Pilot evaluation of sensitive data segmentation technology for privacy. (June 2020)

• Record Type: Journal Article
• Authors: Grando, Adela; Sottara, Davide; Singh, Ripudaman; Murcko, Anita; Soni, Hiral; Tang, Tianyu; Idouraine, Nassim; Todd, Michael; Mote, Mike; Chern, Darwyn; Dye, Christy; Whitfield, Mary Jo
• Is Part Of: International journal of medical informatics. Volume 138(2020)
• Publication Date: 2020-06
• Subjects: Data segmentation -- Data privacy -- Electronic medical records; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2020.104121
• Languages: English
• ISSNs: 1386-5056

Abstract

Graphical abstract: Highlights: Little is known about automatic means to segment sensitive medical records. Consent2Share is the first tool to automatically segment sensitive data. Accuracy of Consent2Share to segment sensitive data was unknown. Poor Consent2Share accuracy to segment sensitive data was found. There is a need to further validate Consent2Share before its use. Abstract: Background: Consent2Share (C2S) is an open source software created by the Office of the National Coordinator Data Segmentation for Privacy initiative to support electronic health record (EHR) granular segmentation. To date, there are no published formal evaluations of Consent2Share. Method: Structured data (e.g. medications) codified using standard clinical terminologies (e.g. RxNorm) was extracted from the EHR of 36 patients with behavioral health conditions from study sites. EHRs were available through a health information exchange and two sites. The EHR data was already classified into data types (e.g. procedures and services). Both Consent2Share and health providers classified EHR data based on value sets (e.g. mental health) and sensitivity (e.g. not sensitive. Descriptive statistics and Chi-square analysis were used to compare differences between data categorizations. Results: From the resulting 1, 080 medical records items, 584 were distinct. Significant differences were found between sensitivity classifications by Consent2Share and providers (χ2 (2, N = 584) = 114.74, p = <0.0001). Sensitivity comparisons led to 56.0 % of agreements, 31.2 % disagreements, and 12.8 % partial agreements. Most (97.8 %) disagreements resulted from information classified as not sensitive by Consent2Share, but sensitive by provider (e.g. behavioral health prevention education service). In terms of data types, most disagreements (57.1 %) focused on procedures and services information (e.g. ligation of fallopian tube). When considering value sets, most disagreements focused on genetic data (100.0 %), followed by sexual and reproductive health (88.9 %). Conclusions: There is a need to further validate Consent2Share before broad use in health care settings. The outcomes from this pilot study will help guide improvements in segmentation logic of tools like Consent2Share and may set the stage for a new generation of personalized consent engines.