Data governance requirements for distributed clinical research networks: triangulating perspectives of diverse stakeholders

There is currently limited information on best practices for the development of governance requirements for distributed research networks (DRNs), an emerging model that promotes clinical data reuse and improves timeliness of comparative effectiveness research. Much of the existing information is based on a single type of stakeholder such as researchers or administrators. This paper reports on a triangulated approach to developing DRN data governance requirements based on a combination of policy analysis with experts, interviews with institutional leaders, and patient focus groups. This approach is illustrated with an example from the Scalable National Network for Effectiveness Research, which resulted in 91 requirements. These requirements were analyzed against the Fair Information Practice Principles (FIPPs) and Health Insurance Portability and Accountability Act (HIPAA) protected versus non-protected health information. The requirements addressed all FIPPs, showing how a DRN's technical infrastructure is able to fulfill HIPAA regulations, protect privacy, and provide a trustworthy platform for research

External validation of LACE+ scores

BACKGROUND: Unplanned hospital readmissions are common adverse events. The LACE+ score has been used to identify patients at the highest risk of unplanned readmission or death, yet the external validity of this score remains uncertain. METHODS: We constructed a cohort of patients admitted to hospital between October 1, 2014 and January 31, 2017 using population-based data from British Columbia (Canada). The primary outcome was a composite of urgent hospital readmission or death within 30 days of index discharge. The primary analysis sought to optimize clinical utility and international generalizability by focusing on the modified LACE+ (mLACE+) score, a variation of the LACE+ score which excludes the Case Mix Group score. Predictive performance was assessed using model calibration and discrimination. RESULTS: Among 368,154 hospitalized individuals, 31,961 (8.7%) were urgently readmitted and 5,428 (1.5%) died within 30 days of index discharge (crude composite risk of readmission or death, 9.95%). The mLACE+ score exhibited excellent calibration (calibration-in-the-large and calibration slope no different than ideal) and adequate discrimination (c-statistic, 0.681; 95%CI, 0.678 to 0.684). Higher risk dichotomized mLACE+ scores were only modestly associated with the primary outcome (positive likelihood ratio 1.95, 95%CI 1.93 to 1.97). Predictive performance of the mLACE+ score was similar to that of the LACE+ and LACE scores. CONCLUSION: The mLACE+, LACE+ and LACE scores predict hospital readmission with excellent calibration and adequate discrimination. These scores can be used to target interventions designed to prevent unplanned hospital readmission.Medicine, Faculty ofNon UBCMedicine, Department ofPopulation and Public Health (SPPH), School ofReviewedFacultyResearcherGraduat