Privacy-by-design: Case studies in interactive record linkage using a hybrid human-computer system

High-quality patient matching from several sources without a common identifier (ID) requires interactive record linkage (RL) using a hybrid human-computer system. MiNDFIRL (MInimum Necessary Disclosure For Interactive Record Linkage) is a hybrid prototype software system that facilitates maximizing linkage accuracy while minimizing information disclosure. We present and evaluate MiNDFIRL using two real-world case studies.

Two user studies were conducted linking 10,000 data pairs from EHR data and 18,240 unique patient IDs from patient generated data. After automated RL, manual review was conducted by three teams of four reviewers (12 total) using MiNDFIRL to resolve potential matches that required human judgment. Reviews for matches were conducted independently and disagreements were resolved through consensus. The teams then participated in a group discussion about MiNDFIRL using a semi-structured interview format.

The best algorithm, Random Forest, found 388 and 539 matches each for EHR and patient generated data algorithmically, but also output an additional 303 and 187 potential pairs that required manual review. 232 and 84 more matches were confirmed manually from these uncertain pairs respectively. Among the full uncertain pairs, only 30% of available identifying information was needed in MiNDFIRL to separate out 77% (232/303) and 45% (84/187) true linkages respectively. When available, first names and emails were the most frequently used fields in making RL decisions.

On-demand access and masking techniques along with risk quantification through a hybrid human-computer system can significantly reduce disclosure while still minimizing false positives and false negatives in real-world RL.