JMIR Publications

ABSTRACT

Background:

Postoperative infections reman a crucial challenge in healthcare, resulting in high morbidity, mortality, and costs. Accurate identification and labeling of patients with postoperative bacterial infections is crucial for developing prediction models, validating biomarkers, and implementing surveillance systems in clinical practice.

Objective:

This scoping review aimed to explore methods for identifying patients with postoperative infections using electronic health record (EHR) data to go beyond the reference standard of manual chart review.

Methods:

We performed a systematic search strategy across PubMed, Embase, Web of Science, the Cochrane Library, and Emcare, targeting studies addressing the prediction and fully automated surveillance of diverse bacterial infections in the postoperative setting. For prediction modeling studies, we assessed the labeling methods used, categorizing them as either manual or automated. We evaluated the different types of electronic health record (EHR) data needed for the surveillance and labeling of postoperative infections, as well as the performance of fully automated surveillance systems compared to manual chart review.

Results:

We identified 75 different methods and definitions used to identify patients with postoperative infections in studies published between 2003 and 2023. Manual labeling was the predominant method in prediction modeling research; 49/75 (65%) of the identified methods use structured data, and 34/75 (45%) use free-text and clinical notes as one of their data sources. Fully automated surveillance systems should be used with caution because the reported positive predictive values (PPVs) are between 0.31 and 0.76.

Conclusions:

As no uniform and reliable methods could be identified for detecting postoperative infections, future research must focus on defining uniform definitions, as well as prioritizing the development of more scalable, automated methods for infection detection using structured EHR data.