care quality measures, including those specific to EoL care, whereas total EoL spending was positively associated with certain quality of care measures. We also found no evidence that elevated EoL spending was associated with patient preferences for EoL care. However, elevated EoL spending was positively and significantly associated with physician preferences for treatment intensity., Conclusions: Our findings suggested that elevated EoL spending captures different resource use from conventional measures of EoL spending and may be more valuable in identifying potentially wasteful spending., (© 2022 Health Research and Educational Trust.)

Objective: To develop and test a measure of patient-centered care (PCC) culture in hospital-based perinatal care., Data Sources: Data were obtained from US perinatal hospitals: one provided survey development data and 14 contributed data for survey testing., Study Design: We used qualitative and quantitative methods to develop the mother-infant centered care (MICC) culture survey. Qualitative methods included observation, focus group, interviews, and expert consultations to adapt items from other settings and create new items capturing dimensions of PCC articulated by The Commonwealth Fund. We quantitatively assessed survey psychometric properties using reliability (Cronbach's α and Pearson correlation coefficients) and validity (exploratory and confirmatory factor analysis [CFA]) statistics, and refined the survey. After confirming aggregation suitability (ICCs), we calculated "MICC culture scores" at the individual, unit, and hospital level and assessed associations between scores and survey-collected, staff-reported outcomes to evaluate concurrent validity., Data Collection: Survey development included 12 site-visit observations, one semi-structured focus group (five participants), two semi-structured interviews, five cognitive interviews, and three expert consultations. Survey testing used online surveys administered to obstetric and neonatal unit staff (N = 316)., Principal Findings: Using responses from 10 hospitals with ≥4 responses from both units (n = 240), the 20-item MICC culture survey demonstrated reliability (Cronbach's α = 0.95) while capturing all PCC dimensions (subscale Cronbach's α = 0.72-0.87). CFA showed validity through goodness-of-fit (overall chi-square = 214 [p-value = 0.012], SRMR = 0.056, RMSEA = 0.041, CFI = 0.97, and TLI = 0.96). Aggregation statistics (ICCs < 0.05) justify unit- and hospital-level aggregation. Demonstrating preliminary validity, individual-, unit-, and hospital-level MICC culture scores were associated with all outcomes (satisfaction with care provided, within-unit team effectiveness, and relational coordination [RC] between units) (p-values < 0.05), except for neonatal unit scores and RC (p-value = 0.11)., Conclusions: The MICC culture survey is a psychometrically sound measure of PCC culture for hospital-based perinatal care. Survey scores are associated with staff-reported outcomes. Future studies with patient outcomes will aid identification of improvement opportunities in perinatal care., (© 2022 Health Research and Educational Trust.)

Objective: To characterize and validate the landscape of algorithms that use International Classification of Disease (ICD) codes to identify low-acuity emergency department (ED) visits., Data Sources: Publicly available ED data from the National Hospital Ambulatory Medical Care Survey (NHAMCS)., Study Design: We systematically searched for studies that specify algorithms consisting of ICD codes that identify preventable or low-acuity ED visits. We classified ED visits in NHAMCS according to these algorithms and compared agreements using the Jaccard index. We then evaluated the performance of each algorithm using positive predictive value (PPV) and sensitivity, with the reference group specified using low-acuity composite (LAC) criteria consisting of both triage and clinical components. In sensitivity analyses, we repeated our primary analysis using only triage or only clinical criteria for reference., Data Collection: We used the 2011-2017 NHAMCS data, totaling 163,576 observations before survey weighting and after dropping observations missing a primary diagnosis. We translated ICD-9 codes (years 2011-2015) to ICD-10 using a standard crosswalk., Principal Findings: We identified 15 papers with an original list of ICD codes used to identify preventable or low-acuity ED presentations. These papers were published between 1992 and 2020, cited an average of 310 (SD 360) times, and included 968 (SD 1175) codes. Pairwise Jaccard similarity indices (0 = no overlap, 1 = perfect congruence) ranged from 0.01 to 0.82, with mean 0.20 (SD 0.13). When validated against the LAC reference group, the algorithms had an average PPV of 0.308 (95% CI [0.253, 0.364]) and sensitivity of 0.183 (95% CI [0.111, 0.256]). Overall, 2.1% of visits identified as low acuity by the algorithms died prehospital or in the ED, or needed surgery, critical care, or cardiac catheterization., Conclusions: Existing algorithms that identify low-acuity ED visits lack congruence and are imperfect predictors of visit acuity., (© 2022 Health Research and Educational Trust.)

Objective: To examine the effects of hospital membership in affiliation networks-franchise-like networks sponsored by high-quality health systems in which affiliate hospitals pay an annual fee for access to sponsor's operational and clinical resources-on clinical quality, patient experience ratings, and financial performance of affiliates and their competitors., Data Sources: Network membership data from press releases and websites of four sponsors (Mayo Clinic, Cleveland Clinic, MD Anderson, Memorial Sloan Kettering), American Hospital Association's Annual Survey, Centers for Medicare & Medicaid Services' Hospital Compare, and Healthcare Cost Report Information System, all for 2005-2016., Study Design: We used a quasi-experimental design and estimated hospital-level regressions with hospital-fixed effects. Dependent variables were measures of clinical quality, patient experience, and financial performance. Independent variables included an indicator for affiliate versus nonaffiliate and fixed effects for hospital characteristics and year. To analyze effects on competitors, we repeated analyses by comparing hospitals in the same county as an affiliate to nonaffiliated, noncompetitor hospitals., Data Collection: Membership was obtained through press releases and network websites then linked across datasets by name and Medicare's identification number., Principal Findings: Across networks, affiliates (N = 199) experienced insignificant clinical quality changes but increased net income by $38,500 and operating margin by 6.6% (p values = 0.01-0.08) compared to nonaffiliates. Multispecialty affiliates improved on no measures. Cancer-specific affiliates improved their net income ($96,900) and operating margin (3.6%; p-values < 0.05). Affiliates' competitors experienced mixed changes in clinical measures relative to hospitals without affiliates in market (p-value < 0.05) but no financial effects. Affiliation was not associated with patient experience ratings for affiliates nor competitors., Conclusions: Despite quality-focused missions, affiliation networks are not guaranteed to improve public measures of quality in affiliated hospitals, although hospitals in these communities improve financially. Future research should assess the conditions and mechanisms by which affiliation improves quality consistently and which forms of quality., (© 2021 Health Research and Educational Trust.)

Objective: To determine whether the variation in prices paid for cardiac medical devices was associated with management practices in cardiac units., Study Setting: Cardiac units in US hospitals., Study Design: We regressed unit prices on management practice scores and other hospital characteristics, with and without controls for device fixed effects, for the 11 top-spending cardiac device categories., Data Collection: A trusted third party that had entered into a confidentiality agreement combined de-identified medical device price data for N = 213 US hospitals from ECRI's Supply Guide benchmarking service, with survey responses regarding management practices in those hospitals' cardiac units; the resulting merged data were made available to researchers for analysis with hospital identifiers removed. N = 1980 hospitals with interventional cardiac catheterization laboratories and at least 25 annual acute myocardial infarction discharges in 2010 were eligible for inclusion; N = 648 responded to the management practices survey; N = 213 subscribed to Supply Guide and purchased at least one of 11 top cardiac medical device categories., Principal Findings: Cardiac units with better management practices paid lower prices for cardiac devices (percent decrease in price for one standard deviation increase in management score = 1.33%, 95% confidence interval 0.99-1.67). This was comparable in magnitude to the price decrease associated with a one standard deviation increase in patient volume., Conclusions: Better management practices were associated with lower device prices. This relationship is robust, but modest in magnitude. This modest magnitude is similar, though, to other events expected to lower input prices, such as transparency in the form of benchmarking information and hospital mergers., (© 2021 Health Research and Educational Trust.)

Objective: This study sought to identify potential disparities among racial/ethnic groups in patient perceptions of integrated care (PPIC) and to explore how methodological differences may influence measured disparities., Data Source: Data from Medicare beneficiaries who completed the 2015 Medicare Current Beneficiary Survey (MCBS) and were enrolled in Part A benefits for an entire year., Study Design: We used 4-point measures of eight dimensions of PPIC and assessed differences in dimensions among racial/ethnic groups. To estimate differences, we applied a "rank and replace" method using multiple regression models in three steps, balancing differences in health status among racial groups and adjusting for differences in socioeconomic status. We reran all analyses with additional SES controls and using standard multiple variable regression., Data Collection/extraction Methods: Not applicable., Principal Findings: We found several significant differences in perceived integrated care between Black versus White (three of eight measures) and Hispanic versus White (one of eight) Medicare beneficiaries. On average, Black beneficiaries perceived more integrated support for self-care than did White beneficiaries (mean difference = 0.14, SE = 0.06, P =.02). Black beneficiaries perceived more integrated specialists' knowledge of past medical history than did White beneficiaries (mean difference = 0.12, SE = 0.06, P =.01). Black and Hispanic beneficiaries also each reported, on average, 0.18 more integrated medication and home health management than did White beneficiaries (P <.01 and P <.01). These findings were robust to sensitivity analyses and model specifications., Conclusions: There exist some aspects of care for which Black and Hispanic beneficiaries may perceive greater integrated care than non-Hispanic White beneficiaries. Further studies should test theories explaining why racial/ethnic groups perceive differences in integrated care., (© 2021 Health Research and Educational Trust.)

Objective: To measure the extent to which the provision of mammograms was impacted by the COVID-19 pandemic and surrounding guidelines., Data Sources: De-identified summary data derived from medical claims and eligibility files were provided by Independence Blue Cross for women receiving mammograms., Study Design: We used a difference-in-differences approach to characterize the change in mammograms performed over time and a queueing formula to estimate the time to clear the queue of missed mammograms., Data Collection: We used data from the first 30 weeks of each year from 2018 to 2020., Principal Findings: Over the 20 weeks following March 11, 2020, the volume of screening mammograms and diagnostic mammograms fell by 58% and 38% of expected levels, on average. Lowest volumes were observed in week 15 (April 8 to 14), when screening and diagnostic mammograms fell by 99% and 74%, respectively. Volumes began to rebound in week 19 (May), with diagnostic mammograms reaching levels to similar to previous years' and screening mammograms remaining 14% below expectations. We estimate it will take a minimum of 22 weeks to clear the queue of missed mammograms in our study sample., Conclusions: The provision of mammograms has been significantly disrupted due to the COVID-19 pandemic., (© 2020 Health Research and Educational Trust.)

Objective: To test the effectiveness of physician incentives for increasing patient medication adherence in three drug classes: diabetes medication, antihypertensives, and statins., Data Sources: Pharmacy and medical claims from a large Medicare Advantage Prescription Drug Plan from January 2011 to December 2012., Study Design: We conducted a randomized experiment (911 primary care practices and 8,935 nonadherent patients) to test the effect of paying physicians for increasing patient medication adherence in three drug classes: diabetes medication, antihypertensives, and statins. We measured patients' medication adherence for 18 (6) months before (after) the intervention., Data Collection/extraction Methods: We obtained data directly from the health insurer., Principal Findings: We found no evidence that physician incentives increased adherence in any drug class. Our results rule out increases in the proportion of days covered by medication larger than 4.2 percentage points., Conclusions: Physician incentives of $50 per patient per drug class are not effective for increasing patient medication adherence among the drug classes and primary care practices studied. Such incentives may be more likely to improve measures under physicians' direct control rather than those that predominantly reflect patient behaviors. Additional research is warranted to disentangle whether physician effort is not responsive to these types of incentives, or medication adherence is not responsive to physician effort. Our results suggest that significant changes in the incentive amount or program design may be necessary to produce responses from physicians or patients., (© Health Research and Educational Trust.)

Objective: To test for differences in patient outcomes when hospital and post-acute care (PAC) providers participate in accountable care organizations (ACOs)., Data/setting: Using Medicare claims, we examined changes in readmission, Medicare spending, and length of stay among patients admitted to ACO-participating hospitals and PAC providers., Design: We compared changes in outcomes among patients discharged from ACO-participating hospitals/PACs before and after participation to changes among patients discharged from non-participating hospitals/PACs over the same time period., Results: Patients discharged from an ACO-participating hospitals and skilled nursing facilities (SNF) had lower readmission rates (-1.7 percentage points, p-value = .03) than before ACO participation and non-participants; and lower per-discharge Medicare spending (-$940, p-value = .001), and length of stay (-3.1 days, p-value <.001) in SNF. Effects among ACO-participating hospitals without a co-participating SNF were smaller. Patients discharged from an ACO-participating hospital and home health agency had lower Medicare per-discharge spending (-$209; p-value = .06) and length of stay (-1.6 days, p-value <.001) for home health compared to before ACO participation and non-participants. Discharge from an ACO-participating hospital and inpatient rehabilitation facility did not impact patient outcomes or spending., Conclusions: Hospital and SNF participation in an ACO was associated with lower readmission rates, Medicare spending on SNF, and SNF length of stay. These results lend support to the ACO payment model., (© Health Research and Educational Trust.)

Objective: To evaluate the prevalence of seven social factors using physician notes as compared to claims and structured electronic health records (EHRs) data and the resulting association with 30-day readmissions., Study Setting: A multihospital academic health system in southeastern Massachusetts., Study Design: An observational study of 49,319 patients with cardiovascular disease admitted from January 1, 2011, to December 31, 2013, using multivariable logistic regression to adjust for patient characteristics., Data Collection/extraction Methods: All-payer claims, EHR data, and physician notes extracted from a centralized clinical registry., Principal Findings: All seven social characteristics were identified at the highest rates in physician notes. For example, we identified 14,872 patient admissions with poor social support in physician notes, increasing the prevalence from 0.4 percent using ICD-9 codes and structured EHR data to 16.0 percent. Compared to an 18.6 percent baseline readmission rate, risk-adjusted analysis showed higher readmission risk for patients with housing instability (readmission rate 24.5 percent; p < .001), depression (20.6 percent; p < .001), drug abuse (20.2 percent; p = .01), and poor social support (20.0 percent; p = .01)., Conclusions: The seven social risk factors studied are substantially more prevalent than represented in administrative data. Automated methods for analyzing physician notes may enable better identification of patients with social needs., (© Health Research and Educational Trust.)

Objective: To examine the influence of physician and hospital market structures on medical technology diffusion, studying the diffusion of drug-eluting stents (DESs), which became available in April 2003., Data Sources/study Setting: Medicare claims linked to physician demographic data from the American Medical Association and to hospital characteristics from the American Hospital Association Survey., Study Design: Retrospective claims data analyses., Data Collection/extraction Methods: All fee-for-service Medicare beneficiaries who received a percutaneous coronary intervention (PCI) with a cardiac stent in 2003 or 2004. Each PCI record was joined to characteristics on the patient, the procedure, the cardiologist, and the hospital where the PCI was delivered. We accounted for the endogeneity of physician and hospital market structure using exogenous variation in the distances between patient, physician, and hospital locations. We estimated multivariate linear probability models that related the use of a DES in the PCI on market structure while controlling for patient, physician, and hospital characteristics., Principal Findings: DESs diffused faster in markets where cardiology practices faced more competition. Conversely, we found no evidence that the structure of the hospital market mattered., Conclusions: Competitive pressure to maintain or expand PCI volume shares compelled cardiologists to adopt DESs more quickly., (© Health Research and Educational Trust.)

Objective: To develop a method to allow a hospital to compare its performance using its entire patient population to the outcomes of very similar patients treated elsewhere., Data Sources/setting: Medicare claims in orthopedics and common general, gynecologic, and urologic surgery from Illinois, New York, and Texas from 2004 to 2006., Study Design: Using two example "focal" hospitals, each hospital's patients were matched to 10 very similar patients selected from 619 other hospitals., Data Collection/extraction Methods: All patients were used at each focal hospital, and we found the 10 closest matched patients from control hospitals with exactly the same principal procedure as each focal patient., Principal Findings: We achieved exact matches on all procedures and very close matches for other patient characteristics for both hospitals. There were few to no differences between each hospital's patients and their matched control patients on most patient characteristics, yet large and significant differences were observed for mortality, failure-to-rescue, and cost., Conclusion: Indirect standardization matching can produce fair audits of quality and cost, allowing for a comprehensive, transparent, and relevant assessment of all patients at a focal hospital. With this approach, hospitals will be better able to benchmark their performance and determine where quality improvement is most needed., (© Health Research and Educational Trust.)

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., (© Health Research and Educational Trust.)

Objective: To examine the willingness to accept new Medicaid patients among certified rural health clinics (RHCs) and other nonsafety net rural providers., Data Sources: Experimental (audit) data from a 10-state study of primary care practices, county-level information from the Area Health Resource File, and RHC information from the Center for Medicare and Medicaid Services., Study Design: We generate appointment rates for rural and nonrural areas by patient-payer type (private, Medicaid, self-pay) to then motivate our focus on within-rural variation by clinic type (RHC vs. non-RHC). Multivariate linear models test for statistical differences and assess the estimates' sensitivity to the inclusion of control variables., Data Collection: The primary data are from a large field study., Principal Findings: Approximately 80 percent of Medicaid callers receive an appointment in rural areas-a rate more than 20 percentage points greater than nonrural areas. Importantly, within rural areas, RHCs offer appointments to prospective Medicaid patients nearly 95 percent of the time, while the rural (nonsafety net) non-RHC Medicaid rate is less than 75 percent. Measured differences are robust to covariate adjustment., Conclusions: Our study suggests that RHC status, with its alternative payment model, is strongly associated with new Medicaid patient acceptance. Altering RHC financial incentives may have consequences for rural Medicaid enrollees., (© Health Research and Educational Trust.)

Objective: To determine if regions with high Medicare expenditures in a given setting remain high cost over time., Data Sources/study Setting: One hundred percent of national Medicare Parts A and B fee-for-service beneficiary claims data and enrollment for 1992-2010., Study Design: Patients are classified into regions. Claims are price-standardized. Risk adjustment is performed at the beneficiary level using the CMS Hierarchical Condition Categories model. Correlation analyses are conducted., Data Collection/extraction Methods: The data were obtained through a contract with CMS for a study performed for the Institute of Medicine., Principal Findings: High-cost regions in 1992 are likely to remain high cost in 2010. Stability in regional spending is highest in the home health, inpatient hospital, and outpatient hospital settings over this time period. Despite the persistence of a region's relative spending over time, a region's spending levels in all settings except home health tend to regress toward the mean., Conclusions: Relatively high-cost regions tend to remain so over long periods of time, even after controlling for patient health status and geographic price variation, suggesting that the observed effect reflects real differences in practice patterns., (© Health Research and Educational Trust.)

Objective: To assess whether adoption of the patient-centered medical home (PCMH) reduces emergency department (ED) utilization among patients with and without chronic illness., Data Sources: Data from approximately 460,000 Independence Blue Cross patients enrolled in 280 primary care practices, all converting to PCMH status between 2008 and 2012., Research Design: We estimate the effect of a practice becoming PCMH-certified on ED visits and costs using a difference-in-differences approach which exploits variation in the timing of PCMH certification, employing either practice or patient fixed effects. We analyzed patients with and without chronic illness across six chronic illness categories., Principal Findings: Among chronically ill patients, transition to PCMH status was associated with 5-8 percent reductions in ED utilization. This finding was robust to a number of specifications, including analyzing avoidable and weekend ED visits alone. The largest reductions in ED visits are concentrated among chronic patients with diabetes and hypertension., Conclusions: Adoption of the PCMH model was associated with lower ED utilization for chronically ill patients, but not for those without chronic illness. The effectiveness of the PCMH model varies by chronic condition. Analysis of weekend and avoidable ED visits suggests that reductions in ED utilization stem from better management of chronic illness rather than expanding access to primary care clinics., (© Health Research and Educational Trust.)

Objective: Develop an improved method for auditing hospital cost and quality tailored to a specific hospital's patient population., Data Sources/setting: Medicare claims in general, gynecologic and urologic surgery, and orthopedics from Illinois, New York, and Texas between 2004 and 2006., Study Design: A template of 300 representative patients from a single index hospital was constructed and used to match 300 patients at 43 hospitals that had a minimum of 500 patients over a 3-year study period., Data Collection/extraction Methods: From each of 43 hospitals we chose 300 patients most resembling the template using multivariate matching., Principal Findings: We found close matches on procedures and patient characteristics, far more balanced than would be expected in a randomized trial. There were little to no differences between the index hospital's template and the 43 hospitals on most patient characteristics yet large and significant differences in mortality, failure-to-rescue, and cost., Conclusion: Matching can produce fair, directly standardized audits. From the perspective of the index hospital, "hospital-specific" template matching provides the fairness of direct standardization with the specific institutional relevance of indirect standardization. Using this approach, hospitals will be better able to examine their performance, and better determine why they are achieving the results they observe., (© Health Research and Educational Trust.)

Objective: Develop an improved method for auditing hospital cost and quality., Data Sources/setting: Medicare claims in general, gynecologic and urologic surgery, and orthopedics from Illinois, Texas, and New York between 2004 and 2006., Study Design: A template of 300 representative patients was constructed and then used to match 300 patients at hospitals that had a minimum of 500 patients over a 3-year study period., Data Collection/extraction Methods: From each of 217 hospitals we chose 300 patients most resembling the template using multivariate matching., Principal Findings: The matching algorithm found close matches on procedures and patient characteristics, far more balanced than measured covariates would be in a randomized clinical trial. These matched samples displayed little to no differences across hospitals in common patient characteristics yet found large and statistically significant hospital variation in mortality, complications, failure-to-rescue, readmissions, length of stay, ICU days, cost, and surgical procedure length. Similar patients at different hospitals had substantially different outcomes., Conclusion: The template-matched sample can produce fair, directly standardized audits that evaluate hospitals on patients with similar characteristics, thereby making benchmarking more believable. Through examining matched samples of individual patients, administrators can better detect poor performance at their hospitals and better understand why these problems are occurring., (© Health Research and Educational Trust.)

Objective: To examine whether expanding coverage for the nonelderly affects primary care utilization among Medicare beneficiaries., Data Source: Zip code-level files from Dartmouth Atlas for Massachusetts and surrounding states, including Medicare utilization for 2005 (pre expansion) and 2007 (post expansion), and health insurance coverage for 2005., Study Design: We use two zip code-level outcomes: arc percent change in primary care visits per Medicare beneficiary per year, and percentage point change in the share of beneficiaries with one or more primary care visits. We use a regression-based difference-in-difference analysis that compares Massachusetts with surrounding states, and zip codes with high, medium, and low uninsurance rates in 2005. The 2005 uninsurance rates correspond to the size of Massachusetts' coverage expansion. We use propensity scores for identification of comparable zip codes and for weighting., Principal Findings: In areas of Massachusetts with the highest uninsurance rates-where insurance expansion had the largest impact-visits per beneficiary fell 6.9 percent (p < .001) relative to areas of Massachusetts with the smallest uninsurance rates., Conclusions: The expansion of coverage for the nonelderly reduced primary care visits, but it did not reduce the percent of beneficiaries with at least one visit. These results could imply restricted access, increased efficiency, or some blend., (© Health Research and Educational Trust.)

Objective: To evaluate the effects of Medicare's hospital pay-for-performance demonstration project on hospital revenues, costs, and margins and on Medicare costs., Data Sources/study Setting: All health care utilization for Medicare beneficiaries hospitalized for acute myocardial infarction (AMI; ICD-9-CM code 410.x1) in fiscal years 2002-2005 from Medicare claims, containing 420,211 admissions with AMI., Study Design: We test for changes in hospital costs and revenues and Medicare payments among 260 hospitals participating in the Medicare hospital pay-for-performance demonstration project and a group of 780 propensity-score-matched comparison hospitals. Effects were estimated using a difference-in-difference model with hospital fixed effects, testing for changes in costs among pay-for-performance hospitals above and beyond changes in comparison hospitals., Principal Findings: We found no significant effect of pay-for-performance on hospital financials (revenues, costs, and margins) or Medicare payments (index hospitalization and 1 year after admission) for AMI patients., Conclusions: Pay-for-performance in the CMS hospital demonstration project had minimal impact on hospital financials and Medicare payments to providers. As P4P extends to all hospitals under the Affordable Care Act, these results provide some estimates of the impact of P4P and emphasize our need for a better understanding of the financial implications of P4P on providers and payers if we want to create sustainable and effective programs to improve health care value., (© Health Research and Educational Trust.)

Objectives: To examine the strength of the volume-outcome relationship among paramedics, a group of providers that has not been previously studied in this context. By identifying the effects of individual learning on performance, we also assess the value of paramedics' retention. The prehospital emergency medical services (EMS) setting allows us to interpret any volume-outcome relationship as learning by doing, uncontaminated by reputation-based referrals because ambulance units are dispatched based on proximity., Data Sources: Incident-level EMS data spanning 1991 to 2005 from the Mississippi Emergency Medical Services Information System collected by the Mississippi Department of Health., Research Design: Using linear and quantile methods with and without provider fixed effects, we estimate the relationship between experience accumulation and performance using the universe of trauma incidents involving injured patients (including motor vehicle crashes, falls, stabbings, and shootings)., Principal Findings: We find that greater individual volume is robustly related to improved performance. In addition, we find that the benefit of learning operates through both recent and past experiences, accrues differentially across tenure groups, and operates on both mean performance and the upper quantiles of the performance distribution., Conclusions: Persistent past and current volume effects suggest that policy and managerial implications in EMS should be directed at retention efforts to take advantage of individual learning by paramedics.

Objective: To estimate racial differences in mortality at 30 days and up to 2 years following a hospital admission for the elderly with common medical conditions., Data Sources: The Medicare Provider Analysis and Review File and the VA Patient Treatment File from 1998 to 2002 were used to extract patients 65 or older admitted with a principal diagnosis of acute myocardial infarction, stroke, hip fracture, gastrointestinal bleeding, congestive heart failure, or pneumonia., Study Design: A retrospective analysis of risk-adjusted mortality after hospital admission for blacks and whites by medical condition and in different hospital settings., Principal Findings: Black Medicare patients had consistently lower adjusted 30-day mortality than white Medicare patients, but the initial survival advantage observed among blacks dissipated beyond 30 days and reversed by 2 years. For VA hospitalizations similar patterns were observed, but the initial survival advantage for blacks dissipated at a slower rate., Conclusions: Racial disparities in health are more likely to be generated in the posthospital phase of the process of care delivery rather than during the hospital stay. The slower rate of increase in relative mortality among black VA patients suggests an integrated health care delivery system like the VA may attenuate racial disparities in health.

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 beta-blocker at arrival and patients given beta-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.

Objective: To describe the work environment of hospital nurses with particular focus on the performance of work systems supplying information, materials, and equipment for patient care., Data Sources: Primary observation, semistructured interviews, and surveys of hospital nurses., Study Design: We sampled a cross-sectional group of six U.S. hospitals to examine the frequency of work system failures and their impact on nurse productivity., Data Collection: We collected minute-by-minute data on the activities of 11 nurses. In addition, we conducted interviews with six of these nurses using questions related to obstacles to care. Finally, we created and administered two surveys in 48 nursing units, one for nurses and one for managers, asking about the frequency of specific work system failures., Principal Findings: Nurses we observed experienced an average of 8.4 work system failures per 8-hour shift. The five most frequent types of failures, accounting for 6.4 of these obstacles, involved medications, orders, supplies, staffing, and equipment. Survey questions asking nurses how frequently they experienced these five categories of obstacles yielded similar frequencies. For an average 8-hour shift, the average task time was only 3.1 minutes, and in spite of this, nurses were interrupted mid-task an average of eight times per shift., Conclusions: Our findings suggest that nurse effectiveness can be increased by creating improvement processes triggered by the occurrence of work system failures, with the goal of reducing future occurrences. Second, given that nursing work is fragmented and unpredictable, designing processes that are robust to interruption can help prevent errors.

Objective: To determine if the availability of public ambulatory clinics affects preventable hospitalization (PH) rates of low-income and elderly populations., Data Sources: PH rates were calculated using elderly and low-income discharges from 1995-97 Virginia hospital discharge data. Other data sources include the 1990 Census, the 1998 Area Resource File, the 1996 American Hospital Association Survey, the Virginia Department of Health, the Virginia Primary Care Association, and the Bureau of Primary Health Care., Study Design: Multiple linear regression was used to evaluate the relationship between ambulatory clinic availability and PH rates, controlling for population and other provider characteristics in a cross-section of zip code clusters., Data Extraction Methods: Clusters with populations of at least 2,000 were assembled from zip codes in each county in the state of Virginia. Overlapping medical market service areas were constructed around the population centroid of each cluster., Principal Findings: Populations in medically underserved areas (MUAs) served by a Federally Qualified Health Center had significantly lower PH rates than did other MUA populations. The presence of a free clinic had a marginally significant association with lower PH rates., Conclusions: The availability of public ambulatory clinics is associated with better access to primary care among low-income and elderly populations.

Objective: To assess the impact of HMO market structure on the formation of physician-hospital strategic alliances from 1993 through 1995. The two trends, managed care and physician-hospital integration have been prominent in reshaping insurance and provider markets over the past decade., Study Design: Pooled cross-sectional data from the InterStudy HMO Census and the Annual Survey conducted by the American Hospital Association (AHA) between 1993 and the end of 1995 to examine the effects of HMO penetration and HMO numbers in a market on the formation of hospital-sponsored alliances with physicians. Because prior research has found nonlinear effects of HMOs on a variety of dependent variables, we operationalized HMO market structure two ways: using a Taylor series expansion and cross-classifying quartile distributions of HMO penetration and numbers into 16 dummy indicators. Alliance formation was operationalized using the presence of any alliance model (IPA, PHO, MSO, and foundation) and the sum of the four models present in the hospital. Because managed care and physician-hospital integration are endogenous (e.g., some hospitals also sponsor HMOs), we used an instrumental variables approach to model the determinants of HMO penetration and HMO numbers. These instruments were then used with other predictors of alliance formation: physician supply characteristics, the extent of hospital competition, hospital-level descriptors, population size and demographic characteristics, and indicators for each year. All equations were estimated at the MSA level using mixed linear models and first-difference models., Principal Findings: Contrary to conventional wisdom, alliance formation is shaped by the number of HMOs in the market rather than by HMO penetration. This confirms a growing perception that hospital-sponsored alliances with physicians are contracting vehicles for managed care: the greater the number of HMOs to contract with, the greater the development of alliances. The models also show that alliance formation is low in markets where a small number of HMOs have deeply penetrated the market. First-difference models further show that alliance formation is linked to HMO consolidation (drop in the number of HMOs in a market) and hospital downsizing. Alliance formation is not linked to changes in hospital costs, profitability, or market competition with other hospitals., Conclusions: Hospitals appear to form alliances with physicians for several reasons. Alliances serve to contract with the growing number of HMOs, to pose a countervailing bargaining force of providers in the face of HMO consolidation, and to accompany hospital downsizing and restructuring efforts., Implications for Policy, Delivery, or Practice: Physician-hospital integration is often mentioned as a provider response to increasing cost-containment pressures due to rising managed care penetration. Our findings do not support this view. Alliances appear to serve the hospital's interest in bargaining with managed care plans on a more even basis.

Objective: This study examines variations in the efficient use of hospital resources across individual physicians., Data Sources and Setting: The study is conducted over a two-year period (1989-1990) in all short-term general hospitals with 50 or more beds in Arizona. We examine hospital discharge data for 43,625 women undergoing cesarean sections and vaginal deliveries without complications. These data include physician identifiers that permit us to link patient information with information on physicians provided by the state medical association., Study Design: The study first measures the contribution of physician characteristics to the explanatory power of regression models that predict resource use. It then tests hypothesized effects on resource utilization exerted by two sets of physician level factors: physician background and physician practice organization. The latter includes effects of hospital practice volume, concentration of hospital practice, percent managed care patients in one's hospital practice, and diversity of patients treated. Efficiency (inefficiency) is measured as the degree of variation in patient charges and length of stay below (above) the average of treating all patients with the same condition in the same hospital in the same year with the same severity of illness, controlling for discharge status and the presence of complications., Principal Findings: After controlling for patient factors, physician characteristics explain a significant amount of the variability in hospital charges and length of stay in the two maternity conditions. Results also support hypotheses that efficiency is influenced by practice organization factors such as patient volume and managed care load. Physicians with larger practices and a higher share of managed care patients appear to be more efficient., Conclusions: The results suggest that health care reform efforts to develop physician-hospital networks and managed competition may promote greater parsimony in physicians' practice behavior.

This study is an attempt to address both the extent to which surgical procedures on an outpatient basis substitute cost-effectively for inpatient procedures, and whether or not an insurance policy's financial incentives increase the volume of outpatient surgical procedures. In particular, given an insurance product of a given composition: What is the probability that the insured will have surgery? and if a surgery does take place, what is the probability that it will occur in an outpatient setting? Finally, the article assesses the implication of such products on the total cost of care by quantifying the insurance plans along two parameters, the relative user price for outpatient versus inpatient surgery and the absolute price for the inpatient surgery. The results indicate that insurance policies that offer relatively lower out-of-pocket payments for ambulatory surgery do not increase the probability that surgery will be done in the ambulatory setting. However, higher out-of-pocket payments for surgery, regardless of site, do reduce the surgery rate. There are other patient and market characteristics, especially the availability of freestanding surgery firms, that do influence the location of surgery.

The use of partial care as a treatment modality for mentally ill patients, particularly the chronically mentally ill, has greatly increased. However, research into what constitutes a "good" program has been scant. This article reports on an evaluation study of staff productivity, cost efficiency, and service effectiveness of adult partial care programs carried out in New Jersey in fiscal year 1984/1985. Five program performance indexes are developed based on comparisons of multiple measures of resources, service activities, and client outcomes. These are used to test various hypotheses regarding the effect of organizational and fiscal variables on partial care program efficiency and effectiveness. The four issues explored are: auspices, organizational complexity, service mix, and fiscal control by the state. These were found to explain about half of the variance in program performance. In addition, partial care programs demonstrating midlevel performance with regard to productivity and efficiency were observed to be the most effective, implying a possible optimal level of efficiency at which effectiveness is maximized.