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Samal L, Kilgallon JL, Lipsitz S, Baer HJ, McCoy A, Gannon M, Noonan S, Dunk R, Chen SW, Chay WI, Fay R, Garabedian PM, Wu E, Wien M, Blecker S, Salmasian H, Bonventre JV, McMahon GM, Bates DW, Waikar SS, Linder JA, Wright A, Dykes P. Clinical Decision Support for Hypertension Management in Chronic Kidney Disease: A Randomized Clinical Trial. JAMA Intern Med 2024; 184:484-492. [PMID: 38466302 PMCID: PMC10928544 DOI: 10.1001/jamainternmed.2023.8315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/11/2023] [Indexed: 03/12/2024]
Abstract
Importance Chronic kidney disease (CKD) affects 37 million adults in the United States, and for patients with CKD, hypertension is a key risk factor for adverse outcomes, such as kidney failure, cardiovascular events, and death. Objective To evaluate a computerized clinical decision support (CDS) system for the management of uncontrolled hypertension in patients with CKD. Design, Setting, and Participants This multiclinic, randomized clinical trial randomized primary care practitioners (PCPs) at a primary care network, including 15 hospital-based, ambulatory, and community health center-based clinics, through a stratified, matched-pair randomization approach February 2021 to February 2022. All adult patients with a visit to a PCP in the last 2 years were eligible and those with evidence of CKD and hypertension were included. Intervention The intervention consisted of a CDS system based on behavioral economic principles and human-centered design methods that delivered tailored, evidence-based recommendations, including initiation or titration of renin-angiotensin-aldosterone system inhibitors. The patients in the control group received usual care from PCPs with the CDS system operating in silent mode. Main Outcomes and Measures The primary outcome was the change in mean systolic blood pressure (SBP) between baseline and 180 days compared between groups. The primary analysis was a repeated measures linear mixed model, using SBP at baseline, 90 days, and 180 days in an intention-to-treat repeated measures model to account for missing data. Secondary outcomes included blood pressure (BP) control and outcomes such as percentage of patients who received an action that aligned with the CDS recommendations. Results The study included 174 PCPs and 2026 patients (mean [SD] age, 75.3 [0.3] years; 1223 [60.4%] female; mean [SD] SBP at baseline, 154.0 [14.3] mm Hg), with 87 PCPs and 1029 patients randomized to the intervention and 87 PCPs and 997 patients randomized to usual care. Overall, 1714 patients (84.6%) were treated for hypertension at baseline. There were 1623 patients (80.1%) with an SBP measurement at 180 days. From the linear mixed model, there was a statistically significant difference in mean SBP change in the intervention group compared with the usual care group (change, -14.6 [95% CI, -13.1 to -16.0] mm Hg vs -11.7 [-10.2 to -13.1] mm Hg; P = .005). There was no difference in the percentage of patients who achieved BP control in the intervention group compared with the control group (50.4% [95% CI, 46.5% to 54.3%] vs 47.1% [95% CI, 43.3% to 51.0%]). More patients received an action aligned with the CDS recommendations in the intervention group than in the usual care group (49.9% [95% CI, 45.1% to 54.8%] vs 34.6% [95% CI, 29.8% to 39.4%]; P < .001). Conclusions and Relevance These findings suggest that implementing this computerized CDS system could lead to improved management of uncontrolled hypertension and potentially improved clinical outcomes at the population level for patients with CKD. Trial Registration ClinicalTrials.gov Identifier: NCT03679247.
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Affiliation(s)
- Lipika Samal
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - John L. Kilgallon
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Hackensack Meridian School of Medicine, Nutley, New Jersey
| | - Stuart Lipsitz
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Heather J. Baer
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Allison McCoy
- Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee
| | - Michael Gannon
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Eastern Virginia Medical School, Norfolk
| | - Sarah Noonan
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- USC School of Medicine Greenville, Greenville, South Carolina
| | - Ryan Dunk
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Sarah W. Chen
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Weng Ian Chay
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Richard Fay
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | | | - Edward Wu
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Alabama College of Osteopathic Medicine, Dothan
| | - Matthew Wien
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Saul Blecker
- Department of Medicine, NYU Grossman School of Medicine, New York, New York
| | | | - Joseph V. Bonventre
- Division of Renal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Gearoid M. McMahon
- Division of Renal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - David W. Bates
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Sushrut S. Waikar
- Section of Nephrology, Boston Medical Center, Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| | - Jeffrey A. Linder
- Division of General Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Adam Wright
- Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee
| | - Patricia Dykes
- Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
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Samal L, Wu E, Aaron S, Kilgallon JL, Gannon M, McCoy A, Blecker S, Dykes PC, Bates DW, Lipsitz S, Wright A. Refining Clinical Phenotypes to Improve Clinical Decision Support and Reduce Alert Fatigue: A Feasibility Study. Appl Clin Inform 2023; 14:528-537. [PMID: 37437601 PMCID: PMC10338104 DOI: 10.1055/s-0043-1768994] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 04/18/2023] [Indexed: 07/14/2023] Open
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.
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Affiliation(s)
- Lipika Samal
- Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Edward Wu
- Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
- Alabama College of Osteopathic Medicine, Dothan, Alabama, United States
| | - Skye Aaron
- Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
| | - John L. Kilgallon
- Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
| | - Michael Gannon
- Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
- Eastern Virginia Medical School, Norfolk, Virginia, United States
| | - Allison McCoy
- Vanderbilt University, Nashville, Tennessee, United States
| | - Saul Blecker
- NYU School of Medicine, New York, New York, United States
| | - Patricia C. Dykes
- Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - David W. Bates
- Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
| | - Stuart Lipsitz
- Department of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
- Harvard Medical School, Boston, Massachusetts, United States
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, United States
| | - Adam Wright
- Vanderbilt University, Nashville, Tennessee, United States
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Garabedian PM, Gannon MP, Aaron S, Wu E, Burns Z, Samal L. Human-centered design of clinical decision support for management of hypertension with chronic kidney disease. BMC Med Inform Decis Mak 2022; 22:217. [PMID: 35964083 PMCID: PMC9375189 DOI: 10.1186/s12911-022-01962-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 08/02/2022] [Indexed: 11/16/2022] Open
Abstract
Background Primary care providers face challenges in recognizing and controlling hypertension in patients with chronic kidney disease (CKD). Clinical decision support (CDS) has the potential to aid clinicians in identifying patients who could benefit from medication changes. This study designed an alert to control hypertension in CKD patients using an iterative human-centered design process. Methods In this study, we present a human-centered design process employing multiple methods for gathering user requirements and feedback on design and usability. Initially, we conducted contextual inquiry sessions to gather user requirements for the CDS. This was followed by group design sessions and one-on-one formative think-aloud sessions to validate requirements, obtain feedback on the design and layout, uncover usability issues, and validate changes. Results This study included 20 participants. The contextual inquiry produced 10 user requirements which influenced the initial alert design. The group design sessions revealed issues related to several themes, including recommendations and clinical content that did not match providers' expectations and extraneous information on the alerts that did not provide value. Findings from the individual think-aloud sessions revealed that participants disagreed with some recommended clinical actions, requested additional information, and had concerns about the placement in their workflow. Following each step, iterative changes were made to the alert content and design. Discussion This study showed that participation from users throughout the design process can lead to a better understanding of user requirements and optimal design, even within the constraints of an EHR alerting system. While raising awareness of design needs, it also revealed concerns related to workflow, understandability, and relevance. Conclusion The human-centered design framework using multiple methods for CDS development informed the creation of an alert to assist in the treatment and recognition of hypertension in patients with CKD. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01962-y.
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Affiliation(s)
- Pamela M Garabedian
- Mass General Brigham, 399 Revolution Drive, Somerville, MA, 857-282-4091, USA.
| | - Michael P Gannon
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Skye Aaron
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Edward Wu
- Alabama College of Osteopathic Medicine, Dothan, AL, USA
| | - Zoe Burns
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Lipika Samal
- Division of General Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
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