Generate Analysis-Ready Data for Real-world Evidence: Tutorial for Harnessing Electronic Health Records With Advanced Informatic Technologies

• Published in: Journal of medical Internet research 2023-05, Vol.25 (1), p.e45662
• Main Authors: Hou, Jue, Zhao, Rachel, Gronsbell, Jessica, Lin, Yucong, Bonzel, Clara-Lea, Zeng, Qingyi, Zhang, Sinian, Beaulieu-Jones, Brett K, Weber, Griffin M, Jemielita, Thomas, Wan, Shuyan Sabrina, Hong, Chuan, Cai, Tianrun, Wen, Jun, Ayakulangara Panickan, Vidul, Liaw, Kai-Li, Liao, Katherine, Cai, Tianxi
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Cancer therapies; Clinical outcomes; Clinical trials; Clinical variables; Codes; Colectomy; Colon cancer; Colonic Neoplasms; Computerized medical records; Data collection; Deployment; Disease; Efficacy; Electronic Health Records; Group therapy; Harmonization; Health records; Humans; Informatics; Laparoscopy; Machine learning; Medical diagnosis; Medical imaging; Medical treatment; Patients; Phenotypes; Reproducibility; Research Design; Statistical analysis; Statistical methods; Tutorial; Variables
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/45662

Although randomized controlled trials (RCTs) are the gold standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world data has been vital in postapproval monitoring and is being promoted for the regulatory process of experimental therapies. An emerging source of real-world data is electronic health records (EHRs), which contain detailed information on patient care in both structured (eg, diagnosis codes) and unstructured (eg, clinical notes and images) forms. Despite the granularity of the data available in EHRs, the critical variables required to reliably assess the relationship between a treatment and clinical outcome are challenging to extract. To address this fundamental challenge and accelerate the reliable use of EHRs for RWE, we introduce an integrated data curation and modeling pipeline consisting of 4 modules that leverage recent advances in natural language processing, computational phenotyping, and causal modeling techniques with noisy data. Module 1 consists of techniques for data harmonization. We use natural language processing to recognize clinical variables from RCT design documents and map the extracted variables to EHR features with description matching and knowledge networks. Module 2 then develops techniques for cohort construction using advanced phenotyping algorithms to both identify patients with diseases of interest and define the treatment arms. Module 3 introduces methods for variable curation, including a list of existing tools to extract baseline variables from different sources (eg, codified, free text, and medical imaging) and end points of various types (eg, death, binary, temporal, and numerical). Finally, module 4 presents validation and robust modeling methods, and we propose a strategy to create gold-standard labels for EHR variables of interest to validate data curation quality and perform subsequent causal modeling for RWE. In addition to the workflow proposed in our pipeline, we also develop a reporting guideline for RWE that covers the necessary information to facilitate transparent reporting and reproducibility of results. Moreover, our pipeline is highly data driven, enhancing study data with a rich variety of publicly available information and knowledge sources. We also showcase our pipeline and provide guidance on the deployment of relevant tools by revisiting the emulation of the Clinical Outcomes of Surgical Therapy Study Group Trial on laparoscop