Anesthesiology Control Tower: Feedback Alerts to Supplement Treatment (ACTFAST) (Missouri)

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Anesthesiology Control Tower: Feedback Alerts to Supplement Treatment (ACTFAST) - Final Report

Citation:
Avidan M. Anesthesiology Control Tower: Feedback Alerts to Supplement Treatment (ACTFAST) - Final Report. (Prepared by the University of Utah under Grant No. R21 HS024581). Rockville, MD: Agency for Healthcare Research and Quality, 2020. (PDF, 243.65 KB)

The findings and conclusions in this document are those of the author(s), who are responsible for its content, and do not necessarily represent the views of AHRQ. No statement in this report should be construed as an official position of AHRQ or of the U.S. Department of Health and Human Services.
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Using algorithms can predict postoperative adverse outcomes with a high degree of accuracy leading to better outcomes for the highest risk patients.

Project Details - Ended

Summary:

Most patients undergo surgery to cure a condition or improve quality of life; however, an estimated 1 to 5 percent of surgical patients die within 1 month of the operation. Furthermore, 10 to 20 percent of these patients experience major complications such as heart attacks, unremitting pain, infections, and blood clots in the weeks to months following their procedures. Some of this morbidity and mortality may be preventable through early identification of risk factors and better communication regarding risk mitigation.

To address these issues, researchers at the Washington University School of Medicine developed and evaluated an air traffic control-like command center for operating rooms (ORs). The Anesthesiology Control Tower: Feedback Alerts to Supplement Treatments or ACTFAST study applied data mining and machine learning to forecast adverse patient outcomes. This study used data from perioperative electronic health records (EHR) and real-time physiological data including patient demographic characteristics, comorbid conditions, preoperative vital signs, selected preoperative laboratory values, intraoperative time series, and selected intraoperative medications. Expert clinicians outside of the OR used Anesthesiology Control Tower (ACT) software to monitor the real-time status of all ORs in the operating suite. Detected events triggered an alert on a clinical dashboard. If the outside clinician decided the alert is important, they provided the attending anesthesiologists with real-time decision support on their personal communication devices. The ACT was tested in a randomized controlled trial (RCT).

The specific aims of this projects were as follows:

  • Develop, refine, and validate forecasting algorithms for adverse outcomes. 
  • Assess the usability of an ACT for the operating suite. 
  • Assess whether the ACT improves clinician compliance with standards of care and surrogate measures of patient outcomes. 

An SQL database was developed from four electronic health data repositories. Novel machine learning algorithms were constructed to predict postoperative 30-day mortality, postoperative acute renal failure, and postoperative acute respiratory failure. Results indicated that the algorithms were able to predict postoperative adverse outcomes with a high degree of accuracy. Researchers began the RCT, but results of this RCT were not available at the end of the grant period because of mid-study EHR vendor changes and the COVID pandemic.

The research team feels that adoption of ACT should be a priority so that providers are made aware of the highest risk patients that would benefit from greater monitoring. The team is confident that the results from the completed RCT will provide value and insight.