Privacy-preserving architecture for providing feedback to clinicians on their clinical performance

Abstract

Abstract Background Learning from routine healthcare data is important for the improvement of the quality of care. Providing feedback on clinicians’ performance in comparison to their peers has been shown to be more efficient for quality improvements. However, the current methods for providing feedback do not fully address the privacy concerns of stakeholders. Methods The paper proposes a distributed architecture for providing feedback to clinicians on their clinical performances while protecting their privacy. The indicators for the clinical performance of a clinician are computed within a healthcare institution based on pseudonymized data extracted from the electronic health record (EHR) system. Group-level indicators of clinicians across healthcare institutions are computed using privacy-preserving distributed data-mining techniques. A clinician receives feedback reports that compare his or her personal indicators with the aggregated indicators of the individual’s peers. Indicators aggregated across different geographical levels are the basis for monitoring changes in the quality of care. The architecture feasibility was practically evaluated in three general practitioner (GP) offices in Norway that consist of about 20,245 patients. The architecture was applied for providing feedback reports to 21 GPs on their antibiotic prescriptions for selected respiratory tract infections (RTIs). Each GP received one feedback report that covered antibiotic prescriptions between 2015 and 2018, stratified yearly. We assessed the privacy protection and computation time of the architecture. Results Our evaluation indicates that the proposed architecture is feasible for practical use and protects the privacy of the patients, clinicians, and healthcare institutions. The architecture also maintains the physical access control of healthcare institutions over the patient data. We sent a single feedback report to each of the 21 GPs. A total of 14,396 cases were diagnosed with the selected RTIs during the study period across the institutions. Of these cases, 2924 (20.3%) were treated with antibiotics, where 40.8% (1194) of the antibiotic prescriptions were narrow-spectrum antibiotics. Conclusions It is feasible to provide feedback to clinicians on their clinical performance in comparison to peers across healthcare institutions while protecting privacy. The architecture also enables monitoring changes in the quality of care following interventions.

Keywords

• Learning healthcare system
• Feedback
• Peer comparison, privacy
• Security
• Antibiotic prescriptions
• Quality improvement