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Updated: Sep 14, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
Published on: July 27, 2018
Leveraging Data Science for Conducting Observational Studies: Highlighting Advantages and Limitations Throughout the
Jorge A Rios-Duarte1,2, Taylor L Pick3, Trece N Robson4
1Department of Dermatology, Mayo Clinic, Rochester, MN.
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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