Impact of an automated patient outcome feedback system on emergency medicine resident patient follow-up: An interrupted time series analysis

Abstract

Objectives

Emergency medicine (EM) residents desire, but often lack, reliable feedback of patient outcomes following handoffs to other providers. This gap is a substantial barrier to calibrating their diagnostic decision making and learning. To address this educational priority, we developed and evaluated the Post-Handoff Reports of Outcomes (PHAROS) system—an automated system within our electronic health record (EHR) to deliver provider-specific patient outcome feedback.

Methods

PHAROS includes: (1) individualized lists of patients seen and brief summaries of each case, (2) flags for important posthandoff events, and (3) links to charts to facilitate review. Starting June 2020, we coupled PHAROS with a resident educational session and individualized emails every 2 weeks outlining patients seen, number of posthandoff events, and instructions on how to access the PHAROS system.

Results

From July 2017 through April 2022, we measured the proportion of handoffs followed by reaccessing patients’ charts between 2 and 14 days posthandoff—a proxy for following up on the patient's outcomes. We performed an interrupted time series analysis on this outcome to determine if PHAROS was associated with a significant change in the trend of our outcome over time. Our secondary outcome was the number of times PHAROS was viewed each month. Our primary outcome had a significant increase in the slope over time (+0.13%/month, p = 0.03) after the introduction of the personalized reports and a nonsignificant change (−1.6%, p = 0.07) at the time of the intervention. The median (IQR) number of views of PHAROS per month was 33.2 (23.75–38.75).

Conclusions

The PHAROS system was associated with a significant increase in the rate of posthandoff chart reaccess among EM residents over time. The PHAROS project demonstrated the feasibility of harnessing the capabilities of the EHR to create an automated system to support EM trainee feedback of patient outcomes—a key component of diagnostic calibration and learning.