Aligning incentives in health care: a multiscale decision theory approach

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.

Simulation-based optimization to improve hospital patient assignment to physicians and clinical units

A fundamental activity in hospital operations is patient assignment, which we define as the process of assigning hospital patients to specific physician services and clinical units based on their diagnosis. When the preferred assignment is not possible, typically due to capacity limits, hospitals often allow for overflow, which is the assignment of patients to other services and/or units. Overflow accelerates assignment, but can also reduce care quality and increase length of stay. This paper develops a discrete-event simulation model to evaluate different assignment strategies. Using a simulation-based optimization approach, we evaluate and heuristically optimize these strategies accounting for expected hospital and physician profit, care quality and patient waiting time. We apply the model using data from the University of Chicago Medical Center. We find that the strategies that use heuristically optimized designation of overflow services and units increase expected profit relative to the capacity-based strategy in which overflow patients are assigned to a service and unit with the most available capacity. We also find further improvement in the strategy that uses heuristically optimized overflow services and units as well as a holding unit that holds patients until a bed in their primary or secondary unit becomes available. Additionally, we demonstrate the effects of these strategies on other performance measures such as patient concentration, waiting time, and outcomes.