Refining Clinical Phenotypes to Improve Clinical Decision Support and Reduce Alert Fatigue: A Feasibility Study

Abstract

Background: Chronic kidney disease (CKD) is common and associated with adverse clinical outcomes. Most care for early CKD is provided in primary care, including hypertension (HTN) management. Computerized clinical decision support (CDS) can improve the quality of care for CKD but can also cause alert fatigue for primary care physicians (PCPs). Computable phenotypes (CPs) are algorithms to identify disease populations using, for example, specific laboratory data criteria.

Objectives: Our objective was to determine the feasibility of implementation of CDS alerts by developing CPs and estimating potential alert burden.

Methods: We utilized clinical guidelines to develop a set of five CPs for patients with stage 3 to 4 CKD, uncontrolled HTN, and indications for initiation or titration of guideline-recommended antihypertensive agents. We then conducted an iterative data analytic process consisting of database queries, data validation, and subject matter expert discussion, to make iterative changes to the CPs. We estimated the potential alert burden to make final decisions about the scope of the CDS alerts. Specifically, the number of times that each alert could fire was limited to once per patient.

Results: In our primary care network, there were 239,339 encounters for 105,992 primary care patients between April 1, 2018 and April 1, 2019. Of these patients, 9,081 (8.6%) had stage 3 and 4 CKD. Almost half of the CKD patients, 4,191 patients, also had uncontrolled HTN. The majority of CKD patients were female, elderly, white, and English-speaking. We estimated that 5,369 alerts would fire if alerts were triggered multiple times per patient, with a mean number of alerts shown to each PCP ranging from 0.07-to 0.17 alerts per week.

Conclusion: Development of CPs and estimation of alert burden allows researchers to iteratively fine-tune CDS prior to implementation. This method of assessment can help organizations balance the tradeoff between standardization of care and alert fatigue.

Trial registration: ClinicalTrials.gov NCT03679247.

Fig. 1

Flowchart to map out key decision points for clinicians.

Fig. 2

( A–E ) Five CDS alerts resulting from five computable phenotypes and associated recommendations.

Fig. 3

Estimated volume of alerts targeting patients who are not prescribed ACEi or ARB (CPs 1A and 1B).

Fig. 4

Estimated volume of alerts targeting patients prescribed a suboptimal dose of ACEi or ARB (CPs 2A and 2B).

Fig. 5

Estimated volume of alerts targeting CP 3A.

Fig. 6

Weekly alert firing rate by CP type.