PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model

• DOI: 10.1093/bioinformatics/btz409
• Publication Date: 2019-06-14T03:14:27Z
• Authors: Benjamin S Glicks...; Boris Oskotsky; Phyllis M Thangaraj; Nicholas Giangreco; Marcus A Badgeley; Kipp W Johnson; Debajyoti Datta; Vivek A Rudrapatna; Nadav Rappoport; Mark M Shervey; Riccardo Miotto; Theodore C Goldstein; Eugenia Rutenberg; Remi Frazier; Nelson Lee; Sharat Israni; Rick Larsen; Bethany Percha; Li Li; Joel T Dudley; Nicholas P Tatonetti; Atul J Butte
• Keywords: Databases, Factual; Bioinformatics; 610; Mathematical sciences; Mathematical Sciences; Databases; 03 medical and health sciences; 0302 clinical medicine; Clinical Research; Information and Computing Sciences; Electronic Health Records; Humans; Factual; Computers; Data Science; Biological Sciences; Applications Notes; 3. Good health; Biological sciences; Observational Studies as Topic; Good Health and Well Being; Networking and Information Technology R&D (NITRD); Information and computing sciences; Patient Safety; Generic health relevance; 2.6 Resources and infrastructure (aetiology); Software; Information Systems

Abstract

Motivation Electronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge.

Results We present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes.

Availability and implementation PatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu.

Supplementary information Supplementary data are available at Bioinformatics online.