Impacting Management of Chronic Kidney Disease Through Primary Care Practice Audits: A Quality Improvement Study

Abstract

Background: Risk prediction tools are important in chronic disease management, but their implementation into clinical workflow is often limited by lack of electronic health record (EHR)-linked solutions. Objective: To implement the Khure Health (KH) clinical decision support platform with an artificial intelligence (AI)-enabled algorithm for chronic kidney disease (CKD) risk detection in 201 primary care provider practices across Ontario. Design: Multi-practice quality improvement study. Setting: The study was conducted in Ontario, Canada. Participants: 201 primary care practices. Measurements: Per-practice CKD risk stratification and clinician action. Methods: Data for estimated glomerular filtration rate (eGFR), albuminuria, demographics, and comorbid conditions were extracted from the EHR using KH’s natural language processing (NLP) algorithms. Patients already on dialysis, visiting a nephrologist, older than 85, or already on a sodium-glucose cotransporter 2 inhibitor (SGLT2i) were excluded. The remaining individuals were risk stratified using the kidney failure risk equation, presence or absence of cardiovascular disease (CVD), or other comorbid conditions. A dashboard with disease-specific educational information and links to the EHRs of the identified patients was created. Results: We screened 361 299 individuals and identified 8194 patients with CKD Stage 3 at risk for progression or cardiovascular events. A total of 620 individuals were at high risk for CKD progression or CVD, and 2592 were at intermediate risk. A total of 2010 individuals (10 patients per practice) at high or moderate risk were selected for a chart audit, and appropriate additional testing (repeat eGFR or albuminuria) or prescription of disease-modifying therapy occurred in 24.32% of these patients. Limitations: Data on comorbidities, medications, or demographic variables are not available for presentation or statistical analysis due to privacy legislation and primary care provider (PCP) custodianship over EHR data. Conclusion: An AI-enabled EHR clinical decision support application that can detect and risk stratify patients with CKD can enable improved laboratory testing and management. Larger trials of clinical decision support and practice audit applications will be needed to impact CKD management nationally.