Observational Health Data Science and Informatics and Hand Surgery Research: Past, Present, and Future

作     者：Hum, Richard Lane, Jennifer C. E. Zhang, Gongliang Selles, Ruud W. Giladi, Aviram M. 

作者机构：Georgetown Univ Sch Med Washington DC USA Queen Mary Univ London Blizard Inst Barts Bone & Joint Hlth London England MedStar Union Mem Hosp Curtis Natl Hand Ctr Baltimore MD USA MedStar Hlth Res Inst Hyattsville MD USA Erasmus MC Univ Med Ctr Rotterdam Dept Rehabil Med Rotterdam Netherlands Erasmus MC Univ Med Ctr Dept Plast & Reconstruct Surg & Hand Surg Rotterdam Netherlands 

出 版 物：《JOURNAL OF HAND SURGERY-AMERICAN VOLUME》 (J. Hand Surg. (USA))

年 卷 期：2025年第50卷第3期

页      面：363-367页

核心收录：

学科分类：1002[医学-临床医学] 100210[医学-外科学(含：普外、骨外、泌尿外、胸心外、神外、整形、烧伤、野战外)] 10[医学] 

基　　金：National Institute for Health Research Barts Biomedical Research Centre [NIHR203330] Orthopaedic Research UK 

主　　题：Hand surgery observational health data science and informatics patient-reported outcomes research 

摘      要：Single center studies are limited by bias, lack of generalizability and variability, and inability to study rare conditions. Multicenter observational research could address many of those concerns, especially in hand surgery where multicenter research is currently quite limited;however, there are numerous barriers including regulatory issues, lack of common terminology, and variable data set structures. The Observational Health Data Sciences and Informatics (OHDSI) program aims to surmount these limitations by enabling large-scale, collaborative research across multiple institutions. The OHDSI uses the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to standardize health care data into a common language, enabling consistent and reliable analysis. The OMOP CDM has been transformative in converting multiple databases into a standardized code with a single vocabulary, allowing for coherent analysis across multiple data sets. Building upon the OMOP CDM, OHDSI provides an extensive suite of open-source tools for all research stages, from data extraction to statistical modeling. By keeping sensitive data local and only sharing summary statistics, OHDSI ensures compliance with privacy regulations while allowing for large-scale analyses. For hand surgery, OHDSI can enhance research depth, understanding of outcomes, risk factors, complications, and device performance, ultimately leading to better patient care. (J Hand Surg Am. 2025;50(3):363e367. Copyright (c) 2025 by the American Society for Surgery of the Hand. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)