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Breathett K, Lewsey S, Brownell NK, Enright K, Evangelista LS, Ibrahim NE, Iturrizaga J, Matlock DD, Ogunniyi MO, Sterling MR, Van Spall HGC. Implementation Science to Achieve Equity in Heart Failure Care: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1143-e1163. [PMID: 38567497 DOI: 10.1161/cir.0000000000001231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Guideline-directed medical therapies and guideline-directed nonpharmacological therapies improve quality of life and survival in patients with heart failure (HF), but eligible patients, particularly women and individuals from underrepresented racial and ethnic groups, are often not treated with these therapies. Implementation science uses evidence-based theories and frameworks to identify strategies that facilitate uptake of evidence to improve health. In this scientific statement, we provide an overview of implementation trials in HF, assess their use of conceptual frameworks and health equity principles, and provide pragmatic guidance for equity in HF. Overall, behavioral nudges, multidisciplinary care, and digital health strategies increased uptake of therapies in HF effectively but did not include equity goals. Few HF studies focused on achieving equity in HF by engaging stakeholders, quantifying barriers and facilitators to HF therapies, developing strategies for equity informed by theory or frameworks, evaluating implementation measures for equity, and titrating strategies for equity. Among these HF equity studies, feasibility was established in using various educational strategies to promote organizational change and equitable care. A couple include ongoing randomized controlled pragmatic trials for HF equity. There is great need for additional HF implementation trials designed to promote delivery of equitable guideline-directed therapy.
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Tan HJ, Spratte BN, Deal AM, Heiling HM, Nazzal EM, Meeks W, Fang R, Teal R, Vu MB, Bennett AV, Blalock SJ, Chung AE, Gotz D, Nielsen ME, Reuland DS, Harris AH, Basch E. Clinical Decision Support for Surgery: A Mixed Methods Study on Design and Implementation Perspectives From Urologists. Urology 2024:S0090-4295(24)00307-8. [PMID: 38697362 DOI: 10.1016/j.urology.2024.04.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/08/2024] [Accepted: 04/20/2024] [Indexed: 05/05/2024]
Abstract
OBJECTIVE To assess urologist attitudes toward clinical decision support (CDS) embedded into the electronic health record (EHR) and define design needs to facilitate implementation and impact. With recent advances in big data and artificial intelligence (AI), enthusiasm for personalized, data-driven tools to improve surgical decision-making has grown, but the impact of current tools remains limited. METHODS A sequential explanatory mixed methods study from 2019 to 2020 was performed. First, survey responses from the 2019 American Urological Association Annual Census evaluated attitudes toward an automatic CDS tool that would display risk/benefit data. This was followed by the purposeful sampling of 25 urologists and qualitative interviews assessing perspectives on CDS impact and design needs. Bivariable, multivariable, and coding-based thematic analysis were applied and integrated. RESULTS Among a weighted sample of 12,366 practicing urologists, the majority agreed CDS would help decision-making (70.9%, 95% CI 68.7%-73.2%), aid patient counseling (78.5%, 95% CI 76.5%-80.5%), save time (58.1%, 95% CI 55.7%-60.5%), and improve patient outcomes (42.9%, 95% CI 40.5%-45.4%). More years in practice was negatively associated with agreement (P <.001). Urologists described how CDS could bolster evidence-based care, personalized medicine, resource utilization, and patient experience. They also identified multiple implementation barriers and provided suggestions on form, functionality, and visual design to improve usefulness and ease of use. CONCLUSION Urologists have favorable attitudes toward the potential for clinical decision support in the EHR. Smart design will be critical to ensure effective implementation and impact.
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Affiliation(s)
- Hung-Jui Tan
- Department of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC; Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC.
| | - Brooke N Spratte
- Department of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC
| | - Allison M Deal
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC
| | - Hillary M Heiling
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC
| | - Elizabeth M Nazzal
- Department of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC
| | - William Meeks
- American Urological Association Data Management and Statistical Services
| | - Raymond Fang
- American Urological Association Data Management and Statistical Services
| | - Randall Teal
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Connected Health Applications and Interventions Core, University of North Carolina, Chapel Hill, NC
| | - Maihan B Vu
- Connected Health Applications and Interventions Core, University of North Carolina, Chapel Hill, NC; Center for Health Promotion and Disease Prevention, University of North Carolina, Chapel Hill, NC
| | - Antonia V Bennett
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC
| | - Susan J Blalock
- Pharmaceutical Outcomes & Policy, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC
| | - Arlene E Chung
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Bioinformatics, Duke University, Durham, NC
| | - David Gotz
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; School of Information and Library Science, University of North Carolina, Chapel Hill, NC
| | - Matthew E Nielsen
- Department of Urology, School of Medicine, University of North Carolina, Chapel Hill, NC; Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC
| | - Daniel S Reuland
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC
| | - Alex Hs Harris
- Department of Surgery, School of Medicine, Stanford University, Palo Alto, CA
| | - Ethan Basch
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina, Chapel Hill, NC; Department of Health Policy & Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC; Department of Medicine, School of Medicine, University of North Carolina, Chapel Hill, NC
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3
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Tang AB, Brownell NK, Roberts JS, Haidar A, Osuna-Garcia A, Cho DJ, Bokhoor P, Fonarow GC. Interventions for Optimization of Guideline-Directed Medical Therapy: A Systematic Review. JAMA Cardiol 2024; 9:397-404. [PMID: 38381449 DOI: 10.1001/jamacardio.2023.5627] [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] [Indexed: 02/22/2024]
Abstract
Importance Implementation of guideline-directed medical therapy (GDMT) in real-world practice remains suboptimal. It is unclear which interventions are most effective at addressing current barriers to GDMT in patients with heart failure with reduced ejection fraction (HFrEF). Objective To perform a systematic review to identify which types of system-level initiatives are most effective at improving GDMT use among patients with HFrEF. Evidence Review PubMed, Embase, Cochrane, CINAHL, and Web of Science databases were queried from January 2010 to November 2023 for randomized clinical trials that implemented a quality improvement intervention with GDMT use as a primary or secondary outcome. References from related review articles were also included for screening. Quality of studies and bias assessment were graded based on the Cochrane Risk of Bias tool and Oxford Centre for Evidence-Based Medicine. Findings Twenty-eight randomized clinical trials were included with an aggregate sample size of 19 840 patients. Studies were broadly categorized as interdisciplinary interventions (n = 15), clinician education (n = 5), electronic health record initiatives (n = 6), or patient education (n = 2). Overall, interdisciplinary titration clinics were associated with significant increases in the proportion of patients on target doses of GDMT with a 10% to 60% and 2% to 53% greater proportion of patients on target doses of β-blockers and renin-angiotensin-aldosterone system inhibitors, respectively, in intervention groups compared with usual care. Other interventions, such as audits, clinician and patient education, or electronic health record alerts, were also associated with some improvements in GDMT utilization, though these findings were inconsistent across studies. Conclusions and Relevance This review summarizes interventions aimed at optimization of GDMT in clinical practice. Initiatives that used interdisciplinary teams, largely comprised of nurses and pharmacists, most consistently led to improvements in GDMT. Additional large, randomized studies are necessary to better understand other types of interventions, as well as their long-term efficacy and sustainability.
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Affiliation(s)
- Amber B Tang
- Department of Medicine, University of California Los Angeles
| | - Nicholas K Brownell
- Department of Medicine, Division of Cardiology, University of California Los Angeles
| | - Jacob S Roberts
- Department of Medicine, University of California Los Angeles
| | - Amier Haidar
- Department of Medicine, University of California Los Angeles
| | - Antonia Osuna-Garcia
- Louise M. Darling Biomedical Library, UCLA Library, University of California Los Angeles
| | - David J Cho
- Department of Medicine, Division of Cardiology, University of California Los Angeles
| | - Pooya Bokhoor
- Department of Medicine, Division of Cardiology, University of California Los Angeles
| | - Gregg C Fonarow
- Department of Medicine, Division of Cardiology, University of California Los Angeles
- Associate Section Editor, JAMA Cardiology
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Heneghan JA, Walker SB, Fawcett A, Bennett TD, Dziorny AC, Sanchez-Pinto LN, Farris RW, Winter MC, Badke C, Martin B, Brown SR, McCrory MC, Ness-Cochinwala M, Rogerson C, Baloglu O, Harwayne-Gidansky I, Hudkins MR, Kamaleswaran R, Gangadharan S, Tripathi S, Mendonca EA, Markovitz BP, Mayampurath A, Spaeder MC. The Pediatric Data Science and Analytics Subgroup of the Pediatric Acute Lung Injury and Sepsis Investigators Network: Use of Supervised Machine Learning Applications in Pediatric Critical Care Medicine Research. Pediatr Crit Care Med 2024; 25:364-374. [PMID: 38059732 PMCID: PMC10994770 DOI: 10.1097/pcc.0000000000003425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Abstract
OBJECTIVE Perform a scoping review of supervised machine learning in pediatric critical care to identify published applications, methodologies, and implementation frequency to inform best practices for the development, validation, and reporting of predictive models in pediatric critical care. DESIGN Scoping review and expert opinion. SETTING We queried CINAHL Plus with Full Text (EBSCO), Cochrane Library (Wiley), Embase (Elsevier), Ovid Medline, and PubMed for articles published between 2000 and 2022 related to machine learning concepts and pediatric critical illness. Articles were excluded if the majority of patients were adults or neonates, if unsupervised machine learning was the primary methodology, or if information related to the development, validation, and/or implementation of the model was not reported. Article selection and data extraction were performed using dual review in the Covidence tool, with discrepancies resolved by consensus. SUBJECTS Articles reporting on the development, validation, or implementation of supervised machine learning models in the field of pediatric critical care medicine. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Of 5075 identified studies, 141 articles were included. Studies were primarily (57%) performed at a single site. The majority took place in the United States (70%). Most were retrospective observational cohort studies. More than three-quarters of the articles were published between 2018 and 2022. The most common algorithms included logistic regression and random forest. Predicted events were most commonly death, transfer to ICU, and sepsis. Only 14% of articles reported external validation, and only a single model was implemented at publication. Reporting of validation methods, performance assessments, and implementation varied widely. Follow-up with authors suggests that implementation remains uncommon after model publication. CONCLUSIONS Publication of supervised machine learning models to address clinical challenges in pediatric critical care medicine has increased dramatically in the last 5 years. While these approaches have the potential to benefit children with critical illness, the literature demonstrates incomplete reporting, absence of external validation, and infrequent clinical implementation.
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Affiliation(s)
- Julia A. Heneghan
- Division of Pediatric Critical Care, University of Minnesota Masonic Children’s Hospital; Minneapolis, MN
| | - Sarah B. Walker
- Department of Pediatrics (Critical Care), Northwestern University Feinberg School of Medicine and Ann & Robert H. Lurie Children’s Hospital of Chicago; Chicago, IL
| | - Andrea Fawcett
- Department of Clinical and Organizational Development; Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL
| | - Tellen D. Bennett
- Departments of Biomedical Informatics and Pediatrics (Critical Care Medicine), University of Colorado School of Medicine; Aurora, CO
| | - Adam C. Dziorny
- Department of Pediatrics, University of Rochester; Rochester, NY
| | - L. Nelson Sanchez-Pinto
- Department of Pediatrics (Critical Care) and Preventive Medicine (Health & Biomedical Informatics), Northwestern University Feinberg School of Medicine and Ann & Robert H. Lurie Children’s Hospital of Chicago; Chicago, IL
| | - Reid W.D. Farris
- Department of Pediatrics, University of Washington and Seattle Children’s Hospital; Seattle, WA
| | - Meredith C. Winter
- Department of Anesthesiology Critical Care Medicine, Children’s Hospital Los Angeles and Keck School of Medicine, University of Southern California; Los Angeles, CA
| | - Colleen Badke
- Department of Pediatrics (Critical Care), Northwestern University Feinberg School of Medicine and Ann & Robert H. Lurie Children’s Hospital of Chicago; Chicago, IL
| | - Blake Martin
- Departments of Biomedical Informatics and Pediatrics (Critical Care Medicine), University of Colorado School of Medicine; Aurora, CO
| | - Stephanie R. Brown
- Section of Pediatric Critical Care, Oklahoma Children’s Hospital and Department of Pediatrics, University of Oklahoma Health Sciences Center, Oklahoma City, OK
| | - Michael C. McCrory
- Department of Anesthesiology, Wake Forest University School of Medicine; Winston Salem, NC
| | | | - Colin Rogerson
- Division of Critical Care, Department of Pediatrics, Indiana University; Indianapolis, IN
| | - Orkun Baloglu
- Pediatric Critical Care Medicine and Pediatric Cardiology, Cleveland Clinic Children’s Center for Artificial Intelligence (C4AI), Cleveland Clinic; Cleveland, OH
| | | | - Matthew R. Hudkins
- Division of Pediatric Critical Care, Department of Pediatrics, Oregon Health & Science University; Portland, OR
| | - Rishikesan Kamaleswaran
- Departments of Biomedical Informatics and Pediatrics, Emory University School of Medicine; Department of Biomedical Engineering, Georgia Institute of Technology; Atlanta, GA
| | - Sandeep Gangadharan
- Department of Pediatrics, Mount Sinai Icahn School of Medicine; New York, NY
| | - Sandeep Tripathi
- Department of Pediatrics. University of Illinois College of Medicine at Peoria/OSF HealthCare, Children’s Hospital of Illinois; Peoria, IL
| | - Eneida A. Mendonca
- Division of Biomedical Informatics, Department of Pediatrics, Cincinnati Children’s Hospital Medical Center and University of Cincinnati; Cincinnati, OH
| | - Barry P. Markovitz
- Division of Pediatric Critical Care, Department of Pediatrics, University of Utah Spencer F Eccles School of Medicine, Intermountain Primary Children’s Hospital; Salt Lake City, UT
| | - Anoop Mayampurath
- Department of Biostatistics & Medical Informatics, University of Wisconsin-Madison; Madison, WI
| | - Michael C. Spaeder
- Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA
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Trinkley KE, Wright G, Allen LA, Bennett TD, Glasgow RE, Hale G, Heckman S, Huebschmann AG, Kahn MG, Kao DP, Lin CT, Malone DC, Matlock DD, Wells L, Wysocki V, Zhang S, Suresh K. Sustained Effect of Clinical Decision Support for Heart Failure: A Natural Experiment Using Implementation Science. Appl Clin Inform 2023; 14:822-832. [PMID: 37852249 PMCID: PMC10584394 DOI: 10.1055/s-0043-1775566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 08/02/2023] [Indexed: 10/20/2023] Open
Abstract
OBJECTIVES In a randomized controlled trial, we found that applying implementation science (IS) methods and best practices in clinical decision support (CDS) design to create a locally customized, "enhanced" CDS significantly improved evidence-based prescribing of β blockers (BB) for heart failure compared with an unmodified commercially available CDS. At trial conclusion, the enhanced CDS was expanded to all sites. The purpose of this study was to evaluate the real-world sustained effect of the enhanced CDS compared with the commercial CDS. METHODS In this natural experiment of 28 primary care clinics, we compared clinics exposed to the commercial CDS (preperiod) to clinics exposed to the enhanced CDS (both periods). The primary effectiveness outcome was the proportion of alerts resulting in a BB prescription. Secondary outcomes included patient reach and clinician adoption (dismissals). RESULTS There were 367 alerts for 183 unique patients and 171 unique clinicians (pre: March 2019-August 2019; post: October 2019-March 2020). The enhanced CDS increased prescribing by 26.1% compared with the commercial (95% confidence interval [CI]: 17.0-35.1%), which is consistent with the 24% increase in the previous study. The odds of adopting the enhanced CDS was 81% compared with 29% with the commercial (odds ratio: 4.17, 95% CI: 1.96-8.85). The enhanced CDS adoption and effectiveness rates were 62 and 14% in the preperiod and 92 and 10% in the postperiod. CONCLUSION Applying IS methods with CDS best practices was associated with improved and sustained clinician adoption and effectiveness compared with a commercially available CDS tool.
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Affiliation(s)
- Katy E. Trinkley
- Department of Family Medicine, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, Colorado, United States
- UCHealth, Aurora, Colorado, United States
- Department of Clinical Pharmacy, University of Colorado Anschutz Medical Campus Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States
| | - Garth Wright
- Department of Clinical Pharmacy, University of Colorado Anschutz Medical Campus Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States
| | - Larry A. Allen
- Adult and Child Center for Outcomes Research and Delivery Science, Aurora, Colorado, United States
- Division of Cardiology, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, Colorado, United States
| | - Tellen D. Bennett
- Adult and Child Center for Outcomes Research and Delivery Science, Aurora, Colorado, United States
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, Colorado, United States
| | - Russell E. Glasgow
- Department of Family Medicine, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, Colorado, United States
- Adult and Child Center for Outcomes Research and Delivery Science, Aurora, Colorado, United States
- Veterans Affairs Eastern Colorado Geriatric Research Education and Clinical Center, Aurora, Colorado, United States
| | - Gary Hale
- UCHealth, Aurora, Colorado, United States
| | | | - Amy G. Huebschmann
- Adult and Child Center for Outcomes Research and Delivery Science, Aurora, Colorado, United States
- Division of Internal Medicine, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, Colorado, United States
- University of Colorado Anschutz Medical Campus Ludeman Family Center for Women's Health Research, Aurora, Colorado, United States
| | - Michael G. Kahn
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, Colorado, United States
| | - David P. Kao
- UCHealth, Aurora, Colorado, United States
- Department of Clinical Pharmacy, University of Colorado Anschutz Medical Campus Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States
| | - Chen-Tan Lin
- UCHealth, Aurora, Colorado, United States
- Division of Internal Medicine, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, Colorado, United States
| | - Daniel C. Malone
- Department of Pharmacotherapy, University of Utah Skaggs College of Pharmacy, Salt Lake City, Utah, United States
| | - Daniel D. Matlock
- Adult and Child Center for Outcomes Research and Delivery Science, Aurora, Colorado, United States
- Veterans Affairs Eastern Colorado Geriatric Research Education and Clinical Center, Aurora, Colorado, United States
- Division of Internal Medicine, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, Colorado, United States
- Division of Geriatrics, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, Colorado, United States
| | - Lauren Wells
- Department of Clinical Pharmacy, University of Colorado Anschutz Medical Campus Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States
| | - Vincent Wysocki
- Department of Clinical Pharmacy, University of Colorado Anschutz Medical Campus Skaggs School of Pharmacy and Pharmaceutical Sciences, Aurora, Colorado, United States
| | - Shelley Zhang
- Department of Family Medicine, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, Colorado, United States
| | - Krithika Suresh
- Adult and Child Center for Outcomes Research and Delivery Science, Aurora, Colorado, United States
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, United States
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6
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Maw AM, Huebschmann AG, Jones CD. Methods progress note: Applying dissemination and implementation science models to enhance hospital-based quality improvement. J Hosp Med 2023; 18:841-844. [PMID: 37225387 DOI: 10.1002/jhm.13139] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 05/04/2023] [Accepted: 05/06/2023] [Indexed: 05/26/2023]
Affiliation(s)
- Anna M Maw
- Division of Hospital Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Amy G Huebschmann
- Division of General Internal Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA
| | - Christine D Jones
- Division of Hospital Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA
- Rocky Mountain Regional VA Medical Center, Eastern Colorado Health Care System, Denver-Seattle Center of Innovation for Veteran-Centered and Value Driven Care, University of Colorado School of Medicine, Aurora, Colorado, USA
- Division of Geriatrics, University of Colorado School of Medicine, Aurora, Colorado, USA
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Shakowski C, Page II RL, Wright G, Lunowa C, Marquez C, Suresh K, Allen LA, Glasgow RE, Lin CT, Wick A, Trinkley KE. Comparative effectiveness of generic commercial versus locally customized clinical decision support tools to reduce prescription of nonsteroidal anti-inflammatory drugs for patients with heart failure. J Am Med Inform Assoc 2023; 30:1516-1525. [PMID: 37352404 PMCID: PMC10436140 DOI: 10.1093/jamia/ocad109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 05/09/2023] [Accepted: 06/08/2023] [Indexed: 06/25/2023] Open
Abstract
OBJECTIVE To compare the effectiveness of 2 clinical decision support (CDS) tools to avoid prescription of nonsteroidal anti-inflammatory drugs (NSAIDs) in patients with heart failure (HF): a "commercial" and a locally "customized" alert. METHODS We conducted a retrospective cohort study of 2 CDS tools implemented within a large integrated health system. The commercial CDS tool was designed according to third-party drug content and EHR vendor specifications. The customized CDS tool underwent a user-centered design process informed by implementation science principles, with input from a cross disciplinary team. The customized CDS tool replaced the commercial CDS tool. Data were collected from the electronic health record via analytic reports and manual chart review. The primary outcome was effectiveness, defined as whether the clinician changed their behavior and did not prescribe an NSAID. RESULTS A random sample of 366 alerts (183 per CDS tool) was evaluated that represented 355 unique patients. The commercial CDS tool was effective for 7 of 172 (4%) patients, while the customized CDS tool was effective for 81 of 183 (44%) patients. After adjusting for age, chronic kidney disease, ejection fraction, NYHA class, concurrent prescription of an opioid or acetaminophen, visit type (inpatient or outpatient), and clinician specialty, the customized alerts were at 24.3 times greater odds of effectiveness compared to the commercial alerts (OR: 24.3 CI: 10.20-58.06). CONCLUSION Investing additional resources to customize a CDS tool resulted in a CDS tool that was more effective at reducing the total number of NSAID orders placed for patients with HF compared to a commercially available CDS tool.
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Affiliation(s)
| | - Robert L Page II
- Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Garth Wright
- Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Cali Lunowa
- Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Clyde Marquez
- Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Krithika Suresh
- Adult and Child Center for Outcomes Research and Delivery Science Center, Aurora, Colorado, USA
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, USA
| | - Larry A Allen
- Adult and Child Center for Outcomes Research and Delivery Science Center, Aurora, Colorado, USA
- Division of Cardiology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Russel E Glasgow
- Adult and Child Center for Outcomes Research and Delivery Science Center, Aurora, Colorado, USA
- Department of Family Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Chen-Tan Lin
- UCHealth, Aurora, Colorado, USA
- Division of Internal Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | | | - Katy E Trinkley
- UCHealth, Aurora, Colorado, USA
- Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Adult and Child Center for Outcomes Research and Delivery Science Center, Aurora, Colorado, USA
- Department of Family Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
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8
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Bongiovanni T, Pletcher MJ, Lau C, Robinson A, Lancaster E, Zhang L, Behrends M, Wick E, Auerbach A. A behavioral intervention to promote use of multimodal pain medication for hospitalized patients: A randomized controlled trial. J Hosp Med 2023; 18:685-692. [PMID: 37357367 PMCID: PMC10578203 DOI: 10.1002/jhm.13153] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 06/01/2023] [Accepted: 06/04/2023] [Indexed: 06/27/2023]
Abstract
BACKGROUND The use of nonsteroidal anti-inflammatory drugs (NSAIDs) can reduce pain and has become a core strategy to decrease opioid use, but there is a lack of data to describe encouraging use when admitting patients using electronic health record systems. OBJECTIVE Assess an electronic health record system to increase ordering of NSAIDs for hospitalized adults. DESIGNS, SETTINGS AND PARTICIPANTS We performed a cluster randomized controlled trial of clinicians admitting adult patients to a health system over a 9-month period. Clinicians were randomized to use a standard admission order set. INTERVENTION Clinicians in the intervention arm were required to actively order or decline NSAIDs; the control arm was shown the same order but without a required response. MAIN OUTCOME AND MEASURES The primary outcome was NSAIDs ordered and administered by the first full hospital day. Secondary outcomes included pain scores and opioid prescribing. RESULTS A total of 20,085 hospitalizations were included. Among these hospitalizations, patients had a mean age of 58 years, and a Charlson comorbidity score of 2.97, while 50% and 56% were female and White, respectively. Overall, 52% were admitted by a clinician randomized to the intervention arm. NSAIDs were ordered in 2267 (22%) interventions and 2093 (22%) control admissions (p = .10). Similarly, there were no statistical differences in NSAID administration, pain scores, or opioid prescribing. Average pain scores (0-5 scale) were 3.36 in the control group and 3.39 in the intervention group (p = .46). There were no differences in clinical harms. CONCLUSIONS AND RELEVANCE Requiring an active decision to order an NSAID at admission had no demonstrable impact on NSAID ordering. Multicomponent interventions, perhaps with stronger decision support, may be necessary to encourage NSAID ordering.
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Affiliation(s)
- Tasce Bongiovanni
- Department of Surgery, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Mark J Pletcher
- Department of Epidemiology and Biostatistics, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Catherine Lau
- Division of Hospital Medicine, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Andrew Robinson
- Department of Epidemiology and Biostatistics, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Elizabeth Lancaster
- Department of Surgery, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Li Zhang
- Department of Epidemiology and Biostatistics, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Matthias Behrends
- Department of Anesthesia, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Elizabeth Wick
- Department of Surgery, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Andrew Auerbach
- Division of Hospital Medicine, University of California San Francisco School of Medicine, San Francisco, California, USA
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Mukhopadhyay A, Reynolds HR, Xia Y, Phillips LM, Aminian R, Diah RA, Nagler AR, Szerencsy A, Saxena A, Horwitz LI, Katz SD, Blecker S. Design and pilot implementation for the BETTER CARE-HF trial: A pragmatic cluster-randomized controlled trial comparing two targeted approaches to ambulatory clinical decision support for cardiologists. Am Heart J 2023; 258:38-48. [PMID: 36640860 PMCID: PMC10023424 DOI: 10.1016/j.ahj.2022.12.016] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 12/15/2022] [Accepted: 12/30/2022] [Indexed: 05/11/2023]
Abstract
BACKGROUND Beart failure with reduced ejection fraction (HFrEF) is a leading cause of morbidity and mortality. However, shortfalls in prescribing of proven therapies, particularly mineralocorticoid receptor antagonist (MRA) therapy, account for several thousand preventable deaths per year nationwide. Electronic clinical decision support (CDS) is a potential low-cost and scalable solution to improve prescribing of therapies. However, the optimal timing and format of CDS tools is unknown. METHODS AND RESULTS We developed two targeted CDS tools to inform cardiologists of gaps in MRA therapy for patients with HFrEF and without contraindication to MRA therapy: (1) an alert that notifies cardiologists at the time of patient visit, and (2) an automated electronic message that allows for review between visits. We designed these tools using an established CDS framework and findings from semistructured interviews with cardiologists. We then pilot tested both CDS tools (n = 596 patients) and further enhanced them based on additional semistructured interviews (n = 11 cardiologists). The message was modified to reduce the number of patients listed, include future visits, and list date of next visit. The alert was modified to improve noticeability, reduce extraneous information on guidelines, and include key information on contraindications. CONCLUSIONS The BETTER CARE-HF (Building Electronic Tools to Enhance and Reinforce CArdiovascular REcommendations for Heart Failure) trial aims to compare the effectiveness of the alert vs. the automated message vs. usual care on the primary outcome of MRA prescribing. To our knowledge, no study has directly compared the efficacy of these two different types of electronic CDS interventions. If effective, our findings can be rapidly disseminated to improve morbidity and mortality for patients with HFrEF, and can also inform the development of future CDS interventions for other disease states. (Trial registration: Clinicaltrials.gov NCT05275920).
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Affiliation(s)
- Amrita Mukhopadhyay
- Leon H. Charney Division of Cardiology, Department of Medicine, New York University Grossman School of Medicine, New York, NY.
| | - Harmony R Reynolds
- Leon H. Charney Division of Cardiology, Department of Medicine, New York University Grossman School of Medicine, New York, NY
| | - Yuhe Xia
- Division of Biostatistics, Department of Population Health, New York, NY
| | - Lawrence M Phillips
- Leon H. Charney Division of Cardiology, Department of Medicine, New York University Grossman School of Medicine, New York, NY
| | - Rod Aminian
- Medical Center Information Technology, New York University Langone Health, New York, NY
| | - Ruth-Ann Diah
- Medical Center Information Technology, New York University Langone Health, New York, NY
| | - Arielle R Nagler
- Ronald O. Perelman Department of Dermatology, New York University School Grossman of Medicine, New York, NY
| | - Adam Szerencsy
- Medical Center Information Technology, New York University Langone Health, New York, NY; Department of Medicine, New York University Grossman School of Medicine, New York, NY
| | - Archana Saxena
- Leon H. Charney Division of Cardiology, Department of Medicine, New York University Grossman School of Medicine, New York, NY; Medical Center Information Technology, New York University Langone Health, New York, NY
| | - Leora I Horwitz
- Department of Medicine, New York University Grossman School of Medicine, New York, NY; Department of Population Health, New York University Grossman School of Medicine, New York, NY
| | - Stuart D Katz
- Leon H. Charney Division of Cardiology, Department of Medicine, New York University Grossman School of Medicine, New York, NY
| | - Saul Blecker
- Department of Medicine, New York University Grossman School of Medicine, New York, NY; Department of Population Health, New York University Grossman School of Medicine, New York, NY.
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Shear K, Rice H, Garabedian PM, Bjarnadottir R, Lathum N, Horgas AL, Harle CA, Dykes PC, Lucero R. Usability Testing of an Interoperable Computerized Clinical Decision Support Tool for Fall Risk Management in Primary Care. Appl Clin Inform 2023; 14:212-226. [PMID: 36599446 PMCID: PMC10017195 DOI: 10.1055/a-2006-4936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 01/02/2023] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Falls are a widespread and persistent problem for community-dwelling older adults. Use of fall prevention guidelines in the primary care setting has been suboptimal. Interoperable computerized clinical decision support systems have the potential to increase engagement with fall risk management at scale. To support fall risk management across organizations, our team developed the ASPIRE tool for use in differing primary care clinics using interoperable standards. OBJECTIVES Usability testing of ASPIRE was conducted to measure ease of access, overall usability, learnability, and acceptability prior to pilot . METHODS Participants were recruited using purposive sampling from two sites with different electronic health records and different clinical organizations. Formative testing rooted in user-centered design was followed by summative testing using a simulation approach. During summative testing participants used ASPIRE across two clinical scenarios and were randomized to determine which scenario they saw first. Single Ease Question and System Usability Scale were used in addition to analysis of recorded sessions in NVivo. RESULTS All 14 participants rated the usability of ASPIRE as above average based on usability benchmarks for the System Usability Scale metric. Time on task decreased significantly between the first and second scenarios indicating good learnability. However, acceptability data were more mixed with some recommendations being consistently accepted while others were adopted less frequently. CONCLUSION This study described the usability testing of the ASPIRE system within two different organizations using different electronic health records. Overall, the system was rated well, and further pilot testing should be done to validate that these positive results translate into clinical practice. Due to its interoperable design, ASPIRE could be integrated into diverse organizations allowing a tailored implementation without the need to build a new system for each organization. This distinction makes ASPIRE well positioned to impact the challenge of falls at scale.
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Affiliation(s)
- Kristen Shear
- Department of Family, Community, and Health Systems Science, College of Nursing, University of Florida, Gainesville, Florida, United States
| | - Hannah Rice
- Department of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Pamela M. Garabedian
- Department of Information Systems, Mass General Brigham, Somerville, Massachusetts, United States
| | - Ragnhildur Bjarnadottir
- Department of Family, Community, and Health Systems Science, College of Nursing, University of Florida, Gainesville, Florida, United States
| | - Nancy Lathum
- Department of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Ann L. Horgas
- Department of Biobehavioral Nursing Science, College of Nursing, University of Florida, Gainesville, Florida, United States
| | - Christopher A. Harle
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States
| | - Patricia C. Dykes
- Department of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Robert Lucero
- UCLA School of Nursing, University of California Los Angeles, Los Angeles, California, United States
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11
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Meunier PY, Raynaud C, Guimaraes E, Gueyffier F, Letrilliart L. Barriers and Facilitators to the Use of Clinical Decision Support Systems in Primary Care: A Mixed-Methods Systematic Review. Ann Fam Med 2023; 21:57-69. [PMID: 36690490 PMCID: PMC9870646 DOI: 10.1370/afm.2908] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 09/08/2022] [Accepted: 10/10/2022] [Indexed: 01/24/2023] Open
Abstract
PURPOSE To identify and quantify the barriers and facilitators to the use of clinical decision support systems (CDSSs) by primary care professionals (PCPs). METHODS A mixed-methods systematic review was conducted using a sequential synthesis design. PubMed/MEDLINE, PsycInfo, Embase, CINAHL, and the Cochrane library were searched in July 2021. Studies that evaluated CDSSs providing recommendations to PCPs and intended for use during a consultation were included. We excluded CDSSs used only by patients, described as concepts or prototypes, used with simulated cases, and decision supports not considered as CDSSs. A framework synthesis was performed according to the HOT-fit framework (Human, Organizational, Technology, Net Benefits), then a quantitative synthesis evaluated the impact of the HOT-fit categories on CDSS use. RESULTS A total of 48 studies evaluating 45 CDSSs were included, and 186 main barriers or facilitators were identified. Qualitatively, barriers and facilitators were classified as human (eg, perceived usefulness), organizational (eg, disruption of usual workflow), and technological (eg, CDSS user-friendliness), with explanatory elements. The greatest barrier to using CDSSs was an increased workload. Quantitatively, the human and organizational factors had negative impacts on CDSS use, whereas the technological factor had a neutral impact and the net benefits dimension a positive impact. CONCLUSIONS Our findings emphasize the need for CDSS developers to better address human and organizational issues, in addition to technological challenges. We inferred core CDSS features covering these 3 factors, expected to improve their usability in primary care.
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Affiliation(s)
- Pierre-Yves Meunier
- Collège universitaire de médecine générale, Université Claude Bernard Lyon 1, Lyon, France
- Research on Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France
| | - Camille Raynaud
- Collège universitaire de médecine générale, Université Claude Bernard Lyon 1, Lyon, France
| | - Emmanuelle Guimaraes
- Collège universitaire de médecine générale, Université Claude Bernard Lyon 1, Lyon, France
| | - François Gueyffier
- Laboratoire de biométrie et biologie évolutive, département biostatistiques et modélisation pour la santé et l'environnement, CNRS UMR5558, Université Claude Bernard Lyon 1, Lyon, France
- Fédération de Recherche Santé Lyon Est, PAM Santé Publique, Hospices Civils de Lyon, Lyon, France
| | - Laurent Letrilliart
- Collège universitaire de médecine générale, Université Claude Bernard Lyon 1, Lyon, France
- Research on Healthcare Performance (RESHAPE), INSERM U1290, Université Claude Bernard Lyon 1, Lyon, France
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Trinkley KE, Ho PM, Glasgow RE, Huebschmann AG. How Dissemination and Implementation Science Can Contribute to the Advancement of Learning Health Systems. ACADEMIC MEDICINE : JOURNAL OF THE ASSOCIATION OF AMERICAN MEDICAL COLLEGES 2022; 97:1447-1458. [PMID: 35796045 PMCID: PMC9547828 DOI: 10.1097/acm.0000000000004801] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Many health systems are working to become learning health systems (LHSs), which aim to improve the value of health care by rapidly, continuously generating evidence to apply to practice. However, challenges remain to advance toward the aspirational goal of becoming a fully mature LHS. While some important challenges have been well described (i.e., building system-level supporting infrastructure and the accessibility of inclusive, integrated, and actionable data), other key challenges are underrecognized, including balancing evaluation rapidity with rigor, applying principles of health equity and classic ethics, focusing on external validity and reproducibility (generalizability), and designing for sustainability. Many LHSs focus on continuous learning cycles, but with limited consideration of issues related to the rapidity of these learning cycles, as well as the sustainability or generalizability of solutions. Some types of data have been consistently underrepresented, including patient-reported outcomes and preferences, social determinants, and behavioral and environmental data, the absence of which can exacerbate health disparities. A promising approach to addressing many challenges that LHSs face may be found in dissemination and implementation (D&I) science. With an emphasis on multilevel dynamic contextual factors, representation of implementation partner engagement, pragmatic research, sustainability, and generalizability, D&I science methods can assist in overcoming many of the challenges facing LHSs. In this article, the authors describe the current state of LHSs and challenges to becoming a mature LHS, propose solutions to current challenges, focusing on the contributions of D&I science with other methods, and propose key components and characteristics of a mature LHS model that others can use to plan and develop their LHSs.
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Affiliation(s)
- Katy E Trinkley
- K.E. Trinkley is associate professor, Departments of Clinical Pharmacy and Medicine and Adult and Child Consortium for Outcomes Research and Delivery Science (ACCORDS), University of Colorado Anschutz Medical Center, and clinical informaticist, Department of Clinical Informatics, UCHealth, Aurora, Colorado; ORCID: http://orcid.org/0000-0003-2041-7404
| | - P Michael Ho
- P.M. Ho is professor, Department of Medicine, University of Colorado Anschutz Medical Campus, and professor, VA Eastern Colorado Health Care System, Aurora, Colorado; ORCID: http://orcid.org/0000-0002-7775-6266
| | - Russell E Glasgow
- R.E. Glasgow is research professor, Department of Family Medicine, and director, Dissemination and Implementation Science Program, ACCORDS, University of Colorado Anschutz Medical Center, Aurora, Colorado; ORCID: http://orcid.org/0000-0003-4218-3231
| | - Amy G Huebschmann
- A.G. Huebschmann is associate professor, Division of General Internal Medicine, ACCORDS and Ludeman Family Center for Women's Health Research, University of Colorado Anschutz Medical Center, Aurora, Colorado; ORCID: http://orcid.org/0000-0002-9329-3142
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Kuo GM, Trinkley KE, Rabin B. Research and Scholarly Methods: Implementation Science Studies. JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY 2022; 5:995-1004. [PMID: 36212610 PMCID: PMC9534307 DOI: 10.1002/jac5.1673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 06/14/2022] [Indexed: 11/08/2022]
Abstract
Traditional research focuses on efficacy or effectiveness of interventions but lacks evaluation of strategies needed for equitable uptake, scalable implementation, and sustainable evidence-based practice transformation. The purpose of this introductory review is to describe key implementation science (IS) concepts as they apply to medication management and pharmacy practice, and to provide guidance on literature review with an IS lens. There are five key ingredients of IS, including: (1) evidence-based intervention; (2) implementation strategies; (3) IS theory, model, or framework; (4) IS outcomes and measures; and (5) stakeholder engagement, which is key to a successful implementation. These key ingredients apply across the three stages of IS research: (1) pre-implementation; (2) implementation; and (3) sustainment. A case example using a combination of IS models, PRISM (Practical, Robust Implementation and Sustainability model) and RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance), is included to describe how an IS study is designed and conducted. This case is a cluster randomized trial comparing two clinical decision support tools to improve guideline-concordant prescribing for patients with heart failure and reduced ejection fraction. The review also includes information on the Standards for Reporting Implementation Studies (StaRI), which is used for literature review and reporting of IS studies,as well as IS-related learning resources.
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Affiliation(s)
- Grace M Kuo
- Texas Tech University Health Sciences Center and Professor Emerita at University of California San Diego; Address: 1300 S. Coulter Street, Suite 104, Amarillo, TX 79106
| | - Katy E Trinkley
- University of Colorado Skaggs Schools of Medicine and Pharmacy and Pharmaceutical Sciences at the Anschutz Medical Campus; Aurora, Colorado
| | - Borsika Rabin
- Herbert Wertheim School of Public Health and Human Longevity Science and Co-Director of the UC San Diego ACTRI Dissemination and Implementation Science Center at University of California San Diego; La Jolla, California
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An exploration of expectations and perceptions of practicing physicians on the implementation of computerized clinical decision support systems using a Qsort approach. BMC Med Inform Decis Mak 2022; 22:185. [PMID: 35842722 PMCID: PMC9288707 DOI: 10.1186/s12911-022-01933-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 07/07/2022] [Indexed: 11/29/2022] Open
Abstract
Background There is increasing interest in incorporating clinical decision support (CDS) into electronic healthcare records (EHR). Successful implementation of CDS systems depends on acceptance of them by healthcare workers. We used a mix of quantitative and qualitative methods starting from Qsort methodology to explore expectations and perceptions of practicing physicians on the use of CDS incorporated in EHR. Methods The study was performed in a large tertiary care academic hospital. We used a mixed approach with a Q-sort based classification of pre-defined reactions to clinical case vignettes combined with a thinking-aloud approach, taking into account COREQ recommendations The open source software of Ken-Q Analysis version 1.0.6. was used for the quantitative analysis, using principal components and a Varimax rotation. For the qualitative analysis, a thematic analysis based on the four main themes was performed based on the audiotapes and field notes. Results Thirty physicians were interviewed (7 in training, 8 junior staff and 15 senior staff; 16 females). Nearly all respondents were strongly averse towards interruptive messages, especially when these also were obstructive. Obstructive interruption was considered to be acceptable only when it increases safety, is adjustable to user expertise level and/or allows deviations when the end-user explains why a deviation is desirable in the case at issue. Transparency was deemed an essential feature, which seems to boil down to providing sufficient clarification on the factors underlying the recommendations of the CDS, so that these can be compared against the physicians’ existing knowledge, beliefs and convictions. Conclusion Avoidance of disruptive workflows and transparency of the underlying decision processes are important points to consider when developing CDS systems incorporated in EHR. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01933-3.
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