ArticleMayo Clinic proceedings. Digital health2026
Leveraging Data Science for Conducting Observational Studies: Highlighting Advantages and Limitations Throughout the Evaluation of a Use Case.
Article in Mayo Clinic proceedings. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To develop a data science pipeline for data extraction and collection to conduct an observational study using institutional medical informatics and artificial intelligence tools. Patients and Methods: Skin excision cases exposed to intra-incisional clindamycin (from February 5 to October 14, 2025) and nonexposed (from January 1 to December 31, 2022) were defined as our cohorts of interest. The outcome of our study was surgical site infection (SSI) 30 days after surgery. A large language model (LLM)-supported data science pipeline was used for cohort identification, case screening, and extraction of procedural information. LLM screening and data extraction were validated in 300 random cases. A generalized estimating equation model was used to analyze the effect of intra-incisional clindamycin on SSI. Results: The LLM achieved high accuracy for screening cases, with accurate identification of procedures done in the head (accuracy, 99.0%; 95% CI, 97.1%-99.8%) and skin excisions (97.0%; 95% CI, 94.4%-98.6%). In addition, it achieved remarkable performance for extraction of clinical information, with the highest accuracy observed for extraction of anatomical location associated with the procedure (accuracy, 100.0%; 95% CI, 98.8%-100.0%). The final dataset included 2247 skin excision cases (594 exposed and 1653 nonexposed) from 1923 patients. Cases that received intra-incisional clindamycin had lower odds of SSI; however, this association was not statistically significant (aOR, 0.57; 95% CI, 0.31-1.07). Conclusion: The implementation of medical informatics tools and robust artificial intelligence algorithms will enable the conduct of end-to-end research pipelines. However, it is important to always consider humans in the loop, as oversight ensures reliability, accuracy, and robustness.
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