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El Tantawi M, Ammar N, Mariño R, Uribe SE, Manton D, Hugo FN, Clément C, Sim CPC, Maret D, Kopycka-Kedzierawski DT, Mbende E, Kruger E, Lan R, Doghri LL, Castelaz M, Alam MK, Ibiyemi O, Naidoo S, Schwarz E, Priya H, Braga MM, Giraudeau N, Foláyan MNO. Developing the teledentistry acceptance survey for dentists - TAS-D: a Delphi study. BMC Oral Health 2024; 24:977. [PMID: 39174955 PMCID: PMC11342607 DOI: 10.1186/s12903-024-04760-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2024] [Accepted: 08/16/2024] [Indexed: 08/24/2024] Open
Abstract
INTRODUCTION The increasing interest in teledentistry since the COVID-19 pandemic warrants an evaluation of dentists' willingness to adopt it. This study aimed to develop a questionnaire to assess dentist's intention to use teledentistry and the associated factors. METHODS A literature search was used to identify items for the questionnaire. The Unified Theory of Acceptance and Use of Technology (UTAUT2) was adopted as framework. A Delphi panel was constituted of researchers with relevant publications and the International Association of Dental Research e-Oral Health Network members. Three Delphi consultations were conducted to establish consensus on items. Consensus was set at 80% agreement and content validity ratio (CVR), reaffirmed iteratively. RESULTS Nineteen out of 25 (76%) invited experts participated in the first round, 17 in the second and 15 in the third. The preliminary questionnaire had 81 items in three sections, reduced to 66, 45 and 33 items in the first, second and third rounds. After revision, the final version comprised eight items assessing dentists' backgrounds in Sect. 1, seven items identifying teledentistry uses in Sect. 2, and 17 items assessing intention to use teledentistry and its determinants in seven dimensions in Sect. 3. The initial CVR was 0.45, which increased to 0.80 at the end of the third round. CONCLUSION A survey tool was developed to assess the acceptance of teledentistry, and its determinants based on the UTAUT2 framework through consensus among teledentistry experts. The tool had excellent validity and needs further evaluation of its psychometric properties.
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Affiliation(s)
- Maha El Tantawi
- Department of Pediatric Dentistry and Dental Public Health, Faculty of Dentistry, Alexandria University, Champollion St, Azarita, Alexandria, Egypt.
| | - Nour Ammar
- Department of Pediatric Dentistry and Dental Public Health, Faculty of Dentistry, Alexandria University, Champollion St, Azarita, Alexandria, Egypt
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilian University of Munich, Munich, Germany
| | - Rodrigo Mariño
- Center for Research in Epidemiology, Economics and Oral Public Health (CIEESPO), Faculty of Dentistry, Universidad de La Frontera, Temuco, Chile
- Melbourne Dental School, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, Australia
| | - Sergio E Uribe
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilian University of Munich, Munich, Germany
- Department of Conservative Dentistry and Oral Health, Riga Stradins University, Riga, Latvia
- Baltic Biomaterials Centre of Excellence, Headquarters at Riga Technical University & RSU Institute of Stomatology, Riga, Latvia
| | - David Manton
- Centrum voor Tandheelkunde en Mondzorgkunde, University of Groningen, UMCG, Groningen, The Netherlands
| | - Fernando N Hugo
- Department of Epidemiology and Health Promotion, New York University College of Dentistry, New York, NY, USA
| | - Celine Clément
- Department of Public Health, Interpsy Research Unit, CHRU Nancy, University Hospital, University of Lorraine, Vandoeuvre-Lès-Nancy, France
| | - Christina P C Sim
- Department of Restorative Dentistry, National Dental Centre Singapore, Singapore, Singapore
- Oral Health Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Delphine Maret
- Department of Dental Surgery, Université Paul Sabatier, Centre Hospitalier Universitaire, Toulouse, France
| | | | - Eliane Mbende
- Department of Public Health, Faculty of Medicine & Biomedical Sciences, University of Yaoundé 1, Yaoundé, Cameroon
| | - Estie Kruger
- School of Allied Health, The University of Western Australia, Perth, Australia
| | - Romain Lan
- Aix-Marseille University, CNRS, EFS, ADES, Marseille, France
| | | | | | - Mohammad Khursheed Alam
- Department of Preventive Dentistry, College of Dentistry, Jouf University, Sakaka, Saudi Arabia
| | | | - Sudeshni Naidoo
- Department of Community Dentistry, Faculty of Dentistry, University of the Western Cape, Cape Town, South Africa
| | - Eli Schwarz
- Oregon Health & Science University, Portland, OR, USA
| | - Harsh Priya
- Department of Public Health Dentistry, Centre for Dental Education and Research, All India Institute of Medical Sciences, New Delhi, India
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Zhang H, Li S, Ma X. Transforming Healthcare with Nanomedicine: A SWOT Analysis of Drug Delivery Innovation. Drug Des Devel Ther 2024; 18:3499-3521. [PMID: 39132625 PMCID: PMC11314449 DOI: 10.2147/dddt.s470210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 07/11/2024] [Indexed: 08/13/2024] Open
Abstract
Objective Nanomedicine represents a transformative approach in biomedical applications. This study aims to delineate the application of nanomedicine in the biomedical field through the strengths, weaknesses, opportunities, and threats (SWOT) analysis to evaluate its efficacy and potential in clinical applications. Methods The SWOT analysis framework was employed to systematically review and assess the internal strengths and weaknesses, along with external opportunities and threats of nanomedicine. This method provides a balanced consideration of the potential benefits and challenges. Results Findings from the SWOT analysis indicate that nanomedicine presents significant potential in drug delivery, diagnostic imaging, and tissue engineering. Nonetheless, it faces substantial hurdles such as safety issues, environmental concerns, and high development costs. Critical areas for development were identified, particularly concerning its therapeutic potential and the uncertainties surrounding long-term effects. Conclusion Nanomedicine holds substantial promise in driving medical innovation. However, successful clinical translation requires addressing safety, cost, and regulatory challenges. Interdisciplinary collaboration and comprehensive strategic planning are crucial for the safe and effective application of nanomedicine.
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Affiliation(s)
- Hao Zhang
- Department of Nuclear Medicine, Affiliated Hospital of North Sichuan Medical College North Sichuan Medical College, Nanchong, 637000, People’s Republic of China
| | - Suping Li
- Department of Nuclear Medicine, Affiliated Hospital of North Sichuan Medical College North Sichuan Medical College, Nanchong, 637000, People’s Republic of China
| | - Xingming Ma
- School of Health Management, Xihua University, Chengdu, 610039, People’s Republic of China
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Nabelsi V, Lévesque-Chouinard A. Successful Electronic Consultation Service Initiative in Quebec, Canada With Primary Care Physicians' and Specialists' Experiences on Acceptance and Use of Technological Innovation: Cross-Sectional Exploratory Study. JMIR Form Res 2024; 8:e52921. [PMID: 38814689 PMCID: PMC11176886 DOI: 10.2196/52921] [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: 09/19/2023] [Revised: 04/10/2024] [Accepted: 04/25/2024] [Indexed: 05/31/2024] Open
Abstract
BACKGROUND Electronic consultation (eConsult) is an eHealth service that allows primary care providers (PCPs) to electronically consult specialists regarding their patients' medical issues. Many studies have demonstrated that eConsult services improve timely access to specialist care; prevent unnecessary referrals; improve PCPs', specialists', and patients' satisfaction; and therefore have a large impact on costs. However, no studies have evaluated PCPs' and specialists' acceptance of eConsult services in Quebec, Canada, and worldwide. OBJECTIVE This exploratory study aims to identify factors affecting eConsult service acceptance by PCPs and specialists in urban and rural primary care clinics across 3 regions in the province of Quebec, Canada, by integrating the Unified Theory of Acceptance and Use of Technology and Task-Technology Fit (TTF) models and user satisfaction. This research was designed to broaden and assist in scaling up this effective eHealth service innovation across the province. METHODS A cross-sectional web-based survey was sent to all PCPs (n=263) and specialists (n=62) who used the eConsult Quebec Service between July 2017 and May 2021. We proposed a unified model integrating the Unified Theory of Acceptance and Use of Technology model and TTF model and user satisfaction by endorsing 11 hypotheses. The partial least squares was used to investigate factors influencing the acceptance of the eConsult Quebec Service. RESULTS Of the 325 end users, 136 (41.8%) users responded (PCPs: 101/263, 38.4%; specialists: 35/62, 57%). The results of the analysis with partial least squares method indicate that 9 of our 11 hypotheses are supported. The direct relationships uniting the various constructs of the model highlighted the importance of several key constructs and predominant correlations. The results suggest that satisfaction is the key driver behind the use of the eConsult Quebec Service. Performance expectancy (P<.001) and effort expectancy (P=.03) can have a positive impact on behavioral intention (BI), and BI (P<.001) can impact adoption. TTF has an influence on performance expectancy (P<.001), adoption (P=.02), and satisfaction (P<.001). However, the results show that there is no direct effect between social influence (P=.38) and BI or between facilitating conditions (P=.17) and adoption. CONCLUSIONS This study provides a better understanding of the factors influencing PCPs' and specialists' intention to adopt the eConsult Quebec Service. Furthermore, this study tests a research model and a technology that have never been explored in Quebec until now. On the basis of the results, the service is a good fit to meet the users' need to improve access to specialized medical advice. Therefore, the results of our study have made a valuable contribution to the implementation of the service by policy makers in order to maximize acceptance, use, adoption, and success across the province of Quebec. Moreover, after 4 successful years, the eConsult Quebec pilot project is now the Conseil Numérique digital consultation service.
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Affiliation(s)
- Véronique Nabelsi
- Department of Administrative Sciences, Université du Québec en Outaouais, Gatineau, QC, Canada
| | - Annabelle Lévesque-Chouinard
- GMF-U de la Haute-Ville du Centre intégré universitaire de santé et des services sociaux de la Capitale-Nationale, Sainte-Foy, QC, Canada
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Sariköse S, Şenol Çelik S. The Effect of Clinical Decision Support Systems on Patients, Nurses, and Work Environment in ICUs: A Systematic Review. Comput Inform Nurs 2024; 42:298-304. [PMID: 38376391 DOI: 10.1097/cin.0000000000001107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
This study aimed to examine the impact of clinical decision support systems on patient outcomes, working environment outcomes, and decision-making processes in nursing. The authors conducted a systematic literature review to obtain evidence on studies about clinical decision support systems and the practices of ICU nurses. For this purpose, the authors searched 10 electronic databases, including PubMed, CINAHL, Web of Science, Scopus, Cochrane Library, Ovid MEDLINE, Science Direct, Tr-Dizin, Harman, and DergiPark. Search terms included "clinical decision support systems," "decision making," "intensive care," "nurse/nursing," "patient outcome," and "working environment" to identify relevant studies published during the period from the year 2007 to October 2022. Our search yielded 619 articles, of which 39 met the inclusion criteria. A higher percentage of studies compared with others were descriptive (20%), conducted through a qualitative (18%), and carried out in the United States (41%). According to the results of the narrative analysis, the authors identified three main themes: "patient care outcomes," "work environment outcomes," and the "decision-making process in nursing." Clinical decision support systems, which target practices of ICU nurses and patient care outcomes, have positive effects on outcomes and show promise in improving the quality of care; however, available studies are limited.
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Affiliation(s)
- Seda Sariköse
- Author Affiliation: Koç University School of Nursing, Istanbul, Turkey
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Hu Z, Wang M, Zheng S, Xu X, Zhang Z, Ge Q, Li J, Yao Y. Clinical Decision Support Requirements for Ventricular Tachycardia Diagnosis Within the Frameworks of Knowledge and Practice: Survey Study. JMIR Hum Factors 2024; 11:e55802. [PMID: 38530337 PMCID: PMC11005434 DOI: 10.2196/55802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/15/2024] [Accepted: 03/02/2024] [Indexed: 03/27/2024] Open
Abstract
BACKGROUND Ventricular tachycardia (VT) diagnosis is challenging due to the similarity between VT and some forms of supraventricular tachycardia, complexity of clinical manifestations, heterogeneity of underlying diseases, and potential for life-threatening hemodynamic instability. Clinical decision support systems (CDSSs) have emerged as promising tools to augment the diagnostic capabilities of cardiologists. However, a requirements analysis is acknowledged to be vital for the success of a CDSS, especially for complex clinical tasks such as VT diagnosis. OBJECTIVE The aims of this study were to analyze the requirements for a VT diagnosis CDSS within the frameworks of knowledge and practice and to determine the clinical decision support (CDS) needs. METHODS Our multidisciplinary team first conducted semistructured interviews with seven cardiologists related to the clinical challenges of VT and expected decision support. A questionnaire was designed by the multidisciplinary team based on the results of interviews. The questionnaire was divided into four sections: demographic information, knowledge assessment, practice assessment, and CDS needs. The practice section consisted of two simulated cases for a total score of 10 marks. Online questionnaires were disseminated to registered cardiologists across China from December 2022 to February 2023. The scores for the practice section were summarized as continuous variables, using the mean, median, and range. The knowledge and CDS needs sections were assessed using a 4-point Likert scale without a neutral option. Kruskal-Wallis tests were performed to investigate the relationship between scores and practice years or specialty. RESULTS Of the 687 cardiologists who completed the questionnaire, 567 responses were eligible for further analysis. The results of the knowledge assessment showed that 383 cardiologists (68%) lacked knowledge in diagnostic evaluation. The overall average score of the practice assessment was 6.11 (SD 0.55); the etiological diagnosis section had the highest overall scores (mean 6.74, SD 1.75), whereas the diagnostic evaluation section had the lowest scores (mean 5.78, SD 1.19). A majority of cardiologists (344/567, 60.7%) reported the need for a CDSS. There was a significant difference in practice competency scores between general cardiologists and arrhythmia specialists (P=.02). CONCLUSIONS There was a notable deficiency in the knowledge and practice of VT among Chinese cardiologists. Specific knowledge and practice support requirements were identified, which provide a foundation for further development and optimization of a CDSS. Moreover, it is important to consider clinicians' specialization levels and years of practice for effective and personalized support.
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Affiliation(s)
- Zhao Hu
- Arrhythmia Center, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College/National Center for Cardiovascular Diseases, Beijing, China
| | - Min Wang
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Si Zheng
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xiaowei Xu
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Zhuxin Zhang
- Arrhythmia Center, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College/National Center for Cardiovascular Diseases, Beijing, China
| | - Qiaoyue Ge
- West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Jiao Li
- Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yan Yao
- Arrhythmia Center, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College/National Center for Cardiovascular Diseases, Beijing, China
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Kashani KB, Awdishu L, Bagshaw SM, Barreto EF, Claure-Del Granado R, Evans BJ, Forni LG, Ghosh E, Goldstein SL, Kane-Gill SL, Koola J, Koyner JL, Liu M, Murugan R, Nadkarni GN, Neyra JA, Ninan J, Ostermann M, Pannu N, Rashidi P, Ronco C, Rosner MH, Selby NM, Shickel B, Singh K, Soranno DE, Sutherland SM, Bihorac A, Mehta RL. Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup. Nat Rev Nephrol 2023; 19:807-818. [PMID: 37580570 PMCID: PMC11285755 DOI: 10.1038/s41581-023-00744-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/06/2023] [Indexed: 08/16/2023]
Abstract
Acute kidney injury (AKI), which is a common complication of acute illnesses, affects the health of individuals in community, acute care and post-acute care settings. Although the recognition, prevention and management of AKI has advanced over the past decades, its incidence and related morbidity, mortality and health care burden remain overwhelming. The rapid growth of digital technologies has provided a new platform to improve patient care, and reports show demonstrable benefits in care processes and, in some instances, in patient outcomes. However, despite great progress, the potential benefits of using digital technology to manage AKI has not yet been fully explored or implemented in clinical practice. Digital health studies in AKI have shown variable evidence of benefits, and the digital divide means that access to digital technologies is not equitable. Upstream research and development costs, limited stakeholder participation and acceptance, and poor scalability of digital health solutions have hindered their widespread implementation and use. Here, we provide recommendations from the Acute Disease Quality Initiative consensus meeting, which involved experts in adult and paediatric nephrology, critical care, pharmacy and data science, at which the use of digital health for risk prediction, prevention, identification and management of AKI and its consequences was discussed.
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Affiliation(s)
- Kianoush B Kashani
- Division of Nephrology and Hypertension, Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN, USA.
| | - Linda Awdishu
- Clinical Pharmacy, San Diego Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Sean M Bagshaw
- Department of Critical Care Medicine, Faculty of Medicine and Dentistry, University of Alberta and Alberta Health Services, Edmonton, Canada
| | | | - Rolando Claure-Del Granado
- Division of Nephrology, Hospital Obrero No 2 - CNS, Cochabamba, Bolivia
- Universidad Mayor de San Simon, School of Medicine, Cochabamba, Bolivia
| | - Barbara J Evans
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, USA
| | - Lui G Forni
- Department of Critical Care, Royal Surrey Hospital NHS Foundation Trust & Department of Clinical & Experimental Medicine, University of Surrey, Guildford, UK
| | - Erina Ghosh
- Philips Research North America, Cambridge, MA, USA
| | - Stuart L Goldstein
- Center for Acute Care Nephrology, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Sandra L Kane-Gill
- Biomedical Informatics and Clinical Translational Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jejo Koola
- UC San Diego Health Department of Biomedical Informatics, Department of Medicine, La Jolla, CA, USA
| | - Jay L Koyner
- Section of Nephrology, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Mei Liu
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Raghavan Murugan
- The Program for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- The Clinical Research, Investigation, and Systems Modelling of Acute Illness Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Girish N Nadkarni
- Division of Data-Driven and Digital Medicine (D3M), Department of Medicine, Icahn School of Medicine at Mount Sinai; Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Javier A Neyra
- Division of Nephrology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jacob Ninan
- Division of Pulmonary, Critical Care and Sleep Medicine, Mayo Clinic, Rochester, MN, USA
| | - Marlies Ostermann
- Department of Critical Care, King's College London, Guy's & St Thomas' Hospital, London, UK
| | - Neesh Pannu
- Division of Nephrology, University of Alberta, Edmonton, Canada
| | - Parisa Rashidi
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, USA
| | - Claudio Ronco
- Università di Padova; Scientific Director Foundation IRRIV; International Renal Research Institute; San Bortolo Hospital, Vicenza, Italy
| | - Mitchell H Rosner
- Department of Medicine, University of Virginia Health, Charlottesville, VA, USA
| | - Nicholas M Selby
- Centre for Kidney Research and Innovation, Academic Unit of Translational Medical Sciences, University of Nottingham, Nottingham, UK
- Department of Renal Medicine, Royal Derby Hospital, Derby, UK
| | - Benjamin Shickel
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, USA
| | - Karandeep Singh
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Danielle E Soranno
- Section of Nephrology, Department of Pediatrics, Indiana University, Riley Hospital for Children, Indianapolis, IN, USA
| | - Scott M Sutherland
- Division of Nephrology, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Azra Bihorac
- Intelligent Critical Care Center, University of Florida, Gainesville, FL, USA.
| | - Ravindra L Mehta
- Division of Nephrology-Hypertension, Department of Medicine, University of California San Diego, La Jolla, CA, USA.
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Fernando M, Abell B, Tyack Z, Donovan T, McPhail SM, Naicker S. Using Theories, Models, and Frameworks to Inform Implementation Cycles of Computerized Clinical Decision Support Systems in Tertiary Health Care Settings: Scoping Review. J Med Internet Res 2023; 25:e45163. [PMID: 37851492 PMCID: PMC10620641 DOI: 10.2196/45163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 08/18/2023] [Accepted: 09/14/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Computerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute care settings such as hospitals. Theories, models, and frameworks may assist in facilitating the implementation processes associated with CDSS innovation and its use within these care settings. These processes include context assessments to identify key determinants, implementation plans for adoption, promoting ongoing uptake, adherence, and long-term evaluation. However, there has been no prior review synthesizing the literature regarding the theories, models, and frameworks that have informed the implementation and adoption of CDSSs within hospitals. OBJECTIVE This scoping review aims to identify the theory, model, and framework approaches that have been used to facilitate the implementation and adoption of CDSSs in tertiary health care settings, including hospitals. The rationales reported for selecting these approaches, including the limitations and strengths, are described. METHODS A total of 5 electronic databases were searched (CINAHL via EBSCOhost, PubMed, Scopus, PsycINFO, and Embase) to identify studies that implemented or adopted a CDSS in a tertiary health care setting using an implementation theory, model, or framework. No date or language limits were applied. A narrative synthesis was conducted using full-text publications and abstracts. Implementation phases were classified according to the "Active Implementation Framework stages": exploration (feasibility and organizational readiness), installation (organizational preparation), initial implementation (initiating implementation, ie, training), full implementation (sustainment), and nontranslational effectiveness studies. RESULTS A total of 81 records (42 full text and 39 abstracts) were included. Full-text studies and abstracts are reported separately. For full-text studies, models (18/42, 43%), followed by determinants frameworks (14/42,33%), were most frequently used to guide adoption and evaluation strategies. Most studies (36/42, 86%) did not list the limitations associated with applying a specific theory, model, or framework. CONCLUSIONS Models and related quality improvement methods were most frequently used to inform CDSS adoption. Models were not typically combined with each other or with theory to inform full-cycle implementation strategies. The findings highlight a gap in the application of implementation methods including theories, models, and frameworks to facilitate full-cycle implementation strategies for hospital CDSSs.
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Affiliation(s)
- Manasha Fernando
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Bridget Abell
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Zephanie Tyack
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Thomasina Donovan
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Steven M McPhail
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
- Digital Health and Informatics Directorate, Metro South Health, Brisbane, Australia
| | - Sundresan Naicker
- Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, Australia
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Yamada J, Kouri A, Simard SN, Lam Shin Cheung J, Segovia S, Gupta S. Improving computerized decision support system interventions: a qualitative study combining the theoretical domains framework with the GUIDES Checklist. BMC Med Inform Decis Mak 2023; 23:226. [PMID: 37853386 PMCID: PMC10585867 DOI: 10.1186/s12911-023-02273-6] [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: 09/17/2022] [Accepted: 08/21/2023] [Indexed: 10/20/2023] Open
Abstract
BACKGROUND Computerized clinical decision support systems (CDSSs) can improve care by bridging knowledge to practice gaps. However, the real-world uptake of such systems in health care settings has been suboptimal. We sought to: (1) use the Theoretical Domains Framework (TDF) to identify determinants (barriers/enablers) of uptake of the Electronic Asthma Management System (eAMS) CDSS; (2) match identified TDF belief statements to elements in the Guideline Implementation with Decision Support (GUIDES) Checklist; and (3) explore the relationship between the TDF and GUIDES frameworks and the usefulness of this sequential approach for identifying opportunities to improve CDSS uptake. METHODS In Phase 1, we conducted semistructured interviews with primary care physicians in Toronto, Canada regarding the uptake of the eAMS CDSS. Using content analysis, two coders independently analyzed interview transcripts guided by the TDF to generate themes representing barriers and enablers to CDSS uptake. In Phase 2, the same reviewers independently mapped each belief statement to a GUIDES domain and factor. We calculated the proportion of TDF belief statements that linked to each GUIDES domain and the proportion of TDF domains that linked to GUIDES factors (and vice-versa) and domains. RESULTS We interviewed 10 participants before data saturation. In Phase 1, we identified 53 belief statements covering 12 TDF domains; 18 (34.0%) were barriers, and 35 (66.0%) were enablers. In Phase 2, 41 statements (77.4%) linked to at least one GUIDES factor, while 12 (22.6%) did not link to any specific factor. The GUIDES Context Domain was linked to the largest number of belief statements (19/53; 35.8%). Each TDF domain linked to one or more GUIDES factor, with 6 TDF domains linking to more than 1 factor and 8 TDF domains linking to more than 1 GUIDES domain. CONCLUSIONS The TDF provides unique insights into barriers and enablers to CDSS uptake, which can then be mapped to GUIDES domains and factors to identify required changes to CDSS context, content, and system. This can be followed by conventional mapping of TDF domains to behaviour change techniques to optimize CDSS implementation. This novel step-wise approach combines two established frameworks to optimize CDSS interventions, and requires prospective validation.
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Affiliation(s)
- Janet Yamada
- Daphne Cockwell School of Nursing, Faculty of Community Services, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
| | - Andrew Kouri
- Division of Respirology, Department of Medicine, Women's College Hospital, 76 Grenville Street, Toronto, ON, M5S 1B2, Canada
| | - Sarah Nicole Simard
- Daphne Cockwell School of Nursing, Faculty of Community Services, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada
| | - Jeffrey Lam Shin Cheung
- Keenan Research Center, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada
| | - Stephanie Segovia
- Division of Respirology, Department of Medicine, University of Toronto, Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, M5B 1W8, Toronto, ON, Canada
| | - Samir Gupta
- Division of Respirology, Department of Medicine, Women's College Hospital, 76 Grenville Street, Toronto, ON, M5S 1B2, Canada.
- Division of Respirology, Department of Medicine, University of Toronto, Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, 30 Bond Street, M5B 1W8, Toronto, ON, Canada.
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Wong A, Berenbrok LA, Snader L, Soh YH, Kumar VK, Javed MA, Bates DW, Sorce LR, Kane-Gill SL. Facilitators and Barriers to Interacting With Clinical Decision Support in the ICU: A Mixed-Methods Approach. Crit Care Explor 2023; 5:e0967. [PMID: 37644969 PMCID: PMC10461946 DOI: 10.1097/cce.0000000000000967] [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] [Indexed: 08/31/2023] Open
Abstract
OBJECTIVES Clinical decision support systems (CDSSs) are used in various aspects of healthcare to improve clinical decision-making, including in the ICU. However, there is growing evidence that CDSS are not used to their full potential, often resulting in alert fatigue which has been associated with patient harm. Clinicians in the ICU may be more vulnerable to desensitization of alerts than clinicians in less urgent parts of the hospital. We evaluated facilitators and barriers to appropriate CDSS interaction and provide methods to improve currently available CDSS in the ICU. DESIGN Sequential explanatory mixed-methods study design, using the BEhavior and Acceptance fRamework. SETTING International survey study. PATIENT/SUBJECTS Clinicians (pharmacists, physicians) identified via survey, with recent experience with clinical decision support. INTERVENTIONS An initial survey was developed to evaluate clinician perspectives on their interactions with CDSS. A subsequent in-depth interview was developed to further evaluate clinician (pharmacist, physician) beliefs and behaviors about CDSS. These interviews were then qualitatively analyzed to determine themes of facilitators and barriers with CDSS interactions. MEASUREMENTS AND MAIN RESULTS A total of 48 respondents completed the initial survey (estimated response rate 15.5%). The majority believed that responding to CDSS alerts was part of their job (75%) but felt they experienced alert fatigue (56.5%). In the qualitative analysis, a total of five facilitators (patient safety, ease of response, specificity, prioritization, and feedback) and four barriers (excess quantity, work environment, difficulty in response, and irrelevance) were identified from the in-depth interviews. CONCLUSIONS In this mixed-methods survey, we identified areas that institutions should focus on to improve appropriate clinician interactions with CDSS, specific to the ICU. Tailoring of CDSS to the ICU may lead to improvement in CDSS and subsequent improved patient safety outcomes.
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Affiliation(s)
- Adrian Wong
- Beth Israel Deaconess Medical Center, Department of Pharmacy, Boston, MA
| | | | - Lauren Snader
- University of Pittsburgh, School of Pharmacy, Pittsburgh, PA
| | - Yu Hyeon Soh
- University of Pittsburgh, School of Pharmacy, Pittsburgh, PA
| | | | | | - David W Bates
- Brigham and Women's Hospital, Division of General Internal Medicine and Primary Care, Boston, MA
- Harvard Medical School, School of Medicine, Boston, MA
| | - Lauren R Sorce
- Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL
- Northwestern University Feinberg School of Medicine, Division of Pediatric Critical Care, Chicago, IL
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Bellón JA, Rodríguez-Morejón A, Conejo-Cerón S, Campos-Paíno H, Rodríguez-Bayón A, Ballesta-Rodríguez MI, Rodríguez-Sánchez E, Mendive JM, López del Hoyo Y, Luna JD, Tamayo-Morales O, Moreno-Peral P. A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study. Front Psychiatry 2023; 14:1163800. [PMID: 37333911 PMCID: PMC10275079 DOI: 10.3389/fpsyt.2023.1163800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 05/02/2023] [Indexed: 06/20/2023] Open
Abstract
The predictD is an intervention implemented by general practitioners (GPs) to prevent depression, which reduced the incidence of depression-anxiety and was cost-effective. The e-predictD study aims to design, develop, and evaluate an evolved predictD intervention to prevent the onset of major depression in primary care based on Information and Communication Technologies, predictive risk algorithms, decision support systems (DSSs), and personalized prevention plans (PPPs). A multicenter cluster randomized trial with GPs randomly assigned to the e-predictD intervention + care-as-usual (CAU) group or the active-control + CAU group and 1-year follow-up is being conducted. The required sample size is 720 non-depressed patients (aged 18-55 years), with moderate-to-high depression risk, under the care of 72 GPs in six Spanish cities. The GPs assigned to the e-predictD-intervention group receive brief training, and those assigned to the control group do not. Recruited patients of the GPs allocated to the e-predictD group download the e-predictD app, which incorporates validated risk algorithms to predict depression, monitoring systems, and DSSs. Integrating all inputs, the DSS automatically proposes to the patients a PPP for depression based on eight intervention modules: physical exercise, social relationships, improving sleep, problem-solving, communication skills, decision-making, assertiveness, and working with thoughts. This PPP is discussed in a 15-min semi-structured GP-patient interview. Patients then choose one or more of the intervention modules proposed by the DSS to be self-implemented over the next 3 months. This process will be reformulated at 3, 6, and 9 months but without the GP-patient interview. Recruited patients of the GPs allocated to the control-group+CAU download another version of the e-predictD app, but the only intervention that they receive via the app is weekly brief psychoeducational messages (active-control group). The primary outcome is the cumulative incidence of major depression measured by the Composite International Diagnostic Interview at 6 and 12 months. Other outcomes include depressive symptoms (PHQ-9) and anxiety symptoms (GAD-7), depression risk (predictD risk algorithm), mental and physical quality of life (SF-12), and acceptability and satisfaction ('e-Health Impact' questionnaire) with the intervention. Patients are evaluated at baseline and 3, 6, 9, and 12 months. An economic evaluation will also be performed (cost-effectiveness and cost-utility analysis) from two perspectives, societal and health systems. Trial registration ClinicalTrials.gov, identifier: NCT03990792.
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Affiliation(s)
- Juan A. Bellón
- Biomedical Research Institute of Malaga (IBIMA Plataforma Bionand), Málaga, Spain
- Prevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain
- Network for Research on Chronicity, Primary Care, and Prevention and Health Promotion (RICAPPS), ISCIII, Madrid, Spain
- ‘El Palo' Health Centre, Servicio Andaluz de Salud (SAS), Málaga, Spain
- Department of Public Health and Psychiatry, University of Málaga (UMA), Málaga, Spain
| | - Alberto Rodríguez-Morejón
- Biomedical Research Institute of Malaga (IBIMA Plataforma Bionand), Málaga, Spain
- Prevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain
- Network for Research on Chronicity, Primary Care, and Prevention and Health Promotion (RICAPPS), ISCIII, Madrid, Spain
- Department of Personality, Evaluation and Psychological Treatment, University of Málaga (UMA), Málaga, Spain
| | - Sonia Conejo-Cerón
- Biomedical Research Institute of Malaga (IBIMA Plataforma Bionand), Málaga, Spain
- Prevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain
- Network for Research on Chronicity, Primary Care, and Prevention and Health Promotion (RICAPPS), ISCIII, Madrid, Spain
| | - Henar Campos-Paíno
- Biomedical Research Institute of Malaga (IBIMA Plataforma Bionand), Málaga, Spain
- Prevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain
- Network for Research on Chronicity, Primary Care, and Prevention and Health Promotion (RICAPPS), ISCIII, Madrid, Spain
| | - Antonina Rodríguez-Bayón
- Prevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain
- Network for Research on Chronicity, Primary Care, and Prevention and Health Promotion (RICAPPS), ISCIII, Madrid, Spain
- Centro de Salud San José, Distrito Sanitario Jaén Norte, Servicio Andaluz de Salud (SAS), Linares, Jaén, Spain
| | - María I. Ballesta-Rodríguez
- Prevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain
- Network for Research on Chronicity, Primary Care, and Prevention and Health Promotion (RICAPPS), ISCIII, Madrid, Spain
- Centro de Salud Federico del Castillo, Distrito Sanitario Jaén, Servicio Andaluz de Salud (SAS), Jaén, Spain
| | - Emiliano Rodríguez-Sánchez
- Prevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain
- Network for Research on Chronicity, Primary Care, and Prevention and Health Promotion (RICAPPS), ISCIII, Madrid, Spain
- Unidad de Investigación de Atención Primaria de Salamanca (APISAL), Gerencia de Atención Primaria de Salamanca, Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, Spain
- Department of Medicine, University of Salamanca (USAL), Salamanca, Spain
| | - Juan M. Mendive
- Prevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain
- Network for Research on Chronicity, Primary Care, and Prevention and Health Promotion (RICAPPS), ISCIII, Madrid, Spain
- ‘La Mina' Health Centre, Institut Català de la Salut (ICS), Barcelona, Spain
| | - Yolanda López del Hoyo
- Prevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain
- Network for Research on Chronicity, Primary Care, and Prevention and Health Promotion (RICAPPS), ISCIII, Madrid, Spain
- Instituto de Investigación Sanitaria de Aragón (IISA), Universidad de Zaragoza (UNIZAR), Zaragoza, Spain
| | - Juan D. Luna
- Prevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain
- Network for Research on Chronicity, Primary Care, and Prevention and Health Promotion (RICAPPS), ISCIII, Madrid, Spain
- Department of Statistics and Operational Research, University of Granada (UGR), Granada, Spain
| | - Olaya Tamayo-Morales
- Prevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain
- Network for Research on Chronicity, Primary Care, and Prevention and Health Promotion (RICAPPS), ISCIII, Madrid, Spain
- Unidad de Investigación de Atención Primaria de Salamanca (APISAL), Gerencia de Atención Primaria de Salamanca, Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, Spain
| | - Patricia Moreno-Peral
- Biomedical Research Institute of Malaga (IBIMA Plataforma Bionand), Málaga, Spain
- Prevention and Health Promotion Research Network (redIAPP), ISCIII, Madrid, Spain
- Network for Research on Chronicity, Primary Care, and Prevention and Health Promotion (RICAPPS), ISCIII, Madrid, Spain
- Department of Personality, Evaluation and Psychological Treatment, University of Málaga (UMA), Málaga, Spain
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Goldstein J, Weitzman D, Lemerond M, Jones A. Determinants for scalable adoption of autonomous AI in the detection of diabetic eye disease in diverse practice types: key best practices learned through collection of real-world data. Front Digit Health 2023; 5:1004130. [PMID: 37274764 PMCID: PMC10232822 DOI: 10.3389/fdgth.2023.1004130] [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: 07/26/2022] [Accepted: 04/25/2023] [Indexed: 06/06/2023] Open
Abstract
Autonomous Artificial Intelligence (AI) has the potential to reduce disparities, improve quality of care, and reduce cost by improving access to specialty diagnoses at the point-of-care. Diabetes and related complications represent a significant source of health disparities. Vision loss is a complication of diabetes, and there is extensive evidence supporting annual eye exams for prevention. Prior to the use of autonomous AI, store-and-forward imaging approaches using remote reading centers (asynchronous telemedicine) attempted to increase diabetes related eye exams with limited success. In 2018, after rigorous clinical validation, the first fully autonomous AI system [LumineticsCore™ (formerly IDx-DR), Digital Diagnostics Inc., Coralville, IA, United States] received U.S. Food and Drug Administration (FDA) De Novo authorization. The system diagnoses diabetic retinopathy (including macular edema) without specialist physician overread at the point-of-care. In addition to regulatory clearance, reimbursement, and quality measure updates, successful adoption requires local optimization of the clinical workflow. The general challenges of frontline care clinical workflow have been well documented in the literature. Because healthcare AI is so new, there remains a gap in the literature about challenges and opportunities to embed diagnostic AI into the clinical workflow. The goal of this review is to identify common workflow themes leading to successful adoption, measured as attainment number of exams per month using the autonomous AI system against targets set for each health center. We characterized the workflow in four different US health centers over a 12-month period. Health centers were geographically dispersed across the Midwest, Southwest, Northeast, and West Coast and varied distinctly in terms of size, staffing, resources, financing and demographics of patient populations. After 1 year, the aggregated number of diabetes-related exams per month increased from 89 after the first month of initial deployment to 174 across all sites. Across the diverse practice types, three primary determinants underscored sustainable adoption: (1) Inclusion of Executive and Clinical Champions; (2) Underlining Health Center Resources; and (3) Clinical workflows that contemplate patient identification (pre-visit), LumineticsCore Exam Capture and Provider Consult (patient visit), and Timely Referral Triage (post-visit). In addition to regulatory clearance, reimbursement and quality measures, our review shows that addressing the core determinants for workflow optimization is an essential part of large-scale adoption of innovation. These best practices can be generalizable to other autonomous AI systems in front-line care settings, thereby increasing patient access, improving quality of care, and addressing health disparities.
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12
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Hysong SJ, Yang C, Wong J, Knox MK, O'Mahen P, Petersen LA. Beyond Information Design: Designing Health Care Dashboards for Evidence-Driven Decision-Making. Appl Clin Inform 2023; 14:465-469. [PMID: 37015343 PMCID: PMC10266903 DOI: 10.1055/a-2068-6699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 03/30/2023] [Indexed: 04/06/2023] Open
Affiliation(s)
- Sylvia J. Hysong
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, United States
- Department of Medicine – Health Services Research Section, Baylor College of Medicine, Houston, Texas, United States
| | - Christine Yang
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, United States
| | - Janine Wong
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, United States
| | - Melissa K. Knox
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, United States
- Department of Medicine – Health Services Research Section, Baylor College of Medicine, Houston, Texas, United States
| | - Patrick O'Mahen
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, United States
- Department of Medicine – Health Services Research Section, Baylor College of Medicine, Houston, Texas, United States
| | - Laura A. Petersen
- Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, Texas, United States
- Department of Medicine – Health Services Research Section, Baylor College of Medicine, Houston, Texas, United States
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13
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Hauschildt J, Lyon-Scott K, Sheppler CR, Larson AE, McMullen C, Boston D, O'Connor PJ, Sperl-Hillen JM, Gold R. Adoption of shared decision-making and clinical decision support for reducing cardiovascular disease risk in community health centers. JAMIA Open 2023; 6:ooad012. [PMID: 36909848 PMCID: PMC10005607 DOI: 10.1093/jamiaopen/ooad012] [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: 11/01/2022] [Revised: 01/13/2023] [Accepted: 02/14/2023] [Indexed: 03/12/2023] Open
Abstract
Objective Electronic health record (EHR)-based shared decision-making (SDM) and clinical decision support (CDS) systems can improve cardiovascular disease (CVD) care quality and risk factor management. Use of the CV Wizard system showed a beneficial effect on high-risk community health center (CHC) patients' CVD risk within an effectiveness trial, but system adoption was low overall. We assessed which multi-level characteristics were associated with system use. Materials and Methods Analyses included 80 195 encounters with 17 931 patients with high CVD risk and/or uncontrolled risk factors at 42 clinics in September 2018-March 2020. Data came from the CV Wizard repository and EHR data, and a survey of 44 clinic providers. Adjusted, mixed-effects multivariate Poisson regression analyses assessed factors associated with system use. We included clinic- and provider-level clustering as random effects to account for nested data. Results Likelihood of system use was significantly higher in encounters with patients with higher CVD risk and at longer encounters, and lower when providers were >10 minutes behind schedule, among other factors. Survey participants reported generally high satisfaction with the system but were less likely to use it when there were time constraints or when rooming staff did not print the system output for the provider. Discussion CHC providers prioritize using this system for patients with the greatest CVD risk, when time permits, and when rooming staff make the information readily available. CHCs' financial constraints create substantial challenges to addressing barriers to improved system use, with health equity implications. Conclusion Research is needed on improving SDM and CDS adoption in CHCs. Trial Registration ClinicalTrials.gov, NCT03001713, https://clinicaltrials.gov/.
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Affiliation(s)
| | | | | | - Annie E Larson
- OCHIN Inc., Research Department, Portland, Oregon 97228-5426, USA
| | - Carmit McMullen
- Kaiser Permanente Center for Health Research, Portland, Oregon 97227, USA
| | - David Boston
- OCHIN Inc., Research Department, Portland, Oregon 97228-5426, USA
| | - Patrick J O'Connor
- HealthPartners Institute, HealthPartners Center for Chronic Care Innovation, Bloomington, Minnesota 55425, USA
| | - JoAnn M Sperl-Hillen
- HealthPartners Institute, HealthPartners Center for Chronic Care Innovation, Bloomington, Minnesota 55425, USA
| | - Rachel Gold
- OCHIN Inc., Research Department, Portland, Oregon 97228-5426, USA.,Kaiser Permanente Center for Health Research, Portland, Oregon 97227, USA
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14
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Wang L, Chen X, Zhang L, Li L, Huang Y, Sun Y, Yuan X. Artificial intelligence in clinical decision support systems for oncology. Int J Med Sci 2023; 20:79-86. [PMID: 36619220 PMCID: PMC9812798 DOI: 10.7150/ijms.77205] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 12/01/2022] [Indexed: 12/23/2022] Open
Abstract
Artificial intelligence (AI) has been widely used in various medical fields, such as image diagnosis, pathological classification, selection of treatment schemes, and prognosis analysis. Especially in the image-aided diagnosis of tumors, the cooperation of human-computer interactions has become mature. However, the ethics of the application of AI as an emerging technology in clinical decision-making have not been fully supported, so the clinical decision support system (CDSS) based on AI technology has not fully realized human-computer interactions in clinical practice as the image-aided diagnosis system. The CDSS was currently used and promoted worldwide including Watson for Oncology, Chinese society of clinical oncology-artificial intelligence (CSCO AI) and so on. This paper summarized the applications and clarified the principle of AI in CDSS, analyzed the difficulties of AI in oncology decisions, and provided a reference scheme for the application of AI in oncology decisions in the future.
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Affiliation(s)
- Lu Wang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Xinyi Chen
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Lu Zhang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Long Li
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - YongBiao Huang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Yinan Sun
- Department of Cardiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
| | - Xianglin Yuan
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China
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15
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Yang JY, Shu KH, Peng YS, Hsu SP, Chiu YL, Pai MF, Wu HY, Tsai WC, Tung KT, Kuo RN. Physician Compliance with Computerized Clinical Decision Support System is a Complete Intermediate Factor in the Anemia Management of Patients with End-Stage Kidney Disease on Hemodialysis: A Retrospective Electronic Health Record Observational Study (Preprint). JMIR Form Res 2022; 7:e44373. [PMID: 37133912 DOI: 10.2196/44373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 03/29/2023] [Accepted: 04/04/2023] [Indexed: 04/07/2023] Open
Abstract
BACKGROUND Previous studies on clinical decision support systems (CDSSs) for the management of renal anemia in patients with end-stage kidney disease undergoing hemodialysis have previously focused solely on the effects of the CDSS. However, the role of physician compliance in the efficacy of the CDSS remains ill-defined. OBJECTIVE We aimed to investigate whether physician compliance was an intermediate variable between the CDSS and the management outcomes of renal anemia. METHODS We extracted the electronic health records of patients with end-stage kidney disease on hemodialysis at the Far Eastern Memorial Hospital Hemodialysis Center (FEMHHC) from 2016 to 2020. FEMHHC implemented a rule-based CDSS for the management of renal anemia in 2019. We compared the clinical outcomes of renal anemia between the pre- and post-CDSS periods using random intercept models. Hemoglobin levels of 10 to 12 g/dL were defined as the on-target range. Physician compliance was defined as the concordance of adjustments of the erythropoietin-stimulating agent (ESA) between the CDSS recommendations and the actual physician prescriptions. RESULTS We included 717 eligible patients on hemodialysis (mean age 62.9, SD 11.6 years; male n=430, 59.9%) with a total of 36,091 hemoglobin measurements (average hemoglobin and on-target rate were 11.1, SD 1.4, g/dL and 59.9%, respectively). The on-target rate decreased from 61.3% (pre-CDSS) to 56.2% (post-CDSS) owing to a high hemoglobin percentage of >12 g/dL (pre: 21.5%; post: 29%). The failure rate (hemoglobin <10 g/dL) decreased from 17.2% (pre-CDSS) to 14.8% (post-CDSS). The average weekly ESA use of 5848 (SD 4211) units per week did not differ between phases. The overall concordance between CDSS recommendations and physician prescriptions was 62.3%. The CDSS concordance increased from 56.2% to 78.6%. In the adjusted random intercept model, the post-CDSS phase showed increased hemoglobin by 0.17 (95% CI 0.14-0.21) g/dL, weekly ESA by 264 (95% CI 158-371) units per week, and 3.4-fold (95% CI 3.1-3.6) increased concordance rate. However, the on-target rate (29%; odds ratio 0.71, 95% CI 0.66-0.75) and failure rate (16%; odds ratio 0.84, 95% CI 0.76-0.92) were reduced. After additional adjustments for concordance in the full models, increased hemoglobin and decreased on-target rate tended toward attenuation (from 0.17 to 0.13 g/dL and 0.71 to 0.73 g/dL, respectively). Increased ESA and decreased failure rate were completely mediated by physician compliance (from 264 to 50 units and 0.84 to 0.97, respectively). CONCLUSIONS Our results confirmed that physician compliance was a complete intermediate factor accounting for the efficacy of the CDSS. The CDSS reduced failure rates of anemia management through physician compliance. Our study highlights the importance of optimizing physician compliance in the design and implementation of CDSSs to improve patient outcomes.
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Affiliation(s)
- Ju-Yeh Yang
- Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
- Center for General Education, Lee-Ming Institute of Technology, New Taipei City, Taiwan
| | - Kai-Hsiang Shu
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Yu-Sen Peng
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Shih-Ping Hsu
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Yen-Ling Chiu
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan, Taiwan
- Graduate Program in Biomedical Informatics, Yuan Ze University, Taoyuan, Taiwan
| | - Mei-Fen Pai
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Hon-Yen Wu
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Wan-Chuan Tsai
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Kuei-Ting Tung
- Division of Nephrology, Department of Internal Medicine, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Raymond N Kuo
- Institute of Health Policy and Management, College of Public Health, National Taiwan University, Taipei, Taiwan
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Gottlieb ER, Mendu M. Clinical Decision Support to Prevent Acute Kidney Injury After Cardiac Catheterization: Moving Beyond Process to Improving Clinical Outcomes. JAMA 2022; 328:831-832. [PMID: 36066539 DOI: 10.1001/jama.2022.14070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Eric R Gottlieb
- Department of Medicine, Mount Auburn Hospital, Cambridge, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge
| | - Mallika Mendu
- Harvard Medical School, Boston, Massachusetts
- Division of Renal Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
- Office of the Chief Medical Officer, Brigham and Women's Hospital, Boston, Massachusetts
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Kukhareva PV, Weir C, Fiol GD, Aarons GA, Taft TY, Schlechter CR, Reese TJ, Curran RL, Nanjo C, Borbolla D, Staes CJ, Morgan KL, Kramer HS, Stipelman CH, Shakib JH, Flynn MC, Kawamoto K. Evaluation in Life Cycle of Information Technology (ELICIT) framework: Supporting the innovation life cycle from business case assessment to summative evaluation. J Biomed Inform 2022; 127:104014. [PMID: 35167977 PMCID: PMC8959015 DOI: 10.1016/j.jbi.2022.104014] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 11/16/2021] [Accepted: 02/02/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Our objective was to develop an evaluation framework for electronic health record (EHR)-integrated innovations to support evaluation activities at each of four information technology (IT) life cycle phases: planning, development, implementation, and operation. METHODS The evaluation framework was developed based on a review of existing evaluation frameworks from health informatics and other domains (human factors engineering, software engineering, and social sciences); expert consensus; and real-world testing in multiple EHR-integrated innovation studies. RESULTS The resulting Evaluation in Life Cycle of IT (ELICIT) framework covers four IT life cycle phases and three measure levels (society, user, and IT). The ELICIT framework recommends 12 evaluation steps: (1) business case assessment; (2) stakeholder requirements gathering; (3) technical requirements gathering; (4) technical acceptability assessment; (5) user acceptability assessment; (6) social acceptability assessment; (7) social implementation assessment; (8) initial user satisfaction assessment; (9) technical implementation assessment; (10) technical portability assessment; (11) long-term user satisfaction assessment; and (12) social outcomes assessment. DISCUSSION Effective evaluation requires a shared understanding and collaboration across disciplines throughout the entire IT life cycle. In contrast with previous evaluation frameworks, the ELICIT framework focuses on all phases of the IT life cycle across the society, user, and IT levels. Institutions seeking to establish evaluation programs for EHR-integrated innovations could use our framework to create such shared understanding and justify the need to invest in evaluation. CONCLUSION As health care undergoes a digital transformation, it will be critical for EHR-integrated innovations to be systematically evaluated. The ELICIT framework can facilitate these evaluations.
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Affiliation(s)
- Polina V. Kukhareva
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Charlene Weir
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Gregory A. Aarons
- Department of Psychiatry, UC San Diego ACTRI Dissemination and Implementation Science Center, UC San Diego, La Jolla, CA, USA
| | - Teresa Y. Taft
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Chelsey R. Schlechter
- Department of Population Health Sciences, Center for Health Outcomes and Population Equity, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Thomas J. Reese
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - Rebecca L. Curran
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Claude Nanjo
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
| | - Damian Borbolla
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
| | | | - Keaton L. Morgan
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Heidi S. Kramer
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | | | - Julie H. Shakib
- Department of Pediatrics, University of Utah, Salt Lake City, UT, USA
| | - Michael C. Flynn
- Department of Family & Preventive Medicine, University of Utah, Salt Lake City, UT, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA.
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18
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Kane-Gill SL, Barreto EF, Bihorac A, Kellum JA. Development of a Theory-Informed Behavior Change Intervention to Reduce Inappropriate Prescribing of Nephrotoxins and Renally Eliminated Drugs. Ann Pharmacother 2021; 55:1474-1485. [PMID: 33855858 DOI: 10.1177/10600280211009567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Goals of managing patients with acute kidney injury (AKI) are mitigating disease progression and ensuring safety while providing supportive care because no effective treatment exists. One strategy recommended in guidelines to meet these goals is optimizing medication management. Unfortunately, guideline implementation appears to be lacking as observed by the frequent occurrence of medication errors and adverse drug events. OBJECTIVE To address this performance gap in the care of hospitalized patients receiving nephrotoxins and renally eliminated drugs, we sought to provide a potential intervention based on theory-informed behavior change. METHODS Formative research with a qualitative analysis identifying what needs to change in patient care was completed by obtaining clinician opinion and expert opinion and reviewing the published literature. Frontline providers, including 8 physicians, 4 pharmacists, and a multiprofessional group of authors, provided insight into possible barriers to appropriate prescribing. Capability, Opportunity, Motivation and Behavior model and Theoretical Domain Framework were applied to characterize behavior change interventions and inform a potential implementation intervention for changing inappropriate prescribing behaviors. RESULTS Lack of knowledge about appropriate drug management in patients at risk for adverse outcomes was provided as a major barrier. Other reported barriers included a lack of: (1) tools to assist with drug management, (2) motivation to make changes, (3) routinization, and (4) an accountable clinician. CONCLUSIONS AND RELEVANCE Assigning a designated clinician to execute a stepwise, routine care process following the checklist provided is a recommended intervention to overcome barriers. The intended impact is behavior change that reduces inappropriate prescribing.
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Affiliation(s)
- Sandra L Kane-Gill
- School of Pharmacy, Pittsburgh, PA, USA.,University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | | | - John A Kellum
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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19
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Laka M, Milazzo A, Merlin T. Factors That Impact the Adoption of Clinical Decision Support Systems (CDSS) for Antibiotic Management. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18041901. [PMID: 33669353 PMCID: PMC7920296 DOI: 10.3390/ijerph18041901] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/08/2021] [Accepted: 02/11/2021] [Indexed: 01/22/2023]
Abstract
The study evaluated individual and setting-specific factors that moderate clinicians’ perception regarding use of clinical decision support systems (CDSS) for antibiotic management. A cross-sectional online survey examined clinicians’ perceptions about CDSS implementation for antibiotic management in Australia. Multivariable logistic regression determined the association between drivers of CDSS adoption and different moderators. Clinical experience, CDSS use and care setting were important predictors of clinicians’ perception concerning CDSS adoption. Compared to nonusers, CDSS users were less likely to lack confidence in CDSS (OR = 0.63, 95%, CI = 0.32, 0.94) and consider it a threat to professional autonomy (OR = 0.47, 95%, CI = 0.08, 0.83). Conversely, there was higher likelihood in experienced clinicians (>20 years) to distrust CDSS (OR = 1.58, 95%, CI = 1.08, 2.23) due to fear of comprising their clinical judgement (OR = 1.68, 95%, CI = 1.27, 2.85). In primary care, clinicians were more likely to perceive time constraints (OR = 1.96, 95%, CI = 1.04, 3.70) and patient preference (OR = 1.84, 95%, CI = 1.19, 2.78) as barriers to CDSS adoption for antibiotic prescribing. Our findings provide differentiated understanding of the CDSS implementation landscape by identifying different individual, organisational and system-level factors that influence system adoption. The individual and setting characteristics can help understand the variability in CDSS adoption for antibiotic management in different clinicians.
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Affiliation(s)
- Mah Laka
- School of Public Health, University of Adelaide, Adelaide 5005, Australia; (M.L.); (A.M.)
| | - Adriana Milazzo
- School of Public Health, University of Adelaide, Adelaide 5005, Australia; (M.L.); (A.M.)
| | - Tracy Merlin
- Adelaide Health Technology Assessment (AHTA), School of Public Health, University of Adelaide, Adelaide 5005, Australia
- Correspondence: ; Tel.: +61-(8)-8313-3575
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