Managing Hospital Care: Data-driven decisions and comparisons

• Main Author: Hu, Wenqi
• Language: English
• Published: 2018
• Subjects: Operations research; Intensive care units > Admission and discharge; Medical care > Decision making; Hospitals > Data processing
• Online Access: https://doi.org/10.7916/D87Q0G8T

This dissertation focuses on utilizing data-driven approaches to objectively measure variation in the quality of care across different hospitals, understand how physicians make dynamic admission and routing decisions for patients, and propose potential changes in practice to improve the quality of care and patient flow management. This analysis was performed in the context of Intensive Care Units (ICUs) and the Emergency Department (ED). In the first part, we assess variation in the overall quality of care provided by both urban and rural hospitals under the same integrated healthcare delivery system when augmenting administrative data with detailed patient severity scores from the electronic medical records (EMRs). Using a new template matching methodology for more objective comparison, we found that the use of granular EMR data significantly reduces the variation across hospitals in common patient severity-of-illness levels. Further, we found that hospital rankings on 30-day mortality and estimates of length-of-stay (LOS) are statistically different from rankings based on administrative data. In the second part, we study ICU admission decision-making dynamically throughout a patient’s stay in the general ward/the Transitional Care Unit (TCU). We first used an instrumental variable approach and modern multivariate matching methods to rigorously estimate the potential benefits and costs of transferring patients to the ICU based on a real-time risk score for deterioration. We then used the quantified impact to calibrate a comprehensive simulation model to evaluate system performances under various new ICU transfer policies. We show that proactively transferring the most severe patients to the ICU could reduce mortality rates and LOS without increasing ICU congestion and causing other adverse effects. In the third part, we focus on understanding how physicians make ICU admission decisions for patients in the ED. We first used two sets of reduced-form regressions to understand 1) what and how patient risk factors and system controls impact the admission decision from the ED; and 2) what are the potential benefits of admitting patients from the ED to the ICU. We then proposed a dynamic discrete choice structural model to estimate to what extent physicians account for the inter-temporal externalities when deciding to admit a specific patient to the ICU, to the ward or let him/her wait in the ED. Note that the structural model estimation is still an ongoing process and more investigation is required to fine tune the details. Therefore, we will not discuss the structural model estimation results in this chapter, but only present the modeling framework and key estimation strategy.