Financial Impacts And Operational Implications Of Private Equity Acquisition Of US Hospitals.

Although private equity acquisition of short-term acute care hospitals purportedly improves efficiency and cost-effectiveness, financial performance after acquisition remains unexamined. We compared changes in the financial performance of 176 hospitals acquired during 2005-14 versus changes in matched control hospitals. Acquisition was associated with a $432 decrease in cost per adjusted discharge and a 1.78-percentage-point increase in operating margin. The majority of acquisitions-134 members of the Hospital Corporation of America, acquired in 2006-were associated with a $559 decrease in cost per adjusted discharge but no change in operating margin. Conversely, non-HCA hospitals exhibited a 3.27-percentage-point increase in operating margin without a concomitant change in cost per adjusted discharge. When we examined markers of hospital capacity, operational efficiency, and costs, we found that private equity acquisition was associated with decreases in total beds, ratio of outpatient to inpatient charges, and staffing (total personnel and nursing full-time equivalents and total full-time equivalents per occupied bed). Therefore, financial performance improved after acquisition, whereas patient throughput and inpatient utilization increased and staffing metrics decreased. Future research is needed to identify any unintended trade-offs with safety and quality.

Development and Performance of a Clinical Decision Support Tool to Inform Resource Utilization for Elective Operations.

Importance: Hospitals ceased most elective procedures during the height of coronavirus disease 2019 (COVID-19) infections. As hospitals begin to recommence elective procedures, it is necessary to have a means to assess how resource intensive a given case may be. Objective: To evaluate the development and performance of a clinical decision support tool to inform resource utilization for elective procedures. Design, Setting, and Participants: In this prognostic study, predictive modeling was used on retrospective electronic health records data from a large academic health system comprising 1 tertiary care hospital and 2 community hospitals of patients undergoing scheduled elective procedures from January 1, 2017, to March 1, 2020. Electronic health records data on case type, patient demographic characteristics, service utilization history, comorbidities, and medications were and abstracted and analyzed. Data were analyzed from April to June 2020. Main Outcomes and Measures: Predicitons of hospital length of stay, intensive care unit length of stay, need for mechanical ventilation, and need to be discharged to a skilled nursing facility. These predictions were generated using the random forests algorithm. Predicted probabilities were turned into risk classifications designed to give assessments of resource utilization risk. Results: Data from the electronic health records of 42 199 patients from 3 hospitals were abstracted for analysis. The median length of stay was 2.3 days (range, 1.3-4.2 days), 6416 patients (15.2%) were admitted to the intensive care unit, 1624 (3.8%) received mechanical ventilation, and 2843 (6.7%) were discharged to a skilled nursing facility. Predictive performance was strong with an area under the receiver operator characteristic ranging from 0.76 to 0.93. Sensitivity of the high-risk and medium-risk groupings was set at 95%. The negative predictive value of the low-risk grouping was 99%. We integrated the models into a daily refreshing Tableau dashboard to guide decision-making. Conclusions and Relevance: The clinical decision support tool is currently being used by surgical leadership to inform case scheduling. This work shows the importance of a learning health care environment in surgical care, using quantitative modeling to guide decision-making.