Curating a longitudinal research resource using linked primary care EHR data—a UK Biobank case study

Figure 1.. Common issues in EHR data collection illustrated with synthetic participant data. These resemble realistic participant types, for example, around 70% of UK Biobank participants have data outside of periods of practice registration. Example 1—individual registered with a practice at birth that subsequently adopted an EHR system in the 1990s (prior records are paper-based). Example 2—individual registered with a practice in 1999 but records are also held from a previous period of registration with another practice. Example 3—multiple periods of registration are available from different practices and/or data providers. Example 4—a combination of the above issues. The boxed areas illustrate the inferred periods of data collection using our algorithm.

Figure 2.. Application of our algorithm to determine periods of complete EHR data collection. The example participant has multiple periods of registration and data outside of registration periods. The boxed areas are the inferred periods of data collection. Further details are included in the Supplementary Materials (Algorithm A1 and Supplementary Figure S1).

Figure 3.. Example output from the longitudinal phenotyping tool for a synthetic participant. Our algorithm was used to identify periods of complete data collection (top panel). Periods of nondiabetic hyperglycemia (prediabetes), type 2 diabetes, and remission were identified. Periods of medication and biomarkers are also shown. We phenotyped periods of complete data collection to reduce the risk of inaccurately identifying the date of incidence of diabetes. Similar phenotyping approaches using linked EHR data can be used to enforce study criteria or identify more complex endpoints.