Generating real-world data from health records: design of a patient-centric study in multiple sclerosis using a commercial health records platform

Objective The FlywheelMS study will explore the use of a real-world health record data set generated by PicnicHealth, a patient-centric health records platform, to improve understanding of disease course and patterns of care for patients with multiple sclerosis (MS). Materials and Methods The FlywheelMS study aims to enroll 5000 adults with MS in the United States to create a large, deidentified, longitudinal data set for clinical research. PicnicHealth obtains health records, including paper charts, electronic health records, and radiology imaging files from any healthcare site. Using a large-scale health record processing pipeline, PicnicHealth abstracts standard and condition-specific data elements from structured (eg, laboratory test results) and unstructured (eg, narrative) text and maps these to standardized medical vocabularies. Researchers can use the resulting data set to answer empirical questions and study participants can access and share their harmonized health records using PicnicHealth's web application. Results As of November 24, 2020, more than 4176 participants from 49 of 50 US states have enrolled in the FlywheelMS study. A median of 200 pages of records have been collected from 14 different doctors over 8 years per participant. Abstraction precision, established through inter-abstractor agreement, is as high as 97.8% when identifying and mapping data elements to a standard ontology. Conclusion Using a commercial health records platform, the FlywheelMS study is generating a real-world, multimodal data set that could provide valuable insights about patients with MS. This approach to data collection and abstraction is disease-agnostic and could be used to address other clinical research questions in the future. Lay Summary Health records contain valuable information about patients and the care they receive in routine clinical practice; however, use of this data source in research is hindered by the difficulty of obtaining complete analyzable data sets. In the United States, health records for each patient are stored in paper and electronic formats across multiple healthcare providers. Furthermore, data must be extracted from health records before they can be analyzed, which is technically difficult for images and free text. In the first part of this paper, we describe how PicnicHealth, a commercial health records platform, collects health records on behalf of patients in any format and from all healthcare sites. Facilitated by tailored software tools and task-specific machine-learning models, data are extracted from health records by human experts in an efficient and precise manner. This enables patients to access and manage their health record data via a web application. The second part of this paper describes the design, rationale, and recruitment metrics for the ongoing FlywheelMS study, which is exploring whether an anonymized data set generated by PicnicHealth can improve our understanding of the disease course and patterns of care for patients with multiple sclerosis.