Decades in the Making: The Evolution of Digital Health Research Infrastructure Through Synthetic Data, Common Data Models, and Federated Learning

Abstract

Traditionally, medical research is based on randomized controlled trials (RCTs) for interventions such as drugs and operative procedures. However, increasingly, there is a need for health research to evolve. RCTs are expensive to run, are generally formulated with a single research question in mind, and analyze a limited dataset for a restricted period. Progressively, health decision makers are focusing on real-world data (RWD) to deliver large-scale longitudinal insights that are actionable. RWD are collected as part of routine care in real time using digital health infrastructure. For example, understanding the effectiveness of an intervention could be enhanced by combining evidence from RCTs with RWD, providing insights into long-term outcomes in real-life situations. Clinicians and researchers struggle in the digital era to harness RWD for digital health research in an efficient and ethically and morally appropriate manner. This struggle encompasses challenges such as ensuring data quality, integrating diverse sources, establishing governance policies, ensuring regulatory compliance, developing analytical capabilities, and translating insights into actionable strategies. The same way that drug trials require infrastructure to support their conduct, digital health also necessitates new and disruptive research data infrastructure. Novel methods such as common data models, federated learning, and synthetic data generation are emerging to enhance the utility of research using RWD, which are often siloed across health systems. A continued focus on data privacy and ethical compliance remains. The past 25 years have seen a notable shift from an emphasis on RCTs as the only source of practice-guiding clinical evidence to the inclusion of modern-day methods harnessing RWD. This paper describes the evolution of synthetic data, common data models, and federated learning supported by strong cross-sector collaboration to support digital health research. Lessons learned are offered as a model for other jurisdictions with similar RWD infrastructure requirements.

Keywords: common data models; digital health research; federated learning; real-world data; synthetic data; university-industry collaboration.

Figure 1

Approaches to accessing data for modern-day health research. AI: artificial intelligence; CDM: common data model.

Figure 2

Generative adversarial network model.

Figure 3

PADARSER (publicly available data approach to the realistic synthetic electronic health record) framework reproduced from Walonoski J et al [49], which is published under Creative Commons Attribution 4.0 International License [52]. EHR: electronic health record.

Figure 4

SynSys model adapted from Dahmen J et al [30], which is published under Creative Commons Attribution 4.0 International License [53].

Figure 5

Health Care Systems Research Network Virtual Data Warehouse common data model, modified from Ross TR et al [62], which is published under a Creative Commons Attribution 4.0 International License [65]. AHFS: American hospital formulary service; DX: diagnostic; EverNDC: Ever National Drug Code; GPI: generic product identifier; LOINC: Logical Observation Identifiers Names and Codes; MD: medical doctor; NDC: National Drug Code; Rx: prescription.

Figure 6

Observational Medical Outcomes Partnership Common Data Model, reproduced from Jiang G et al [72], which is published under Creative Commons Attribution 4.0 International License [52].

Figure 7

Extract, transform, and load process adapted from the work published by Abd Al-Rahman SQ et al [73], under the CC-BY-SA license [74].