A data-driven concept schema for defining clinical research data needs. (July 2016)

• Record Type: Journal Article
• Authors: Hruby, Gregory W.; Hoxha, Julia; Ravichandran, Praveen Chandar; Mendonça, Eneida A.; Hanauer, David A; Weng, Chunhua
• Is Part Of: International journal of medical informatics. Volume 91(2016:Jul.)
• Page Start: 1
• Page End: 9
• Publication Date: 2016-07
• Subjects: Medical informatics -- Comparative effectiveness research -- Needs assessment -- Data collection -- Models -- Theoretical; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2016.03.008
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: We validated and enriched an existing data needs framework using data-driven methods. The new schema can generalize beyond cancer research. The schema can serve as a template for specifying medical researchers' data needs. The schema can facilitate the indexing of EHR data requests and modular data queries to improve EHR data query reuse. Abstract: Objectives: The Patient, Intervention, Control/Comparison, and Outcome (PICO) framework is an effective technique for framing a clinical question. We aim to develop the counterpart of PICO to structure clinical research data needs. Methods: We use a data-driven approach to abstracting key concepts representing clinical research data needs by adapting and extending an expert-derived framework originally developed for defining cancer research data needs. We annotated clinical trial eligibility criteria, EHR data request logs, and data queries to electronic health records (EHR), to extract and harmonize concept classes representing clinical research data needs. We evaluated the class coverage, class preservation from the original framework, schema generalizability, schema understandability, and schema structural correctness through a semi-structured interview with eight multidisciplinary domain experts. We iteratively refined the schema based on the evaluations. Results: Our data-driven schema preserved 68% of the 63 classes from the original framework and covered 88% (73/82) of the classes proposed by evaluators. Class coverage for participants of different backgrounds ranged from 60% to 100% with a median value of 95% agreement among the individual evaluators. The schema was found understandable and structurally sound. Conclusions: Our proposed schema may serve as the counterpart to PICO for improving the research data needs communication between researchers and informaticians.