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Alexiuk M, Elgubtan H, Tangri N. Clinical Decision Support Tools in the Electronic Medical Record. Kidney Int Rep 2024; 9:29-38. [PMID: 38312784 PMCID: PMC10831391 DOI: 10.1016/j.ekir.2023.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 10/23/2023] [Indexed: 02/06/2024] Open
Abstract
The integration of clinical decision support (CDS) tools into electronic medical record (EMR) systems has become common. Although there are many benefits for both patients and providers from successful integration, barriers exist that prevent consistent and effective use of these tools. Such barriers include tool alert fatigue, lack of interoperability between tools and medical record systems, and poor acceptance of tools by care providers. However, successful integration of CDS tools into EMR systems have been reported; examples of these include the Statin Choice Decision Aid, and the Kidney Failure Risk Equation (KFRE). This article reviews the history of EMR systems and its integration with CDS tools, the barriers preventing successful integration, and the benefits reported from successful integration. This article also provides suggestions and strategies for improving successful integration, making these tools easier to use and more effective for care providers.
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Affiliation(s)
- Mackenzie Alexiuk
- Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada
- Community Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Heba Elgubtan
- Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada
- Community Health Sciences, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Navdeep Tangri
- Chronic Disease Innovation Centre, Winnipeg, Manitoba, Canada
- Department of Internal Medicine, Max Rady College of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
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Gaps and Disparities in Primary Prevention Statin Prescription During Outpatient Care. Am J Cardiol 2021; 161:36-41. [PMID: 34794616 DOI: 10.1016/j.amjcard.2021.08.070] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 08/29/2021] [Accepted: 08/31/2021] [Indexed: 12/13/2022]
Abstract
The 2018 American College of Cardiology/American Heart Association Guideline on the Management of Blood Cholesterol recommends statin therapy for eligible patients to reduce the risk of atherosclerotic cardiovascular disease (ASCVD). We extracted electronic health record data for patients with at least one primary care or cardiology visit between October 2018 and January 2020 at an urban, academic medical center in New York City. Clinical and demographic data were used to identify patients eligible for primary prevention statin therapy. Statin prescription status was extracted from the electronic health record, and multivariate logistic regression was used to assess the association between statin prescription and age, gender, race, ethnicity, and other clinical and demographic covariables. In 7,550 patients eligible for primary prevention statin therapy, 3,994 (52.9%) were prescribed statins on at least 1 visit. Statin prescription was highest in patients with diabetes mellitus (73.6%) and with a 10-year ASCVD risk ≥20% (60.6%) and was lowest for those with a 10-year ASCVD risk between 5% and 7.5% (18.7%). Compared with those never prescribed statins, patients prescribed statins were less likely to be women, mainly driven by lower statin prescription rates for women with diabetes. In a fully adjusted model, women remained less likely to be prescribed statin therapy (adjusted odds ratios 0.79, 95% confidence interval 0.71 to 0.88). In conclusion, primary prevention statin therapy remains underutilized.
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Scalia P, Ahmad F, Schubbe D, Forcino R, Durand MA, Barr PJ, Elwyn G. Integrating Option Grid Patient Decision Aids in the Epic Electronic Health Record: Case Study at 5 Health Systems. J Med Internet Res 2021; 23:e22766. [PMID: 33938806 PMCID: PMC8129884 DOI: 10.2196/22766] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 10/20/2020] [Accepted: 02/17/2021] [Indexed: 11/26/2022] Open
Abstract
Background Some researchers argue that the successful implementation of patient decision aids (PDAs) into clinical workflows depends on their integration into electronic health records (EHRs). Anecdotally, we know that EHR integration is a complex and time-consuming task; yet, the process has not been examined in detail. As part of an implementation project, we examined the work involved in integrating an encounter PDA for symptomatic uterine fibroids into Epic EHR systems. Objective This study aims to identify the steps and time required to integrate a PDA into the Epic EHR system and examine facilitators and barriers to the integration effort. Methods We conducted a case study at 5 academic medical centers in the United States. A clinical champion at each institution liaised with their Epic EHR team to initiate the integration of the uterine fibroid Option Grid PDAs into clinician-facing menus. We scheduled regular meetings with the Epic software analysts and an expert Epic technologist to discuss how best to integrate the tools into Epic for use by clinicians with patients. The meetings were then recorded and transcribed. Two researchers independently coded the transcripts and field notes before categorizing the codes and conducting a thematic analysis to identify the facilitators and barriers to EHR integration. The steps were reviewed and edited by an Epic technologist to ensure their accuracy. Results Integrating the uterine fibroid Option Grid PDA into clinician-facing menus required an 18-month timeline and a 6-step process, as follows: task priority negotiation with Epic software teams, security risk assessment, technical review, Epic configuration; troubleshooting, and launch. The key facilitators of the process were the clinical champions who advocated for integration at the institutional level and the presence of an experienced technologist who guided Epic software analysts during the build. Another facilitator was the use of an emerging industry standard app platform (Health Level 7 Substitutable Medical Applications and Reusable Technologies on Fast Healthcare Interoperability Resources) as a means of integrating the Option Grid into existing systems. This standard platform enabled clinicians to access the tools by using single sign-on credentials and prevented protected health information from leaving the EHR. Key barriers were the lack of control over the Option Grid product developed by EBSCO (Elton B Stephens Company) Health; the periodic Epic upgrades that can result in a pause on new software configurations; and the unforeseen software problems with Option Grid (ie, inability to print the PDA), which delayed the launch of the PDA. Conclusions The integration of PDAs into the Epic EHR system requires a 6-step process and an 18-month timeline. The process required support and prioritization from a clinical champion, guidance from an experienced technologist, and a willing EHR software developer team.
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Affiliation(s)
| | | | | | | | | | | | - Glyn Elwyn
- Dartmouth College, Lebanon, NH, United States
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Ridgeway JL, Branda ME, Gravholt D, Brito JP, Hargraves IG, Hartasanchez SA, Leppin AL, Gomez YL, Mann DM, Nautiyal V, Thomas RJ, Behnken EM, Torres Roldan VD, Shah ND, Khurana CS, Montori VM. Increasing risk-concordant cardiovascular care in diverse health systems: a mixed methods pragmatic stepped wedge cluster randomized implementation trial of shared decision making (SDM4IP). Implement Sci Commun 2021; 2:43. [PMID: 33883035 PMCID: PMC8058970 DOI: 10.1186/s43058-021-00145-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 04/05/2021] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND The primary prevention of cardiovascular (CV) events is often less intense in persons at higher CV risk and vice versa. Clinical practice guidelines recommend that clinicians and patients use shared decision making (SDM) to arrive at an effective and feasible prevention plan that is congruent with each person's CV risk and informed preferences. However, SDM does not routinely happen in practice. This study aims to integrate into routine care an SDM decision tool (CV PREVENTION CHOICE) at three diverse healthcare systems in the USA and study strategies that foster its adoption and routine use. METHODS This is a mixed method, hybrid type III stepped wedge cluster randomized study to estimate (a) the effectiveness of implementation strategies on SDM uptake and utilization and (b) the extent to which SDM results in prevention plans that are risk-congruent. Formative evaluation methods, including clinician and stakeholder interviews and surveys, will identify factors likely to impact feasibility, acceptability, and adoption of CV PREVENTION CHOICE as well as normalization of CV PREVENTION CHOICE in routine care. Implementation facilitation will be used to tailor implementation strategies to local needs, and implementation strategies will be systematically adjusted and tracked for assessment and refinement. Electronic health record data will be used to assess implementation and effectiveness outcomes, including CV PREVENTION CHOICE reach, adoption, implementation, maintenance, and effectiveness (measured as risk-concordant care plans). A sample of video-recorded clinical encounters and patient surveys will be used to assess fidelity. The study employs three theoretical approaches: a determinant framework that calls attention to categories of factors that may foster or inhibit implementation outcomes (the Consolidated Framework for Implementation Research), an implementation theory that guides explanation or understanding of causal influences on implementation outcomes (Normalization Process Theory), and an evaluation framework (RE-AIM). DISCUSSION By the project's end, we expect to have (a) identified the most effective implementation strategies to embed SDM in routine practice and (b) estimated the effectiveness of SDM to achieve feasible and risk-concordant CV prevention in primary care. TRIAL REGISTRATION ClinicalTrials.gov, NCT04450914 . Posted June 30, 2020 TRIAL STATUS: This study received ethics approval on April 17, 2020. The current trial protocol is version 2 (approved February 17, 2021). The first subject had not yet been enrolled at the time of submission.
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Affiliation(s)
- Jennifer L Ridgeway
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
- Knowledge and Evaluation Research Unit, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
| | - Megan E Branda
- Knowledge and Evaluation Research Unit, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado-Denver Anschutz Medical Campus, 13001 East 17th Place, 3rd Floor, Mail Stop B119, Aurora, CO, 80045, USA
| | - Derek Gravholt
- Knowledge and Evaluation Research Unit, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Juan P Brito
- Knowledge and Evaluation Research Unit, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
- Division of Diabetes, Endocrinology, Metabolism, and Nutrition, Department of Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Ian G Hargraves
- Knowledge and Evaluation Research Unit, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Sandra A Hartasanchez
- Knowledge and Evaluation Research Unit, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Aaron L Leppin
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
- Knowledge and Evaluation Research Unit, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Yvonne L Gomez
- Altru Health System, 1380 S. Columbia Road, Grand Forks, ND, 58206, USA
| | - Devin M Mann
- Department of Population Health, NYU Grossman School of Medicine, 530 1st Avenue, New York, NY, 10016, USA
| | - Vivek Nautiyal
- Wellstar Cardiovascular Medicine, 55 Whitcher Street, NE, Suite 350, Marietta, GA, 30060, USA
| | - Randal J Thomas
- Division of Preventive Cardiology, Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Emma M Behnken
- Knowledge and Evaluation Research Unit, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Victor D Torres Roldan
- Knowledge and Evaluation Research Unit, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Nilay D Shah
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
| | - Charanjit S Khurana
- Virginia Hospital Center Physician Group-Cardiology, 1715 North George Mason Drive, Arlington, VA, 22205, USA
| | - Victor M Montori
- Knowledge and Evaluation Research Unit, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
- Division of Diabetes, Endocrinology, Metabolism, and Nutrition, Department of Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA
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