September: A busy month for Perioperative Data Science

It’s been a busy time in Perioperative Data Science.

Research and Publications

In just the last month, we received reviews on three papers and have already resubmitted two of those. Both have been accepted pending a minor revision — one in Anesthesia and Analgesia (A&A) and one in British Journal of Anesthesia (BJA). The A&A paper is an “Open Mind” article debating the pros and cons of having journal-level requirements for sharing code used in research. The BJA article discusses a retrospective study we performed to calculate personalized intraoperative blood pressure recommendations for patients using the Sickbay platform. Our third paper we received feedback on in the last month is also aimed at A&A and proposes a clinician-friendly taxonomy for classifying and understanding the utility of Machine Learning (ML) and Artificial Intelligence (AI) algorithms.

Also in the last month, we wrapped up two projects — one on predictors of renal failure and another on cost savings of a specialized perioperative service — and are currently writing manuscripts for them. Along with those, we also submitted an article to Journal of Clinical Anesthesia on predicting blood transfusion need.

Quality Improvement

Perioperative Data Science was asked to help out with a high-profile, institution-wide project to understand the impact of an Opioid Stewardship Program implemented a few years ago. Those results will be presented to hospital leadership soon and may inform decisions on next steps for the institutions Opioid Stewardship Program.

Additionally, Perioperative Data Science led a recent discussion on the next steps for a departmental hypotension prevention initiative. In this instance, we are predicting adverse patient outcomes related to hypotension, planning to convey those to providers so that they can be fully equipped with an understanding of the impact of blood pressure management on the patients under their care.


In this same time frame, Administrative team asked IT and Perioperative Data Science for assistance understanding the impacts of longer working days on department resources. I think it comes as no surprise that the continued pandemic has greatly impact every aspect of our work in the Department of Anesthesiology. And now we are attempting to quantify just how much the pandemic has stretched our resources. The results of this project are scheduled to be presented to Department leadership soon.


We took on two projects with medical students over the last month. One of those is a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis of AI/ML in Perioperative Medicine. The other is a document-processing project to automatically process reports from echocardiograms into a structured, tabular format that can be used in AI/ML algorithms. This project will also make this data more accessible to researchers, QI practitioners, and educators.


This last month we finalized the joint recruitment of a data scientist shared with Radiology. We’ll be announcing the details on this new hire’s start date. Stay tuned!