The Hospital Compare mortality model and the volume-outcome relationship

Geographic Variation In Effective Contraceptive Use Among Medicaid Recipients In 2018

Medicaid is the largest payer for publicly funded contraception, serving millions of women across the United States. However, relatively little is known about the extent to which effective contraceptive services vary geographically for Medicaid recipients. This study used national Medicaid claims to assess county-level variation in rates of provision of the most or moderately effective methods of contraception and provision of long-acting reversible contraception (LARC) across forty states and Washington, D.C., in 2018. County-level rates of most or moderately effective contraceptive use varied almost fourfold across states, from a low of 10.8 percent to a high of 44.4 percent. Rates of LARC provision varied almost tenfold, from a low of 1.0 percent to a high of 9.6 percent. Despite the fact that contraception is a core benefit within Medicaid, access and use vary substantially across and within states. Medicaid agencies have a variety of options to ensure that people have access to a choice of the full range of contraceptive methods, including removing or loosening utilization controls, incorporating quality metrics or value-based payments into contraceptive services, and adjusting reimbursement to remove barriers to the clinical provision of LARC.

Is there an association between hospital staffing levels and inpatient-COVID-19 mortality rates?

Objective

This study aims to investigate the relationship between RNs and hospital-based medical specialties staffing levels with inpatient COVID-19 mortality rates.

Methods

We relied on data from AHA Annual Survey Database, Area Health Resource File, and UnitedHealth Group Clinical Discovery Database. In phase 1 of the analysis, we estimated the risk-standardized event rates (RSERs) based on 95,915 patients in the UnitedHealth Group Database 1,398 hospitals. We then used beta regression to analyze the association between hospital- and county- level factors with risk-standardized inpatient COVID-19 mortality rates from March 1, 2020, through December 31, 2020.

Results

Higher staffing levels of RNs and emergency medicine physicians were associated with lower COVID-19 mortality rates. Moreover, larger teaching hospitals located in urban settings had higher COVID-19 mortality rates. Finally, counties with greater social vulnerability, specifically in terms of housing type and transportation, and those with high infection rates had the worst patient mortality rates.

Conclusion

Higher staffing levels are associated with lower inpatient mortality rates for COVID-19 patients. More research is needed to determine appropriate staffing levels and how staffing levels interact with other factors such as teams, leadership, and culture to impact patient care during pandemics.

The first 20 months of the COVID-19 pandemic: Mortality, intubation and ICU rates among 104,590 patients hospitalized at 21 United States health systems

Main objective

There is limited information on how patient outcomes have changed during the COVID-19 pandemic. This study characterizes changes in mortality, intubation, and ICU admission rates during the first 20 months of the pandemic.

Study design and methods

University of Wisconsin researchers collected and harmonized electronic health record data from 1.1 million COVID-19 patients across 21 United States health systems from February 2020 through September 2021. The analysis comprised data from 104,590 adult hospitalized COVID-19 patients. Inclusion criteria for the analysis were: (1) age 18 years or older; (2) COVID-19 ICD-10 diagnosis during hospitalization and/or a positive COVID-19 PCR test in a 14-day window (+/- 7 days of hospital admission); and (3) health system contact prior to COVID-19 hospitalization. Outcomes assessed were: (1) mortality (primary), (2) endotracheal intubation, and (3) ICU admission.

Results and significance

The 104,590 hospitalized participants had a mean age of 61.7 years and were 50.4% female, 24% Black, and 56.8% White. Overall risk-standardized mortality (adjusted for age, sex, race, ethnicity, body mass index, insurance status and medical comorbidities) declined from 16% of hospitalized COVID-19 patients (95% CI: 16% to 17%) early in the pandemic (February-April 2020) to 9% (CI: 9% to 10%) later (July-September 2021). Among subpopulations, males (vs. females), those on Medicare (vs. those on commercial insurance), the severely obese (vs. normal weight), and those aged 60 and older (vs. younger individuals) had especially high mortality rates both early and late in the pandemic. ICU admission and intubation rates also declined across these 20 months.

Conclusions

Mortality, intubation, and ICU admission rates improved markedly over the first 20 months of the pandemic among adult hospitalized COVID-19 patients although gains varied by subpopulation. These data provide important information on the course of COVID-19 and identify hospitalized patient groups at heightened risk for negative outcomes.

Trial registration

ClinicalTrials.gov Identifier: NCT04506528 (https://clinicaltrials.gov/ct2/show/NCT04506528).

MEASURING PERFORMANCE FOR END-OF-LIFE CARE

Although not without controversy, readmission is entrenched as a hospital quality metric with statistical analyses generally based on fitting a logistic-Normal generalized linear mixed model. Such analyses, however, ignore death as a competing risk, although doing so for clinical conditions with high mortality can have profound effects; a hospital's seemingly good performance for readmission may be an artifact of it having poor performance for mortality. in this paper we propose novel multivariate hospital-level performance measures for readmission and mortality that derive from framing the analysis as one of cluster-correlated semi-competing risks data. We also consider a number of profiling-related goals, including the identification of extreme performers and a bivariate classification of whether the hospital has higher-/lower-than-expected readmission and mortality rates via a Bayesian decision-theoretic approach that characterizes hospitals on the basis of minimizing the posterior expected loss for an appropriate loss function. in some settings, particularly if the number of hospitals is large, the computational burden may be prohibitive. To resolve this, we propose a series of analysis strategies that will be useful in practice. Throughout, the methods are illustrated with data from CMS on N = 17,685 patients diagnosed with pancreatic cancer between 2000-2012 at one of J = 264 hospitals in California.

Hospital variation in admissions for low back pain following an emergency department presentation: a retrospective study

Background

One in 6 patients with low back pain (LBP) presenting to emergency departments (EDs) are subsequently admitted to hospital each year, making LBP the ninth most common reason for hospital admission in Australia. No studies have investigated and quantified the extent of clinical variation in hospital admission following an ED presentation for LBP.

Methods

We used routinely collected ED data from public hospitals within the state of New South Wales, Australia, to identify presentations of patients aged between 18 and 111 with a discharge diagnosis of LBP. We fitted a series of random effects multilevel logistic regression models adjusted by case-mix and hospital variables. The main outcome was the hospital-adjusted admission rate (HAAR). Data were presented as funnel plots with 95% and 99.8% confidence limits. Hospitals with a HAAR outside the 95% confidence limit were considered to have a HAAR significantly different to the state average.

Results

We identified 176,729 LBP presentations across 177 public hospital EDs and 44,549 hospital admissions (25.2%). The mean (SD) age was 51.8 (19.5) and 52% were female. Hospital factors explained 10% of the variation (ICC = 0.10), and the median odds ratio (MOR) was 2.03. We identified marked variation across hospitals, with HAAR ranging from 6.9 to 65.9%. After adjusting for hospital variables, there was still marked variation between hospitals with similar characteristics.

Conclusion

We found substantial variation in hospital admissions following a presentation to the ED due to LBP even after controlling by case-mix and hospital characteristics. Given the substantial costs associated with these admissions, our findings indicate the need to investigate sources of variation and to determine instances where the observed variation is warranted or unwarranted.

Supplementary information

The online version contains supplementary material available at 10.1186/s12913-022-08134-8.

Center Variability in Medicare Claims-Based Publicly Reported Transcatheter Aortic Valve Replacement Outcome Measures

Background

Public reporting of transcatheter aortic valve replacement (TAVR) claims–based outcome measures is used to identify high‐ and low‐performing centers. Whether claims‐based TAVR outcomes can reliably be used for center‐level comparisons is unknown. In this study, we sought to evaluate center variability in claims‐based TAVR outcomes used in public reporting.

Methods and Results

The study sample included 119 554 Medicare beneficiaries undergoing TAVR between January 2014 and October 2018 based on procedure codes in 100% Medicare inpatient claims. Multivariable hierarchical logistic regression was used to estimate center‐specific adjusted rates and reliability (R) of 30‐day mortality, discharge not to home/self‐care, 30‐day stroke, and 30‐day readmission. Reliability was defined as the ratio of between‐hospital variation to the sum of the between‐ and within‐hospital variation. The median (interquartile range [IQR]) center‐level adjusted outcome rates were 3.1% (2.9%–3.4%) for 30‐day mortality, 41.4% (31.3%–53.4%) for discharge not to home, 2.5% (2.3%–2.7%) for 30‐day stroke, and 14.9% (14.4%–15.5%) for 30‐day readmission. Median reliability was highest for the discharge not to home measure (R=0.95; IQR, 0.94–0.97), followed by the 30‐day stroke (R=0.92; IQR, 0.87–0.94), 30‐day mortality (R=0.86; IQR, 0.81–0.91), and 30‐day readmission measures (R=0.42; IQR, 0.35–0.51). Across outcomes, there was an inverse relationship between center volume and measure reliability.

Conclusions

Claims‐based TAVR outcome measures for mortality, discharge not to home, and stroke were reliable measures for center‐level comparisons, but readmission measures were unreliable. Stakeholders should consider these findings when evaluating claims‐based measures to compare center‐level TAVR performance.

Application of the Risk Stratification Index to Multilevel Models of All-condition 30-Day Mortality in Hospitalized Populations Over the Age of 65

BACKGROUND

The Risk Stratification Index (RSI) is superior to Hierarchical Conditions Categories (HCC) in patient-level regressions but has not been applied to assess hospital effects.

OBJECTIVE

The objective of this study was to measure the accuracy of RSI in modeling 30-day hospital mortality across all conditions using multilevel logistic regression.

SUBJECTS AND DATA SOURCES

A 100% sample of Medicare inpatient stays from 2009 to 2014, restricted to patients greater than 65 years of age in general hospitals, resulting in 64 million stays at 3504 hospitals.

RESEARCH DESIGN

We calculated RSI and HCC scores for patient stays using multilevel logistic regression in 3 populations: all inpatients, surgical, and nonsurgical. Correlations of risk-standardized mortality rates with rates of specific case types assessed case-mix balance. Patient stay volume was included to assess smaller hospitals.

RESULTS

We found a negligible correlation of all-conditions risk-standardized mortality rates with hospitals' proportions of orthopedic, cardiac, or pneumonia cases. RSI outperformed HCC in multilevel regressions containing both patient and hospital-level effects. C-statistics using RSI were 0.87 for the all-inpatients group, 0.87 for surgical, and 0.86 for nonsurgical stays. With HCC they were 0.82, 0.82, and 0.81. Akaike Information Criteria and Bayesian Information Criteria values were higher with HCC. RSI shifted 41% of hospitals' rankings by >1 decile. Hospitals with smaller volumes had higher 30-day observed and standardized mortality: 11.2% in the lowest volume quintile versus 8.5% in the highest volume quintile.

CONCLUSION

RSI has superior accuracy and results in a significant shift in rankings compared with HCC in multilevel models of 30-day hospital mortality across all conditions.

Divided by choice? For-profit providers, patient choice and mechanisms of patient sorting in the English National Health Service

This paper studies patient choice of provider following government reforms in the 2000s, which allowed for-profit surgical centers to compete with existing public National Health Service (NHS) hospitals in England. For-profit providers offer significant benefits, notably shorter waiting times. We estimate the extent to which different types of patients benefit from the reforms, and we investigate mechanisms that cause differential benefits. Our counterfactual simulations show that, in terms of the value of access, entry of for-profit providers benefitted the richest patients twice as much as the poorest, and white patients six times as much as ethnic minority patients. Half of these differences is explained by healthcare geography and patient health, while primary care referral practice plays a lesser, though non-negligible role. We also show that, with capitated reimbursement, different compositions of patient risks between for-profit surgical centers and existing public hospitals put public hospitals at a competitive disadvantage.

Variation in US Hospital Mortality Rates for Patients Admitted With COVID-19 During the First 6 Months of the Pandemic

Importance

It is unknown how much the mortality of patients with coronavirus disease 2019 (COVID-19) depends on the hospital that cares for them, and whether COVID-19 hospital mortality rates are improving.

Objective

To identify variation in COVID-19 mortality rates and how those rates have changed over the first months of the pandemic.

Design, Setting, and Participants

This cohort study assessed 38 517 adults who were admitted with COVID-19 to 955 US hospitals from January 1, 2020, to June 30, 2020, and a subset of 27 801 adults (72.2%) who were admitted to 398 of these hospitals that treated at least 10 patients with COVID-19 during 2 periods (January 1 to April 30, 2020, and May 1 to June 30, 2020).

Exposures

Hospital characteristics, including size, the number of intensive care unit beds, academic and profit status, hospital setting, and regional characteristics, including COVID-19 case burden.

Main Outcomes and Measures

The primary outcome was the hospital's risk-standardized event rate (RSER) of 30-day in-hospital mortality or referral to hospice adjusted for patient-level characteristics, including demographic data, comorbidities, community or nursing facility admission source, and time since January 1, 2020. We examined whether hospital characteristics were associated with RSERs or their change over time.

Results

The mean (SD) age among participants (18 888 men [49.0%]) was 70.2 (15.5) years. The mean (SD) hospital-level RSER for the 955 hospitals was 11.8% (2.5%). The mean RSER in the worst-performing quintile of hospitals was 15.65% compared with 9.06% in the best-performing quintile (absolute difference, 6.59 percentage points; 95% CI, 6.38%-6.80%; P < .001). Mean RSERs in all but 1 of the 398 hospitals improved; 376 (94%) improved by at least 25%. The overall mean (SD) RSER declined from 16.6% (4.0%) to 9.3% (2.1%). The absolute difference in rates of mortality or referral to hospice between the worst- and best-performing quintiles of hospitals decreased from 10.54 percentage points (95% CI, 10.03%-11.05%; P < .001) to 5.59 percentage points (95% CI, 5.33%-5.86%; P < .001). Higher county-level COVID-19 case rates were associated with worse RSERs, and case rate declines were associated with improvement in RSERs.

Conclusions and Relevance

Over the first months of the pandemic, COVID-19 mortality rates in this cohort of US hospitals declined. Hospitals did better when the prevalence of COVID-19 in their surrounding communities was lower.

Association between coronary artery bypass graft center volume and year-to-year outcome variability: New York and California statewide analysis

OBJECTIVE

We evaluated whether volume-based, rather than time-based, annual reporting of center outcomes for coronary artery bypass grafting may improve inference of quality, assuming that large center-level year-to-year outcome variability is related to statistical noise.

METHODS

We analyzed 2012 to 2016 data on isolated coronary artery bypass grafting using statewide outcome reports from New York and California. Annual changes in center-level observed-to-expected mortality ratio represented stability of year-to-year outcomes. Cubic spline fit related the annual observed-to-expected ratio change and center volume. Volume above the inflection point of the spline curve indicated centers with low year-to-year change in outcome. We compared observed-to-expected ratio changes between centers below and above the volume threshold and observed-to-expected ratio changes between consecutive annual and biennial measurements.

RESULTS

There were 155 centers with median annual volume of 89 (interquartile range, 55-160) for isolated coronary artery bypass grafting. The inflection point of observed-to-expected ratio variability was observed at 111 cases/year. Median year-to-year observed-to-expected ratio change for centers performing less than 111 cases (62 centers) was greater at 0.83 (0.26-1.59) compared with centers performing 111 cases or more (93 centers) at 0.49 (022-0.87) (P < .001). By aggregating the outcome over 2 years, centers above the 111-case threshold increased from 93 centers (60%) to 118 centers (76%), but the median observed-to-expected change for all centers was similar between annual aggregates at 0.70 (0.26-1.22) compared with observed-to-expected change between biennial aggregates at 0.54 (0.23-1.02) (P = .095).

CONCLUSIONS

Center-level, risk-adjusted coronary artery bypass grafting mortality varies significantly from one year to the next. Reporting outcomes by specific case volume may complement annual reports.

Hospital Variation in Geriatric Surgical Safety for Emergency Operation

BACKGROUND

The American College of Surgeons maintains that surgical care in the US has not reached optimal safety and quality. This can be driven partially by higher-risk, emergency operations in geriatric patients. We therefore sought to answer 2 questions: First, to what degree does standardized postoperative mortality vary in hospitals performing nonelective operations in geriatric patients? Second, can the differences in hospital-based mortality be explained by patient-, operative-, and hospital-level characteristics among outlier institutions?

STUDY DESIGN

Patients 65 years and older who underwent 1 of 8 common emergency general surgery operations were identified using the California State Inpatient Database (2010 to 2011). Expected mortality was obtained from hierarchical, Bayesian mixed-effects logistic regression models. A risk-adjusted hospital-level standardized mortality ratio (SMR) was calculated from observed-to-expected in-hospital deaths. "Outlier" hospitals had an SMR 80% CI that did not cross the mean SMR of 1.0. High-mortality (SMR >1.0) and low-mortality (SMR <1.0) outliers were compared.

RESULTS

We included 24,207 patients from 107 hospitals. SMRs varied widely, from 2.3 (highest) to 0.3 (lowest). Eleven hospitals (10.3%) were poor-performing high-SMR outliers, and 10 hospitals (9.3%) were exceptional-performing low-SMR outliers. SMR was 3 times worse in the high-SMR compared with the low-SMR group (1.7 vs 0.6; p < 0.001). Patient-, operation-, and hospital-level characteristics were equivalent among outlier-hospitals.

CONCLUSIONS

Significant hospital variation exists in standardized mortality after common general surgery operations done emergently in older patients. More than 10% of institutions have substantial excess mortality. These findings confirm that the safety of emergency operation in geriatric patients can be significantly improved by decreasing the wide variability in mortality outcomes.

On the relations between focus, experience, and hospital performance

BACKGROUND

In the United States, a long-standing debate has existed over advantages/disadvantages of general versus specialty hospitals. A recent stream of research has investigated whether general hospitals accrue performance benefits from a focus strategy; a strategy of specializing in certain clinical conditions while remaining a multiproduct firm. In contrast, a substantial and long-standing body of research on hospitals has been concerned with the absolute volume of cases in a service area as an indication of experience based largely on the idea that absolute volume confers learning opportunities.

PURPOSE

We investigated whether hospital focus and experience in a service area have complementary effects or are largely substitutive for hospital performance.

METHODOLOGY/APPROACH

Key data sources were patient discharge records and hospital discharge profiles from California's Office of Statewide Health Policy and Development for years 2010-2014. We specified hospital focus as the proportion of total cardiology-related discharges and hospital experience as the cumulative volume of cardiology-related discharges for each hospital. Performance was specified using quality (inpatient mortality and 30-day readmission) and efficiency (length of stay and cost) patient-level performance metrics. We analyzed the data using logistic and log-linear ordinary least squares regression models.

RESULTS

Study results generally supported our hypotheses that focus and experience are related to better quality and efficiency performance and that the effects are largely substitutive for hospitals.

CONCLUSION

Our study extends the literature by finding that hospitals exhibit distinct and stable patterns regarding their positioning on focus and experience and that these patterns have important implications for hospitals' performance in terms of quality and efficiency.

PRACTICE IMPLICATIONS

Many general hospitals in the United States may be stretched too thin across service areas for which they lack necessary patient volumes for clinical proficiency. A viable alternative is to select a limited set of service areas on which to focus.

Improving The Accuracy Of Hospital Quality Ratings By Focusing On The Association Between Volume And Outcome

The Centers for Medicare and Medicaid Services (CMS) uses hierarchical modeling to stabilize its hospital quality star ratings by shrinking the performance of low-volume hospitals toward the performance of average hospitals. Responding to criticism that the methodology may distort the performance of low-volume hospitals, a CMS expert panel recommended that the agency consider using "shrinkage targets" to more accurately classify hospital quality performance. To test the "shrinkage targets" approach, we created two parallel sets of performance measures. We found that there was moderate-to-substantial agreement between the standard CMS approach and the approach based on shrinkage targets in hospital star ratings for all but the lowest-volume hospitals. These findings suggest that the standard CMS risk-adjustment methodology does not distort the star ratings of hospitals as long as case volumes exceed the current cutoff (twenty-five cases) used by CMS for public reporting.

Hospital Volume Threshold Associated with Higher Survival after Endovascular Recanalization Therapy for Acute Ischemic Stroke

BACKGROUND AND PURPOSE

Endovascular recanalization therapy (ERT) is becoming increasingly important in the management of acute ischemic stroke (AIS). However, the hospital volume threshold for optimal ERT remains unknown. We investigated the relationship between hospital volume of ERT and risk-adjusted patient outcomes.

METHODS

From the National Health Insurance claims data in Korea, 11,745 patients with AIS who underwent ERT from July 2011 to June 2016 in 111 hospitals were selected. We measured the hospital's ERT volume and patient outcomes, including the 30-day mortality, readmission, and postprocedural intracranial hemorrhage (ICH) rates. For each outcome measure, we constructed risk-adjusted prediction models incorporating demographic variables, the modified Charlson comorbidity index, and the stroke severity index (SSI), and validated them. Risk-adjusted outcomes of AIS cases were compared across hospital quartiles to confirm the volume-outcome relationship (VOR) in ERT. Spline regression was performed to determine the volume threshold.

RESULTS

The mean AIS volume was 14.8 cases per hospital/year and the unadjusted means of mortality, readmission, and ICH rates were 11.6%, 4.6%, and 8.6%, respectively. The VOR was observed in the risk-adjusted 30-day mortality rate across all quartile groups, and in the ICH rate between the first and fourth quartiles (P<0.05). The volume threshold was 24 cases per year.

CONCLUSIONS

There was an association between hospital volume and outcomes, and the volume threshold in ERT was identified. Policies should be developed to ensure the implementation of the AIS volume threshold for hospitals performing ERT.

Evaluating mortality outlier hospitals to improve the quality of care in emergency general surgery

BACKGROUND

Expected performance rates for various outcome metrics are a hallmark of hospital quality indicators used by Agency of Healthcare Research and Quality, Center for Medicare and Medicaid Services, and National Quality Forum. The identification of outlier hospitals with above- and below-expected mortality for emergency general surgery (EGS) operations is therefore of great value for EGS quality improvement initiatives. The aim of this study was to determine hospital variation in mortality after EGS operations, and compare characteristics between outlier hospitals.

METHODS

Using data from the California State Inpatient Database (2010-2011), we identified patients who underwent one of eight common EGS operations. Expected mortality was obtained from a Bayesian model, adjusting for both patient- and hospital-level variables. A hospital-level standardized mortality ratio (SMR) was constructed (ratio of observed to expected deaths). Only hospitals performing three or more of each operation were included. An "outlier" hospital was defined as having an SMR with 80% confidence interval that did not cross 1.0. High- and low-mortality SMR outliers were compared.

RESULTS

There were 140,333 patients included from 220 hospitals. Standardized mortality ratio varied from a high of 2.6 (mortality, 160% higher than expected) to a low of 0.2 (mortality, 80% lower than expected); 12 hospitals were high SMR outliers, and 28 were low SMR outliers. Standardized mortality was over three times worse in the high SMR outliers compared with the low SMR outliers (1.7 vs. 0.5; p < 0.001). Hospital-, patient-, and operative-level characteristics were equivalent in each outlier group.

CONCLUSION

There exists significant hospital variation in standardized mortality after EGS operations. High SMR outliers have significant excess mortality, while low SMR outliers have superior EGS survival. Common hospital-level characteristics do not explain the wide gap between underperforming and overperforming outlier institutions. These findings suggest that SMR can help guide assessment of EGS performance across hospitals; further research is essential to identify and define the hospital processes of care which translate into optimal EGS outcomes.

LEVEL OF EVIDENCE

Epidemiologic Study, level III.

Impact of Missing Stroke Severity Data on the Accuracy of Hospital Ischemic Stroke Mortality Profiling

BACKGROUND

The Centers for Medicare and Medicaid Services have proposed 30-day ischemic stroke risk-standardized mortality rates that include adjustment for stroke severity using the National Institute of Health Stroke Scale (NIHSS), which is often undocumented. We used simulations to quantify the effect of missing NIHSS data on the accuracy of hospital-level ischemic stroke risk-standardized mortality rate profiling for 100 hypothetical hospitals with different case volumes.

METHODS AND RESULTS

We generated simulated data sets of patients with NIHSS scores and other predictors of 30-day mortality based on empirical analysis of data from 7654 patients with ischemic stroke in the Michigan Stroke Registry. We assigned and rank-ordered a true (known) 30-day mortality rate to each hospital in the simulated data sets of N=100 hospitals with either low (100 patients with stroke), medium (300), or high (500) case volumes. We then estimated and rank-ordered 30-day risk-standardized mortality rates for the N=100 hospitals in each simulated data set using hierarchical logistic regression models. In each data set, we systematically varied the rate of missing NIHSS data and whether missing NIHSS data was independent (missing completely at random) or dependent (missing not at random) on the NIHSS score. With no missing NIHSS data, the Spearman correlation between the true hospital performance rank order assigned during data set generation and the estimated 30-day risk-standardized mortality rate rank order was 0.72, 0.88, and 0.91 in low, medium, and high volume hospitals, respectively. However, this fell to as low as 0.50, 0.71, and 0.79 as missing NIHSS data reached 70%.

CONCLUSIONS

Missing NIHSS data had substantial detrimental effects on the accuracy of profiling of ischemic stroke mortality, especially in lower volume hospitals. Even with complete NIHSS documentation, significant limitations in ischemic stroke mortality profiling remain.

Should we pay attention to surgeon or hospital volume in total knee arthroplasty? Evidence from a nationwide population-based study

Background

Although prior research into the relationship between volume and outcome indicates that this relationship is not linear and that an optimal volume should be specified, consensus is lacking regarding the ideal value of this optimal volume. The purposes of this study were to use a visual method to identify surgeon- and hospital-volume thresholds and to examine the relationships of surgeon and hospital volume thresholds to 30-day readmission.

Methods

A retrospective nationwide population-based study design was adopted. Patients who received total knee replacement surgery between 2007 and 2008 in any hospital in Taiwan were included. After adjusting for patient, physician, and hospital characteristics, a restricted cubic spline regression model was used to identify optimal surgeon- and hospital-volume thresholds. Further, a patient-level mixed effect model was conducted to test the respective relationships between these thresholds and 30-day readmission.

Results

A total of 30,828 patients who had received their surgeries from 1,468 surgeons in 437 hospitals were included in this study. Thresholds of 50 cases a year for surgeons and 75 cases a year for hospitals were identified using a restricted cubic spline regression model. However, only the surgeon volume threshold was associated with 30-day readmission using a patient-level mixed effect model after adjusting for patient-, surgeon- and hospital-level covariates.

Conclusions

According to the results of the restricted cubic spline models, the optimal volume thresholds for surgeons and hospitals are 50 cases and 75 cases a year, respectively. However, only the surgeon volume threshold is associated with 30-day readmission.

Physician-Level Variation in Outcomes of Mechanically Ventilated Patients

RATIONALE

Physicians are increasingly being held accountable for patient outcomes, yet their specific contribution to the outcomes remains uncertain.

OBJECTIVES

To determine variation in outcomes of mechanically ventilated patients among intensivists, as well as associations between intensivist experience and patient outcomes.

METHODS

We performed a retrospective cohort study of mechanically ventilated Medicare fee-for-service patients in acute care hospitals in Pennsylvania using administrative, clinical, and physician data from Centers for Medicare and Medicaid Services and the American Medical Association from 2008 and 2009. We identified intensivists by training background, board certification, and claims for services provided to patients admitted to an intensive care unit. We assigned patients to intensivists for outcome attribution based on submitted claims for critical care and in-patient services. We estimated the physician-specific adjusted odds ratios (ORs) for 30-day mortality using a hierarchical model with a random effect for physician, adjusted for patient and hospital characteristics. We tested for independent association of physician experience with patient outcomes using mixed-effects regression for the primary outcome of 30-day mortality. We defined physician experience in two ways: years since training completion ("duration") and annual number of mechanically ventilated patients ("volume").

RESULTS

We assigned 345 physicians to 11,268 patients. The 30-day mortality was 43% and median hospital length of stay was 11 days (interquartile range = 6-18). The physician adjusted OR varied from 0.72 to 1.64 (median = 0.99; interquartile range = 0.92-1.09). A total of 48% of physicians was outliers, with an adjusted OR significantly different from 1. However, among intensivists, physician experience was not associated with 30-day mortality (duration OR = 1.00 per additional year; 95% confidence interval = 1.00-1.01; volume OR = 1.00 per additional patient; 95% confidence interval = 1.00-1.00).

CONCLUSIONS

Intensivists independently contribute to outcomes of Medicare patients who undergo mechanical ventilation, as evidenced by the variation in risk-adjusted mortality across intensivists. However, physician experience does not underlie this relationship between intensivists, suggesting the need to identify modifiable physician factors to improve outcomes.

Differential Safety Between Top-Ranked Cancer Hospitals and Their Affiliates for Complex Cancer Surgery

IMPORTANCE

Leading cancer hospitals have increasingly shared their brands with other hospitals through growing networks of affiliations. However, the brand of top-ranked cancer hospitals may evoke distinct reputations for safety and quality that do not extend to all hospitals within these networks.

OBJECTIVE

To assess perioperative mortality of Medicare beneficiaries after complex cancer surgery across hospitals participating in networks with top-ranked cancer hospitals.

DESIGN, SETTING, AND PARTICIPANTS

A cross-sectional study was performed of the Centers for Medicare & Medicaid Services 100% Medicare Provider and Analysis Review file from January 1, 2013, to December 31, 2016, for top-ranked cancer hospitals (as assessed by U.S. News and World Report) and affiliated hospitals that share their brand. Participants were 29 228 Medicare beneficiaries older than 65 years who underwent complex cancer surgery (lobectomy, esophagectomy, gastrectomy, colectomy, and pancreaticoduodenectomy [Whipple procedure]) between January 1, 2013, and October 1, 2016.

EXPOSURES

Undergoing complex cancer surgery at a top-ranked cancer hospital vs an affiliated hospital.

MAIN OUTCOMES AND MEASURES

Risk-adjusted 90-day mortality estimated using hierarchical logistic regression and comparison of the relative safety of hospitals within each cancer network estimated using standardized mortality ratios.

RESULTS

A total of 17 300 patients (59.2%; 8612 women and 8688 men; mean [SD] age, 74.7 [6.2] years) underwent complex cancer surgery at 59 top-ranked hospitals and 11 928 patients (40.8%; 6287 women and 5641 men; mean [SD] age, 76.2 [6.9] years) underwent complex cancer surgery at 343 affiliated hospitals. Overall, surgery performed at affiliated hospitals was associated with higher 90-day mortality (odds ratio, 1.40; 95% CI, 1.23-1.59; P < .001), with odds ratios that ranged from 1.32 (95% CI, 1.12-1.56; P = .001) for colectomy to 2.04 (95% CI, 1.41-2.95; P < .001) for gastrectomy. When the relative safety of each top-ranked cancer hospital was compared with its collective affiliates, the top-ranked hospital was safer than the affiliates in 41 of 49 studied networks (83.7%; 95% CI, 73.1%-93.3%).

CONCLUSIONS AND RELEVANCE

The likelihood of surviving complex cancer surgery appears to be greater at top-ranked cancer hospitals compared with the affiliated hospitals that share their brand. Further investigation of performance across trusted cancer networks could enhance informed decision making for complex cancer care.

Versorgungsstrukturen und Mengen-Ergebnis-Beziehung beim akuten Herzinfarkt – Verlaufsbetrachtung der deutschlandweiten Krankenhausabrechnungsdaten von 2005 bis 2015

Hintergrund Studien haben beschrieben, dass höhere Herzinfarktfallzahlen des behandelnden Krankenhauses mit besseren Behandlungsergebnissen assoziiert sind. Vor diesem Hintergrund wird die Entwicklung der akutstationären Herzinfarktversorgung sowie der Mengen-Ergebnisbeziehung im Zeitverlauf analysiert. Ziel der Arbeit ist, die Entwicklungen zu bewerten und Anhaltspunkte für eine Verbesserung der Herzinfarktversorgung in Deutschland abzuleiten.

Methode Anhand der deutschlandweiten Krankenhausabrechnungsdaten (DRG-Statistik) von 2005 bis 2015 wurden Patienten mit akutem Herzinfarkt im erstbehandelnden Krankenhaus identifiziert und anhand der jährlichen Herzinfarktfallzahl des behandelnden Krankenhauses in fallzahlgleiche Quintile eingeteilt.

Ergebnisse Im Beobachtungszeitraum zeigte sich ein zunehmender Anteil interventionell versorgter Herzinfarktpatienten. Die Krankenhaussterblichkeit im erstbehandelnden Krankenhaus ging insgesamt von 12,1 auf 8,7% zurück. In allen Jahren wurde in den höheren Fallzahlquintilen eine geringere Sterblichkeit im Vergleich zum unteren Fallzahlquintil beobachtet. Im Jahr 2015 zeigte sich im Vergleich zur Behandlung in Krankenhäusern mit sehr geringer Fallzahl ein um 20% reduziertes Sterberisiko (adjustiertes OR jeweils 0,8 [95% KI 0,7–0,9]) in Krankenhäusern mit mittlerer, hoher oder sehr hoher Fallzahl. Mehr als 40% der Krankenhäuser mit sehr geringer Fallzahl waren in städtischen Regionen lokalisiert.

Schlussfolgerung Eine gezieltere Steuerung von Patienten mit Herzinfarktsymptomen in Krankenhäuser mit hohen Herzinfarktfallzahlen könnte die Versorgung weiter verbessern. Eine solche Versorgungssteuerung ist sowohl aus Gründen der medizinischen Qualität als auch der Wirtschaftlichkeit insbesondere in städtischen Regionen erforderlich.

Relationship between hospital or surgeon volume and outcomes in joint arthroplasty: protocol for a suite of systematic reviews and dose-response meta-analyses

INTRODUCTION

Joint arthroplasty is a particularly complex orthopaedic surgical procedure performed on joints, including the hip, knee, shoulder, ankle, elbow, wrist and even digit joints. Increasing evidence from volume-outcomes research supports the finding that patients undergoing joint arthroplasty in high-volume hospitals or by high-volume surgeons achieve better outcomes, and minimum case load requirements have been established in some areas. However, the relationships between hospital/surgeon volume and outcomes in patients undergoing arthroplasty are not fully understood. Furthermore, whether elective arthroplasty should be restricted to high-volume hospitals or surgeons remains in dispute, and little is known regarding where the thresholds should be set for different types of joint arthroplasties.

METHODS AND ANALYSES

This is a protocol for a suite of systematic reviews and dose-response meta-analyses, which will be amended and updated in conjunction with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols. Electronic databases, including PubMed and Embase, will be searched for observational studies examining the relationship between the hospital or surgeon volume and clinical outcomes in adult patients undergoing primary or revision of joint arthroplasty. We will use records management software for study selection and a predefined standardised file for data extraction and management. Quality will be assessed using the Newcastle-Ottawa Scale, and the meta-analysis, subgroup analysis and sensitivity analysis will be performed using Stata statistical software. Once the volume-outcome relationships are established, we will examine the potential non-linear relationships between hospital/surgeon volume and outcomes and detect whether thresholds or turning points exist.

ETHICS AND DISSEMINATION

Ethical approval is not required, because these studies are based on aggregated published data. The results of this suite of systematic reviews and meta-analyses will be submitted to peer-reviewed journals for publication.

PROSPERO REGISTRATION NUMBER

CRD42017056639.

Association Between 30-Day Mortality After Percutaneous Coronary Intervention and Education and Certification Variables for New York State Interventional Cardiologists

BACKGROUND

Patients and other providers have access to few publicly available physician attributes that identify interventional cardiologists with better postprocedural outcomes, particularly in states without public reporting of outcomes. Interventional cardiology board certification, maintenance of certification, graduation from a US medical school, medical school ranking, and length of practice represent such publicly available attributes. Previous studies on these measures have shown mixed results.

METHODS AND RESULTS

We included interventional cardiologists practicing in New York State in the years 2011 to 2013. The primary outcome was 30-day risk-standardized mortality rate (RSMR) after percutaneous coronary intervention. Hierarchical regression modeling was used to analyze the physician attributes and was adjusted for provider caseload. A total of 356 providers were studied. The average 30-day RSMR was 1.1 (SD=0.1) deaths per 100 cases for all percutaneous coronary interventions and 0.7 (SD=0.1) deaths per 100 cases for nonemergent procedures. The primary outcome was slightly lower among providers with interventional cardiology board certification compared with noncertified providers (1.06 [SD=0.14] versus 1.14 [SD=0.14] deaths per 100 cases; P<0.001). In multivariable hierarchical regression modeling, after adjusting for provider caseload, none of the physician attributes were associated with the primary outcome. Provider caseload was significantly associated with 30-day RSMR independent of the other attributes.

CONCLUSIONS

Interventional cardiology board-certified providers had a modestly lower 30-day RSMR before accounting for caseload. However, after adjusting for provider caseload, none of the examined publicly available physician attributes, including interventional cardiology board certification, were independently associated with 30-day RSMR.

Outcomes of Care for Ischemic Heart Disease and Chronic Heart Failure in the Veterans Health Administration

Importance

The Department of Veterans Affairs (VA) operates a nationwide system of hospitals and hospital-affiliated clinics, providing health care to more than 2 million veterans with cardiovascular disease. While data permitting hospital comparisons of the outcomes of acute cardiovascular care (eg, myocardial infarction) are publicly available, little is known about variation across VA medical centers (VAMCs) in outcomes of care for populations of patients with chronic, high-risk cardiovascular conditions.

Objective

To determine whether there are substantial differences in cardiovascular outcomes across VAMCs.

Design, Setting, and Participants

Retrospective cohort study comprising 138 VA hospitals and each hospital's affiliated outpatient clinics. Patients were identified who received VA inpatient or outpatient care between 2010 and 2014. Separate cohorts were constructed for patients diagnosed as having either ischemic heart disease (IHD) or chronic heart failure (CHF). The data were analyzed between June 24, 2015, and November 21, 2017.

Exposures

Hierarchical linear models with VAMC-level random effects were estimated to compare risk-standardized mortality rates for IHD and for CHF across 138 VAMCs. Mortality estimates were risk standardized using a wide array of patient-level covariates derived from both VA and Medicare health care encounters.

Main Outcomes and Measures

All-cause mortality.

Results

The cohorts comprised 930 079 veterans with IHD and 348 015 veterans with CHF; both cohorts had a mean age of 77 years and were predominantly white (IHD, n = 822 665 [89%] and CHF, n = 287 871 [83%]) and male (IHD, n = 916 684 [99%] and CHF n = 341 352 [98%]). The VA-wide crude annual mortality rate was 7.4% for IHD and 14.5% for CHF. For IHD, VAMCs' risk-standardized mortality varied from 5.5% (95% CI, 5.2%-5.7%) to 9.4% (95% CI, 9.0%-9.9%) (P < .001 for the difference). For CHF, VAMCs' risk-standardized mortality varied from 11.1% (95% CI, 10.3%-12.1%) to 18.9% (95% CI, 18.3%-19.5%) (P < .001 for the difference). Twenty-nine VAMCs had IHD mortality rates that significantly exceeded the national mean, while 35 VAMCs had CHF mortality rates that significantly exceeded the national mean. Veterans Affairs medical centers' mortality rates among their IHD and CHF populations were not associated with 30-day mortality rates for myocardial infarction (R2 = 0.01; P = .35) and weakly associated with hospitalized heart failure 30-day mortality (R2 = 0.16; P < .001) and the VA's star rating system (R2 = 0.06; P = .005).

Conclusions and Relevance

Risk-standardized mortality rates for IHD and CHF varied widely across the VA health system, and this variation was not well explained by differences in demographics or comorbidities. This variation may signal substantial differences in the quality of cardiovascular care between VAMCs.

Creating Individual Surgeon Performance Assessments in a Statewide Hospital Surgical Quality Improvement Collaborative

BACKGROUND

Surgeon performance profiling is of great interest to surgeons, hospitals, health plans, and the public, yet efforts to date have been contested, with stakeholders at odds over the selection, reliability, and validity of metrics used. We sought to create surgeon-level comparative assessments within the Illinois Surgical Quality Improvement Collaborative.

STUDY DESIGN

American College of Surgeons NSQIP data were obtained for 51 Illinois hospitals covering a 30-month period from 2014 to 2016. Surgeon-level, risk-adjusted outcomes rates were estimated from 3-level crossed random effects logistic regression models and classified as low, as expected, or high for each of 7 postoperative outcomes. Model intra-class correlations and provider-specific reliability statistics were calculated.

RESULTS

A total of 123,141 cases were analyzed for 2,724 surgeons. Median provider case volume was 17 (interquartile range 4 to 54). Overall crude complication rates ranged from 0.62% to 7.14% across the 7 outcomes investigated. Surgeon-level variance estimates were low (intra-class correlation coefficients between 0.007 and 0.074). No performance outliers were detected for 3 of the outcomes measures, while a small number of outliers were identified for any morbidity (11 surgeons), surgical site infection (10 surgeons), death or serious morbidity (8 surgeons), and reoperation (1 surgeon). Among all physicians, median reliability was below 0.1 for each outcome.

CONCLUSIONS

Few individual surgeon performance outliers could be detected in NSQIP clinical registry data for a statewide hospital collaborative over a 30-month period using postoperative patient outcomes. Low surgeon-specific case volumes and minimal variance between surgeons may limit the utility of American College of Surgeons NSQIP outcomes measures for individual profiling. Alternative metrics, such as process measures, patient experience, composite measures, or technical skill assessments should be explored for surgeon-level measurement.

Adjustment for Atherosclerosis Diagnosis Distorts the Effects of Percutaneous Coronary Intervention and the Ranking of Hospital Performance

BACKGROUND

Coronary atherosclerosis raises the risk of acute myocardial infarction (AMI), and is usually included in AMI risk-adjustment models. Percutaneous coronary intervention (PCI) does not cause atherosclerosis, but may contribute to the notation of atherosclerosis in administrative claims. We investigated how adjustment for atherosclerosis affects rankings of hospitals that perform PCI.

METHODS AND RESULTS

This was a retrospective cohort study of 414 715 Medicare beneficiaries hospitalized for AMI between 2009 and 2011. The outcome was 30-day mortality. Regression models determined the association between patient characteristics and mortality. Rankings of the 100 largest PCI and non-PCI hospitals were assessed with and without atherosclerosis adjustment. Patients admitted to PCI hospitals or receiving interventional cardiology more frequently had an atherosclerosis diagnosis. In adjustment models, atherosclerosis was associated, implausibly, with a 42% reduction in odds of mortality (odds ratio=0.58, P<0.0001). Without adjustment for atherosclerosis, the number of expected lives saved by PCI hospitals increased by 62% (P<0.001). Hospital rankings also changed: 72 of the 100 largest PCI hospitals had better ranks without atherosclerosis adjustment, while 77 of the largest non-PCI hospitals had worse ranks (P<0.001).

CONCLUSIONS

Atherosclerosis is almost always noted in patients with AMI who undergo interventional cardiology but less often in medically managed patients, so adjustment for its notation likely removes part of the effect of interventional treatment. Therefore, hospitals performing more extensive imaging and more PCIs have higher atherosclerosis diagnosis rates, making their patients appear healthier and artificially reducing the expected mortality rate against which they are benchmarked. Thus, atherosclerosis adjustment is detrimental to hospitals providing more thorough AMI care.

Hospital Variation in Risk-Adjusted Pediatric Sepsis Mortality

OBJECTIVES

With continued attention to pediatric sepsis at both the clinical and policy levels, it is important to understand the quality of hospitals in terms of their pediatric sepsis mortality. We sought to develop a method to evaluate hospital pediatric sepsis performance using 30-day risk-adjusted mortality and to assess hospital variation in risk-adjusted sepsis mortality in a large state-wide sample.

DESIGN

Retrospective cohort study using administrative claims data.

SETTINGS

Acute care hospitals in the state of Pennsylvania from 2011 to 2013.

PATIENTS

Patients between the ages of 0-19 years admitted to a hospital with sepsis defined using validated International Classification of Diseases, Ninth revision, Clinical Modification, diagnosis and procedure codes.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

During the study period, there were 9,013 pediatric sepsis encounters in 153 hospitals. After excluding repeat visits and hospitals with annual patient volumes too small to reliably assess hospital performance, there were 6,468 unique encounters in 24 hospitals. The overall unadjusted mortality rate was 6.5% (range across all hospitals: 1.5-11.9%). The median number of pediatric sepsis cases per hospital was 67 (range across all hospitals: 30-1,858). A hierarchical logistic regression model for 30-day risk-adjusted mortality controlling for patient age, gender, emergency department admission, infection source, presence of organ dysfunction at admission, and presence of chronic complex conditions showed good discrimination (C-statistic = 0.80) and calibration (slope and intercept of calibration plot: 0.95 and -0.01, respectively). The hospital-specific risk-adjusted mortality rates calculated from this model varied minimally, ranging from 6.0% to 7.4%.

CONCLUSIONS

Although a risk-adjustment model for 30-day pediatric sepsis mortality had good performance characteristics, the use of risk-adjusted mortality rates as a hospital quality measure in pediatric sepsis is not useful due to the low volume of cases at most hospitals. Novel metrics to evaluate the quality of pediatric sepsis care are needed.

Does adding clinical data to administrative data improve agreement among hospital quality measures?

BACKGROUND

Hospital performance measures based on patient mortality and readmission have indicated modest rates of agreement. We examined if combining clinical data on laboratory tests and vital signs with administrative data leads to improved agreement with each other, and with other measures of hospital performance in the nation's largest integrated health care system.

METHODS

We used patient-level administrative and clinical data, and hospital-level data on quality indicators, for 2007-2010 from the Veterans Health Administration (VA). For patients admitted for acute myocardial infarction (AMI), heart failure (HF) and pneumonia we examined changes in hospital performance on 30-d mortality and 30-d readmission rates as a result of adding clinical data to administrative data. We evaluated whether this enhancement yielded improved measures of hospital quality, based on concordance with other hospital quality indicators.

RESULTS

For 30-d mortality, data enhancement improved model performance, and significantly changed hospital performance profiles; for 30-d readmission, the impact was modest. Concordance between enhanced measures of both outcomes, and with other hospital quality measures - including Joint Commission process measures, VA Surgical Quality Improvement Program (VASQIP) mortality and morbidity, and case volume - remained poor.

CONCLUSIONS

Adding laboratory tests and vital signs to measure hospital performance on mortality and readmission did not improve the poor rates of agreement across hospital quality indicators in the VA.

INTERPRETATION

Efforts to improve risk adjustment models should continue; however, evidence of validation should precede their use as reliable measures of quality.

Relationship Between State Malpractice Environment and Quality of Health Care in the United States

BACKGROUND

One major intent of the medical malpractice system in the United States is to deter negligent care and to create incentives for delivering high-quality health care. A study was conducted to assess whether state-level measures of malpractice risk were associated with hospital quality and patient safety.

METHODS

In an observational study of short-term, acute-care general hospitals in the United States that publicly reported in the Centers for Medicaid & Medicare Services Hospital Compare in 2011, hierarchical regression models were used to estimate associations between state-specific malpractice environment measures (rates of paid claims, average Medicare Malpractice Geographic Practice Cost Index [MGPCI], absence of tort reform laws, and a composite measure) and measures of hospital quality (processes of care, imaging utilization, 30-day mortality and readmission, Agency for Healthcare Research and Quality Patient Safety Indicators, and patient experience from the Hospital Consumer Assessment of Healthcare Providers and Systems [HCAHPS]).

RESULTS

No consistent association between malpractice environment and hospital process-of-care measures was found. Hospitals in areas with a higher MGPCI were associated with lower adjusted odds of magnetic resonance imaging overutilization for lower back pain but greater adjusted odds of overutilization of cardiac stress testing and brain/sinus computed tomography (CT) scans. The MGPCI was negatively associated with 30-day mortality measures but positively associated with 30-day readmission measures. Measures of malpractice risk were also negatively associated with HCAHPS measures of patient experience.

CONCLUSIONS

Overall, little evidence was found that greater malpractice risk improves adherence to recommended clinical standards of care, but some evidence was found that malpractice risk may encourage defensive medicine.

Improving Hospital Performance Rankings Using Discrete Patient Diagnoses for Risk Adjustment of Outcomes

OBJECTIVE

To assess the changes in patient outcome prediction and hospital performance ranking when incorporating diagnoses as risk adjusters rather than comorbidity indices.

DATA SOURCES

Healthcare Cost and Utilization Project State Inpatient Databases for New York State, 2005-2009.

STUDY DESIGN

Conducted tree-based classification for mortality and readmission by incorporating discrete patient diagnoses as predictors, comparing with traditional comorbidity indices such as those used for Centers for Medicare and Medicaid Services (CMS) outcome models.

PRINCIPAL FINDINGS

Diagnosis codes as predictors increased predictive accuracy 5.6 percent (95% CI: 4.5-6.9 percent) relative to CMS condition categories for heart failure 30-day mortality. Most other outcomes exhibited statistically significant accuracy gains and facility ranking shifts. Sensitivity analysis showed improvements even when predictors were limited to only the diagnoses included in CMS models.

CONCLUSIONS

Discretizing patient severity information beyond the levels of traditional comorbidity indices improves patient outcome predictions and substantially shifts facility rankings.

Free to Choose? Reform, Choice, and Consideration Sets in the English National Health Service

Choice in public services is controversial. We exploit a reform in the English National Health Service to assess the effect of removing constraints on patient choice. We estimate a demand model that explicitly captures the removal of the choice constraints imposed on patients. We find that, post-removal, patients became more responsive to clinical quality. This led to a modest reduction in mortality and a substantial increase in patient welfare. The elasticity of demand faced by hospitals increased substantially post- reform and we find evidence that hospitals responded to the enhanced incentives by improving quality. This suggests greater choice can raise quality.

Higher Hospital Spending on Occupational Therapy Is Associated With Lower Readmission Rates

Hospital executives are under continual pressure to control spending and improve quality. While prior studies have focused on the relationship between overall hospital spending and quality, the relationship between spending on specific services and quality has received minimal attention. The literature thus provides executives limited guidance regarding how they should allocate scarce resources. Using Medicare claims and cost report data, we examined the association between hospital spending for specific services and 30-day readmission rates for heart failure, pneumonia, and acute myocardial infarction. We found that occupational therapy is the only spending category where additional spending has a statistically significant association with lower readmission rates for all three medical conditions. One possible explanation is that occupational therapy places a unique and immediate focus on patients’ functional and social needs, which can be important drivers of readmission if left unaddressed.

Measuring the Volume-Outcome Relation for Complex Hospital Surgery

BACKGROUND

Prominent studies continue to measure the hospital volume-outcome relation using simple logistic or random-effects models. These regression models may not appropriately account for unobserved differences across hospitals (such as differences in organizational effectiveness) which could be mistaken for a volume outcome relation.

OBJECTIVE

To explore alternative estimation methods for measuring the volume-outcome relation for six major cancer operations, and to determine which estimation method is most appropriate.

METHODS

We analyzed patient-level hospital discharge data from three USA states and data from the American Hospital Association Annual Survey of Hospitals from 2000 to 2011. We studied six major cancer operations using three regression frameworks (logistic, fixed-effects, and random-effects) to determine the correlation between patient outcome (mortality) and hospital volume.

RESULTS

For our data, logistic and random-effects models suggest a non-zero volume effect, whereas fixed-effects models do not. Model-specification tests support the fixed-effects or random-effects model, depending on the surgical procedure; the basic logistic model is always rejected. Esophagectomy and rectal resection do not exhibit significant volume effects, whereas colectomy, pancreatic resection, pneumonectomy, and pulmonary lobectomy do.

CONCLUSIONS

The statistical significance of the hospital volume-outcome relation depends critically on the regression model. A simple logistic model cannot control for unobserved differences across hospitals that may be mistaken for a volume effect. Even when one applies panel-data methods, one must carefully choose between fixed- and random-effects models.

Relationship between hospital financial performance and publicly reported outcomes

BACKGROUND

Hospitals that have robust financial performance may have improved publicly reported outcomes.

OBJECTIVES

To assess the relationship between hospital financial performance and publicly reported outcomes of care, and to assess whether improved outcome metrics affect subsequent hospital financial performance.

DESIGN

Observational cohort study.

SETTING AND PATIENTS

Hospital financial data from the Office of Statewide Health Planning and Development in California in 2008 and 2012 were linked to data from the Centers for Medicare and Medicaid Services Hospital Compare website.

MEASUREMENTS

Hospital financial performance was measured by net revenue by operations, operating margin, and total margin. Outcomes were 30-day risk-standardized mortality and readmission rates for acute myocardial infarction (AMI), congestive heart failure (CHF), and pneumonia (PNA).

RESULTS

Among 279 hospitals, there was no consistent relationship between measures of financial performance in 2008 and publicly reported outcomes from 2008 to 2011 for AMI and PNA. However, improved hospital financial performance (by any of the 3 measures) was associated with a modest increase in CHF mortality rates (ie, 0.26% increase in CHF mortality rate for every 10% increase in operating margin [95% confidence interval: 0.07%-0.45%]). Conversely, there were no significant associations between outcomes from 2008 to 2011 and subsequent financial performance in 2012 (P > 0.05 for all).

CONCLUSIONS

Robust financial performance is not associated with improved publicly reported outcomes for AMI, CHF, and PNA. Financial incentives in addition to public reporting, such as readmissions penalties, may help motivate hospitals with robust financial performance to further improve publicly reported outcomes. Reassuringly, improved mortality and readmission rates do not necessarily lead to loss of revenue. Journal of Hospital Medicine 2016;11:481-488. © 2016 Society of Hospital Medicine.

Improving Medicare's Hospital Compare Mortality Model

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.

The prediction accuracy of dynamic mixed-effects models in clustered data

BACKGROUND

Clinical prediction models often fail to generalize in the context of clustered data, because most models fail to account for heterogeneity in outcome values and covariate effects across clusters. Furthermore, standard approaches for modeling clustered data, including generalized linear mixed-effects models, would not be expected to provide accurate predictions in novel clusters, because such predictions are typically based on the hypothetical mean cluster. We hypothesized that dynamic mixed-effects models, which incorporate data from previous predictions to refine the model for future predictions, would allow for cluster-specific predictions in novel clusters as the model is updated over time, thus improving overall model generalizability.

RESULTS

We quantified the potential gains in prediction accuracy from using a dynamic modeling strategy in a simulation study. Furthermore, because clinical prediction models in the context of clustered data often involve outcomes that are dependent on patient volume, we examined whether using dynamic mixed-effects models would be robust to misspecification of the volume-outcome relationship. Our results indicated that dynamic mixed-effects models led to substantial improvements in prediction accuracy in clustered populations over a broad range of conditions, and were uniformly superior to static models. In addition, dynamic mixed-effects models were particularly robust to misspecification of the volume-outcome relationship and to variation in the frequency of model updating. The extent of the improvement in prediction accuracy that was observed with dynamic mixed-effects models depended on the relative impact of fixed and random effects on the outcome as well as the degree of misspecification of model fixed effects.

CONCLUSIONS

Dynamic mixed-effects models led to substantial improvements in prediction model accuracy across a broad range of simulated conditions. Therefore, dynamic mixed-effects models could be a useful alternative to standard static models for improving the generalizability of clinical prediction models in the setting of clustered data, and, thus, well worth the logistical challenges that may accompany their implementation in practice.

Composite Measures of Health Care Provider Performance: A Description of Approaches

CONTEXT

Since the Institute of Medicine's 2001 report Crossing the Quality Chasm, there has been a rapid proliferation of quality measures used in quality-monitoring, provider-profiling, and pay-for-performance (P4P) programs. Al-though individual performance measures are useful for identifying specific processes and outcomes for improvement and tracking progress, they do not easily provide an accessible overview of performance. Composite measures aggregate individual performance measures into a summary score. By reducing the amount of data that must be processed, they facilitate (1) benchmarking of an organization's performance, encouraging quality improvement initiatives to match performance against high-performing organizations, and (2) profiling and P4P programs based on an organization's overall performance.

METHODS

We describe different approaches to creating composite measures,discuss their advantages and disadvantages, and provide examples of their use.

FINDINGS

The major issues in creating composite measures are (1) whether to aggregate measures at the patient level through all-or-none approaches or the facility level, using one of the several possible weighting schemes; (2) when combining measures on different scales, how to rescale measures (using z scores,range percentages, ranks, or 5-star categorizations); and (3) whether to use shrinkage estimators, which increase precision by smoothing rates from smaller facilities but also decrease transparency.

CONCLUSIONS

Because provider rankings and rewards under P4P programs may be sensitive to both context and the data, careful analysis is warranted before deciding to implement a particular method. A better understanding of both when and where to use composite measures and the incentives created by composite measures are likely to be important areas of research as the use of composite measures grows.

The relationship between historical risk-adjusted 30-day mortality and subsequent hip fracture outcomes: Retrospective cohort study

BACKGROUND

While 30-day risk-adjusted mortality is a performance measure for hip fracture care, it has not been shown to predict long-term outcomes. We assessed whether hospital rankings based on historical 30-day mortality predicted subsequent hip fracture outcomes.

METHODS

Using national Medicare data, we calculated annual hospital performance rankings based on standardized 30-day hip fracture mortality ratios. We used logistic regression to measure the association of patients' survival at 180 days with their hospital's ranking for the year prior to admission. Subgroup analyses assessed whether associations between hospital performance and 180-day outcomes were similar for community-dwelling patients as well as those living in nursing homes prior to fracture.

RESULTS

Out of 378,077 patients hospitalized with hip fractures between January 1, 2007 and June 30, 2009, 81,653 (21.6%) died by 180 days. Worse historical hospital performance was associated with a greater adjusted odds of 30 day mortality (odds ratio (OR), fourth vs. first quartile: 1.24, 95% confidence interval (CI): 1.18, 1.29, P<0.001) and 180 day mortality (OR, fourth vs. first quartile: 1.15, 95% CI 1.11, 1.18, P<0.001). Past hospital performance was associated with death or new nursing home placement among community dwellers (OR, fourth vs. first quartile: 1.09, 95% CI 1.05, 1.13, P<0.001), but was not associated with death or new dependence in locomotion among nursing home residents (OR 1.05, 95% CI 0.97, 1.15, P=0.229).

CONCLUSIONS

Better historical hospital hip fracture mortality predicts modest decreases in mortality at 180 days for subsequent patients, but is inconsistently associated with changes in functional outcomes.

LEVEL OF EVIDENCE

Level 3 (Non-randomized controlled cohort study).

Benchmarking Outpatient Rehabilitation Clinics Using Functional Status Outcomes

OBJECTIVE

To utilize functional status (FS) outcomes to benchmark outpatient therapy clinics.

DATA SOURCES

Outpatient therapy data from clinics using Focus on Therapeutic Outcomes (FOTO) assessments.

STUDY DESIGN

Retrospective analysis of 538 clinics, involving 2,040 therapists and 90,392 patients admitted July 2006-June 2008. FS at discharge was modeled using hierarchical regression methods with patients nested within therapists within clinics. Separate models were estimated for all patients, for those with lumbar, and for those with shoulder impairments. All models risk-adjusted for intake FS, age, gender, onset, surgery count, functional comorbidity index, fear-avoidance level, and payer type. Inverse probability weighting adjusted for censoring.

DATA COLLECTION METHODS

Functional status was captured using computer adaptive testing at intake and at discharge.

PRINCIPAL FINDINGS

Clinic and therapist effects explained 11.6 percent of variation in FS. Clinics ranked in the lowest quartile had significantly different outcomes than those in the highest quartile (p < .01). Clinics ranked similarly in lumbar and shoulder impairments (correlation = 0.54), but some clinics ranked in the highest quintile for one condition and in the lowest for the other.

CONCLUSIONS

Benchmarking models based on validated FS measures clearly separated high-quality from low-quality clinics, and they could be used to inform value-based-payment policies.

On the Hospital Volume and Outcome Relationship: Does Specialization Matter More Than Volume?

OBJECTIVE

To evaluate the relationship between hospital volume and outcome by focusing on alternative measures of volume that capture specialization and overall throughput of hospitals.

DATA SOURCES/STUDY SETTING

Hospital administrative data from the state of Victoria, Australia; data contain 1,798,474 admitted episodes reported by 135 public and private acute-care hospitals.

STUDY DESIGN

This study contrasts the volume-outcome relationship using regression models with different measures of volume; two-step and single-step risk-adjustment methods are used.

DATA COLLECTION/EXTRACTION METHODS

The sample is restricted to ischemic heart disease (IHD) patients (ICD-10 codes: I20-I25) admitted during 2001/02 to 2004/05.

PRINCIPAL FINDINGS

Overall hospital throughput and degree of specialization display more substantive implications for the volume-outcome relationship than conventional caseload volume measure. Two-step estimation when corrected for heteroscedasticity produces comparable results to single-step methods.

CONCLUSIONS

Different measures of volume could lead to vastly different conclusions about the volume-outcome relationship. Hospital specialization and throughput should both be included as measures of volume to capture the notion of size, focus, and possible congestion effects.

Variation in treatment of neonatal abstinence syndrome in US children's hospitals, 2004-2011

OBJECTIVE

Neonatal abstinence syndrome (NAS) is a drug withdrawal syndrome experienced by opioid-exposed infants. There is no standard treatment for NAS and surveys suggest wide variation in pharmacotherapy for NAS. Our objective was to determine whether different pharmacotherapies for NAS are associated with differences in outcomes and to determine whether pharmacotherapy and outcome vary by hospital.

STUDY DESIGN

We used the Pediatric Health Information System Database from 2004 to 2011 to identify a cohort of infants with NAS requiring pharmacotherapy. Mixed effects hierarchical negative binomial models evaluated the association between pharmacotherapy and hospital with length of stay (LOS), length of treatment (LOT) and hospital charges, after adjusting for socioeconomic variables and comorbid clinical conditions.

RESULT

Our cohort included 1424 infants with NAS from 14 children's hospitals. Among hospitals in our sample, six used morphine, six used methadone and two used phenobarbital as primary initial treatment for NAS. In multivariate analysis, when compared with NAS patients initially treated with morphine, infants treated with methadone had shorter LOT (incidence rate ratio (IRR) = 0.55; P < 0.0001) and LOS (IRR = 0.60; P < 0.0001). Phenobarbital as a second-line agent was associated with increased LOT (IRR = 2.09; P<0.0001), LOS (IRR = 1.78; P < 0.0001) and higher hospital charges (IRR = 1.84; P < 0.0001). After controlling for case-mix, hospitals varied in LOT, LOS and hospital charges.

CONCLUSION

We found variation in hospital in treatment for NAS among major US children's hospitals. In analyses controlling for possible confounders, methadone as initial treatment was associated with reduced LOT and hospital stay.

A hospital-specific template for benchmarking its cost and quality

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.

Challenges to measuring variation in readmission rates of neonatal intensive care patients

OBJECTIVE

To examine the viability of a hospital readmission quality metric for infants requiring neonatal intensive care.

METHODS

Two cohorts were constructed. First, a cohort was constructed from infants born in California from 1995 to 2009 at 23 to 34 weeks' gestation, using birth certificates linked to maternal and infant inpatient records (N = 343,625). Second, the Medicaid Analytic eXtract (MAX) identified Medicaid-enrolled infants admitted to the neonatal intensive care unit (NICU) during their birth hospitalization in 18 states during 2006 to 2008 (N = 254,722). Hospital and state-level unadjusted readmission rates and rates adjusted for gestational age, birth weight, insurance status, gender, and common complications of preterm birth were calculated.

RESULTS

Within California, there were wide variations in hospital-level readmission rates that were not completely explained through risk adjustment. Similar unadjusted variation was seen between states using MAX data, but risk adjustment and calculation of hospital-level rates were not possible because of missing gestational age, birth weight, and birth hospital data.

CONCLUSIONS

The California cohort shows significant variation in hospital-level readmission rates after risk adjustment, supporting the premise that readmission rates of prematurely born infants may reflect care quality. However, state data do not include term and early term infants requiring neonatal intensive care. MAX allows for multistate comparisons of all infants requiring NICU care. However, there were extensive missing data in the few states with sufficient information on managed care patients to calculate state-level measures. Constructing a valid readmission measure for NICU care across diverse states and regions requires improved data collection, including potential linkage between MAX data and vital statistics records.

Hospitals with higher nurse staffing had lower odds of readmissions penalties than hospitals with lower staffing

The Affordable Care Act's Hospital Readmissions Reduction Program (HRRP) penalizes hospitals based on excess readmission rates among Medicare beneficiaries. The aim of the program is to reduce readmissions while aligning hospitals' financial incentives with payers' and patients' quality goals. Many evidence-based interventions that reduce readmissions, such as discharge preparation, care coordination, and patient education, are grounded in the fundamentals of basic nursing care. Yet inadequate staffing can hinder nurses' efforts to carry out these processes of care. We estimated the effect that nurse staffing had on the likelihood that a hospital was penalized under the HRRP. Hospitals with higher nurse staffing had 25 percent lower odds of being penalized compared to otherwise similar hospitals with lower staffing. Investment in nursing is a potential system-level intervention to reduce readmissions that policy makers and hospital administrators should consider in the new regulatory environment as they examine the quality of care delivered to US hospital patients.

Fixed effects modelling for provider mortality outcomes: Analysis of the Australia and New Zealand Intensive Care Society (ANZICS) Adult Patient Data-base

BACKGROUND

Risk adjusted mortality for intensive care units (ICU) is usually estimated via logistic regression. Random effects (RE) or hierarchical models have been advocated to estimate provider risk-adjusted mortality on the basis that standard estimators increase false outlier classification. The utility of fixed effects (FE) estimators (separate ICU-specific intercepts) has not been fully explored.

METHODS

Using a cohort from the Australian and New Zealand Intensive Care Society Adult Patient Database, 2009-2010, the model fit of different logistic estimators (FE, random-intercept and random-coefficient) was characterised: Bayesian Information Criterion (BIC; lower values better), receiver-operator characteristic curve area (AUC) and Hosmer-Lemeshow (H-L) statistic. ICU standardised hospital mortality ratios (SMR) and 95%CI were compared between models. ICU site performance (FE), relative to the grand observation-weighted mean (GO-WM) on odds ratio (OR), risk ratio (RR) and probability scales were assessed using model-based average marginal effects (AME).

RESULTS

The data set consisted of 145355 patients in 128 ICUs, years 2009 (47.5%) & 2010 (52.5%), with mean(SD) age 60.9(18.8) years, 56% male and ICU and hospital mortalities of 7.0% and 10.9% respectively. The FE model had a BIC = 64058, AUC = 0.90 and an H-L statistic P-value = 0.22. The best-fitting random-intercept model had a BIC = 64457, AUC = 0.90 and H-L statistic P-value = 0.32 and random-coefficient model, BIC = 64556, AUC = 0.90 and H-L statistic P-value = 0.28. Across ICUs and over years no outliers (SMR 95% CI excluding null-value = 1) were identified and no model difference in SMR spread or 95%CI span was demonstrated. Using AME (OR and RR scale), ICU site-specific estimates diverged from the GO-WM, and the effect spread decreased over calendar years. On the probability scale, a majority of ICUs demonstrated calendar year decrease, but in the for-profit sector, this trend was reversed.

CONCLUSIONS

The FE estimator had model advantage compared with conventional RE models. Using AME, between and over-year ICU site-effects were easily characterised.

HIV quality report cards: impact of case-mix adjustment and statistical methods

BACKGROUND

There will be increasing pressure to publicly report and rank the performance of healthcare systems on human immunodeficiency virus (HIV) quality measures. To inform discussion of public reporting, we evaluated the influence of case-mix adjustment when ranking individual care systems on the viral control quality measure.

METHODS

We used data from the Veterans Health Administration (VHA) HIV Clinical Case Registry and administrative databases to estimate case-mix adjusted viral control for 91 local systems caring for 12 368 patients. We compared results using 2 adjustment methods, the observed-to-expected estimator and the risk-standardized ratio.

RESULTS

Overall, 10 913 patients (88.2%) achieved viral control (viral load ≤400 copies/mL). Prior to case-mix adjustment, system-level viral control ranged from 51% to 100%. Seventeen (19%) systems were labeled as low outliers (performance significantly below the overall mean) and 11 (12%) as high outliers. Adjustment for case mix (patient demographics, comorbidity, CD4 nadir, time on therapy, and income from VHA administrative databases) reduced the number of low outliers by approximately one-third, but results differed by method. The adjustment model had moderate discrimination (c statistic = 0.66), suggesting potential for unadjusted risk when using administrative data to measure case mix.

CONCLUSIONS

Case-mix adjustment affects rankings of care systems on the viral control quality measure. Given the sensitivity of rankings to selection of case-mix adjustment methods-and potential for unadjusted risk when using variables limited to current administrative databases-the HIV care community should explore optimal methods for case-mix adjustment before moving forward with public reporting.