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CC BY-NC-ND 4.0 · Appl Clin Inform 2023; 14(03): 528-537
DOI: 10.1055/s-0043-1768994
Research Article

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

Lipika Samal
1   Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
2   Harvard Medical School, Boston, Massachusetts, United States
,
Edward Wu
1   Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
3   Alabama College of Osteopathic Medicine, Dothan, Alabama, United States
,
Skye Aaron
1   Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
,
John L. Kilgallon
1   Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
,
Michael Gannon
1   Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
4   Eastern Virginia Medical School, Norfolk, Virginia, United States
,
Allison McCoy
5   Vanderbilt University, Nashville, Tennessee, United States
,
Saul Blecker
6   NYU School of Medicine, New York, New York, United States
,
Patricia C. Dykes
1   Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
2   Harvard Medical School, Boston, Massachusetts, United States
,
David W. Bates
1   Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
2   Harvard Medical School, Boston, Massachusetts, United States
,
Stuart Lipsitz
1   Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
2   Harvard Medical School, Boston, Massachusetts, United States
7   Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States
,
Adam Wright
5   Vanderbilt University, Nashville, Tennessee, United States
› Author Affiliations Funding This study was funded by the National Institutes of Health NIDDK (grant number: R01DK116898).
› Further Information
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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.

Keywords

clinical decision support - chronic kidney disease - alert fatigue - electronic health record - data quality

Protection of Human and Animal Subjects

The Human Subjects Institutional Review Board at Brigham and Women's Hospital approved this study (IRB protocol no: 2018P000692).


Clinicaltrials.gov Trial Registration

This study is registered with Clinicaltrials.gov (identifier: NCT03679247).


Supplementary Material



Publication History

Received: 12 July 2022

Accepted: 18 April 2023

Article published online:
12 July 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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