TY - JOUR
T1 - How to assess the impact of process improvement interventions with routinely collected longitudinal hospital data
AU - Martinez, Diego
AU - Jalalpour, Mehdi
AU - Efron, David
AU - Levin, Scott
N1 - Publisher Copyright:
© 2019, © 2019 “IISE”.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/10/2
Y1 - 2019/10/2
N2 - Objective: To show how interrupted time series can be used to isolate and measure the impact of process improvement while accounting for confounders often present in complex hospital operations. Methods: Retrospective cohort study comparing the volume of operating room exit delays (OR holds) 52 weeks before and 62 weeks after implementation of a surgical patient throughput optimization program at a tertiary academic hospital. Time-stamped electronic medical records data were collected and analyzed using interrupted time-series design. Segmented regression and Box-Jenkins time series analysis were used to measure OR hold volume pre- and post-implementation, controlling for secular trends in surgical volume, downstream capacity, and the loss of high-volume surgeons. Results: A total of 8,983 surgical patients were discharged during the pre-intervention period and 9,855 during the post-intervention period. The median weekly discharge volume pre-intervention was 175 (interquartile range [IQR] 164–180), and the median bed occupancy was 86% (IQR 84.6–88.1%). The median weekly discharge volume post-intervention was 163 (IQR 150.5–169.8), and the median bed occupancy was 82.1% (IQR 78.9–84.7%). Post-intervention, there was an immediate 60% (95% confidence interval, 54–70%) reduction in the number of OR holds that was sustained over the 14-month post-intervention period.
AB - Objective: To show how interrupted time series can be used to isolate and measure the impact of process improvement while accounting for confounders often present in complex hospital operations. Methods: Retrospective cohort study comparing the volume of operating room exit delays (OR holds) 52 weeks before and 62 weeks after implementation of a surgical patient throughput optimization program at a tertiary academic hospital. Time-stamped electronic medical records data were collected and analyzed using interrupted time-series design. Segmented regression and Box-Jenkins time series analysis were used to measure OR hold volume pre- and post-implementation, controlling for secular trends in surgical volume, downstream capacity, and the loss of high-volume surgeons. Results: A total of 8,983 surgical patients were discharged during the pre-intervention period and 9,855 during the post-intervention period. The median weekly discharge volume pre-intervention was 175 (interquartile range [IQR] 164–180), and the median bed occupancy was 86% (IQR 84.6–88.1%). The median weekly discharge volume post-intervention was 163 (IQR 150.5–169.8), and the median bed occupancy was 82.1% (IQR 78.9–84.7%). Post-intervention, there was an immediate 60% (95% confidence interval, 54–70%) reduction in the number of OR holds that was sustained over the 14-month post-intervention period.
KW - Care delivery process
KW - healthcare management
KW - length of stay
KW - patient flow
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U2 - 10.1080/24725579.2019.1658660
DO - 10.1080/24725579.2019.1658660
M3 - Article
AN - SCOPUS:85073801987
VL - 9
SP - 371
EP - 375
JO - IISE Transactions on Healthcare Systems Engineering
JF - IISE Transactions on Healthcare Systems Engineering
SN - 2472-5579
IS - 4
ER -