How hospitals can improve their public quality metrics: a decision-theoretic model

The public reporting of hospitals' quality of care is providing additional motivation for hospitals to deliver high-quality patient care. Hospital Compare, a consumer-oriented website by the Centers for Medicare and Medicaid Services (CMS), provides patients with detailed quality of care data on most US hospitals. Given that many quality metrics are the aggregate result of physicians' individual clinical decisions, the question arises if and how hospitals could influence their physicians so that their decisions positively contribute to hospitals' quality goals. In this paper, we develop a decision-theoretic model to explore how three different hospital interventions-incentivization, training, and nudging-may affect physicians' decisions. We focus our analysis on Outpatient Measure 14 (OP-14), which is an imaging quality metric that reports the percentage of outpatients with a brain computed tomography (CT) scan, who also received a same-day sinus CT scan. In most cases, same-day brain and sinus CT scans are considered unnecessary, and high utilizing hospitals aim to reduce their OP-14 metric. Our model captures the physicians' imaging decision process accounting for medical and behavioral factors, in particular the uncertainty in clinical assessment and a physician's diagnostic ability. Our analysis shows how hospital interventions of incentivization, training, and nudging affect physician decisions and consequently OP-14. This decision-theoretic model provides a foundation to develop insights for policy makers on the multi-level effects of their policy decisions.

Correlation of Subjective Hospital Compare Metrics With Objective Outcomes of Cranial Neurosurgical Procedures in New York State

Background

Public reporting is at the forefront of health care reform.

Objective

To investigate whether patient satisfaction as expressed in a public reporting platform correlates with objective outcomes for cranial neurosurgery patients.

Methods

We performed a cohort study involving patients undergoing cranial neurosurgery from 2009 to 2013 who were registered in the Statewide Planning and Research Cooperative System database. This cohort was merged with the corresponding data from the Centers for Medicare and Medicaid Services Hospital Compare website. The association of patient satisfaction metrics with outcomes was examined with the use of a propensity-adjusted regression model.

Results

Overall, 19 591 patients underwent cranial neurosurgery during the study. Using a propensity-adjusted multivariable regression analysis, we demonstrated that hospitals with a greater percentage of patient-assigned "high" scores had decreased mortality (OR, 0.60; 95% CI, 0.53-0.67), rate of discharge to rehabilitation (OR, 0.93; 95% CI, 0.88-0.98), length of stay (adjusted difference, -1.29; 95% CI, -1.46 to -1.13), and hospitalization charges (adjusted difference, -23%; 95% CI, -36% to -9%) after cranial neurosurgery. Similar associations were identified for hospitals with a higher percentage of patients, who would recommend these institutions to others.

Conclusion

In a Centers for Medicare and Medicaid Services Hospital Compare-Statewide Planning and Research Cooperative System merged dataset, we observed an association of higher performance in patient satisfaction measures with decreased mortality, rate of discharge to rehabilitation, hospitalization charges, and length of stay.

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.