Impact of performance-based budgeting on quality outcomes in U.S. military health care facilities.

BACKGROUND: Performance-based budgeting (PBB) is a variation of pay for performance that has been used in government hospitals but could be applicable to any integrated system. It works by increasing or decreasing funding based on preestablished performance thresholds, which incentivizes organizations to improve performance. In late 2006, the U.S. Army implemented a PBB program that tied hospital-level funding decisions to performance on key cost and quality-related metrics. PURPOSE: The aim of this study was to estimate the impact of PBB on quality improvement in U.S. Army health care facilities. APPROACH: This study used a retrospective difference-in-differences analysis of data from two Defense Health Agency data repositories. The merged data set encompassed administrative, demographic, and performance information about 428 military health care facilities. Facility-level performance data on quality indicators were compared between 187 Army PBB facilities and a comparison group of 241 non-PBB Navy and Air Force facilities before and after program implementation. RESULTS: The Army's PBB programs had a positive impact on quality performance. Relative to comparison facilities, facilities that participated in PBB programs increased performance for over half of the indicators under investigation. Furthermore, performance was either sustained or continued to improve over 5 years for five of the six performance indicators examined long term. CONCLUSION: Study findings indicate that PBB may be an effective policy mechanism for improving facility-level performance on quality indicators. PRACTICE IMPLICATIONS: This study adds to the extant literature on pay for performance by examining the specific case of PBB. It demonstrates that quality performance can be influenced internally through centralized budgeting processes. Though specific to military hospitals, the findings might have applicability to other public and private sector hospitals who wish to incentivize performance internally in their organizational subunits through centralized budgeting processes.

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.

Evolving Radiologist Participation in Medicare Shared Savings Program Accountable Care Organizations.

PURPOSE: The aim of this study was to temporally characterize radiologist participation in Medicare Shared Savings Program (MSSP) accountable care organizations (ACOs). METHODS: Using CMS Physician and Other Supplier Public Use Files, ACO provider-level Research Identifiable Files, and Shared Savings Program ACO Public-Use Files for 2013 through 2018, characteristics of radiologist ACO participation were assessed over time. RESULTS: Between 2013 and 2018, the percentage of Medicare-participating radiologists affiliated with MSSP ACOs increased from 10.4% to 34.9%. During that time, the share of large ACOs (>20,000 beneficiaries) with participating radiologists averaged 87.0%, and the shares of medium ACOs (10,000-20,000) and small ACOs (<10,000) with participating radiologists rose from 62.5% to 66.0% and from 26.3% to 51.6%, respectively. The number of physicians in MSSP ACOs with radiologists was substantially larger than those without radiologists (mean range across years, 573-945 versus 107-179). Primary care physicians constituted a larger percentage of the physician population for ACOs without radiologists (average across years, 66.3% versus 38.5%), and ACOs with radiologists had a higher rate of specialist representation (56.0% versus 33.7%). Beneficiary age, race, and sex demographics were similar among radiologist-participating versus nonparticipating ACOs. CONCLUSIONS: In recent years, radiologist participation in MSSP ACOs has increased substantially. ACOs with radiologist participation are large and more diverse in their physician specialty composition. Nonparticipating radiologists should prepare accordingly.

Closing the Digital Health Evidence Gap: Development of a Predictive Score to Maximize Patient Outcomes.

Background: Clinical studies of telemedicine (TM) programs for chronic illness have demonstrated mixed results across settings and populations. With recent uptake in use of digital health modalities, more precise patient classification may improve outcomes, efficiency, and effectiveness. Objective: The purpose of the research was to develop a predictive score that measures the influence of patient characteristics on TM interventions. The central hypothesis is that disease type, illness severity, and the social determinants of health influence outcomes, including resource utilization, and can be precisely characterized. Methods: The retrospective study evaluated the feasibility of creating a patient "Telemedicine ImPact" (TIP) score derived from a Virginia Medicare and Medicaid claims data set. Claims were randomly selected, stratified by disease type, and matched by illness severity into a TM intervention group (N = 7,782) and a nontelemedicine "usual care" control cohort (N = 7,981). The individual records were then summarized into 15,762 cases with 80% of the cases used to develop, train, and test four predictive models (hospital utilization, readmissions, total utilization, and mortality) using 10-fold cross-validation. Results: Bayesian supervised machine learning achieved reference model performance index area under the curve for receiver operating characteristic (AUC/ROC) ≥0.85. Posterior probabilities for each outcome model were generated on a "hold-back" set of 3,082 cases. Robust parametric statistical methods enabled dimension reduction, model validation, and derivation of a reliable composite scaled score that quantified the overall health risk for each case. The TM intervention cohort demonstrated higher total utilization (representing the sum of inpatient, outpatient, and prescription use) and lower mean inpatient utilization than the usual standard of care. This finding suggests TM-based care may shift the composition of health resource utilization, reducing hospitalizations while increasing outpatient services, adjusted for patient differences. Conclusions: The creation of a patient score using machine learning to predict the effect of TM on outcomes is feasible. Adoption of the TIP score may reduce variability in results by more precisely accounting for the effects of patient characteristics on health outcomes and utilization. More consistent outcome prediction may lead to greater support for digital health.

Continuing Patient Care during Electronic Health Record Downtime.

INTRODUCTION: Electronic health record (EHR) downtime is any period during which the EHR system is fully or partially unavailable. These periods are operationally disruptive and pose risks to patients. EHR downtime has not sufficiently been studied in the literature, and most hospitals are not adequately prepared. OBJECTIVE: The objective of this study was to assess the operational implications of downtime with a focus on the clinical laboratory, and to derive recommendations for improved downtime contingency planning. METHODS: A hybrid qualitative-quantitative study based on historic performance data and semistructured interviews was performed at two mid-Atlantic hospitals. In the quantitative analysis, paper records from downtime events were analyzed and compared with normal operations. To enrich this quantitative analysis, interviews were conducted with 17 hospital employees, who had experienced several downtime events, including a hospital-wide EHR shutdown. RESULTS: During downtime, laboratory testing results were delayed by an average of 62% compared with normal operation. However, the archival data were incomplete due to inconsistencies in the downtime paper records. The qualitative interview data confirmed that delays in laboratory result reporting are significant, and further uncovered that the delays are often due to improper procedural execution, and incomplete or incorrect documentation. Interviewees provided a variety of perspectives on the operational implications of downtime, and how to best address them. Based on these insights, recommendations for improved downtime contingency planning were derived, which provide a foundation to enhance Safety Assurance Factors for EHR Resilience guides. CONCLUSION: This study documents the extent to which downtime events are disruptive to hospital operations. It further highlights the challenge of quantitatively assessing the implication of downtimes events, due to a lack of otherwise EHR-recorded data. Organizations that seek to improve and evaluate their downtime contingency plans need to find more effective methods to collect data during these times.

Implications of electronic health record downtime: an analysis of patient safety event reports.

Objective: We sought to understand the types of clinical processes, such as image and medication ordering, that are disrupted during electronic health record (EHR) downtime periods by analyzing the narratives of patient safety event report data. Materials and Methods: From a database of 80 381 event reports, 76 reports were identified as explicitly describing a safety event associated with an EHR downtime period. These reports were analyzed and categorized based on a developed code book to identify the clinical processes that were impacted by downtime. We also examined whether downtime procedures were in place and followed. Results: The reports were coded into categories related to their reported clinical process: Laboratory, Medication, Imaging, Registration, Patient Handoff, Documentation, History Viewing, Delay of Procedure, and General. A majority of reports (48.7%, n = 37) were associated with lab orders and results, followed by medication ordering and administration (14.5%, n = 11). Incidents commonly involved patient identification and communication of clinical information. A majority of reports (46%, n = 35) indicated that downtime procedures either were not followed or were not in place. Only 27.6% of incidents (n = 21) indicated that downtime procedures were successfully executed. Discussion: Patient safety report data offer a lens into EHR downtime-related safety hazards. Important areas of risk during EHR downtime periods were patient identification and communication of clinical information; these should be a focus of downtime procedure planning to reduce safety hazards. Conclusion: EHR downtime events pose patient safety hazards, and we highlight critical areas for downtime procedure improvement.

Modeling and designing health care payment innovations for medical imaging.

Payment innovations that better align incentives in health care are a promising approach to reduce health care costs and improve quality of care. Designing effective payment systems, however, is challenging due to the complexity of the health care system with its many stakeholders and their often conflicting objectives. There is a lack of mathematical models that can comprehensively capture and efficiently analyze the complex, multi-level interactions and thereby predict the effect of new payment systems on stakeholder decisions and system-wide outcomes. To address the need for multi-level health care models, we apply multiscale decision theory (MSDT) and build upon its recent advances. In this paper, we specifically study the Medicare Shared Savings Program (MSSP) for Accountable Care Organizations (ACOs) and determine how this incentive program affects computed tomography (CT) use, and how it could be redesigned to minimize unnecessary CT scans. The model captures the multi-level interactions, decisions and outcomes for the key stakeholders, i.e., the payer, ACO, hospital, primary care physicians, radiologists and patients. Their interdependent decisions are analyzed game theoretically, and equilibrium solutions - which represent stakeholders' normative decision responses - are derived. Our results provide decision-making insights for the payer on how to improve MSSP, for ACOs on how to distribute MSSP incentives among their members, and for hospitals on whether to invest in new CT imaging systems.

Computed tomography scan use variation: patient, hospital, and geographic factors.

OBJECTIVES: To examine patient, hospital, and geographic characteristics influencing variation in computed tomography (CT) scan use in inpatients in New York State. STUDY DESIGN: Retrospective cohort study. METHODS: We used the 2007 healthcare cost and utilization project's state inpatient database from the agency for healthcare research and quality and applied descriptive univariate statistics and logistic regression models to quantify the influence of each factor on CT scan use. RESULTS: The primary contributors to variation in CT scan use were the inpatients' diagnosis, age, and hospital county, whereas inpatients' sex and method of payment and hospitals' teaching status and size had very little effect. Inpatients diagnosed with trauma had the highest CT scan use; CT scan use increased with age for inpatients over 30 years; and CT scan use varied widely between counties. CONCLUSIONS: After controlling for patient and hospital characteristics, significant geographic variation remained at the level of the county, which indicates that additional research investigating the use of CT scans is necessary to understand the reasons behind small-area variation. Understanding the distribution and practice patterns of specific physician specialties may be helpful in curtailing underuse and overuse.