Improving Medicare's Hospital Compare Mortality Model

Payer-Negotiated Price Variation and Relationship to Surgical Outcomes for the Most Common Cancers at NCI-Designated Cancer Centers

BACKGROUND

Federal rules mandate that hospitals publish payer-specific negotiated prices for all services. Little is known about variation in payer-negotiated prices for surgical oncology services or their relationship to clinical outcomes. We assessed variation in payer-negotiated prices associated with surgical care for common cancers at National Cancer Institute (NCI)-designated cancer centers and determined the effect of increasing payer-negotiated prices on the odds of morbidity and mortality.

MATERIALS AND METHODS

A cross-sectional analysis of 63 NCI-designated cancer center websites was employed to assess variation in payer-negotiated prices. A retrospective cohort study of 15,013 Medicare beneficiaries undergoing surgery for colon, pancreas, or lung cancers at an NCI-designated cancer center between 2014 and 2018 was conducted to determine the relationship between payer-negotiated prices and clinical outcomes. The primary outcome was the effect of median payer-negotiated price on odds of a composite outcome of 30 days mortality and serious postoperative complications for each cancer cohort.

RESULTS

Within-center prices differed by up to 48.8-fold, and between-center prices differed by up to 675-fold after accounting for geographic variation in costs of providing care. Among the 15,013 patients discharged from 20 different NCI-designated cancer centers, the effect of normalized median payer-negotiated price on the composite outcome was clinically negligible, but statistically significantly positive for colon [aOR 1.0094 (95% CI 1.0051-1.0138)], lung [aOR 1.0145 (1.0083-1.0206)], and pancreas [aOR 1.0080 (1.0040-1.0120)] cancer cohorts.

CONCLUSIONS

Payer-negotiated prices are statistically significantly but not clinically meaningfully related to morbidity and mortality for the surgical treatment of common cancers. Higher payer-negotiated prices are likely due to factors other than clinical quality.

Multilevel Quality Indicators: Methodology and Monte Carlo Evidence

BACKGROUND

Quality indicators are frequently used to assess the performance of health care providers, in particular hospitals. Established approaches to the design of such indicators are subject to distortions due to indirect standardization and high variance of estimators. Indicators for geographical regions are rarely considered.

OBJECTIVES

To develop and evaluate a methodology of multilevel quality indicators (MQIs) for both health care providers and geographical regions.

RESEARCH DESIGN

We formally derived MQIs from a statistical multilevel model, which may include characteristics of patients, providers, and regions. We used Monte Carlo simulation to assess the performance of MQIs relative to established approaches based on the standardized mortality/morbidity ratio (SMR) and the risk-standardized mortality rate (RSMR).

MEASURES

Rank correlation between true provider/region effects and quality indicator estimates; shares of the 10% best and 10% worst providers identified by the quality indicators.

RESULTS

The proposed MQIs are (1) standardized hospital outcome rate (SHOR), (2) regional SHOR, and (3) regional standardized patient outcome rate. Monte Carlo simulations indicated that the SHOR provides substantially better estimates of provider performance than the SMR and risk-standardized mortality rate in almost all scenarios. The regional standardized patient outcome rate was slightly more stable than the regional SMR. We also found that modeling of regional characteristics generally improves the adequacy of provider-level estimates.

CONCLUSIONS

MQIs methodology facilitates adequate and efficient estimation of quality indicators for both health care providers and geographical regions.

Reformulating provider profiling by grouping providers treating similar patients prior to evaluating performance

Standard approaches to comparing health providers' performance rely on hierarchical logistic regression models that adjust for patient characteristics at admission. Estimates from these models may be misleading when providers treat different patient populations and the models are misspecified. To address this limitation, we propose a novel profiling approach that identifies groups of providers treating similar populations of patients and then evaluates providers' performance within each group. The groups of providers are identified using a Bayesian multilevel finite mixture of general location models. To compare the performance of our proposed profiling approach to standard methods, we use patient-level data from 119 skilled nursing facilities in Massachusetts. We use simulated and observed outcome data to explore the performance of these profiling methods in different settings. In simulations, our proposed method classifies providers to groups with similar patients' admission characteristics. In addition, in the presence of limited overlap in patient characteristics across providers and misspecifications of the outcome model, the provider-level estimates obtained using our approach identified providers that under- and overperformed compared to the standard regression-based approaches more accurately.

High-Dimensional Fixed Effects Profiling Models and Applications in End-Stage Kidney Disease Patients: Current State and Future Directions

Profiling analysis aims to evaluate health care providers, including hospitals, nursing homes, or dialysis facilities among others with respect to a patient outcome, such as 30-day unplanned hospital readmission or mortality. Fixed effects (FE) profiling models have been developed over the last decade, motivated by the overall need to (a) improve accurate identification or "flagging" of under-performing providers, (b) relax assumptions inherent in random effects (RE) profiling models, and (c) take into consideration the unique disease characteristics and care/treatment processes of end-stage kidney disease (ESKD) patients on dialysis. In this paper, we review the current state of FE methodologies and their rationale in the ESKD population and illustrate applications in four key areas: profiling dialysis facilities for (1) patient hospitalizations over time (longitudinally) using standardized dynamic readmission ratio (SDRR), (2) identification of dialysis facility characteristics (e.g., staffing level) that contribute to hospital readmission, and (3) adverse recurrent events using standardized event ratio (SER). Also, we examine the operating characteristics with a focus on FE profiling models. Throughout these areas of applications to the ESKD population, we identify challenges for future research in both methodology and clinical studies.

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).

Improving target price calculations in Medicare bundled payment programs

OBJECTIVE

To compare the predictive accuracy of two approaches to target price calculations under Bundled Payments for Care Improvement-Advanced (BPCI-A): the traditional Centers for Medicare and Medicaid Services (CMS) methodology and an empirical Bayes approach designed to mitigate the effects of regression to the mean.

DATA SOURCES

Medicare fee-for-service claims for beneficiaries discharged from acute care hospitals between 2010 and 2016.

STUDY DESIGN

We used data from a baseline period (discharges between January 1, 2010 and September 30, 2013) to predict spending in a performance period (discharges between October 1, 2015 and June 30, 2016). For 23 clinical episode types in BPCI-A, we compared the average prediction error across hospitals associated with each statistical approach. We also calculated an average across all clinical episode types and explored differences by hospital size.

DATA COLLECTION/EXTRACTION METHODS

We used a 20% sample of Medicare claims, excluding hospitals and episode types with small numbers of observations.

PRINCIPAL FINDINGS

The empirical Bayes approach resulted in significantly more accurate episode spending predictions for 19 of 23 clinical episode types. Across all episode types, prediction error averaged $8456 for the CMS approach versus $7521 for the empirical Bayes approach. Greater improvements in accuracy were observed with increasing hospital size.

CONCLUSIONS

CMS should consider using empirical Bayes methods to calculate target prices for BPCI-A.

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.

Hospital-acquired pressure ulcer incident rates among hospitals that implement an education program for staff, patients, and family caregivers inclusive of an after discharge follow-up program in Saudi Arabia

A tertiary public hospital in Saudi Arabia set out in 2015 to establish a team focused on reducing hospital-acquired pressure ulcers (HAPUs). The pressure ulcer prevention program (PUPP) had a multifaceted approach and data were collected for a period of 5 years. The results showed a definite reduction in the incidences of HAPUs. Many such programs show similar positive results and echo many of the same considerations of risk, prevention strategies, and the need for early intervention. However, none of the other studies either replicate the hospital's PUPP nor the extent of the positive and lasting effect of the program. Eager to determine the contributing factor(s) in order that the project success could be continued and possibly replicated in other quality improvement projects, it was decided that an examination and comparison of other similar programs and their results would be necessary in order to uncover the answer. It was determined that the in-person in-home discharge follow-up portion of the program most likely had the largest effect on the outcomes. Outcomes that were supported by the pre-work completed during the hospital portion of the PUPP towards reducing HAPUs and readmissions.

Association of Do-Not-Resuscitate Patient Case Mix With Publicly Reported Risk-Standardized Hospital Mortality and Readmission Rates

IMPORTANCE

The Centers for Medicare and Medicaid Services's (CMS's) 30-day risk-standardized mortality rate (RSMR) and risk-standardized readmission rate (RSRR) models do not adjust for do-not-resuscitate (DNR) status of hospitalized patients and may bias Hospital Readmissions Reduction Program (HRRP) financial penalties and Overall Hospital Quality Star Ratings.

OBJECTIVE

To identify the association between hospital-level DNR prevalence and condition-specific 30-day RSMR and RSRR and the implications of this association for HRRP financial penalty.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study obtained patient-level data from the Medicare Limited Data Set Inpatient Standard Analytical File and hospital-level data from the CMS Hospital Compare website for all consecutive Medicare inpatient encounters from July 1, 2015, to June 30, 2018, in 4484 US hospitals. Hospitalized patients had a principal diagnosis of acute myocardial infarction (AMI), heart failure (HF), stroke, pneumonia, or chronic obstructive pulmonary disease (COPD). Incoming acute care transfers, discharges against medical advice, and patients coming from or discharged to hospice were among those excluded from the analysis.

EXPOSURES

Present-on-admission (POA) DNR status was defined as an International Classification of Diseases, Ninth Revision diagnosis code of V49.86 (before October 1, 2015) or as an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis code of Z66 (beginning October 1, 2015). Hospital-level prevalence of POA DNR status was calculated for each of the 5 conditions.

MAIN OUTCOMES AND MEASURES

Hospital-level 30-day RSMRs and RSRRs for 5 condition-specific cohorts (mortality cohorts: AMI, HF, stroke, pneumonia, and COPD; readmission cohorts: AMI, HF, pneumonia, and COPD) and HRRP financial penalty status (yes or no).

RESULTS

Included in the study were 4 884 237 inpatient encounters across condition-specific 30-day mortality cohorts (patient mean [SD] age, 78.8 [8.5] years; 2 608 182 women [53.4%]) and 4 450 378 inpatient encounters across condition-specific 30-day readmission cohorts (patient mean [SD] age, 78.6 [8.5] years; 2 349 799 women [52.8%]). Hospital-level median (interquartile range [IQR]) prevalence of POA DNR status in the mortality cohorts varied: 11% (7%-16%) for AMI, 13% (7%-23%) for HF, 14% (9%-22%) for stroke, 17% (9%-26%) for pneumonia, and 10% (5%-18%) for COPD. For the readmission cohorts, the hospital-level median (IQR) POA DNR prevalence was 9% (6%-15%) for AMI, 12% (6%-22%) for HF, 16% (8%-24%) for pneumonia, and 9% (4%-17%) for COPD. The 30-day RSMRs were significantly higher for hospitals in the highest quintiles vs the lowest quintiles of DNR prevalence (eg, AMI: 12.9 [95% CI, 12.8-13.1] vs 12.5 [95% CI, 12.4-12.7]; P < .001). The inverse was true among the readmission cohorts, with the highest quintiles of DNR prevalence exhibiting the lowest RSRRs (eg, AMI: 15.3 [95% CI, 15.1-15.5] vs 15.9 [95% CI, 15.7-16.0]; P < .001). A 1% absolute increase in risk-adjusted hospital-level DNR prevalence was associated with greater odds of avoiding HRRP financial penalty (odds ratio, 1.06; 95% CI, 1.04-1.08; P < .001).

CONCLUSIONS AND RELEVANCE

This cross-sectional study found that the lack of adjustment in CMS 30-day RSMR and RSRR models for POA DNR status of hospitalized patients may be associated with biased readmission penalization and hospital-level performance.

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.

Profiling dialysis facilities for adverse recurrent events

Profiling analysis aims to evaluate health care providers, such as hospitals, nursing homes, or dialysis facilities, with respect to a patient outcome. Previous profiling methods have considered binary outcomes, such as 30-day hospital readmission or mortality. For the unique population of dialysis patients, regular blood works are required to evaluate effectiveness of treatment and avoid adverse events, including dialysis inadequacy, imbalance mineral levels, and anemia among others. For example, anemic events (when hemoglobin levels exceed normative range) are recurrent and common for patients on dialysis. Thus, we propose high-dimensional Poisson and negative binomial regression models for rate/count outcomes and introduce a standardized event ratio measure to compare the event rate at a specific facility relative to a chosen normative standard, typically defined as an "average" national rate across all facilities. Our proposed estimation and inference procedures overcome the challenge of high-dimensional parameters for thousands of dialysis facilities. Also, we investigate how overdispersion affects inference in the context of profiling analysis. The proposed methods are illustrated with profiling dialysis facilities for recurrent anemia events.

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.

Replication Bayes factors from evidence updating

We describe a general method that allows experimenters to quantify the evidence from the data of a direct replication attempt given data already acquired from an original study. These so-called replication Bayes factors are a reconceptualization of the ones introduced by Verhagen and Wagenmakers (Journal of Experimental Psychology: General, 143(4), 1457-1475 2014) for the common t test. This reconceptualization is computationally simpler and generalizes easily to most common experimental designs for which Bayes factors are available.

Performance Characteristics of Profiling Methods and the Impact of Inadequate Case-mix Adjustment

Profiling or evaluation of health care providers involves the application of statistical models to compare each provider's performance with respect to a patient outcome, such as unplanned 30-day hospital readmission, adjusted for patient case-mix characteristics. The nationally adopted method is based on random effects (RE) hierarchical logistic regression models. Although RE models are sensible for modeling hierarchical data, novel high dimensional fixed effects (FE) models have been proposed which may be well-suited for the objective of identifying sub-standard performance. However, there are limited comparative studies. Thus, we examine their relative performance, including the impact of inadequate case-mix adjustment.

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.

Hospitals Known for Nursing Excellence Perform Better on Value Based Purchasing Measures

It is well-established that hospitals recognized for good nursing care - Magnet hospitals - are associated with better patient outcomes. Less is known about how Magnet hospitals compare to non-Magnets on quality measures linked to Medicare reimbursement. The purpose of this study was to determine how Magnet hospitals perform compared to matched non-Magnet hospitals on Hospital Value Based Purchasing (VBP) measures. A cross-sectional analysis of three linked data sources was performed. The sample included 3,021 non-federal acute care hospitals participating in the VBP program (323 Magnets; 2,698 non-Magnets). Propensity score matching was used to match Magnet and non-Magnet hospitals with similar hospital characteristics. After matching, linear and logistic regression models were used to examine the relationship between Magnet status and VBP performance. After matching and adjusting for hospital characteristics, Magnet recognition predicted higher scores on Total Performance (Regression Coefficient [RC] = 1.66, p < 0.05), Clinical Processes (RC = 3.85; p < 0.01), and Patient Experience (RC = 6.33; p < 0.001). The relationships between Magnet recognition and the Outcome and Efficiency domains were not statistically significant. Magnet hospitals known for nursing excellence perform better on Hospital VBP measures. As healthcare systems adapt to evolving incentives that reward value, attention to nurses at the front lines may be central to ensuring high-value care for patients.

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.

Potential Implications of Recent and Proposed Changes in the Regulatory Oversight of Solid Organ Transplantation in the United States

Every 6 months, the Scientific Registry of Transplant Recipients (SRTR) publishes evaluations of every solid organ transplant program in the United States, including evaluations of 1-year patient and graft survival. The Centers for Medicare & Medicaid Services (CMS) and the Organ Procurement and Transplantation Network (OPTN) Membership and Professional Standards Committee (MPSC) use SRTR's 1-year evaluations for regulatory review of transplant programs. Concern has been growing that the regulatory scrutiny of transplant programs with lower-than-expected outcomes is harmful, causing programs to undertake fewer high-risk transplants and leading to unnecessary organ discards. As a result, CMS raised its threshold for a "Condition-Level Deficiency" designation of observed relative to expected 1-year graft or patient survival from 1.50 to 1.85. Exceeding this threshold in the current SRTR outcomes report and in one of the four previous reports leads to scrutiny that may result in loss of Medicare funding. For its part, OPTN is reviewing a proposal from the MPSC to also change its performance criteria thresholds for program review, to review programs with "substantive clinical differences." We review the details and implications of these changes in transplant program oversight.

New Evidence on What Works in Effective Public Reporting

OBJECTIVE

To describe the current state of the public reporting field and provide guidance to public report producers based on the evidence.

PRINCIPAL FINDINGS

Public reports should address the questions and priorities that consumers actually have; present information credibly and in a way that is understood by the intended audience; reach the intended audience; and enable consumers to act on the information.

CONCLUSIONS

Public reports have advanced greatly in recent years, but there remains much room for improvement. Report producers should continually evaluate their reports and apply the latest evidence to maximize their usefulness and impact.