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Hakimjavadi R, Hong HA, Fallah N, Humphreys S, Kingwell S, Stratton A, Tsai E, Wai EK, Walden K, Noonan VK, Phan P. Enabling knowledge translation: implementation of a web-based tool for independent walking prediction after traumatic spinal cord injury. Front Neurol 2023; 14:1219307. [PMID: 38116110 PMCID: PMC10728823 DOI: 10.3389/fneur.2023.1219307] [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: 06/06/2023] [Accepted: 11/13/2023] [Indexed: 12/21/2023] Open
Abstract
Introduction Several clinical prediction rules (CPRs) have been published, but few are easily accessible or convenient for clinicians to use in practice. We aimed to develop, implement, and describe the process of building a web-based CPR for predicting independent walking 1-year after a traumatic spinal cord injury (TSCI). Methods Using the published and validated CPR, a front-end web application called "Ambulation" was built using HyperText Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript. A survey was created using QualtricsXM Software to gather insights on the application's usability and user experience. Website activity was monitored using Google Analytics. Ambulation was developed with a core team of seven clinicians and researchers. To refine the app's content, website design, and utility, 20 professionals from different disciplines, including persons with lived experience, were consulted. Results After 11 revisions, Ambulation was uploaded onto a unique web domain and launched (www.ambulation.ca) as a pilot with 30 clinicians (surgeons, physiatrists, and physiotherapists). The website consists of five web pages: Home, Calculation, Team, Contact, and Privacy Policy. Responses from the user survey (n = 6) were positive and provided insight into the usability of the tool and its clinical utility (e.g., helpful in discharge planning and rehabilitation), and the overall face validity of the CPR. Since its public release on February 7, 2022, to February 28, 2023, Ambulation had 594 total users, 565 (95.1%) new users, 26 (4.4%) returning users, 363 (61.1%) engaged sessions (i.e., the number of sessions that lasted 10 seconds/longer, had one/more conversion events e.g., performing the calculation, or two/more page or screen views), and the majority of the users originating from the United States (39.9%) and Canada (38.2%). Discussion Ambulation is a CPR for predicting independent walking 1-year after TSCI and it can assist frontline clinicians with clinical decision-making (e.g., time to surgery or rehabilitation plan), patient education and goal setting soon after injury. This tool is an example of adapting a validated CPR for independent walking into an easily accessible and usable web-based tool for use in clinical practice. This study may help inform how other CPRs can be adopted into clinical practice.
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Affiliation(s)
| | - Heather A. Hong
- Praxis Spinal Cord Institute, Blusson Spinal Cord Centre, Vancouver, BC, Canada
| | - Nader Fallah
- Praxis Spinal Cord Institute, Blusson Spinal Cord Centre, Vancouver, BC, Canada
- Division of Neurology, Department of Medicine, Faculty of Medicine, The University of British Columbia, UBC Hospital, Vancouver, BC, Canada
| | - Suzanne Humphreys
- Praxis Spinal Cord Institute, Blusson Spinal Cord Centre, Vancouver, BC, Canada
| | - Stephen Kingwell
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Alexandra Stratton
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Eve Tsai
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Eugene K. Wai
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Kristen Walden
- Praxis Spinal Cord Institute, Blusson Spinal Cord Centre, Vancouver, BC, Canada
| | - Vanessa K. Noonan
- Praxis Spinal Cord Institute, Blusson Spinal Cord Centre, Vancouver, BC, Canada
- International Collaboration on Repair Discoveries (ICORD), University of British Columbia, Vancouver, BC, Canada
| | - Philippe Phan
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
- Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
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Duran HT, Kingeter M, Reale C, Weinger MB, Salwei ME. Decision-making in anesthesiology: will artificial intelligence make intraoperative care safer? Curr Opin Anaesthesiol 2023; 36:691-697. [PMID: 37865848 PMCID: PMC11100504 DOI: 10.1097/aco.0000000000001318] [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] [Indexed: 10/23/2023]
Abstract
PURPOSE OF REVIEW This article explores the impact of recent applications of artificial intelligence on clinical anesthesiologists' decision-making. RECENT FINDINGS Naturalistic decision-making, a rich research field that aims to understand how cognitive work is accomplished in complex environments, provides insight into anesthesiologists' decision processes. Due to the complexity of clinical work and limits of human decision-making (e.g. fatigue, distraction, and cognitive biases), attention on the role of artificial intelligence to support anesthesiologists' decision-making has grown. Artificial intelligence, a computer's ability to perform human-like cognitive functions, is increasingly used in anesthesiology. Examples include aiding in the prediction of intraoperative hypotension and postoperative complications, as well as enhancing structure localization for regional and neuraxial anesthesia through artificial intelligence integration with ultrasound. SUMMARY To fully realize the benefits of artificial intelligence in anesthesiology, several important considerations must be addressed, including its usability and workflow integration, appropriate level of trust placed on artificial intelligence, its impact on decision-making, the potential de-skilling of practitioners, and issues of accountability. Further research is needed to enhance anesthesiologists' clinical decision-making in collaboration with artificial intelligence.
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Affiliation(s)
- Huong-Tram Duran
- University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | | | - Carrie Reale
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Megan E. Salwei
- Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Wilson S, Tolley C, Mc Ardle R, Beswick E, Slight SP. Key Considerations When Developing and Implementing Digital Technology for Early Detection of Dementia-Causing Diseases Among Health Care Professionals: Qualitative Study. J Med Internet Res 2023; 25:e46711. [PMID: 37606986 PMCID: PMC10481214 DOI: 10.2196/46711] [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: 02/22/2023] [Revised: 05/30/2023] [Accepted: 06/15/2023] [Indexed: 08/23/2023] Open
Abstract
BACKGROUND The World Health Organization (WHO) promotes using digital technologies to accelerate global attainment of health and well-being. This has led to a growth in research exploring the use of digital technology to aid early detection and preventative interventions for dementia-causing diseases such as Alzheimer disease. The opinions and perspectives of health care professionals must be incorporated into the development and implementation of technology to promote its successful adoption in clinical practice. OBJECTIVE This study aimed to explore health care professionals' perspectives on the key considerations of developing and implementing digital technologies for the early detection of dementia-causing diseases in the National Health Service (NHS). METHODS Health care professionals with patient-facing roles in primary or secondary care settings in the NHS were recruited through various web-based NHS clinical networks. Participants were interviewed to explore their experiences of the current dementia diagnostic practices, views on early detection and use of digital technology to aid these practices, and the challenges of implementing such interventions in health care. An inductive thematic analysis approach was applied to identify central concepts and themes in the interviews, allowing the data to determine our themes. A list of central concepts and themes was applied systematically to the whole data set using NVivo (version 1.6.1; QSR International). Using the constant comparison technique, the researchers moved backward and forward between these data and evolving explanations until a fit was made. RESULTS Eighteen semistructured interviews were conducted, with 11 primary and 7 secondary care health care professionals. We identified 3 main categories of considerations relevant to health care service users, health care professionals, and the digital health technology itself. Health care professionals recognized the potential of using digital technology to collect real-time data and the possible benefits of detecting dementia-causing diseases earlier if an effective intervention were available. However, some were concerned about postdetection management, questioning the point of an early detection of dementia-causing diseases if an effective intervention cannot be provided and feared this would only lead to increased anxiety in patients. Health care professionals also expressed mixed opinions on who should be screened for early detection. Some suggested it should be available to everyone to mitigate the chance of excluding those who are not in touch with their health care or are digitally excluded. Others were concerned about the resources that would be required to make the technology available to everyone. CONCLUSIONS This study highlights the need to design digital health technology in a way that is accessible to all and does not add burden to health care professionals. Further work is needed to ensure inclusive strategies are used in digital research to promote health equity.
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Affiliation(s)
- Sarah Wilson
- School of Pharmacy, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Clare Tolley
- School of Pharmacy, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Riona Mc Ardle
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Emily Beswick
- School of Pharmacy, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Sarah P Slight
- School of Pharmacy, Newcastle University, Newcastle upon Tyne, United Kingdom
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Cavaliere F, Biancofiore G, Bignami E, DE Robertis E, Giannini A, Grasso S, McCREDIE VA, Piastra M, Scolletta S, Taccone FS, Terragni P. A year in review in Minerva Anestesiologica 2022: critical care. Minerva Anestesiol 2023; 89:115-124. [PMID: 36745125 DOI: 10.23736/s0375-9393.22.17211-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Franco Cavaliere
- IRCCS A. Gemelli University Polyclinic Foundation, Sacred Heart Catholic University, Rome, Italy -
| | - Gianni Biancofiore
- Department of Transplant Anesthesia and Critical Care, University School of Medicine, Pisa, Italy
| | - Elena Bignami
- Division of Anesthesiology, Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Edoardo DE Robertis
- Section of Anesthesia, Analgesia and Intensive Care, Department of Surgical and Biomedical Sciences, University of Perugia, Perugia, Italy
| | - Alberto Giannini
- Unit of Pediatric Anesthesia and Intensive Care, Children's Hospital - ASST Spedali Civili di Brescia, Brescia, Italy
| | - Salvatore Grasso
- Section of Anesthesiology and Intensive Care, Department of Emergency and Organ Transplantation, Polyclinic Hospital, Aldo Moro University, Bari, Italy
| | - Victoria A McCREDIE
- Interdepartmental Division of Critical Care, University of Toronto, Toronto, ON, Canada
| | - Marco Piastra
- Unit of Pediatric Intensive Care and Trauma Center, IRCCS A. Gemelli University Polyclinic Foundation, Sacred Heart Catholic University, Rome, Italy
| | - Sabino Scolletta
- Department of Emergency-Urgency and Organ Transplantation, Anesthesia and Intensive Care, University Hospital of Siena, Siena, Italy
| | - Fabio S Taccone
- Department of Intensive Care, Erasme Hospital, Université Libre de Bruxelles (ULB), Brussels, Belgium
| | - Pierpaolo Terragni
- Division of Anesthesia and General Intensive Care, Department of Medical, Surgical and Experimental Sciences, University Hospital of Sassari, University of Sassari, Sassari, Italy
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Parmar J, Sacrey LA, Anderson S, Charles L, Dobbs B, McGhan G, Shapkin K, Tian P, Triscott J. Facilitators, barriers and considerations for the implementation of healthcare innovation: A qualitative rapid systematic review. HEALTH & SOCIAL CARE IN THE COMMUNITY 2022; 30:856-868. [PMID: 34558143 DOI: 10.1111/hsc.13578] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 09/08/2021] [Accepted: 09/10/2021] [Indexed: 06/13/2023]
Abstract
Family caregiving scholars recommend that health providers receive competency-based education to partner with and support family caregivers to care and to maintain their own health. While it may be relatively easy to develop competency-based education for healthcare providers, ensuring widespread uptake and spread and scale of healthcare education is critical to ensuring consistent person-centered support for all family caregivers (FCGs) throughout the care trajectory. The development of novel healthcare innovations requires implementation strategies for uptake and spread, with implementation involving the use of strategies to integrate a novel innovation into healthcare. Research suggests that there are many factors involved in successful implementation and a synthesis of potential factors is warranted. The purpose of this review is to provide an in-depth examination of facilitators, barriers and considerations for implementation of a novel healthcare innovation that will be used to develop an implementation plan for spread and scale of our competency-based education for health providers to learn about person-centered care for FCGs. A systematic review of published and grey literature was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA [Moher et al., 2015]) guidelines. The systematic review involved searching four databases for original research articles that described barriers, facilitators and/or other considerations when implementing innovations. Twenty-eight articles were included in the qualitative thematic analyses and described three areas of implementation research: barriers, facilitators and recommendations. There were major and parallel themes that emerged under facilitators and barriers. There were a wide variety of strategies that were identified as recommendations. The findings were synthesised into five considerations for implementation: Research and information sharing, intentional implementation planning, organisational underpinnings, creating the clinical context and facilitative training. This review provides an integrative overview of identified facilitators, barriers and recommendations for implementation that may aid in developing implementation strategies that can be tailored to the local context or innovation being implemented.
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Affiliation(s)
- Jasneet Parmar
- Department of Family Medicine, University of Alberta, Edmonton, Alberta, Canada
- Edmonton Zone Home Living, Alberta Health Services, Edmonton, Alberta, Canada
| | - Lori Ann Sacrey
- Department of Pediatrics, University of Alberta, Edmonton, Alberta, Canada
| | - Sharon Anderson
- Department of Family Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Lesley Charles
- Department of Family Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Bonnie Dobbs
- Department of Family Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Gwen McGhan
- Faculty of Nursing, University of Calgary, Alberta, Canada
| | | | - Peter Tian
- Department of Family Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Jean Triscott
- Department of Family Medicine, University of Alberta, Edmonton, Alberta, Canada
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Mosch LK, Poncette AS, Spies C, Weber-Carstens S, Schieler M, Krampe H, Balzer F. Creation of an Evidence-Based Implementation Framework for Digital Health Technology in the Intensive Care Unit: Qualitative Study. JMIR Form Res 2022; 6:e22866. [PMID: 35394445 PMCID: PMC9034425 DOI: 10.2196/22866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 02/01/2021] [Accepted: 11/27/2021] [Indexed: 12/01/2022] Open
Abstract
Background Digital health technologies such as continuous remote monitoring and artificial intelligence–driven clinical decision support systems could improve clinical outcomes in intensive care medicine. However, comprehensive evidence and guidelines for the successful implementation of digital health technologies into specific clinical settings such as the intensive care unit (ICU) are scarce. We evaluated the implementation of a remote patient monitoring platform and derived a framework proposal for the implementation of digital health technology in an ICU. Objective This study aims to investigate barriers and facilitators to the implementation of a remote patient monitoring technology and to develop a proposal for an implementation framework for digital health technology in the ICU. Methods This study was conducted from May 2018 to March 2020 during the implementation of a tablet computer–based remote patient monitoring system. The system was installed in the ICU of a large German university hospital as a supplementary monitoring device. Following a hybrid qualitative approach with inductive and deductive elements, we used the Consolidated Framework for Implementation Research and the Expert Recommendations for Implementing Change to analyze the transcripts of 7 semistructured interviews with clinical ICU stakeholders and descriptive questionnaire data. The results of the qualitative analysis, together with the findings from informal meetings, field observations, and previous explorations, provided the basis for the derivation of the proposed framework. Results This study revealed an insufficient implementation process due to lack of staff engagement and few perceived benefits from the novel solution. Further implementation barriers were the high staff presence and monitoring coverage in the ICU. The implementation framework includes strategies to be applied before and during implementation, targeting the implementation setting by involving all ICU stakeholders, assessing the intervention’s adaptability, facilitating the implementation process, and maintaining a vital feedback culture. Setting up a unit responsible for implementation, considering the guidance of an implementation advisor, and building on existing institutional capacities could improve the institutional context of implementation projects in the ICU. Conclusions Implementation of digital health in the ICU should involve a thorough preimplementation assessment of the ICU’s need for innovation and its readiness to change, as well as an ongoing evaluation of the implementation conditions. Involvement of all stakeholders, transparent communication, and continuous feedback in an equal atmosphere are essential, but leadership roles must be clearly defined and competently filled. Our proposed framework may guide health care providers with concrete, evidence-based, and step-by-step recommendations for implementation practice, facilitating the introduction of digital health in intensive care. Trial Registration ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173
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Affiliation(s)
- Lina Katharina Mosch
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,Department of Anesthesiology and Intensive Care Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Akira-Sebastian Poncette
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.,Department of Anesthesiology and Intensive Care Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Claudia Spies
- Department of Anesthesiology and Intensive Care Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Steffen Weber-Carstens
- Department of Anesthesiology and Intensive Care Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Monique Schieler
- Department of Anesthesiology and Intensive Care Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Henning Krampe
- Department of Anesthesiology and Intensive Care Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Felix Balzer
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
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Jacobsohn GC, Leaf M, Liao F, Maru AP, Engstrom CJ, Salwei ME, Pankratz GT, Eastman A, Carayon P, Wiegmann DA, Galang JS, Smith MA, Shah MN, Patterson BW. Collaborative design and implementation of a clinical decision support system for automated fall-risk identification and referrals in emergency departments. HEALTHCARE (AMSTERDAM, NETHERLANDS) 2022; 10:100598. [PMID: 34923354 PMCID: PMC8881336 DOI: 10.1016/j.hjdsi.2021.100598] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 11/15/2021] [Accepted: 11/22/2021] [Indexed: 11/04/2022]
Abstract
Of the 3 million older adults seeking fall-related emergency care each year, nearly one-third visited the Emergency Department (ED) in the previous 6 months. ED providers have a great opportunity to refer patients for fall prevention services at these initial visits, but lack feasible tools for identifying those at highest-risk. Existing fall screening tools have been poorly adopted due to ED staff/provider burden and lack of workflow integration. To address this, we developed an automated clinical decision support (CDS) system for identifying and referring older adult ED patients at risk of future falls. We engaged an interdisciplinary design team (ED providers, health services researchers, information technology/predictive analytics professionals, and outpatient Falls Clinic staff) to collaboratively develop a system that successfully met user requirements and integrated seamlessly into existing ED workflows. Our rapid-cycle development and evaluation process employed a novel combination of human-centered design, implementation science, and patient experience strategies, facilitating simultaneous design of the CDS tool and intervention implementation strategies. This included defining system requirements, systematically identifying and resolving usability problems, assessing barriers and facilitators to implementation (e.g., data accessibility, lack of time, high patient volumes, appointment availability) from multiple vantage points, and refining protocols for communicating with referred patients at discharge. ED physician, nurse, and patient stakeholders were also engaged through online surveys and user testing. Successful CDS design and implementation required integration of multiple new technologies and processes into existing workflows, necessitating interdisciplinary collaboration from the onset. By using this iterative approach, we were able to design and implement an intervention meeting all project goals. Processes used in this Clinical-IT-Research partnership can be applied to other use cases involving automated risk-stratification, CDS development, and EHR-facilitated care coordination.
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Affiliation(s)
- Gwen Costa Jacobsohn
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.
| | - Margaret Leaf
- Applied Data Science, Enterprise Analytics, UW Health, Madison, WI, USA.
| | - Frank Liao
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA; Applied Data Science, Enterprise Analytics, UW Health, Madison, WI, USA.
| | - Apoorva P. Maru
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Collin J. Engstrom
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA,Department of Computer Science, Winona State University, Rochester, MN, USA
| | - Megan E. Salwei
- Department of Industrial and Systems Engineering, University of Wisconsin, Madison, Wisconsin, USA,Center for Quality and Productivity Improvement, University of Wisconsin-Madison, Madison, Wisconsin, USA,Center for Research and Innovation in Systems Safety, Departments of Anesthesiology and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Gerald T Pankratz
- Department of Medicine, Division of Geriatrics and Gerontology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
| | - Alexis Eastman
- Department of Medicine, Division of Geriatrics and Gerontology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
| | - Pascale Carayon
- Department of Industrial and Systems Engineering, University of Wisconsin, Madison, WI, USA; Center for Quality and Productivity Improvement, University of Wisconsin-Madison, Madison, WI, USA.
| | - Douglas A. Wiegmann
- Department of Industrial and Systems Engineering, University of Wisconsin, Madison, Wisconsin, USA,Center for Quality and Productivity Improvement, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Joel S. Galang
- Applied Data Science, Enterprise Analytics, UW Health, Madison, Wisconsin, USA
| | - Maureen A. Smith
- Health Innovation Program, University of Wisconsin-Madison, Madison, Wisconsin, USA,Department of Population Health Sciences, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Manish N. Shah
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA,Department of Medicine, Division of Geriatrics and Gerontology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA,Department of Population Health Sciences, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA
| | - Brian W. Patterson
- BerbeeWalsh Department of Emergency Medicine, University of Wisconsin-Madison School of Medicine and Public Health, Madison, Wisconsin, USA,Health Innovation Program, University of Wisconsin-Madison, Madison, Wisconsin, USA
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Bellini V, Valente M, Gaddi AV, Pelosi P, Bignami E. Artificial intelligence and telemedicine in anesthesia: potential and problems. Minerva Anestesiol 2022; 88:729-734. [PMID: 35164492 DOI: 10.23736/s0375-9393.21.16241-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
INTRODUCTION The application of novel technologies like Artificial Intelligence (AI), Machine Learning (ML) and telemedicine in anesthesiology could play a role in transforming the future of health care. In the present review we discuss the current applications of AI and telemedicine in anesthesiology and perioperative care, exploring their potential influence and the possible hurdles. EVIDENCE ACQUISITION AI technologies have the potential to deeply impact all phases of perioperative care from accurate risk prediction to operating room organization, leading to increased cost-effective care quality and better outcomes. Telemedicine is reported as a successful mean within the anaesthetic pathway, including preoperative evaluation, remote patient monitoring, and postoperative care. EVIDENCE SYNTHESIS The utilization of AI and telemedicine is promising encouraging results in perioperative management, nevertheless several hurdles remain to be overcome before these tools could be integrated in our daily practice. CONCLUSIONS AI models and telemedicine can significantly influence all phases of perioperative care, helping physicians in the development of precision medicine.
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Affiliation(s)
- Valentina Bellini
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Marina Valente
- General Surgery Unit, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Antonio V Gaddi
- Center for Metabolic diseases and Atherosclerosis, University of Bologna, Bologna, Italy
| | - Paolo Pelosi
- Department of Anesthesia and Intensive Care, Ospedale Policlinico San Martino, IRCCS for Oncology and Neuroscience, Genoa, Italy.,Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy
| | - Elena Bignami
- Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy -
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de Hond AAH, Leeuwenberg AM, Hooft L, Kant IMJ, Nijman SWJ, van Os HJA, Aardoom JJ, Debray TPA, Schuit E, van Smeden M, Reitsma JB, Steyerberg EW, Chavannes NH, Moons KGM. Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. NPJ Digit Med 2022; 5:2. [PMID: 35013569 PMCID: PMC8748878 DOI: 10.1038/s41746-021-00549-7] [Citation(s) in RCA: 105] [Impact Index Per Article: 52.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 12/13/2021] [Indexed: 12/23/2022] Open
Abstract
While the opportunities of ML and AI in healthcare are promising, the growth of complex data-driven prediction models requires careful quality and applicability assessment before they are applied and disseminated in daily practice. This scoping review aimed to identify actionable guidance for those closely involved in AI-based prediction model (AIPM) development, evaluation and implementation including software engineers, data scientists, and healthcare professionals and to identify potential gaps in this guidance. We performed a scoping review of the relevant literature providing guidance or quality criteria regarding the development, evaluation, and implementation of AIPMs using a comprehensive multi-stage screening strategy. PubMed, Web of Science, and the ACM Digital Library were searched, and AI experts were consulted. Topics were extracted from the identified literature and summarized across the six phases at the core of this review: (1) data preparation, (2) AIPM development, (3) AIPM validation, (4) software development, (5) AIPM impact assessment, and (6) AIPM implementation into daily healthcare practice. From 2683 unique hits, 72 relevant guidance documents were identified. Substantial guidance was found for data preparation, AIPM development and AIPM validation (phases 1-3), while later phases clearly have received less attention (software development, impact assessment and implementation) in the scientific literature. The six phases of the AIPM development, evaluation and implementation cycle provide a framework for responsible introduction of AI-based prediction models in healthcare. Additional domain and technology specific research may be necessary and more practical experience with implementing AIPMs is needed to support further guidance.
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Affiliation(s)
- Anne A H de Hond
- Department of Information Technology and Digital Innovation, Leiden University Medical Center, Leiden, The Netherlands.
- Clinical AI Implementation and Research Lab, Leiden University Medical Center, Leiden, The Netherlands.
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.
| | - Artuur M Leeuwenberg
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
| | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Ilse M J Kant
- Department of Information Technology and Digital Innovation, Leiden University Medical Center, Leiden, The Netherlands
- Clinical AI Implementation and Research Lab, Leiden University Medical Center, Leiden, The Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Steven W J Nijman
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Hendrikus J A van Os
- Clinical AI Implementation and Research Lab, Leiden University Medical Center, Leiden, The Netherlands
- National eHealth Living Lab, Leiden, The Netherlands
| | - Jiska J Aardoom
- National eHealth Living Lab, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Thomas P A Debray
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Ewoud Schuit
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Maarten van Smeden
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Johannes B Reitsma
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Ewout W Steyerberg
- Clinical AI Implementation and Research Lab, Leiden University Medical Center, Leiden, The Netherlands
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Niels H Chavannes
- National eHealth Living Lab, Leiden, The Netherlands
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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10
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Lieu TA, Herrinton LJ, Needham T, Ford M, Liu L, Lyons D, Macapinlac J, Neugebauer R, Ng D, Prausnitz S, Robertson W, Schultz K, Stewart K, Van Den Eeden SK, Baer DM. A prognostic information system for real-time personalized care: Lessons for embedded researchers. HEALTHCARE-THE JOURNAL OF DELIVERY SCIENCE AND INNOVATION 2021; 8 Suppl 1:100486. [PMID: 34175099 DOI: 10.1016/j.hjdsi.2020.100486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Revised: 09/12/2020] [Accepted: 10/14/2020] [Indexed: 10/21/2022]
Abstract
Embedded researchers could play a central role in developing tools to personalize care using electronic medical records (EMRs). However, few studies have described the steps involved in developing such tools, or evaluated the key factors in success and failure. This case study describes how we used an EMR-derived data warehouse to develop a prototype informatics tool to help oncologists counsel patients with pancreatic cancer about their prognosis. The tool generated real-time prognostic information based on tumor type and stage, age, comorbidity status and lab tests. Our multidisciplinary team included embedded researchers, application developers, user experience experts, and an oncologist leader.This prototype succeeded in establishing proof of principle, but did not reach adoption into actual practice. In pilot testing, oncologists succeeded in generating prognostic information in real time. A few found it helpful in patient encounters, but all identified critical areas for further development before implementation. Generalizable lessons included the need to (1) include a wide range of potential use cases and stakeholders when selecting use cases for such tools; (2) develop talking points for clinicians to explain results from predictive tools to patients; (3) develop ways to reduce lag time between events and data availability; and (4) keep the options presented in the user interface very simple. This case demonstrates that embedded researchers can lead collaborations using EMR-derived data to create systems for real-time personalized patient counseling, and highlights challenges that such teams can anticipate.
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Affiliation(s)
- Tracy A Lieu
- Division of Research, Kaiser Permanente Northern California, USA; The Permanente Medical Group, Oakland, CA, USA.
| | - Lisa J Herrinton
- Division of Research, Kaiser Permanente Northern California, USA
| | | | - Michael Ford
- Division of Research, Kaiser Permanente Northern California, USA
| | - Liyan Liu
- Division of Research, Kaiser Permanente Northern California, USA
| | | | | | | | - Daniel Ng
- Division of Research, Kaiser Permanente Northern California, USA
| | | | | | | | | | | | - David M Baer
- The Permanente Medical Group, Oakland, CA, USA; Department of Oncology, Kaiser Permanente Oakland Medical Center, CA, Oakland, USA
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11
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Archambault P, Turcotte S, Smith PY, Said Abasse K, Paquet C, Côté A, Gomez D, Khechine H, Gagnon MP, Tremblay M, Elazhary N, Légaré F. Intention to Use Wiki-Based Knowledge Tools: Survey of Quebec Emergency Health Professionals. JMIR Med Inform 2021; 9:e24649. [PMID: 34142977 PMCID: PMC8277401 DOI: 10.2196/24649] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 02/16/2021] [Accepted: 05/07/2021] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Clinical decision support systems are information technologies that assist clinicians in making better decisions. Their adoption has been limited because their content is difficult to adapt to local contexts and slow to adapt to emerging evidence. Collaborative writing applications such as wikis have the potential to increase access to existing and emerging evidence-based knowledge at the point of care, standardize emergency clinical decision making, and quickly adapt this knowledge to local contexts. However, little is known about the factors influencing health professionals' use of wiki-based knowledge tools. OBJECTIVE This study aims to measure emergency physicians' (EPs) and other acute care health professionals' (ACHPs) intentions to use wiki-based knowledge tools in trauma care and identify determinants of this intention that can be used in future theory-based interventions for promoting the use of wiki-based knowledge tools in trauma care. METHODS In total, 266 EPs and 907 ACHPs (nurses, respiratory therapists, and pharmacists) from 12 Quebec trauma centers were asked to answer a survey based on the theory of planned behavior (TPB). The TPB constructs were measured using a 7-point Likert scale. Descriptive statistics and Pearson correlations between the TPB constructs and intention were calculated. Multiple linear regression analysis was conducted to identify the salient beliefs. RESULTS Among the eligible participants, 57.1% (152/266) of EPs and 31.9% (290/907) of ACHPs completed the questionnaire. For EPs, we found that attitude, perceived behavioral control (PBC), and subjective norm (SN) were significant determinants of the intention to use wiki-based knowledge tools and explained 62% of its variance. None of the sociodemographic variables were related to EPs' intentions to use wiki-based knowledge tools. The regression model identified two normative beliefs ("approval by physicians" and "approval by patients") and two behavioral beliefs ("refreshes my memory" and "reduces errors"). For ACHPs, attitude, PBC, SN, and two sociodemographic variables (profession and the previous personal use of a wiki) were significantly related to the intention to use wiki-based knowledge tools and explained 60% of the variance in behavioral intention. The final regression model for ACHPs included two normative beliefs ("approval by the hospital trauma team" and "people less comfortable with information technology"), one control belief ("time constraints"), and one behavioral belief ("access to evidence"). CONCLUSIONS The intentions of EPs and ACHPs to use wiki-based knowledge tools to promote best practices in trauma care can be predicted in part by attitude, SN, and PBC. We also identified salient beliefs that future theory-based interventions should promote for the use of wiki-based knowledge tools in trauma care. These interventions will address the barriers to using wiki-based knowledge tools, find ways to ensure the quality of their content, foster contributions, and support the exploration of wiki-based knowledge tools as potential effective knowledge translation tools in trauma care.
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Affiliation(s)
- Patrick Archambault
- Département de médecine d'urgence, Centre intégré de santé et de services sociaux de Chaudière-Appalaches, Lévis, QC, Canada
- Department of Family Medicine and Emergency Medicine, Faculty of Medicine, Université Laval, Québec, QC, Canada
- VITAM - Centre de recherche en santé durable, Université Laval, Québec, QC, Canada
| | - Stéphane Turcotte
- Centre intégré de santé et de services sociaux de Chaudière-Appalaches, Lévis, QC, Canada
| | - Pascal Y Smith
- Centre intégré de santé et de services sociaux de Chaudière-Appalaches, Lévis, QC, Canada
| | - Kassim Said Abasse
- Département de management, Faculté des sciences de l'administration, Université Laval, Québec, QC, Canada
| | - Catherine Paquet
- Département de marketing, Faculté des sciences de l'administration, Université Laval, Québec, QC, Canada
| | - André Côté
- Département de management, Faculté des sciences de l'administration, Université Laval, Québec, QC, Canada
| | - Dario Gomez
- Département de systèmes d'information organisationnels, Faculté des sciences de l'administration, Université Laval, Québec, QC, Canada
| | - Hager Khechine
- Département de systèmes d'information organisationnels, Faculté des sciences de l'administration, Université Laval, Québec, QC, Canada
| | - Marie-Pierre Gagnon
- VITAM - Centre de recherche en santé durable, Université Laval, Québec, QC, Canada
- Faculté des sciences infirmières, Université Laval, Québec, QC, Canada
| | - Melissa Tremblay
- Department of Family Medicine and Emergency Medicine, Faculty of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Nicolas Elazhary
- Department of Family Medicine and Emergency Medicine, Faculty of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - France Légaré
- Department of Family Medicine and Emergency Medicine, Faculty of Medicine, Université Laval, Québec, QC, Canada
- VITAM - Centre de recherche en santé durable, Université Laval, Québec, QC, Canada
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12
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Identifying Low Value Malnutrition Care Activities for De-Implementation and Systematised, Interdisciplinary Alternatives-A Multi-Site, Nominal Group Technique Approach. Nutrients 2021; 13:nu13062063. [PMID: 34208675 PMCID: PMC8234755 DOI: 10.3390/nu13062063] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 06/11/2021] [Accepted: 06/12/2021] [Indexed: 01/07/2023] Open
Abstract
Malnutrition risk is identified in over one-third of inpatients; reliance on dietetics-delivered nutrition care for all “at-risk” patients is unsustainable, inefficient, and ineffective. This study aimed to identify and prioritise low-value malnutrition care activities for de-implementation and articulate systematised interdisciplinary opportunities. Nine workshops, at eight purposively sampled hospitals, were undertaken using the nominal group technique. Participants were asked “What highly individualised malnutrition care activities do you think we could replace with systematised, interdisciplinary malnutrition care?” and “What systematised, interdisciplinary opportunities do you think we should do to provide more effective and efficient nutrition care in our ward/hospital?” Sixty-three participants were provided five votes per question. The most voted de-implementation activities were low-value nutrition reviews (32); education by dietitian (28); assessments by dietitian for patients with malnutrition screening tool score of two (22); assistants duplicating malnutrition screening (19); and comprehensive, individualised nutrition assessments where unlikely to add value (15). The top voted alternative opportunities were delegated/skill shared interventions (55), delegated/skill shared education (24), abbreviated malnutrition care processes where clinically appropriate (23), delegated/skill shared supportive food/fluids (14), and mealtime assistance (13). Findings highlight opportunities to de-implement perceived low-value malnutrition care activities and replace them with systems and skill shared alternatives across hospital settings.
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13
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Zhang T, Shen N, Booth R, LaChance J, Jackson B, Strudwick G. Supporting the use of patient portals in mental health settings: a scoping review. Inform Health Soc Care 2021; 47:62-79. [PMID: 34032528 DOI: 10.1080/17538157.2021.1929998] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
With the increased use of patient portals in acute and chronic care settings as a strategy to support patient care and improve patient-centric care, there is still little known about the impact of patient portals in mental health contexts. The purposes of this review were to: 1) identify the critical success factors for successful patient portal implementation and adoption among end-users that could be utilized in a mental health setting; 2) uncover what we know about existing mental health portals and their effectiveness for end-users; and 3) determine what indicators are being used to evaluate existing patient portals for end-users that may be applied in a mental health context. This scoping review was conducted through a search of six electronic databases including Medline, EMBASE, PsycINFO, and CINAHL for articles published between 2007 and 2021. A total of 31 articles were included in the review. Critical success factors of patient portal implementation included those related to education, usefulness, usability, culture, and resources. Only two patient portals had articles published related to their effectiveness for end-users (one in Canada and the other in the United States). More than 100 measures of process (n = 73) and outcome (n = 59) indicators were extracted from the studies and mapped to the Benefits Evaluation Framework. Patient portals carry great potential to improve patient care, but more attention needs to be given to ensure they are being evaluated through the development and implementation phases with the end-users in mind. Further understanding of process indicators relating to use are essential for long-term patient adoption of portals to obtain their potential benefits.
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Affiliation(s)
- Timothy Zhang
- Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, ON, Canada.,Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nelson Shen
- Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, ON, Canada
| | - Richard Booth
- Arthur Labatt Family School of Nursing, Western University, London, ON, Canada
| | - Jessica LaChance
- Arthur Labatt Family School of Nursing, Western University, London, ON, Canada
| | - Brianna Jackson
- Arthur Labatt Family School of Nursing, Western University, London, ON, Canada.,Yale School of Nursing, Yale University, Orange, Connecticut, USA
| | - Gillian Strudwick
- Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, ON, Canada
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14
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Melnick ER, Harry E, Sinsky CA, Dyrbye LN, Wang H, Trockel MT, West CP, Shanafelt T. Perceived Electronic Health Record Usability as a Predictor of Task Load and Burnout Among US Physicians: Mediation Analysis. J Med Internet Res 2020; 22:e23382. [PMID: 33289493 PMCID: PMC7785404 DOI: 10.2196/23382] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Revised: 10/15/2020] [Accepted: 12/07/2020] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Electronic health record (EHR) usability and physician task load both contribute to physician professional burnout. The association between perceived EHR usability and workload has not previously been studied at a national level. Better understanding these interactions could give further information as to the drivers of extraneous task load. OBJECTIVE This study aimed to determine the relationship between physician-perceived EHR usability and workload by specialty and evaluate for associations with professional burnout. METHODS A secondary analysis of a cross-sectional survey of US physicians from all specialties was conducted from October 2017 to March 2018. Among the 1250 physicians invited to respond to the subsurvey analyzed here, 848 (67.8%) completed it. EHR usability was assessed with the System Usability Scale (SUS; range: 0-100). Provider task load (PTL) was assessed using the mental demand, physical demand, temporal demand, and effort required subscales of the National Aeronautics and Space Administration Task Load Index (range: 0-400). Burnout was measured using the Maslach Burnout Inventory. RESULTS The mean scores were 46.1 (SD 22.1) for SUS and 262.5 (SD 71.7) for PTL. On multivariable analysis adjusting for age, gender, relationship status, medical specialty, practice setting, hours worked per week, and number of nights on call per week, physician-rated EHR usability was associated with PTL, with each 1-point increase in SUS score (indicating more favorable) associated with a 0.57-point decrease in PTL score (P<.001). On mediation analysis, higher SUS score was associated with lower PTL score, which was associated with lower odds of burnout. CONCLUSIONS A strong association was observed between EHR usability and workload among US physicians, with more favorable usability associated with less workload. Both outcomes were associated with the odds of burnout, with task load acting as a mediator between EHR usability and burnout. Improving EHR usability while decreasing task load has the potential to allow practicing physicians more working memory for medical decision making and patient communication.
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Affiliation(s)
- Edward R Melnick
- Department of Emergency Medicine, Yale University School of Medicine, New Haven, CT, United States
| | - Elizabeth Harry
- University of Colorado School of Medicine, Aurora, CO, United States
| | - Christine A Sinsky
- Professional Satisfaction and Practice Sustainability, American Medical Association, Chicago, IL, United States
| | - Liselotte N Dyrbye
- Department of Medicine Physician Well-Being Program, Mayo Clinic, Rochester, MN, United States
| | - Hanhan Wang
- Department of Medicine, Stanford School of Medicine, Palo Alto, CA, United States
| | - Mickey Todd Trockel
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA, United States
| | - Colin P West
- Department of Internal Medicine, Mayo Clinic, Rochester, MN, United States
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Tait Shanafelt
- Department of Medicine, Stanford School of Medicine, Palo Alto, CA, United States
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15
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Huang C, Koppel R, McGreevey JD, Craven CK, Schreiber R. Transitions from One Electronic Health Record to Another: Challenges, Pitfalls, and Recommendations. Appl Clin Inform 2020; 11:742-754. [PMID: 33176389 DOI: 10.1055/s-0040-1718535] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
Abstract
OBJECTIVE We address the challenges of transitioning from one electronic health record (EHR) to another-a near ubiquitous phenomenon in health care. We offer mitigating strategies to reduce unintended consequences, maximize patient safety, and enhance health care delivery. METHODS We searched PubMed and other sources to identify articles describing EHR-to-EHR transitions. We combined these references with the authors' extensive experience to construct a conceptual schema and to offer recommendations to facilitate transitions. RESULTS Our PubMed query retrieved 1,351 citations: 43 were relevant for full paper review and 18 met the inclusion criterion of focus on EHR-to-EHR transitions. An additional PubMed search yielded 1,014 citations, for which we reviewed 74 full papers and included 5. We supplemented with additional citations for a total of 70 cited. We distinguished 10 domains in the literature that overlap yet present unique and salient opportunities for successful transitions and for problem mitigation. DISCUSSION There is scant literature concerning EHR-to-EHR transitions. Identified challenges include financial burdens, personnel resources, patient safety threats from limited access to legacy records, data integrity during migration, cybersecurity, and semantic interoperability. Transition teams must overcome inadequate human infrastructure, technical challenges, security gaps, unrealistic providers' expectations, workflow changes, and insufficient training and support-all factors affecting potential clinician burnout. CONCLUSION EHR transitions are remarkably expensive, laborious, personnel devouring, and time consuming. The paucity of references in comparison to the topic's salience reinforces the necessity for this type of review and analysis. Prudent planning may streamline EHR transitions and reduce expenses. Mitigating strategies, such as preservation of legacy data, managing expectations, and hiring short-term specialty consultants can overcome some of the greatest hurdles. A new medical subject headings (MeSH) term for EHR transitions would facilitate further research on this topic.
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Affiliation(s)
- Chunya Huang
- Geisinger Commonwealth School of Medicine, Scranton, Pennsylvania, United States.,Department of Anesthesiology and Perioperative Medicine, University of Louisville School of Medicine-Louisville, Kentucky, United States
| | - Ross Koppel
- Deparments of Biomedical Informatics and of Sociology, University of Pennsylvania, Philadelphia, Pennsylvania, United States.,Department of Biomedical Informatics, University at Buffalo (SUNY), Buffalo, New York, United States
| | - John D McGreevey
- Division of General Internal Medicine, Section of Hospital Medicine, Perelman School of Medicine at the University of Pennsylvania, University of Pennsylvania Health System, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Catherine K Craven
- Department of Population Health Science and Policy, Clinical Informatics Group, IT Department, Mount Sinai Health System, Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, United States
| | - Richard Schreiber
- Physician Informatics and Department of Medicine, Geisinger Holy Spirit, Geisinger Commonwealth School of Medicine, Camp Hill, Pennsylvania, United States
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16
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Crotty BH, Somai M. The Bugs In the Virtual Clinic: Confronting Telemedicine’s Challenges Through Empathy and Support (Preprint). J Particip Med 2020; 14:e25688. [PMID: 35452399 PMCID: PMC9077509 DOI: 10.2196/25688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/28/2020] [Accepted: 01/05/2022] [Indexed: 11/13/2022] Open
Abstract
Although telemedicine has been an important conduit for clinical care during the COVID-19 pandemic, not all patients have been able to meaningfully participate in this mode of health care provision. Challenges with accessing telemedicine using consumer technology can interfere with the ability of patients and clinicians to meaningfully connect and lead to significant investments in time by clinicians and their staff. In this narrative case, we identify issues related to patients’ use of technology, make comparisons between telehealth adoption and the deployment of electronic health records, and propose that building intuitive and supported digital care experiences for patients is required to make virtual care sustainable.
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Affiliation(s)
- Bradley H Crotty
- Inception Labs, Collaborative for Healthcare Delivery Science, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Melek Somai
- Inception Labs, Collaborative for Healthcare Delivery Science, Medical College of Wisconsin, Milwaukee, WI, United States
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17
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Trinkley KE, Kahn MG, Bennett TD, Glasgow RE, Haugen H, Kao DP, Kroehl ME, Lin CT, Malone DC, Matlock DD. Integrating the Practical Robust Implementation and Sustainability Model With Best Practices in Clinical Decision Support Design: Implementation Science Approach. J Med Internet Res 2020; 22:e19676. [PMID: 33118943 PMCID: PMC7661234 DOI: 10.2196/19676] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/18/2020] [Accepted: 09/15/2020] [Indexed: 11/13/2022] Open
Abstract
Background Clinical decision support (CDS) design best practices are intended to provide a narrative representation of factors that influence the success of CDS tools. However, they provide incomplete direction on evidence-based implementation principles. Objective This study aims to describe an integrated approach toward applying an existing implementation science (IS) framework with CDS design best practices to improve the effectiveness, sustainability, and reproducibility of CDS implementations. Methods We selected the Practical Robust Implementation and Sustainability Model (PRISM) IS framework. We identified areas where PRISM and CDS design best practices complemented each other and defined methods to address each. Lessons learned from applying these methods were then used to further refine the integrated approach. Results Our integrated approach to applying PRISM with CDS design best practices consists of 5 key phases that iteratively interact and inform each other: multilevel stakeholder engagement, designing the CDS, design and usability testing, thoughtful deployment, and performance evaluation and maintenance. The approach is led by a dedicated implementation team that includes clinical informatics and analyst builder expertise. Conclusions Integrating PRISM with CDS design best practices extends user-centered design and accounts for the multilevel, interacting, and dynamic factors that influence CDS implementation in health care. Integrating PRISM with CDS design best practices synthesizes the many known contextual factors that can influence the success of CDS tools, thereby enhancing the reproducibility and sustainability of CDS implementations. Others can adapt this approach to their situation to maximize and sustain CDS implementation success.
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Affiliation(s)
- Katy E Trinkley
- Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.,Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.,Clinical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.,Adult and Child Consortium for Outcomes Research and Delivery Science, Aurora, CO, United States
| | - Michael G Kahn
- Section of Informatics and Data Science, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Tellen D Bennett
- Adult and Child Consortium for Outcomes Research and Delivery Science, Aurora, CO, United States.,Section of Informatics and Data Science, Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Russell E Glasgow
- Adult and Child Consortium for Outcomes Research and Delivery Science, Aurora, CO, United States.,Department of Family Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Heather Haugen
- Colorado Clinical and Translational Sciences Institute, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - David P Kao
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.,Clinical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Miranda E Kroehl
- Charter Communications Corporation, Greenwood Village, CO, United States
| | - Chen-Tan Lin
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.,Clinical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Daniel C Malone
- Department of Pharmacotherapy, Skaggs College of Pharmacy, University of Utah, Salt Lake City, UT, United States
| | - Daniel D Matlock
- Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States.,Adult and Child Consortium for Outcomes Research and Delivery Science, Aurora, CO, United States.,VA Eastern Colorado Geriatric Research Education and Clinical Center, Aurora, CO, United States
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18
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Hoffman JM, Flynn AJ, Juskewitch JE, Freimuth RR. Biomedical Data Science and Informatics Challenges to Implementing Pharmacogenomics with Electronic Health Records. Annu Rev Biomed Data Sci 2020. [DOI: 10.1146/annurev-biodatasci-020320-093614] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Pharmacogenomic information must be incorporated into electronic health records (EHRs) with clinical decision support in order to fully realize its potential to improve drug therapy. Supported by various clinical knowledge resources, pharmacogenomic workflows have been implemented in several healthcare systems. Little standardization exists across these efforts, however, which limits scalability both within and across clinical sites. Limitations in information standards, knowledge management, and the capabilities of modern EHRs remain challenges for the widespread use of pharmacogenomics in the clinic, but ongoing efforts are addressing these challenges. Although much work remains to use pharmacogenomic information more effectively within clinical systems, the experiences of pioneering sites and lessons learned from those programs may be instructive for other clinical areas beyond genomics. We present a vision of what can be achieved as informatics and data science converge to enable further adoption of pharmacogenomics in the clinic.
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Affiliation(s)
- James M. Hoffman
- Department of Pharmaceutical Sciences and the Office of Quality and Patient Care, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | - Allen J. Flynn
- Department of Learning Health Sciences, Medical School, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Justin E. Juskewitch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Robert R. Freimuth
- Division of Digital Health Sciences, Department of Health Sciences Research, Center for Individualized Medicine, and Information and Knowledge Management, Mayo Clinic, Rochester, Minnesota 55905, USA
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Hofer IS, Burns M, Kendale S, Wanderer JP. Realistically Integrating Machine Learning Into Clinical Practice: A Road Map of Opportunities, Challenges, and a Potential Future. Anesth Analg 2020; 130:1115-1118. [PMID: 32287118 DOI: 10.1213/ane.0000000000004575] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Ira S Hofer
- From the Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California
| | - Michael Burns
- Department of Anesthesiology, University of Michigan School of Medicine, Ann Arbor, Michigan
| | - Samir Kendale
- Department of Anesthesiology, Perioperative Care and Pain Medicine, New York University Langone School of Medicine, New York, New York
| | - Jonathan P Wanderer
- Departments of Anesthesiology and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee
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