Introducing machine learning-based prediction models in the perioperative setting

Abstract

Facing the challenge of every second patient developing complications arising from surgery1, it is intriguing that the next major advancements in perioperative care will be technological2. These technological advancements can be as simple as digitalization of information, facilitating standardization, and aiding decision support3. A recent example of this was the demonstration of a reduction in postoperative morbidity and mortality in patients undergoing surgery for pancreatic cancer4. The authors selected evidence-based recommendations for individualizing postoperative clinical observations as well as the use and timing of standard imaging techniques. This standardized and individualized algorithm was the real innovation; however, the platform accelerates and facilitates its implementation. With the development of digitalization in the healthcare sector and new additional data sources, including data from electronic health records, wearables, and patient-reported outcome measures, we are on the verge of a revolution in healthcare. This can only be a reality, however, if these data can be transformed and understood by clinicians.

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