A Pragmatic Cluster Randomized Trial of an Electronic Clinical Decision Support System to Improve Chronic Kidney Disease Management in Primary Care: Design, Rationale, and Implementation Experience

Elaine C Khoong, Leah Karliner, Lowell Lo, Marilyn Stebbins, Andrew Robinson, Sarita Pathak, Jasmine Santoyo-Olsson, Rebecca Scherzer, Carmen A Peralta, Elaine C Khoong, Leah Karliner, Lowell Lo, Marilyn Stebbins, Andrew Robinson, Sarita Pathak, Jasmine Santoyo-Olsson, Rebecca Scherzer, Carmen A Peralta

Abstract

Background: The diagnosis of chronic kidney disease (CKD) is based on laboratory results easily extracted from electronic health records; therefore, CKD identification and management is an ideal area for targeted electronic decision support efforts. Early CKD management frequently occurs in primary care settings where primary care providers (PCPs) may not implement all the best practices to prevent CKD-related complications. Few previous studies have employed randomized trials to assess a CKD electronic clinical decision support system (eCDSS) that provided recommendations to PCPs tailored to each patient based on laboratory results.

Objective: The aim of this study was to report the trial design and implementation experience of a CKD eCDSS in primary care.

Methods: This was a 3-arm pragmatic cluster-randomized trial at an academic general internal medicine practice. Eligible patients had 2 previous estimated-glomerular-filtration-rates by serum creatinine (eGFRCr) <60 mL/min/1.73m2 at least 90 days apart. Randomization occurred at the PCP level. For patients of PCPs in either of the 2 intervention arms, the research team ordered triple-marker testing (serum creatinine, serum cystatin-c, and urine albumin-creatinine-ratio) at the beginning of the study period, to be completed when acquiring labs for regular clinical care. The eCDSS launched for PCPs and patients in the intervention arms during a regular PCP visit subsequent to completing the triple-marker testing. The eCDSS delivered individualized guidance on cardiovascular risk-reduction, potassium and proteinuria management, and patient education. Patients in the eCDSS+ arm also received a pharmacist phone call to reinforce CKD-related education. The primary clinical outcome is blood pressure change from baseline at 6 months after the end of the trial, and the main secondary outcome is provider awareness of CKD diagnosis. We also collected process, patient-centered, and implementation outcomes.

Results: A multidisciplinary team (primary care internist, nephrologists, pharmacist, and informaticist) designed the eCDSS to integrate into the current clinical workflow. All 81 PCPs contacted agreed to participate and were randomized. Of 995 patients initially eligible by eGFRCr, 413 were excluded per protocol and 58 opted out or withdrew, resulting in 524 patient participants (188 usual care; 165 eCDSS; and 171 eCDSS+). During the 12-month intervention period, 53.0% (178/336) of intervention patient participants completed triple-marker labs. Among these, 138/178 (77.5%) had a PCP appointment after the triple-marker labs resulted; the eCDSS was opened for 73.9% (102/138), with orders or education signed for 81.4% (83/102).

Conclusions: Successful integration of an eCDSS into primary care workflows and high eCDSS utilization rates at eligible visits suggest this tailored electronic approach is feasible and has the potential to improve guideline-concordant CKD care.

Trial registration: ClinicalTrials.gov NCT02925962; https://ichgcp.net/clinical-trials-registry/NCT02925962 (Archived by WebCite at http://www.webcitation.org/78qpx1mjR).

International registered report identifier (irrid): DERR1-10.2196/14022.

Keywords: chronic kidney disease; clinical decision support systems; electronic health records; pragmatic clinical trial.

Conflict of interest statement

Conflicts of Interest: CP is currently the chief medical officer of Cricket Health. The other authors declare they have no conflicts of interest.

©Elaine C Khoong, Leah Karliner, Lowell Lo, Marilyn Stebbins, Andrew Robinson, Sarita Pathak, Jasmine Santoyo-Olsson, Rebecca Scherzer, Carmen A Peralta. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 07.06.2019.

Figures

Figure 1
Figure 1
Flow diagram of participant selection. Note that 1 clinician who was randomized did not ultimately participate because their only eligible patient opted out. eGFRCr: estimated glomerular filtration rate by serum creatinine; eCDSS: electronic clinical decision support system trial arm; eCDSS+: electronic clinical decision support system and pharmacist follow-up trial arm; N/A: not applicable.
Figure 2
Figure 2
Flow diagram of study workflow for intervention arms. BPA: best practice advisory; PCP: primary care provider.
Figure 3
Figure 3
Electronic clinical decision support system trial arm low-risk best practice advisory. CKD: chronic kidney disease; Cr: serum creatinine; eGFR: estimated glomerular filtration rate.
Figure 4
Figure 4
Electronic clinical decision support system trial arm high-risk best practice advisory. BP: blood pressure; CKD: chronic kidney disease; mg/g: milligrams per grams.
Figure 5
Figure 5
Electronic clinical decision support system trial arm example SmartSet. ACEi/ARB: angiotensin converting enzyme inhibitor / angiotensin II receptor blocker; AVS: after visit summary; CKD: chronic kidney disease; eCDSS: electronic clinical decision support system; CDSS+: electronic clinical decision support system and pharmacist follow-up trial arm; K+: potassium; mg/g: milligrams per grams; NSAID: nonsteroidal anti-inflammatory drug; OTC: over-the-counter;.

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