Millions of adults have chronic kidney disease (CKD), leading to substantial morbidity, mortality and health care costs. These effects are concentrated in patients with high-risk disease. Several provider- and system-level barriers lead to well-described gaps in care for these patients, contributing to poor outcomes. Given the growing CKD population, the relative dearth of nephrologists, and the fragmented care that high-risk patients receive, novel tools are needed to improve the quality and safety of CKD care and clinical outcomes. Population health management (PHM) improves health by aggregating and analyzing data across a population to drive consistent, evidence-based care. CKD PHM using electronic health records (EHRs) can be a sustainable strategy to overcome physician- and system-level barriers. EHR-based PHM could improve the identification of patients with high-risk CKD; increase the use of evidence-based, widely available, and cost- effective interventions; and enhance medication safety. We have developed the tools needed to implement and examine the impact of an EHR-based PHM intervention. Our multidisciplinary team has constructed a dynamic CKD registry containing real-time data and embedded metrics for identifying high-risk CKD using validated risk-prediction models, reviewing medication exposures, and monitoring processes of care. We have successfully leveraged this tool to pilot an EHR-based PHM intervention that targets: 1) timely communication of high-risk CKD, 2) implementation of remote nephrology guidance (E-consult) to improve evidence-based CKD care and the provision of medication safety reviews, and 3) standardized CKD patient education. The overarching aim of this proposal is to perform a mixed-methods evaluation of the effectiveness of a multifaceted EHR-based PHM intervention to improve the delivery of evidence-based CKD care in high-risk patients. The University of Pittsburgh's extensive PCP network offers the ideal setting to evaluate the intervention with 330 PCPs caring for over 480,000 patients while using the Epic EHR. We will perform an 18- month pragmatic, cluster randomized controlled trial in 625 patients with high-risk CKD managed by their PCPs to determine whether EHR-based PHM improves key processes of care. We hypothesize that EHR-based PHM will improve hypertension control, use of renin angiotensin aldosterone system inhibitors in patients with albuminuria, and avoidance of renally contraindicated medications (Aim 1). Also, we will qualitatively examine barriers and facilitators of the intervention's effectiveness as well as predictors of response heterogeneity (Aim 2). Our novel intervention benefits from customization based on PCP stakeholder feedback, executive level support from the medical center and health plan, and enthusiastic support from PCPs. This study directly responds to calls from national primary care organizations to use EHRs to implement PHM while limiting the PCP burden. CKD PHM may provide a sustainable, resourceful approach to improving CKD care, outcomes, and safety and our research will produce code for PHM dashboards in the most widely adopted ambulatory EHR in the US, facilitating dissemination and a potential transformation in CKD care.

Public Health Relevance

CKD is associated with an unacceptably high human and financial cost. Gaps in CKD care contribute to catastrophic outcomes such as dialysis dependence. Novel system-based interventions are needed to improve CKD care. Real time risk stratification and population health management using electronic health records can improve CKD care and outcomes in the patients who need it most.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Demonstration and Dissemination Projects (R18)
Project #
1R18DK118460-01
Application #
9590189
Study Section
Special Emphasis Panel (ZDK1)
Program Officer
Narva, Andrew
Project Start
2018-09-01
Project End
2021-07-31
Budget Start
2018-09-01
Budget End
2019-07-31
Support Year
1
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Pittsburgh
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
004514360
City
Pittsburgh
State
PA
Country
United States
Zip Code
15213