Data linkages between patient-powered research networks and health plans: a foundation for collaborative research

Abstract

Objective: Patient-powered research networks (PPRNs) are a valuable source of patient-generated information. Diagnosis code-based algorithms developed by PPRNs can be used to query health plans' claims data to identify patients for research opportunities. Our objective was to implement privacy-preserving record linkage processes between PPRN members' and health plan enrollees' data, compare linked and nonlinked members, and measure disease-specific confirmation rates for specific health conditions.

Materials and methods: This descriptive study identified overlapping members from 4 PPRN registries and 14 health plans. Our methods for the anonymous linkage of overlapping members used secure Health Insurance Portability and Accountability Act-compliant, 1-way, cryptographic hash functions. Self-reported diagnoses by PPRN members were compared with claims-based computable phenotypes to calculate confirmation rates across varying durations of health plan coverage.

Results: Data for 21 616 PPRN members were hashed. Of these, 4487 (21%) members were linked, regardless of any expected overlap with the health plans. Linked members were more likely to be female and younger than nonlinked members were. Irrespective of duration of enrollment, the confirmation rates for the breast or ovarian cancer, rheumatoid or psoriatic arthritis or psoriasis, multiple sclerosis, or vasculitis PPRNs were 72%, 50%, 75%, and 67%, increasing to 91%, 67%, 93%, and 80%, respectively, for members with ≥5 years of continuous health plan enrollment.

Conclusions: This study demonstrated that PPRN membership and health plan data can be successfully linked using privacy-preserving record linkage methodology, and used to confirm self-reported diagnosis. Identifying and confirming self-reported diagnosis of members can expedite patient selection for research opportunities, shorten study recruitment timelines, and optimize costs.

Keywords: anonymous linkage methods; claims-based computable phenotypes; data hashing; patient-powered research networks; patient-reported information.