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Richardson S, Dauber-Decker KL, Solomon J, Seelamneni P, Khan S, Barnaby DP, Chelico J, Qiu M, Liu Y, Sanghani S, Izard SM, Chiuzan C, Mann D, Pekmezaris R, McGinn T, Diefenbach MA. Effect of a behavioral nudge on adoption of an electronic health record-agnostic pulmonary embolism risk prediction tool: a pilot cluster nonrandomized controlled trial. JAMIA Open 2024; 7:ooae064. [PMID: 39091509 PMCID: PMC11293639 DOI: 10.1093/jamiaopen/ooae064] [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: 11/18/2023] [Revised: 05/24/2024] [Accepted: 06/25/2024] [Indexed: 08/04/2024] Open
Abstract
Objective Our objective was to determine the feasibility and preliminary efficacy of a behavioral nudge on adoption of a clinical decision support (CDS) tool. Materials and Methods We conducted a pilot cluster nonrandomized controlled trial in 2 Emergency Departments (EDs) at a large academic healthcare system in the New York metropolitan area. We tested 2 versions of a CDS tool for pulmonary embolism (PE) risk assessment developed on a web-based electronic health record-agnostic platform. One version included behavioral nudges incorporated into the user interface. Results A total of 1527 patient encounters were included in the trial. The CDS tool adoption rate was 31.67%. Adoption was significantly higher for the tool that included behavioral nudges (39.11% vs 20.66%; P < .001). Discussion We demonstrated feasibility and preliminary efficacy of a PE risk prediction CDS tool developed using insights from behavioral science. The tool is well-positioned to be tested in a large randomized clinical trial. Trial Registration Clinicaltrials.gov (NCT05203185).
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Affiliation(s)
- Safiya Richardson
- New York University (NYU) Langone, New York, NY 10016, United States
| | | | - Jeffrey Solomon
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, United States
| | - Pradeep Seelamneni
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, United States
| | - Sundas Khan
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX 77030, United States
- Baylor College of Medicine, Houston, TX 77030, United States
| | - Douglas P Barnaby
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, United States
- Northwell/Zucker School of Medicine, Hempstead, NY 11549, United States
| | - John Chelico
- CommonSpirit Health, Chicago, IL 60606, United States
| | - Michael Qiu
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, United States
| | - Yan Liu
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, United States
| | - Shreya Sanghani
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, United States
| | - Stephanie M Izard
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, United States
| | - Codruta Chiuzan
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, United States
| | - Devin Mann
- New York University (NYU) Langone, New York, NY 10016, United States
| | - Renee Pekmezaris
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, United States
- Northwell/Zucker School of Medicine, Hempstead, NY 11549, United States
| | - Thomas McGinn
- Baylor College of Medicine, Houston, TX 77030, United States
- CommonSpirit Health, Chicago, IL 60606, United States
| | - Michael A Diefenbach
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, United States
- Northwell/Zucker School of Medicine, Hempstead, NY 11549, United States
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Zaphir JS, Murphy KA, MacQuarrie AJ, Stainer MJ. Understanding The Role of Cognitive Load In Paramedical Contexts: A Systematic Review. PREHOSP EMERG CARE 2024:1-23. [PMID: 38922409 DOI: 10.1080/10903127.2024.2370491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Accepted: 06/11/2024] [Indexed: 06/27/2024]
Abstract
Objectives: Cognitive load refers to the working memory resources required during a task. When the load is too high or too low this has implications for an individual's task performance. In the context of paramedicine and emergency medical services (EMS) broadly, high cognitive load could potentially put patient and personnel safety at risk. This systematic review aimed to determine the current understanding of the role of cognitive load in paramedical contexts.Methods: To do this, five databases were searched (Elsevier Embase, ProQuest Psychology, CINAHL, Ovid Medline, and Ovid PsychINFO) using synonyms of cognitive load and paramedical contexts. Included articles were full text, peer reviewed empirical research, with a focus on cognitive load and EMS work. Two reviewers screened titles, abstracts, and full text using a traffic light system against the inclusion and exclusion criteria. The quality of evidence was assessed using the GRADE framework. This study was registered on PROSPERO (CRD42022384246). No funding was received for this research.Results: The searches identified 73 unique articles and after title/abstract and full text screening, 25 articles were included in the final review. Synthesis of the research revealed 10 categories of findings in the area. These are clinical performance, cognitive processes, emotional responses, physical expenditure, physiological responses, equipment and ergonomics, expertise and experience, multiple loads, cognitive load measures, and task complexity.Conclusions: From these findings it was determined that there is agreement in terms of what factors influence cognitive load in paramedical contexts, such as cognitive processes, task complexity, physical expenditure, level of experience, multiple types of loads, and the use of equipment. Cognitive load influences clinical task performance and has a bi-directional relationship with emotion. However, the literature is mixed regarding physiological responses to cognitive load, and how they are best measured. These findings highlight potential intervention points where cognitive load can be managed or reduced to improve working conditions for EMS clinicians and safety for their patients.
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Affiliation(s)
- Jasmine S Zaphir
- School of Applied Psychology, Griffith University, Queensland, Australia 4222
| | - Karen A Murphy
- School of Applied Psychology, Griffith University, Queensland, Australia 4222
| | | | - Matthew J Stainer
- School of Applied Psychology, Griffith University, Queensland, Australia 4222
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Williams RJ, Brintz BJ, Ribeiro Dos Santos G, Huang AT, Buddhari D, Kaewhiran S, Iamsirithaworn S, Rothman AL, Thomas S, Farmer A, Fernandez S, Cummings DAT, Anderson KB, Salje H, Leung DT. Integration of population-level data sources into an individual-level clinical prediction model for dengue virus test positivity. SCIENCE ADVANCES 2024; 10:eadj9786. [PMID: 38363842 PMCID: PMC10871531 DOI: 10.1126/sciadv.adj9786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 01/17/2024] [Indexed: 02/18/2024]
Abstract
The differentiation of dengue virus (DENV) infection, a major cause of acute febrile illness in tropical regions, from other etiologies, may help prioritize laboratory testing and limit the inappropriate use of antibiotics. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, population-level data sources may improve predictive ability. To create a clinical prediction model that integrates patient-extrinsic data for identifying DENV among febrile patients presenting to a hospital in Thailand, we fit random forest classifiers combining clinical data with climate and population-level epidemiologic data. In cross-validation, compared to a parsimonious model with the top clinical predictors, a model with the addition of climate data, reconstructed susceptibility estimates, force of infection estimates, and a recent case clustering metric significantly improved model performance.
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Affiliation(s)
- Robert J. Williams
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - Ben J. Brintz
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | | | - Angkana T. Huang
- Department of Genetics, University of Cambridge, Cambridge, UK
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Darunee Buddhari
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | | | | | - Alan L. Rothman
- Institute for Immunology and Informatics and Department of Cell and Molecular Biology, University of Rhode Island, Providence, RI, USA
| | - Stephen Thomas
- Department of Microbiology and Immunology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Aaron Farmer
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Stefan Fernandez
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Derek A. T. Cummings
- Department of Biology, University of Florida, Gainesville, FL, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Kathryn B. Anderson
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
- Department of Microbiology and Immunology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Henrik Salje
- Department of Genetics, University of Cambridge, Cambridge, UK
| | - Daniel T. Leung
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
- Division of Microbiology and Immunology, Department of Pathology, University of Utah, Salt Lake City, UT, USA
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Casey SD, Reed ME, LeMaster C, Mark DG, Gaskin J, Norris RP, Sax DR. Physicians' Perceptions of Clinical Decision Support to Treat Patients With Heart Failure in the ED. JAMA Netw Open 2023; 6:e2344393. [PMID: 37988076 PMCID: PMC10663967 DOI: 10.1001/jamanetworkopen.2023.44393] [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: 07/19/2023] [Accepted: 10/12/2023] [Indexed: 11/22/2023] Open
Abstract
Importance Clinical decision support (CDS) could help emergency department (ED) physicians treat patients with heart failure (HF) by estimating risk, collating relevant history, and assisting with medication prescribing if physicians' perspectives inform its design and implementation. Objective To evaluate CDS usability and workflow integration in the hands of ED physician end users who use it in clinical practice. Design, Setting, and Participants This mixed-methods qualitative study administered semistructured interviews to ED physicians from 2 community EDs of Kaiser Permanente Northern California in 2023. The interview guide, based on the Usability Heuristics for User Interface Design and the Sociotechnical Environment models, yielded themes used to construct an electronic survey instrument sent to all ED physicians. Main Outcomes and Measures Main outcomes were physicians' perceptions of using CDS to complement clinical decision-making, usability, and integration into ED clinical workflow. Results Seven key informant physicians (5 [71.4%] female, median [IQR] 15.0 [9.5-15.0] years in practice) were interviewed and survey responses from 51 physicians (23 [45.1%] female, median [IQR] 14.0 [9.5-17.0] years in practice) were received from EDs piloting the CDS intervention. Response rate was 67.1% (51 of 76). Physicians suggested changes to CDS accessibility, functionality, and workflow integration. Most agreed that CDS would improve patient care and fewer than half of physicians expressed hesitation about their capacity to consistently comply with its recommendations, citing workload concerns. Physicians preferred a passive prompt that encouraged, but did not mandate, interaction with the CDS. Conclusions and Relevance In this qualitative study of physicians who were using a novel CDS intervention to assist with ED management of patients with acute HF, several opportunities were identified to improve usability as well as several key barriers and facilitators to CDS implementation.
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Affiliation(s)
- Scott D. Casey
- Kaiser Permanente Division of Research, Oakland, California
- The Kaiser Permanente CREST Network, Oakland, California
| | - Mary E. Reed
- Kaiser Permanente Division of Research, Oakland, California
- The Kaiser Permanente CREST Network, Oakland, California
| | | | | | - Jesse Gaskin
- The Permanente Medical Group Consulting Services, The Permanente Medical Group, Oakland, California
| | | | - Dana R. Sax
- Kaiser Permanente Division of Research, Oakland, California
- The Kaiser Permanente CREST Network, Oakland, California
- The Permanente Medical Group, Oakland, California
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Solomon J, Dauber-Decker K, Richardson S, Levy S, Khan S, Coleman B, Persaud R, Chelico J, King D, Spyropoulos A, McGinn T. Integrating Clinical Decision Support Into Electronic Health Record Systems Using a Novel Platform (EvidencePoint): Developmental Study. JMIR Form Res 2023; 7:e44065. [PMID: 37856193 PMCID: PMC10623239 DOI: 10.2196/44065] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 06/21/2023] [Accepted: 07/31/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Through our work, we have demonstrated how clinical decision support (CDS) tools integrated into the electronic health record (EHR) assist providers in adopting evidence-based practices. This requires confronting technical challenges that result from relying on the EHR as the foundation for tool development; for example, the individual CDS tools need to be built independently for each different EHR. OBJECTIVE The objective of our research was to build and implement an EHR-agnostic platform for integrating CDS tools, which would remove the technical constraints inherent in relying on the EHR as the foundation and enable a single set of CDS tools that can work with any EHR. METHODS We developed EvidencePoint, a novel, cloud-based, EHR-agnostic CDS platform, and we will describe the development of EvidencePoint and the deployment of its initial CDS tools, which include EHR-integrated applications for clinical use cases such as prediction of hospitalization survival for patients with COVID-19, venous thromboembolism prophylaxis, and pulmonary embolism diagnosis. RESULTS The results below highlight the adoption of the CDS tools, the International Medical Prevention Registry on Venous Thromboembolism-D-Dimer, the Wells' criteria, and the Northwell COVID-19 Survival (NOCOS), following development, usability testing, and implementation. The International Medical Prevention Registry on Venous Thromboembolism-D-Dimer CDS was used in 5249 patients at the 2 clinical intervention sites. The intervention group tool adoption was 77.8% (4083/5249 possible uses). For the NOCOS tool, which was designed to assist with triaging patients with COVID-19 for hospital admission in the event of constrained hospital resources, the worst-case resourcing scenario never materialized and triaging was never required. As a result, the NOCOS tool was not frequently used, though the EvidencePoint platform's flexibility and customizability enabled the tool to be developed and deployed rapidly under the emergency conditions of the pandemic. Adoption rates for the Wells' criteria tool will be reported in a future publication. CONCLUSIONS The EvidencePoint system successfully demonstrated that a flexible, user-friendly platform for hosting CDS tools outside of a specific EHR is feasible. The forthcoming results of our outcomes analyses will demonstrate the adoption rate of EvidencePoint tools as well as the impact of behavioral economics "nudges" on the adoption rate. Due to the EHR-agnostic nature of EvidencePoint, the development process for additional forms of CDS will be simpler than traditional and cumbersome IT integration approaches and will benefit from the capabilities provided by the core system of EvidencePoint.
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Affiliation(s)
- Jeffrey Solomon
- Institute of Health System Science, Feinstein Institutes for Medical Research, Manhasset, NY, United States
| | - Katherine Dauber-Decker
- Institute of Health System Science, Feinstein Institutes for Medical Research, Manhasset, NY, United States
| | - Safiya Richardson
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Sera Levy
- Department of Psychiatry, Heersink School of Medicine, University of Alabama at Birmingham Medicine, Birmingham, AL, United States
| | - Sundas Khan
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States
- Center for Innovations in Quality, Effectiveness, and Safety, Michael E DeBakey Veterans Affairs Medical Center, Houston, TX, United States
| | - Benjamin Coleman
- Institute of Health System Science, Feinstein Institutes for Medical Research, Manhasset, NY, United States
| | - Rupert Persaud
- Institute of Health System Science, Feinstein Institutes for Medical Research, Manhasset, NY, United States
| | - John Chelico
- Physician Enterprise, CommonSpirit Health, Chicago, IL, United States
| | - D'Arcy King
- School of Psychology, Fielding Graduate University, Santa Barbara, CA, United States
| | - Alex Spyropoulos
- Institute of Health System Science, Feinstein Institutes for Medical Research, Manhasset, NY, United States
- Department of Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Thomas McGinn
- Department of Medicine, Baylor College of Medicine, Houston, TX, United States
- Physician Enterprise, CommonSpirit Health, Chicago, IL, United States
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Wosny M, Strasser LM, Hastings J. Experience of Health Care Professionals Using Digital Tools in the Hospital: Qualitative Systematic Review. JMIR Hum Factors 2023; 10:e50357. [PMID: 37847535 PMCID: PMC10618886 DOI: 10.2196/50357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 08/25/2023] [Accepted: 08/25/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND The digitalization of health care has many potential benefits, but it may also negatively impact health care professionals' well-being. Burnout can, in part, result from inefficient work processes related to the suboptimal implementation and use of health information technologies. Although strategies to reduce stress and mitigate clinician burnout typically involve individual-based interventions, emerging evidence suggests that improving the experience of using health information technologies can have a notable impact. OBJECTIVE The aim of this systematic review was to collect evidence of the benefits and challenges associated with the use of digital tools in hospital settings with a particular focus on the experiences of health care professionals using these tools. METHODS We conducted a systematic literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to explore the experience of health care professionals with digital tools in hospital settings. Using a rigorous selection process to ensure the methodological quality and validity of the study results, we included qualitative studies with distinct data that described the experiences of physicians and nurses. A panel of 3 independent researchers performed iterative data analysis and identified thematic constructs. RESULTS Of the 1175 unique primary studies, we identified 17 (1.45%) publications that focused on health care professionals' experiences with various digital tools in their day-to-day practice. Of the 17 studies, 10 (59%) focused on clinical decision support tools, followed by 6 (35%) studies focusing on electronic health records and 1 (6%) on a remote patient-monitoring tool. We propose a theoretical framework for understanding the complex interplay between the use of digital tools, experience, and outcomes. We identified 6 constructs that encompass the positive and negative experiences of health care professionals when using digital tools, along with moderators and outcomes. Positive experiences included feeling confident, responsible, and satisfied, whereas negative experiences included frustration, feeling overwhelmed, and feeling frightened. Positive moderators that may reinforce the use of digital tools included sufficient training and adequate workflow integration, whereas negative moderators comprised unfavorable social structures and the lack of training. Positive outcomes included improved patient care and increased workflow efficiency, whereas negative outcomes included increased workload, increased safety risks, and issues with information quality. CONCLUSIONS Although positive and negative outcomes and moderators that may affect the use of digital tools were commonly reported, the experiences of health care professionals, such as their thoughts and emotions, were less frequently discussed. On the basis of this finding, this study highlights the need for further research specifically targeting experiences as an important mediator of clinician well-being. It also emphasizes the importance of considering differences in the nature of specific tools as well as the profession and role of individual users. TRIAL REGISTRATION PROSPERO CRD42023393883; https://tinyurl.com/2htpzzxj.
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Affiliation(s)
- Marie Wosny
- School of Medicine, University of St Gallen (HSG), St Gallen, Switzerland
| | | | - Janna Hastings
- School of Medicine, University of St Gallen (HSG), St Gallen, Switzerland
- Institute for Implementation Science in Health Care, University of Zurich (UZH), Zurich, Switzerland
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Williams RJ, Brintz BJ, Santos GRD, Huang A, Buddhari D, Kaewhiran S, Iamsirithaworn S, Rothman AL, Thomas S, Farmer A, Fernandez S, Cummings DAT, Anderson KB, Salje H, Leung DT. Integration of population-level data sources into an individual-level clinical prediction model for dengue virus test positivity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.08.23293840. [PMID: 37609267 PMCID: PMC10441499 DOI: 10.1101/2023.08.08.23293840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
The differentiation of dengue virus (DENV) infection, a major cause of acute febrile illness in tropical regions, from other etiologies, may help prioritize laboratory testing and limit the inappropriate use of antibiotics. While traditional clinical prediction models focus on individual patient-level parameters, we hypothesize that for infectious diseases, population-level data sources may improve predictive ability. To create a clinical prediction model that integrates patient-extrinsic data for identifying DENV among febrile patients presenting to a hospital in Thailand, we fit random forest classifiers combining clinical data with climate and population-level epidemiologic data. In cross validation, compared to a parsimonious model with the top clinical predictors, a model with the addition of climate data, reconstructed susceptibility estimates, force of infection estimates, and a recent case clustering metric, significantly improved model performance.
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Affiliation(s)
- RJ Williams
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, USA
| | - Ben J. Brintz
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, USA
- Division of Epidemiology, Department of Internal Medicine, University of Utah, Salt Lake City, USA
| | | | - Angkana Huang
- Department of Genetics, University of Cambridge, United Kingdom
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Darunee Buddhari
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | | | | | - Alan L. Rothman
- Institute for Immunology and Informatics and Department of Cell and Molecular Biology, University of Rhode Island, Providence, USA
| | - Stephen Thomas
- Department of Microbiology and Immunology, SUNY Upstate Medical University, Syracuse, USA
| | - Aaron Farmer
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Stefan Fernandez
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
| | - Derek A T Cummings
- Department of Biology, University of Florida, Gainesville, USA
- Emerging Pathogens Institute, University of Florida, Gainesville, USA
| | - Kathryn B Anderson
- Department of Virology, Armed Forces Research Institute of Medical Sciences, Bangkok, Thailand
- Department of Microbiology and Immunology, SUNY Upstate Medical University, Syracuse, USA
| | - Henrik Salje
- Department of Genetics, University of Cambridge, United Kingdom
| | - Daniel T. Leung
- Division of Infectious Diseases, Department of Internal Medicine, University of Utah, Salt Lake City, USA
- Division of Microbiology and Immunology, Department of Pathology, University of Utah, Salt Lake City, USA
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Labarta JI, Dimitri P, Keiser M, Koledova E, Rivera-Romero O. Evaluating the Usefulness and Ease of Use of a Next-Generation-Connected Drug Delivery Device for Growth Hormone Therapy: Qualitative Study of Health Care Professionals' Perceptions. JMIR Hum Factors 2023; 10:e46893. [PMID: 37531173 PMCID: PMC10433030 DOI: 10.2196/46893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 06/09/2023] [Accepted: 06/17/2023] [Indexed: 08/03/2023] Open
Abstract
BACKGROUND Digital solutions targeting children's health have become an increasingly important element in the provision of integrated health care. For the treatment of growth hormone deficiency (GHD), a unique connected device is available to facilitate the delivery of recombinant human growth hormone (r-hGH) by automating the daily injection process and collecting injection data such that accurate adherence information is available to health care professionals (HCPs), caregivers, and patients. The adoption of such digital solutions requires a good understanding of the perspectives of HCPs as key stakeholders because they leverage data collection and prescribe these solutions to their patients. OBJECTIVE This study aimed to evaluate the third generation of the easypod device (EP3) for the delivery of r-hGH treatment from the HCP perspective, with a focus on perceived usefulness and ease of use. METHODS A qualitative study was conducted, based on a participatory workshop conducted in Zaragoza, Spain, with 10 HCPs experienced in the management of pediatric GHD from 7 reference hospitals in Spain. Several activities were designed to promote discussion among participants about predefined topics based on the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology to provide their perceptions about the new device. RESULTS Participants reported 2 key advantages of EP3 over previous easypod generations: the touch screen interface and the real-time data transmission functionality. All participants (10/10, 100%) agreed that the new device should be part of a digital health ecosystem that provides complementary functionalities including data analysis. CONCLUSIONS This study explored the perceived value of the EP3 autoinjector device for the treatment of GHD by HCPs. HCPs rated the new capabilities of the device as having substantial improvements and concluded that it was highly recommendable for clinical practice. EP3 will enhance decision-making and allow for more personalized care of patients receiving r-hGH.
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Affiliation(s)
- José I Labarta
- Unit of Endocrinology, Department of Pediatrics, Hospital Universitario Miguel Servet, Zaragoza, Spain
- Instituto de Investigación Sanitaria Aragón, Zaragoza, Spain
| | - Paul Dimitri
- Department of Paediatric Endocrinology, Sheffield Children's NHS Foundation Trust, Sheffield, United Kingdom
| | - Matthew Keiser
- Ares Trading SA (an affiliate of Merck KGaA), Eysins, Switzerland
| | - Ekaterina Koledova
- Global Medical Affairs Cardiometabolic & Endocrinology, Merck Healthcare KGaA, Darmstadt, Germany
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9
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Leung T, Dauber-Decker K, Solomon J, Khan S, Barnaby D, Chelico J, Qiu M, Liu Y, Mann D, Pekmezaris R, McGinn T, Diefenbach M. Nudging Health Care Providers' Adoption of Clinical Decision Support: Protocol for the User-Centered Development of a Behavioral Economics-Inspired Electronic Health Record Tool. JMIR Res Protoc 2023; 12:e42653. [PMID: 36652293 PMCID: PMC9892982 DOI: 10.2196/42653] [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: 09/14/2022] [Revised: 10/21/2022] [Accepted: 10/25/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND The improvements in care resulting from clinical decision support (CDS) have been significantly limited by consistently low health care provider adoption. Health care provider attitudes toward CDS, specifically psychological and behavioral barriers, are not typically addressed during any stage of CDS development, although they represent an important barrier to adoption. Emerging evidence has shown the surprising power of using insights from the field of behavioral economics to address psychological and behavioral barriers. Nudges are formal applications of behavioral economics, defined as positive reinforcement and indirect suggestions that have a nonforced effect on decision-making. OBJECTIVE Our goal is to employ a user-centered design process to develop a CDS tool-the pulmonary embolism (PE) risk calculator-for PE risk stratification in the emergency department that incorporates a behavior theory-informed nudge to address identified behavioral barriers to use. METHODS All study activities took place at a large academic health system in the New York City metropolitan area. Our study used a user-centered and behavior theory-based approach to achieve the following two aims: (1) use mixed methods to identify health care provider barriers to the use of an active CDS tool for PE risk stratification and (2) develop a new CDS tool-the PE risk calculator-that addresses behavioral barriers to health care providers' adoption of CDS by incorporating nudges into the user interface. These aims were guided by the revised Observational Research Behavioral Information Technology model. A total of 50 clinicians who used the original version of the tool were surveyed with a quantitative instrument that we developed based on a behavior theory framework-the Capability-Opportunity-Motivation-Behavior framework. A semistructured interview guide was developed based on the survey responses. Inductive methods were used to analyze interview session notes and audio recordings from 12 interviews. Revised versions of the tool were developed that incorporated nudges. RESULTS Functional prototypes were developed by using Axure PRO (Axure Software Solutions) software and usability tested with end users in an iterative agile process (n=10). The tool was redesigned to address 4 identified major barriers to tool use; we included 2 nudges and a default. The 6-month pilot trial for the tool was launched on October 1, 2021. CONCLUSIONS Clinicians highlighted several important psychological and behavioral barriers to CDS use. Addressing these barriers, along with conducting traditional usability testing, facilitated the development of a tool with greater potential to transform clinical care. The tool will be tested in a prospective pilot trial. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/42653.
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Affiliation(s)
| | | | - Jeffrey Solomon
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Sundas Khan
- Baylor College of Medicine, Houston, TX, United States
| | - Douglas Barnaby
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | | | - Michael Qiu
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Yan Liu
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Devin Mann
- New York University Grossman School of Medicine, New York, NY, United States
| | - Renee Pekmezaris
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Thomas McGinn
- Baylor College of Medicine, Houston, TX, United States.,CommonSpirit Health, Chicago, IL, United States
| | - Michael Diefenbach
- Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, United States
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Sax DR, Sturmer LR, Mark DG, Rana JS, Reed ME. Barriers and Opportunities Regarding Implementation of a Machine Learning-Based Acute Heart Failure Risk Stratification Tool in the Emergency Department. Diagnostics (Basel) 2022; 12:diagnostics12102463. [PMID: 36292152 PMCID: PMC9600201 DOI: 10.3390/diagnostics12102463] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/26/2022] [Accepted: 10/01/2022] [Indexed: 11/16/2022] Open
Abstract
Hospital admissions for patients with acute heart failure (AHF) remain high. There is an opportunity to improve alignment between patient risk and admission decision. We recently developed a machine learning (ML)-based model that stratifies emergency department (ED) patients with AHF based on predicted risk of a 30-day severe adverse event. Prior to deploying the algorithm and paired clinical decision support, we sought to understand barriers and opportunities regarding successful implementation. We conducted semi-structured interviews with eight front-line ED providers and surveyed 67 ED providers. Audio-recorded interviews were transcribed and analyzed using thematic analysis, and we had a 65% response rate to the survey. Providers wanted decision support to be streamlined into workflows with minimal disruptions. Most providers wanted assistance primarily with ED disposition decisions, and secondarily with medical management and post-discharge follow-up care. Receiving feedback on patient outcomes after risk tool use was seen as an opportunity to increase acceptance, and few providers (<10%) had significant hesitations with using an ML-based tool after education on its use. Engagement with key front-line users on optimal design of the algorithm and decision support may contribute to broader uptake, acceptance, and adoption of recommendations for clinical decisions.
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Affiliation(s)
- Dana R. Sax
- Kaiser Permanente Northern California Division of Research, Oakland, CA 94612, USA
- Department of Emergency Medicine, The Permanente Medical Group, Oakland, CA 94612, USA
- Correspondence:
| | - Lillian R. Sturmer
- College of Osteopathic Medicine, Touro University, Vallejo, CA 94592, USA
| | - Dustin G. Mark
- Kaiser Permanente Northern California Division of Research, Oakland, CA 94612, USA
- Department of Emergency Medicine, The Permanente Medical Group, Oakland, CA 94612, USA
| | - Jamal S. Rana
- Kaiser Permanente Northern California Division of Research, Oakland, CA 94612, USA
- Department of Cardiology, The Permanente Medical Group, Oakland, CA 94612, USA
| | - Mary E. Reed
- Kaiser Permanente Northern California Division of Research, Oakland, CA 94612, USA
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Abdulla S, Swarup V, Soomro A, de Wit K. Mapping emergency physician reasoning for adhering to evidence-based pulmonary embolism testing. Acad Emerg Med 2022; 29:658-661. [PMID: 35233857 DOI: 10.1111/acem.14476] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 02/24/2022] [Indexed: 01/17/2023]
Affiliation(s)
| | | | | | - Kerstin de Wit
- McMaster University Hamilton Ontorio Canada
- Queen's University Kingston Ontorio Canada
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