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Lu X, Yang C, Liang L, Hu G, Zhong Z, Jiang Z. Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review. J Am Med Inform Assoc 2024:ocae243. [PMID: 39259922 DOI: 10.1093/jamia/ocae243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 08/15/2024] [Accepted: 09/02/2024] [Indexed: 09/13/2024] Open
Abstract
OBJECTIVE The objective of our research is to conduct a comprehensive review that aims to systematically map, describe, and summarize the current utilization of artificial intelligence (AI) in the recruitment and retention of participants in clinical trials. MATERIALS AND METHODS A comprehensive electronic search was conducted using the search strategy developed by the authors. The search encompassed research published in English, without any time limitations, which utilizes AI in the recruitment process of clinical trials. Data extraction was performed using a data charting table, which included publication details, study design, and specific outcomes/results. RESULTS The search yielded 5731 articles, of which 51 were included. All the studies were designed specifically for optimizing recruitment in clinical trials and were published between 2004 and 2023. Oncology was the most covered clinical area. Applying AI to recruitment in clinical trials has demonstrated several positive outcomes, such as increasing efficiency, cost savings, improving recruitment, accuracy, patient satisfaction, and creating user-friendly interfaces. It also raises various technical and ethical issues, such as limited quantity and quality of sample size, privacy, data security, transparency, discrimination, and selection bias. DISCUSSION AND CONCLUSION While AI holds promise for optimizing recruitment in clinical trials, its effectiveness requires further validation. Future research should focus on using valid and standardized outcome measures, methodologically improving the rigor of the research carried out.
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Affiliation(s)
- Xiaoran Lu
- Department of Philosophy, School of the Art, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Chen Yang
- Department of Philosophy, School of Humanities, Central South University, Changsha, Hunan 410075, P.R. China
| | - Lu Liang
- Department of Philosophy, School of Humanities, Central South University, Changsha, Hunan 410075, P.R. China
| | - Guanyu Hu
- School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shanxi 710049, P.R. China
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, United Kingdom
| | - Ziyi Zhong
- Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 3BX, United Kingdom
| | - Zihao Jiang
- School of Marxism, Shenzhen Polytechnic University, Shenzhen, Guangdong 518055, P.R. China
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Sheffer-Hilel G, Kachal J, Biderman A, Shahar DR, Amar S. The attitudes and knowledge of family physicians regarding malnutrition in the elderly: a call for action. Isr J Health Policy Res 2024; 13:42. [PMID: 39223630 PMCID: PMC11367879 DOI: 10.1186/s13584-024-00631-x] [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: 02/27/2024] [Accepted: 08/25/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Malnutrition in the elderly places a significant burden on healthcare, social, and aged-care systems, yet it often remains undiagnosed and untreated. This study aims to evaluate family physicians' knowledge and attitudes towards the diagnosis and treatment of malnutrition in the elderly. METHODS Based on a literature review, an online questionnaire was developed, comprised of seven knowledge-related items and eight attitude-related questions regarding malnutrition in elderly populations. We also assessed the feasibility of including two malnutrition screening questions in regular clinic visits for individuals aged ≥ 70 years. RESULTS Surveys were completed by 126 physicians (35% response rate), mean age 47.2 ± 12.6 years; 15.6 ± 12.5 years of practice; 67% females; and 92% board-certified family physicians. Moreover, 77.6% agreed that diagnosing malnutrition is important in patients with decreased appetite. Most respondents demonstrated knowledge of nutritional screening principles (63.5%) and recognized that even obese elderly individuals could be malnourished (83.2%). There was partial agreement (60%) that normal BMI values in the elderly differ from those in younger populations. Almost complete agreement was seen for incorporating two nutritional status questions in medical visits (91%), with physicians expressing willingness to receive training in malnutrition identification and screening tools. Despite challenges such as time constraints and limited knowledge, participants were open to conducting biannual malnutrition risk screening for elderly patients. CONCLUSION We recommend malnutrition screening in primary care followed by malnutrition diagnosis and referral of malnourished patients to the proper intervention.
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Affiliation(s)
- Galia Sheffer-Hilel
- Nutrition Sciences Department, Faculty of Sciences at, Tel-Hai Academic College, Kiryat Shmona, Israel.
| | - Josefa Kachal
- Nutrition Sciences Department, Faculty of Sciences at, Tel-Hai Academic College, Kiryat Shmona, Israel
| | - Aya Biderman
- Department of Family Medicine and Siaal Research Center for Family Medicine and Primary Care, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Clalit Health Services, Southern District, Beer-Sheva, Israel
| | - Danit Rivka Shahar
- Department of Public Health, The Daniel Abraham International Center for Health and Nutrition, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Shimon Amar
- Department of Family Medicine and Siaal Research Center for Family Medicine and Primary Care, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- Clalit Health Services, Southern District, Beer-Sheva, Israel
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Francisco KM, Burns CM. An Approach to Potentially Increasing Adoption of an Artificial Intelligence-Enabled Electronic Medical Record Encounter in Canadian Primary Care: Protocol for a User-Centered Design. JMIR Res Protoc 2024; 13:e54365. [PMID: 39024011 PMCID: PMC11294781 DOI: 10.2196/54365] [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: 11/07/2023] [Revised: 03/19/2024] [Accepted: 06/13/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND Primary care physicians are at the forefront of the clinical process that can lead to diagnosis, referral, and treatment. With electronic medical records (EMRs) being introduced and, over time, gaining acceptance by primary care users, they have now become a standard part of care. EMRs have the potential to be further optimized with the introduction of artificial intelligence (AI). There has yet to be a widespread exploration of the use of AI in primary health care and how clinicians envision AI use to encourage further uptake. OBJECTIVE The primary objective of this research is to understand if the user-centered design approach, rooted in contextual design, can lead to an increased likelihood of adoption of an AI-enabled encounter module embedded in a primary care EMR. In this study, we use human factor models and the technology acceptance model to understand the results. METHODS To accomplish this, a partnership has been established with an industry partner, TELUS Health, to use their EMR, the collaborative health record. The overall intention is to understand how to improve the user experience by using user-centered design to inform how AI should be embedded in an EMR encounter. Given this intention, a user-centered approach will be used to accomplish it. The approach of user-centered design requires qualitative interviewing to gain a clear understanding of users' approaches, intentions, and other key insights to inform the design process. A total of 5 phases have been designed for this study. RESULTS As of March 2024, a total of 14 primary care clinician participants have been recruited and interviewed. First-cycle coding of all qualitative data results is being conducted to inform redesign considerations. CONCLUSIONS Some limitations need to be acknowledged related to the approach of this study. There is a lack of market maturity of AI-enabled EMR encounters in primary care, requiring research to take place through scenario-based interviews. However, this participant group will still help inform design considerations for this tool. This study is targeted for completion in the late fall of 2024. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/54365.
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Affiliation(s)
- Krizia Mae Francisco
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Catherine M Burns
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
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Duarte M, Salamanca M, Gonzalez JM, Roman Laporte R, Gattamorta K, Lopez Martinez FE, Clochesy J, Rincon Acuna JC. Prediction of Positive Patient Health Questionnaire-2 Screening Using Area Deprivation Index in Primary Care. Clin Nurs Res 2024; 33:355-369. [PMID: 38801166 DOI: 10.1177/10547738241252887] [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] [Indexed: 05/29/2024]
Abstract
Depression is recognized as a significant public health issue in the United States. The National Survey on Drug Use and Health reports that 21.0 million adults aged 18 or older had major depressive disorder in 2020, including 14.8 million experiencing a major depressive episode with severe impairment. The aim is to predict the positivity of Patient Health Questionnaire-2 (PHQ-2) outcomes among patients in primary care settings by analyzing a range of variables, including socioeconomic status, demographic characteristics, and health behaviors, thereby identifying those at increased risk for depression. Employing a machine learning approach, the study utilizes retrospective data from electronic health records across 15 primary care clinics in South Florida to explore the relationship between social determinants of health (SDoH), including area of deprivation index (ADI) and PHQ-2 positivity. The study encompasses 15 primary care clinics located in South Florida, where a diverse patient population receives care. Analysis included 94,572 patient visits; 74,636 records were included in the study. If a zip+4 was not available or an ADI score did not exist, the visit was not included in the final analysis. Screening involved the PHQ-2, assessing depressed mood and anhedonia, with a cutoff >2 indicating positive screening. ADI was used to assess SDoH by matching patients' residential postal codes to ADI national percentiles. Demographics, sexual history, tobacco use, caffeine intake, and community involvement were also evaluated in the study. Over 40 machine learning algorithms were explored for their accuracy in predicting PHQ-2 outcomes, using software tools including Scikit-learn and stats models in Python. Variables were normalized, scored, and then subjected to predictive regression models, with Random Forest showing outstanding performance. Feature engineering and correlation analysis identified ADI, age, education, visit type, coffee intake, and marital status as significant predictors of PHQ-2 positivity. The area under the curve and model accuracies varied across clinics, with specific clinics showing higher predictive accuracy and others (p > .05). The study concludes that the ADI, as a proxy for SDoH, alongside other individual factors, can predict PHQ-2 positivity. Health organizations can use this information to anticipate health needs and resource allocation.
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Affiliation(s)
| | | | - Juan M Gonzalez
- University of Miami School of Nursing and Health Studies, Coral Gables, FL, USA
| | | | - Karina Gattamorta
- University of Miami School of Nursing and Health Studies, Coral Gables, FL, USA
| | | | - John Clochesy
- University of Miami School of Nursing and Health Studies, Coral Gables, FL, USA
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Waheed MA, Liu L. Perceptions of Family Physicians About Applying AI in Primary Health Care: Case Study From a Premier Health Care Organization. JMIR AI 2024; 3:e40781. [PMID: 38875531 PMCID: PMC11063883 DOI: 10.2196/40781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 05/25/2023] [Accepted: 03/07/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND The COVID-19 pandemic has led to the rapid proliferation of artificial intelligence (AI), which was not previously anticipated; this is an unforeseen development. The use of AI in health care settings is increasing, as it proves to be a promising tool for transforming health care systems, improving operational and business processes, and efficiently simplifying health care tasks for family physicians and health care administrators. Therefore, it is necessary to assess the perspective of family physicians on AI and its impact on their job roles. OBJECTIVE This study aims to determine the impact of AI on the management and practices of Qatar's Primary Health Care Corporation (PHCC) in improving health care tasks and service delivery. Furthermore, it seeks to evaluate the impact of AI on family physicians' job roles, including associated risks and ethical ramifications from their perspective. METHODS We conducted a cross-sectional survey and sent a web-based questionnaire survey link to 724 practicing family physicians at the PHCC. In total, we received 102 eligible responses. RESULTS Of the 102 respondents, 72 (70.6%) were men and 94 (92.2%) were aged between 35 and 54 years. In addition, 58 (56.9%) of the 102 respondents were consultants. The overall awareness of AI was 80 (78.4%) out of 102, with no difference between gender (P=.06) and age groups (P=.12). AI is perceived to play a positive role in improving health care practices at PHCC (P<.001), managing health care tasks (P<.001), and positively impacting health care service delivery (P<.001). Family physicians also perceived that their clinical, administrative, and opportunistic health care management roles were positively influenced by AI (P<.001). Furthermore, perceptions of family physicians indicate that AI improves operational and human resource management (P<.001), does not undermine patient-physician relationships (P<.001), and is not considered superior to human physicians in the clinical judgment process (P<.001). However, its inclusion is believed to decrease patient satisfaction (P<.001). AI decision-making and accountability were recognized as ethical risks, along with data protection and confidentiality. The optimism regarding using AI for future medical decisions was low among family physicians. CONCLUSIONS This study indicated a positive perception among family physicians regarding AI integration into primary care settings. AI demonstrates significant potential for enhancing health care task management and overall service delivery at the PHCC. It augments family physicians' roles without replacing them and proves beneficial for operational efficiency, human resource management, and public health during pandemics. While the implementation of AI is anticipated to bring benefits, the careful consideration of ethical, privacy, confidentiality, and patient-centric concerns is essential. These insights provide valuable guidance for the strategic integration of AI into health care systems, with a focus on maintaining high-quality patient care and addressing the multifaceted challenges that arise during this transformative process.
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Affiliation(s)
| | - Lu Liu
- Bath Business School, Bath Spa University, Bath, United Kingdom
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Hampton J, Mugambi P, Caggiano E, Eugene R, Valente A, Taylor M, Carreiro S. Closing the Digital Divide in Interventions for Substance Use Disorder. JOURNAL OF PSYCHIATRY AND BRAIN SCIENCE 2024; 9:e240002. [PMID: 38726224 PMCID: PMC11081399 DOI: 10.20900/jpbs.20240002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2024]
Abstract
Digital health interventions are exploding in today's medical practice and have tremendous potential to support the treatment of substance use disorders (SUD). Developers and healthcare providers alike must be cognizant of the potential for digital interventions to exacerbate existing inequities in SUD treatment, particularly as they relate to Social Determinants of Health (SDoH). To explore this evolving area of study, this manuscript will review the existing concepts of the digital divide and digital inequities, and the role SDoH play as drivers of digital inequities. We will then explore how the data used and modeling strategies can create bias in digital health tools for SUD. Finally, we will discuss potential solutions and future directions to bridge these gaps including smartphone ownership, Wi-Fi access, digital literacy, and mitigation of historical, algorithmic, and measurement bias. Thoughtful design of digital interventions is quintessential to reduce the risk of bias, decrease the digital divide, and create equitable health outcomes for individuals with SUD.
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Affiliation(s)
- Jazmin Hampton
- Division of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
- Washington University of Health and Science, San Pedro, Belize, Central America
- Division of Public Health, Walden University, Minneapolis, MN 55401, USA
| | - Purity Mugambi
- Manning College of Information and Computer Sciences, University of Massachusetts-Amherst, Amherst, MA 01003, USA
| | - Emily Caggiano
- Division of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | - Reynalde Eugene
- Division of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | - Alycia Valente
- Division of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | - Melissa Taylor
- Division of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
| | - Stephanie Carreiro
- Division of Toxicology, Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA 01655, USA
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Kueper JK, Emu M, Banbury M, Bjerre LM, Choudhury S, Green M, Pimlott N, Slade S, Tsuei SH, Sisler J. Artificial intelligence for family medicine research in Canada: current state and future directions: Report of the CFPC AI Working Group. CANADIAN FAMILY PHYSICIAN MEDECIN DE FAMILLE CANADIEN 2024; 70:161-168. [PMID: 38499374 PMCID: PMC11280631 DOI: 10.46747/cfp.7003161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
OBJECTIVE To understand the current landscape of artificial intelligence (AI) for family medicine (FM) research in Canada, identify how the College of Family Physicians of Canada (CFPC) could support near-term positive progress in this field, and strengthen the community working in this field. COMPOSITION OF THE COMMITTEE Members of a scientific planning committee provided guidance alongside members of a CFPC staff advisory committee, led by the CFPC-AMS TechForward Fellow and including CFPC, FM, and AI leaders. METHODS This initiative included 2 projects. First, an environmental scan of published and gray literature on AI for FM produced between 2018 and 2022 was completed. Second, an invitational round table held in April 2022 brought together AI and FM experts and leaders to discuss priorities and to create a strategy for the future. REPORT The environmental scan identified research related to 5 major domains of application in FM (preventive care and risk profiling, physician decision support, operational efficiencies, patient self-management, and population health). Although there had been little testing or evaluation of AI-based tools in practice settings, progress since previous reviews has been made in engaging stakeholders to identify key considerations about AI for FM and opportunities in the field. The round-table discussions further emphasized barriers to and facilitators of high-quality research; they also indicated that while there is immense potential for AI to benefit FM practice, the current research trajectory needs to change, and greater support is needed to achieve these expected benefits and to avoid harm. CONCLUSION Ten candidate action items that the CFPC could adopt to support near-term positive progress in the field were identified, some of which an AI working group has begun pursuing. Candidate action items are roughly divided into avenues where the CFPC is well-suited to take a leadership role in tackling priority issues in AI for FM research and specific activities or initiatives the CFPC could complete. Strong FM leadership is needed to advance AI research that will contribute to positive transformation in FM.
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Affiliation(s)
- Jacqueline K Kueper
- Adjunct Research Professor in the Department of Epidemiology and Biostatistics in the Schulich School of Medicine and Dentistry at Western University in London, Ont
| | - Mahzabeen Emu
- Doctoral candidate in the School of Computing at Queen's University in Kingston, Ont
| | - Mark Banbury
- Executive Director, Information and Technology Services, at the College of Family Physicians of Canada
| | - Lise M Bjerre
- Associate Professor at the University of Ottawa in Ontario and Chair in Family Medicine at the Institut du Savoir Montfort in Ottawa
| | - Salimur Choudhury
- Associate Professor in the School of Computing at Queen's University
| | - Michael Green
- Professor in the Department of Family Medicine at Queen's University and President of the College of Family Physicians of Canada
| | - Nicholas Pimlott
- Professor in the Department of Family and Community Medicine in the Temerty Faculty of Medicine at the University of Toronto in Ontario and Editor of Canadian Family Physician
| | - Steve Slade
- Research Director at the College of Family Physicians of Canada
| | - Sian H Tsuei
- Clinical Assistant Professor in the Department of Family Practice at the University of British Columbia in Vancouver and Adjunct Professor in the Faculty of Health Sciences at Simon Fraser University in Victoria, BC
| | - Jeff Sisler
- Former Executive Director of Professional Development and Practice Support at the College of Family Physicians of Canada
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O'Connor S, Vercell A, Wong D, Yorke J, Fallatah FA, Cave L, Anny Chen LY. The application and use of artificial intelligence in cancer nursing: A systematic review. Eur J Oncol Nurs 2024; 68:102510. [PMID: 38310664 DOI: 10.1016/j.ejon.2024.102510] [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: 10/05/2023] [Revised: 01/07/2024] [Accepted: 01/10/2024] [Indexed: 02/06/2024]
Abstract
PURPOSE Artificial Intelligence is being applied in oncology to improve patient and service outcomes. Yet, there is a limited understanding of how these advanced computational techniques are employed in cancer nursing to inform clinical practice. This review aimed to identify and synthesise evidence on artificial intelligence in cancer nursing. METHODS CINAHL, MEDLINE, PsycINFO, and PubMed were searched using key terms between January 2010 and December 2022. Titles, abstracts, and then full texts were screened against eligibility criteria, resulting in twenty studies being included. Critical appraisal was undertaken, and relevant data extracted and analysed. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. RESULTS Artificial intelligence was used in numerous areas including breast, colorectal, liver, and ovarian cancer care among others. Algorithms were trained and tested on primary and secondary datasets to build predictive models of health problems related to cancer. Studies reported this led to improvements in the accuracy of predicting health outcomes or identifying variables that improved outcome prediction. While nurses led most studies, few deployed an artificial intelligence based digital tool with cancer nurses in a real-world setting as studies largely focused on developing and validating predictive models. CONCLUSION Electronic cancer nursing datasets should be established to enable artificial intelligence techniques to be tested and if effective implemented in digital prediction and other AI-based tools. Cancer nurses need more education on machine learning and natural language processing, so they can lead and contribute to artificial intelligence developments in oncology.
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Affiliation(s)
- Siobhan O'Connor
- Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College London, London, United Kingdom.
| | - Amy Vercell
- Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College London, London, United Kingdom; The Christie NHS Foundation Trust, Wilmslow Rd, Manchester, M20 4BX, United Kingdom.
| | - David Wong
- Leeds Institute for Health Informatics, University of Leeds, Leeds, United Kingdom.
| | - Janelle Yorke
- Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care, King's College London, London, United Kingdom; The Christie NHS Foundation Trust, Wilmslow Rd, Manchester, M20 4BX, United Kingdom.
| | - Fatmah Abdulsamad Fallatah
- Department of Nursing Affairs, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia.
| | - Louise Cave
- NHS Transformation Directorate, NHS England, England, United Kingdom.
| | - Lu-Yen Anny Chen
- Institute of Clinical Nursing, College of Nursing, National Yang Ming Chiao Tung University, Taipei, Taiwan.
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Vidal-Alaball J, Panadés Zafra R, Escalé-Besa A, Martinez-Millana A. The artificial intelligence revolution in primary care: Challenges, dilemmas and opportunities. Aten Primaria 2024; 56:102820. [PMID: 38056048 PMCID: PMC10714322 DOI: 10.1016/j.aprim.2023.102820] [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: 11/06/2023] [Accepted: 11/08/2023] [Indexed: 12/08/2023] Open
Abstract
Artificial intelligence (AI) can be a valuable tool for primary care (PC), as, among other things, it can help healthcare professionals improve diagnostic accuracy, chronic disease management and the overall efficiency of the care they provide. It is important to emphasise that AI should not be seen as a replacement tool, but as an aid to PC professionals. Although AI is capable of processing large amounts of data and generating accurate predictions, it cannot replace the skill and expertise of professionals in clinical decision making. AI still requires the interpretation and clinical judgement of a trained healthcare professional and cannot provide the empathy and emotional support often required in healthcare.
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Affiliation(s)
- Josep Vidal-Alaball
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Barcelona, Spain; Grup de Recerca Promoció de la Salut en l'Àmbit Rural, Gerència d'Atenció Primària i a la Comunitat de Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Barcelona, Spain; Facultat de Medicina, Universitat de Vic-Universitat Central de Catalunya, Vic, Barcelona, Spain; Grup de Salut Digital CAMFIC, Barcelona, Spain
| | - Robert Panadés Zafra
- Grup de Recerca Promoció de la Salut en l'Àmbit Rural, Gerència d'Atenció Primària i a la Comunitat de Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Barcelona, Spain; Grup de Salut Digital CAMFIC, Barcelona, Spain; Equip d'Atenció Primària d'Anoia Rural, Gerència d'Atenció Primària i a la Comunitat de Catalunya Central, Institut Català de la Salut, Jorba i Copons, Barcelona, Spain
| | - Anna Escalé-Besa
- Grup de Recerca Promoció de la Salut en l'Àmbit Rural, Gerència d'Atenció Primària i a la Comunitat de Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Barcelona, Spain; Grup de Salut Digital CAMFIC, Barcelona, Spain; Equip d'Atenció Primària Navàs-Balsareny, Gerència d'Atenció Primària i a la Comunitat de Catalunya Central, Institut Català de la Salut, Navàs, Barcelona, Spain.
| | - Antonio Martinez-Millana
- Grup de Salut Digital CAMFIC, Barcelona, Spain; Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
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Allen MR, Webb S, Mandvi A, Frieden M, Tai-Seale M, Kallenberg G. Navigating the doctor-patient-AI relationship - a mixed-methods study of physician attitudes toward artificial intelligence in primary care. BMC PRIMARY CARE 2024; 25:42. [PMID: 38281026 PMCID: PMC10821550 DOI: 10.1186/s12875-024-02282-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Accepted: 01/19/2024] [Indexed: 01/29/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is a rapidly advancing field that is beginning to enter the practice of medicine. Primary care is a cornerstone of medicine and deals with challenges such as physician shortage and burnout which impact patient care. AI and its application via digital health is increasingly presented as a possible solution. However, there is a scarcity of research focusing on primary care physician (PCP) attitudes toward AI. This study examines PCP views on AI in primary care. We explore its potential impact on topics pertinent to primary care such as the doctor-patient relationship and clinical workflow. By doing so, we aim to inform primary care stakeholders to encourage successful, equitable uptake of future AI tools. Our study is the first to our knowledge to explore PCP attitudes using specific primary care AI use cases rather than discussing AI in medicine in general terms. METHODS From June to August 2023, we conducted a survey among 47 primary care physicians affiliated with a large academic health system in Southern California. The survey quantified attitudes toward AI in general as well as concerning two specific AI use cases. Additionally, we conducted interviews with 15 survey respondents. RESULTS Our findings suggest that PCPs have largely positive views of AI. However, attitudes often hinged on the context of adoption. While some concerns reported by PCPs regarding AI in primary care focused on technology (accuracy, safety, bias), many focused on people-and-process factors (workflow, equity, reimbursement, doctor-patient relationship). CONCLUSION Our study offers nuanced insights into PCP attitudes towards AI in primary care and highlights the need for primary care stakeholder alignment on key issues raised by PCPs. AI initiatives that fail to address both the technological and people-and-process concerns raised by PCPs may struggle to make an impact.
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Affiliation(s)
- Matthew R Allen
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA.
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA, 92093, USA.
| | - Sophie Webb
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ammar Mandvi
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Marshall Frieden
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Ming Tai-Seale
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Gene Kallenberg
- Department of Family Medicine, University of California San Diego, La Jolla, CA, 92093, USA
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Cummerow J, Wienecke C, Engler N, Marahrens P, Gruening P, Steinhäuser J. Identifying Existing Evidence to Potentially Develop a Machine Learning Diagnostic Algorithm for Cough in Primary Care Settings: Scoping Review. J Med Internet Res 2023; 25:e46929. [PMID: 38096024 PMCID: PMC10755665 DOI: 10.2196/46929] [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: 03/06/2023] [Revised: 07/19/2023] [Accepted: 10/27/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Primary care is known to be one of the most complex health care settings because of the high number of theoretically possible diagnoses. Therefore, the process of clinical decision-making in primary care includes complex analytical and nonanalytical factors such as gut feelings and dealing with uncertainties. Artificial intelligence is also mandated to offer support in finding valid diagnoses. Nevertheless, to translate some aspects of what occurs during a consultation into a machine-based diagnostic algorithm, the probabilities for the underlying diagnoses (odds ratios) need to be determined. OBJECTIVE Cough is one of the most common reasons for a consultation in general practice, the core discipline in primary care. The aim of this scoping review was to identify the available data on cough as a predictor of various diagnoses encountered in general practice. In the context of an ongoing project, we reflect on this database as a possible basis for a machine-based diagnostic algorithm. Furthermore, we discuss the applicability of such an algorithm against the background of the specifics of general practice. METHODS The PubMed, Scopus, Web of Science, and Cochrane Library databases were searched with defined search terms, supplemented by the search for gray literature via the German Journal of Family Medicine until April 20, 2023. The inclusion criterion was the explicit analysis of cough as a predictor of any conceivable disease. Exclusion criteria were articles that did not provide original study results, articles in languages other than English or German, and articles that did not mention cough as a diagnostic predictor. RESULTS In total, 1458 records were identified for screening, of which 35 articles met our inclusion criteria. Most of the results (11/35, 31%) were found for chronic obstructive pulmonary disease. The others were distributed among the diagnoses of asthma or unspecified obstructive airway disease, various infectious diseases, bronchogenic carcinoma, dyspepsia or gastroesophageal reflux disease, and adverse effects of angiotensin-converting enzyme inhibitors. Positive odds ratios were found for cough as a predictor of chronic obstructive pulmonary disease, influenza, COVID-19 infections, and bronchial carcinoma, whereas the results for cough as a predictor of asthma and other nonspecified obstructive airway diseases were inconsistent. CONCLUSIONS Reliable data on cough as a predictor of various diagnoses encountered in general practice are scarce. The example of cough does not provide a sufficient database to contribute odds to a machine learning-based diagnostic algorithm in a meaningful way.
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Affiliation(s)
- Julia Cummerow
- Institute of Family Medicine, University Medical Centre Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Christin Wienecke
- Institute of Family Medicine, University Medical Centre Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Nicola Engler
- Institute of Family Medicine, University Medical Centre Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Philip Marahrens
- Institute of Family Medicine, University Medical Centre Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Philipp Gruening
- Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany
| | - Jost Steinhäuser
- Institute of Family Medicine, University Medical Centre Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
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12
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Abdulazeem H, Whitelaw S, Schauberger G, Klug SJ. A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data. PLoS One 2023; 18:e0274276. [PMID: 37682909 PMCID: PMC10491005 DOI: 10.1371/journal.pone.0274276] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 08/29/2023] [Indexed: 09/10/2023] Open
Abstract
With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.
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Affiliation(s)
- Hebatullah Abdulazeem
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
| | - Sera Whitelaw
- Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada
| | - Gunther Schauberger
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
| | - Stefanie J. Klug
- Chair of Epidemiology, Department of Sport and Health Sciences, Technical University of Munich (TUM), Munich, Germany
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13
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Robinson R, Liday C, Lee S, Williams IC, Wright M, An S, Nguyen E. Artificial Intelligence in Health Care-Understanding Patient Information Needs and Designing Comprehensible Transparency: Qualitative Study. JMIR AI 2023; 2:e46487. [PMID: 38333424 PMCID: PMC10851077 DOI: 10.2196/46487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/10/2023] [Accepted: 05/14/2023] [Indexed: 02/10/2024]
Abstract
Background Artificial intelligence (AI) is as a branch of computer science that uses advanced computational methods such as machine learning (ML), to calculate and/or predict health outcomes and address patient and provider health needs. While these technologies show great promise for improving healthcare, especially in diabetes management, there are usability and safety concerns for both patients and providers about the use of AI/ML in healthcare management. Objectives To support and ensure safe use of AI/ML technologies in healthcare, the team worked to better understand: 1) patient information and training needs, 2) the factors that influence patients' perceived value and trust in AI/ML healthcare applications; and 3) on how best to support safe and appropriate use of AI/ML enabled devices and applications among people living with diabetes. Methods To understand general patient perspectives and information needs related to the use of AI/ML in healthcare, we conducted a series of focus groups (n=9) and interviews (n=3) with patients (n=40) and interviews with providers (n=6) in Alaska, Idaho, and Virginia. Grounded Theory guided data gathering, synthesis, and analysis. Thematic content and constant comparison analysis were used to identify relevant themes and sub-themes. Inductive approaches were used to link data to key concepts including preferred patient-provider-interactions, patient perceptions of trust, accuracy, value, assurances, and information transparency. Results Key summary themes and recommendations focused on: 1) patient preferences for AI/ML enabled device and/or application information; 2) patient and provider AI/ML-related device and/or application training needs; 3) factors contributing to patient and provider trust in AI/ML enabled devices and/or application; and 4) AI/ML-related device and/or application functionality and safety considerations. A number of participant (patients and providers) recommendations to improve device functionality to guide information and labeling mandates (e.g., links to online video resources, and access to 24/7 live in-person or virtual emergency support). Other patient recommendations include: 1) access to practice devices; 2) connection to local supports and reputable community resources; 3) simplified display and alert limits. Conclusion Recommendations from both patients and providers could be used by Federal Oversight Agencies to improve utilization of AI/ML monitoring of technology use in diabetes, improving device safety and efficacy.
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Affiliation(s)
- Renee Robinson
- College of Pharmacy, Idaho State University, Anchorage, AK, US
| | - Cara Liday
- College of Pharmacy, Idaho State University, Pocatello, ID, US
| | - Sarah Lee
- College of Pharmacy, Idaho State University, Meridian, ID, US
| | - Ishan C Williams
- School of Nursing, University of Virginia, Charlottesville, VA, US
| | - Melanie Wright
- College of Pharmacy, Idaho State University, Meridian, ID, US
| | - Sungjoon An
- College of Pharmacy, Idaho State University, Meridian, ID, US
| | - Elaine Nguyen
- College of Pharmacy, Idaho State University, Meridian, ID, US
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14
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Black JE, Kueper JK, Williamson TS. An introduction to machine learning for classification and prediction. Fam Pract 2023; 40:200-204. [PMID: 36181463 DOI: 10.1093/fampra/cmac104] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Classification and prediction tasks are common in health research. With the increasing availability of vast health data repositories (e.g. electronic medical record databases) and advances in computing power, traditional statistical approaches are being augmented or replaced with machine learning (ML) approaches to classify and predict health outcomes. ML describes the automated process of identifying ("learning") patterns in data to perform tasks. Developing an ML model includes selecting between many ML models (e.g. decision trees, support vector machines, neural networks); model specifications such as hyperparameter tuning; and evaluation of model performance. This process is conducted repeatedly to find the model and corresponding specifications that optimize some measure of model performance. ML models can make more accurate classifications and predictions than their statistical counterparts and confer greater flexibility when modelling unstructured data or interactions between covariates; however, many ML models require larger sample sizes to achieve good classification or predictive performance and have been criticized as "black box" for their poor transparency and interpretability. ML holds potential in family medicine for risk profiling of patients' disease risk and clinical decision support to present additional information at times of uncertainty or high demand. In the future, ML approaches are positioned to become commonplace in family medicine. As such, it is important to understand the objectives that can be addressed using ML approaches and the associated techniques and limitations. This article provides a brief introduction into the use of ML approaches for classification and prediction tasks in family medicine.
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Affiliation(s)
- Jason E Black
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Jacqueline K Kueper
- Department of Epidemiology and Biostatistics, Western University Schulich School of Medicine & Dentistry, London, ON, Canada.,Department of Computer Science, Western University Faculty of Science, London, ON, Canada
| | - Tyler S Williamson
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,O'Brien Institute for Public Health, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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15
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Machine learning in general practice: scoping review of administrative task support and automation. BMC PRIMARY CARE 2023; 24:14. [PMID: 36641467 PMCID: PMC9840326 DOI: 10.1186/s12875-023-01969-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 01/04/2023] [Indexed: 01/15/2023]
Abstract
BACKGROUND Artificial intelligence (AI) is increasingly used to support general practice in the early detection of disease and treatment recommendations. However, AI systems aimed at alleviating time-consuming administrative tasks currently appear limited. This scoping review thus aims to summarize the research that has been carried out in methods of machine learning applied to the support and automation of administrative tasks in general practice. METHODS Databases covering the fields of health care and engineering sciences (PubMed, Embase, CINAHL with full text, Cochrane Library, Scopus, and IEEE Xplore) were searched. Screening for eligible studies was completed using Covidence, and data was extracted along nine research-based attributes concerning general practice, administrative tasks, and machine learning. The search and screening processes were completed during the period of April to June 2022. RESULTS 1439 records were identified and 1158 were screened for eligibility criteria. A total of 12 studies were included. The extracted attributes indicate that most studies concern various scheduling tasks using supervised machine learning methods with relatively low general practitioner (GP) involvement. Importantly, four studies employed the latest available machine learning methods and the data used frequently varied in terms of setting, type, and availability. CONCLUSION The limited field of research developing in the application of machine learning to administrative tasks in general practice indicates that there is a great need and high potential for such methods. However, there is currently a lack of research likely due to the unavailability of open-source data and a prioritization of diagnostic-based tasks. Future research would benefit from open-source data, cutting-edge methods of machine learning, and clearly stated GP involvement, so that improved and replicable scientific research can be done.
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16
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Chalutz Ben-Gal H. Artificial intelligence (AI) acceptance in primary care during the coronavirus pandemic: What is the role of patients' gender, age and health awareness? A two-phase pilot study. Front Public Health 2023; 10:931225. [PMID: 36699881 PMCID: PMC9868720 DOI: 10.3389/fpubh.2022.931225] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 12/15/2022] [Indexed: 01/11/2023] Open
Abstract
Background Artificial intelligence (AI) is steadily entering and transforming the health care and Primary Care (PC) domains. AI-based applications assist physicians in disease detection, medical advice, triage, clinical decision-making, diagnostics and digital public health. Recent literature has explored physicians' perspectives on the potential impact of digital public health on key tasks in PC. However, limited attention has been given to patients' perspectives of AI acceptance in PC, specifically during the coronavirus pandemic. Addressing this research gap, we administered a pilot study to investigate criteria for patients' readiness to use AI-based PC applications by analyzing key factors affecting the adoption of digital public health technology. Methods The pilot study utilized a two-phase mixed methods approach. First, we conducted a qualitative study with 18 semi-structured interviews. Second, based on the Technology Readiness and Acceptance Model (TRAM), we conducted an online survey (n = 447). Results The results indicate that respondents who scored high on innovativeness had a higher level of readiness to use AI-based technology in PC during the coronavirus pandemic. Surprisingly, patients' health awareness and sociodemographic factors, such as age, gender and education, were not significant predictors of AI-based technology acceptance in PC. Conclusions This paper makes two major contributions. First, we highlight key social and behavioral determinants of acceptance of AI-enabled health care and PC applications. Second, we propose that to increase the usability of digital public health tools and accelerate patients' AI adoption, in complex digital public health care ecosystems, we call for implementing adaptive, population-specific promotions of AI technologies and applications.
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17
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Hwang GJ, Chang PY, Tseng WY, Chou CA, Wu CH, Tu YF. Research Trends in Artificial Intelligence-Associated Nursing Activities Based on a Review of Academic Studies Published From 2001 to 2020. Comput Inform Nurs 2022; 40:814-824. [PMID: 36516032 DOI: 10.1097/cin.0000000000000897] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The present study referred to the technology-based learning model to conduct a systematic review of the dimensions of nursing activities, research samples, research methods, roles of artificial intelligence, applied artificial intelligence algorithms, evaluation measure of algorithms, and research foci. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedure, this study obtained and analyzed a total of 102 high-quality artificial intelligence-associated nursing activities studies published from 2001 to 2020 in the Web of Science database. The results showed: (1) In terms of nursing activities, nursing management was explored the most, followed by nursing assessment; (2) quantitative methods were most frequently adopted in artificial intelligence-associated nursing activities studies to investigate issues related to patients, followed by nursing staff; (3) the most adopted roles of artificial intelligence in artificial intelligence-associated nursing activities studies were profiling and prediction, followed by assessment and evaluation; (4) artificial intelligence-associated nursing activities studies frequently mixed applied artificial intelligence algorithms and evaluation measure of algorithms; (5) in the dimension of research foci, most studies mainly paid attention to the design or evaluation of the artificial intelligence systems/instruments, followed by investigating the correlation and affect issues. Based on the findings, several recommendations are raised as a reference for future researchers, educators, and policy makers.
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Affiliation(s)
- Gwo-Jen Hwang
- Author Affiliations : Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology (Dr Hwang, Ms Chang, Ms Tseng, Mr Chou, and Ms Wu); and Department of Library and Information Science, Bachelor's Program in Information Innovation and Digital life, Research and Development Center for Physical Education, Health, and Information Technology, Fu Jen Catholic University (Dr Tu), Taiwan
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18
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Ng ZQP, Ling LYJ, Chew HSJ, Lau Y. The role of artificial intelligence in enhancing clinical nursing care: A scoping review. J Nurs Manag 2022; 30:3654-3674. [PMID: 34272911 DOI: 10.1111/jonm.13425] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 06/17/2021] [Accepted: 07/15/2021] [Indexed: 12/30/2022]
Abstract
AIM To present an overview of how artificial intelligence has been used to improve clinical nursing care. BACKGROUND Artificial intelligence has been reshaping the healthcare industry but little is known about its applicability in enhancing nursing care. EVALUATION A scoping review was conducted. Seven electronic databases (CINAHL, Cochrane Library, EMBASE, IEEE Xplore, PubMed, Scopus, and Web of Science) were searched from 1 January 2010 till 20 December 2020. Grey literature and reference lists of included articles were also searched. KEY ISSUES Thirty-seven studies encapsulating the use of artificial intelligence in improving clinical nursing care were included in this review. Six use cases were identified - documentation, formulating nursing diagnoses, formulating nursing care plans, patient monitoring, patient care prediction such as falls prediction (most common) and wound management. Various techniques of machine learning and classification were used for predictive analyses and to improve nurses' preparedness and management of patients' conditions CONCLUSION: This review highlighted the potential of artificial intelligence in improving the quality of nursing care. However, more randomized controlled trials in real-life healthcare settings should be conducted to enhance the rigor of evidence. IMPLICATIONS FOR NURSING MANAGEMENT Education in the application of artificial intelligence should be promoted to empower nurses to lead technological transformations and not passively trail behind others.
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Affiliation(s)
- Zi Qi Pamela Ng
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Li Ying Janice Ling
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Han Shi Jocelyn Chew
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ying Lau
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
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19
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Rushlow DR, Croghan IT, Inselman JW, Thacher TD, Friedman PA, Yao X, Pellikka PA, Lopez-Jimenez F, Bernard ME, Barry BA, Attia IZ, Misra A, Foss RM, Molling PE, Rosas SL, Noseworthy PA. Clinician Adoption of an Artificial Intelligence Algorithm to Detect Left Ventricular Systolic Dysfunction in Primary Care. Mayo Clin Proc 2022; 97:2076-2085. [PMID: 36333015 DOI: 10.1016/j.mayocp.2022.04.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 03/09/2022] [Accepted: 04/04/2022] [Indexed: 03/19/2023]
Abstract
OBJECTIVE To compare the clinicians' characteristics of "high adopters" and "low adopters" of an artificial intelligence (AI)-enabled electrocardiogram (ECG) algorithm that alerted for possible low left ventricular ejection fraction (EF) and the subsequent effectiveness of detecting patients with low EF. METHODS Clinicians in 48 practice sites of a US Midwest health system were cluster-randomized by the care team to usual care or to receive a notification that suggested ordering an echocardiogram in patients flagged as potentially having low EF based on an AI-ECG algorithm. Enrollment was between June 26, 2019, and July 30, 2019; participation concluded on March 31, 2020. This report is focused on those clinicians randomized to receive the notification of the AI-ECG algorithm. At the patient level, data were analyzed for the proportion of patients with positive AI-ECG results. Adoption was defined as the clinician order of an echocardiogram after prompted by the alert. RESULTS A total of 165 clinicians and 11,573 patients were included in this analysis. Among patients with positive AI-ECG, high adopters (n=41) were twice as likely to diagnose patients with low EF (33.9%) vs low adopters, n=124, (16.9%); odds ratio, 1.62; 95% CI, 1.21 to 2.17). High adopters were more often advanced practice providers (eg, nurse practitioners and physician assistants) vs physicians, Family Medicine vs Internal Medicine specialty, and tended to have less complex patients. CONCLUSION Clinicians who most frequently followed the recommendations of an AI tool were twice as likely to diagnose low EF. Those clinicians with less complex patients were more likely to be high adopters. TRIAL REGISTRATION Clinicaltrials.gov Identifier: NCT04000087.
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Affiliation(s)
- David R Rushlow
- Department of Family Medicine, Mayo Clinic, Rochester, MN, USA.
| | - Ivana T Croghan
- Department of Medicine, Division of General Internal Medicine, Mayo Clinic, Rochester, MN, USA; Department of Health Sciences Research, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA; Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Jonathan W Inselman
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Tom D Thacher
- Department of Family Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Xiaoxi Yao
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Barbara A Barry
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Itzhak Z Attia
- Department of Cardiology, Mayo Clinic, Rochester, MN, USA
| | - Artika Misra
- Department of Family Medicine, Mayo Clinic Health System, Mankato, MN, USA
| | - Randy M Foss
- Department of Family Medicine, Mayo Clinic Health System, Lake City, MN, USA
| | - Paul E Molling
- Department of Family Medicine, Mayo Clinic Health System, Onalaska, WI, USA
| | - Steven L Rosas
- Department of Family Medicine, Mayo Clinic Health System, Menomonie, WI, USA
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20
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Terry AL, Kueper JK, Beleno R, Brown JB, Cejic S, Dang J, Leger D, McKay S, Meredith L, Pinto AD, Ryan BL, Stewart M, Zwarenstein M, Lizotte DJ. Is primary health care ready for artificial intelligence? What do primary health care stakeholders say? BMC Med Inform Decis Mak 2022; 22:237. [PMID: 36085203 PMCID: PMC9461192 DOI: 10.1186/s12911-022-01984-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 09/02/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Effective deployment of AI tools in primary health care requires the engagement of practitioners in the development and testing of these tools, and a match between the resulting AI tools and clinical/system needs in primary health care. To set the stage for these developments, we must gain a more in-depth understanding of the views of practitioners and decision-makers about the use of AI in primary health care. The objective of this study was to identify key issues regarding the use of AI tools in primary health care by exploring the views of primary health care and digital health stakeholders.
Methods
This study utilized a descriptive qualitative approach, including thematic data analysis. Fourteen in-depth interviews were conducted with primary health care and digital health stakeholders in Ontario. NVivo software was utilized in the coding of the interviews.
Results
Five main interconnected themes emerged: (1) Mismatch Between Envisioned Uses and Current Reality—denoting the importance of potential applications of AI in primary health care practice, with a recognition of the current reality characterized by a lack of available tools; (2) Mechanics of AI Don’t Matter: Just Another Tool in the Toolbox– reflecting an interest in what value AI tools could bring to practice, rather than concern with the mechanics of the AI tools themselves; (3) AI in Practice: A Double-Edged Sword—the possible benefits of AI use in primary health care contrasted with fundamental concern about the possible threats posed by AI in terms of clinical skills and capacity, mistakes, and loss of control; (4) The Non-Starters: A Guarded Stance Regarding AI Adoption in Primary Health Care—broader concerns centred on the ethical, legal, and social implications of AI use in primary health care; and (5) Necessary Elements: Facilitators of AI in Primary Health Care—elements required to support the uptake of AI tools, including co-creation, availability and use of high quality data, and the need for evaluation.
Conclusion
The use of AI in primary health care may have a positive impact, but many factors need to be considered regarding its implementation. This study may help to inform the development and deployment of AI tools in primary health care.
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Kaswa R, Nair A, Murphy S, Von Pressentin KB. Artificial intelligence: A strategic opportunity for enhancing primary care in South Africa. S Afr Fam Pract (2004) 2022; 64:e1-e2. [PMID: 36073098 PMCID: PMC9558344 DOI: 10.4102/safp.v64i1.5596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 06/22/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Ramprakash Kaswa
- Department of Family Medicine and Rural Health, Walter Sisulu University, Mthatha, South Africa; and, Mthatha General Hospital, Mthatha.
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22
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Raymond L, Castonguay A, Doyon O, Paré G. Nurse practitioners' involvement and experience with AI-based health technologies: A systematic review. Appl Nurs Res 2022; 66:151604. [DOI: 10.1016/j.apnr.2022.151604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 06/21/2022] [Indexed: 10/17/2022]
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Sharma M, Savage C, Nair M, Larsson I, Svedberg P, Nygren JM. Artificial Intelligence Applications in Health Care Practice: A Scoping Review (Preprint). J Med Internet Res 2022; 24:e40238. [PMID: 36197712 PMCID: PMC9582911 DOI: 10.2196/40238] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Revised: 08/19/2022] [Accepted: 08/30/2022] [Indexed: 11/25/2022] Open
Abstract
Background Artificial intelligence (AI) is often heralded as a potential disruptor that will transform the practice of medicine. The amount of data collected and available in health care, coupled with advances in computational power, has contributed to advances in AI and an exponential growth of publications. However, the development of AI applications does not guarantee their adoption into routine practice. There is a risk that despite the resources invested, benefits for patients, staff, and society will not be realized if AI implementation is not better understood. Objective The aim of this study was to explore how the implementation of AI in health care practice has been described and researched in the literature by answering 3 questions: What are the characteristics of research on implementation of AI in practice? What types and applications of AI systems are described? What characteristics of the implementation process for AI systems are discernible? Methods A scoping review was conducted of MEDLINE (PubMed), Scopus, Web of Science, CINAHL, and PsycINFO databases to identify empirical studies of AI implementation in health care since 2011, in addition to snowball sampling of selected reference lists. Using Rayyan software, we screened titles and abstracts and selected full-text articles. Data from the included articles were charted and summarized. Results Of the 9218 records retrieved, 45 (0.49%) articles were included. The articles cover diverse clinical settings and disciplines; most (32/45, 71%) were published recently, were from high-income countries (33/45, 73%), and were intended for care providers (25/45, 56%). AI systems are predominantly intended for clinical care, particularly clinical care pertaining to patient-provider encounters. More than half (24/45, 53%) possess no action autonomy but rather support human decision-making. The focus of most research was on establishing the effectiveness of interventions (16/45, 35%) or related to technical and computational aspects of AI systems (11/45, 24%). Focus on the specifics of implementation processes does not yet seem to be a priority in research, and the use of frameworks to guide implementation is rare. Conclusions Our current empirical knowledge derives from implementations of AI systems with low action autonomy and approaches common to implementations of other types of information systems. To develop a specific and empirically based implementation framework, further research is needed on the more disruptive types of AI systems being implemented in routine care and on aspects unique to AI implementation in health care, such as building trust, addressing transparency issues, developing explainable and interpretable solutions, and addressing ethical concerns around privacy and data protection.
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Affiliation(s)
- Malvika Sharma
- Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Medical Management Centre, Stockholm, Sweden
| | - Carl Savage
- Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Medical Management Centre, Stockholm, Sweden
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Monika Nair
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Ingrid Larsson
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Petra Svedberg
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens M Nygren
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
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Kueper JK, Terry A, Bahniwal R, Meredith L, Beleno R, Brown JB, Dang J, Leger D, McKay S, Pinto A, Ryan BL, Zwarenstein M, Lizotte DJ. Connecting artificial intelligence and primary care challenges: findings from a multi stakeholder collaborative consultation. BMJ Health Care Inform 2022; 29:bmjhci-2021-100493. [PMID: 35091423 PMCID: PMC8804627 DOI: 10.1136/bmjhci-2021-100493] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 01/06/2022] [Indexed: 11/15/2022] Open
Abstract
Despite widespread advancements in and envisioned uses for artificial intelligence (AI), few examples of successfully implemented AI innovations exist in primary care (PC) settings.
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Affiliation(s)
- Jacqueline K Kueper
- Department of Epidemiology and Biostatistics, Western University Schulich School of Medicine & Dentistry, London, Ontario, Canada .,Department of Computer Science, Western University Faculty of Science, London, Ontario, Canada
| | - Amanda Terry
- Department of Epidemiology and Biostatistics, Western University Schulich School of Medicine & Dentistry, London, Ontario, Canada.,Department of Family Medicine, Western University Schulich School of Medicine & Dentistry, London, Ontario, Canada.,Schulich Interfaculty Program in Public Health, Western University Schulich School of Medicine & Dentistry, London, Ontario, Canada
| | - Ravninder Bahniwal
- Schulich Interfaculty Program in Public Health, Western University Schulich School of Medicine & Dentistry, London, Ontario, Canada
| | - Leslie Meredith
- Department of Family Medicine, Western University Schulich School of Medicine & Dentistry, London, Ontario, Canada
| | - Ron Beleno
- Age-Well NCE, Toronto Rehabilitation Institute, Toronto, Ontario, Canada
| | - Judith Belle Brown
- Department of Family Medicine, Western University Schulich School of Medicine & Dentistry, London, Ontario, Canada
| | - Janet Dang
- London Middlesex Primary Care Alliance, London, Ontario, Canada.,Thames Valley Family Health Team, London, Ontario, Canada
| | - Daniel Leger
- Department of Family Medicine, Western University Schulich School of Medicine & Dentistry, London, Ontario, Canada
| | - Scott McKay
- Department of Family Medicine, Western University Schulich School of Medicine & Dentistry, London, Ontario, Canada
| | - Andrew Pinto
- Department of Family and Community Medicine, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.,Department of Family and Community Medicine, Li Ka Shing Knowledge Institute, Unity Health Toronto, Toronto, Ontario, Canada
| | - Bridget L Ryan
- Department of Epidemiology and Biostatistics, Western University Schulich School of Medicine & Dentistry, London, Ontario, Canada.,Department of Family Medicine, Western University Schulich School of Medicine & Dentistry, London, Ontario, Canada
| | - Merrick Zwarenstein
- Department of Epidemiology and Biostatistics, Western University Schulich School of Medicine & Dentistry, London, Ontario, Canada.,Department of Family Medicine, Western University Schulich School of Medicine & Dentistry, London, Ontario, Canada
| | - Daniel J Lizotte
- Department of Epidemiology and Biostatistics, Western University Schulich School of Medicine & Dentistry, London, Ontario, Canada.,Department of Computer Science, Western University Faculty of Science, London, Ontario, Canada
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25
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Li B, Fan X, Xia Q. Evaluation of the Effect of Changing the Normalised Appointment Mode During the Coronavirus Disease 2019 Epidemic on the Development of Day Surgery. Patient Prefer Adherence 2022; 16:3221-3227. [PMID: 36531303 PMCID: PMC9748151 DOI: 10.2147/ppa.s377139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/04/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE To investigate the effect of changing the normal appointment mode on day surgery. METHODS From December 2020 to December 2021, 302 patients with day surgery admitted to the hospital by using the unified reservation mode of the intelligent bed system were selected as the experimental group, while 302 patients with day surgery admitted to the hospital by using the decentralised bed reservation mode were randomly selected as the control group. The same-day surgery cancellation rate, bed utilisation rate and patient satisfaction were analysed and compared between the two groups. RESULTS The treatment experience of the patients in the experimental group was higher than that in the control group. The same-day surgery cancellation rate was lower than that of the control group, with a statistically significant difference (P < 0.05). CONCLUSION The unified computer reservation mode of the intelligent bed reservation system is superior to the decentralised reservation mode across departments. A daytime intelligent bed reservation mode was adopted for unifying bed appointments, which could effectively reduce the same-day cancellation rate of day surgery, improve bed utilisation and improve patient satisfaction.
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Affiliation(s)
- Bangju Li
- Day Surgery Ward, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001 People's Republic of China
| | - Xizhen Fan
- Day Surgery Ward, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001 People's Republic of China
| | - Qun Xia
- Day Surgery Ward, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, 230001 People's Republic of China
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26
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Pettit RW, Fullem R, Cheng C, Amos CI. Artificial intelligence, machine learning, and deep learning for clinical outcome prediction. Emerg Top Life Sci 2021; 5:ETLS20210246. [PMID: 34927670 PMCID: PMC8786279 DOI: 10.1042/etls20210246] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 12/03/2021] [Accepted: 12/07/2021] [Indexed: 12/12/2022]
Abstract
AI is a broad concept, grouping initiatives that use a computer to perform tasks that would usually require a human to complete. AI methods are well suited to predict clinical outcomes. In practice, AI methods can be thought of as functions that learn the outcomes accompanying standardized input data to produce accurate outcome predictions when trialed with new data. Current methods for cleaning, creating, accessing, extracting, augmenting, and representing data for training AI clinical prediction models are well defined. The use of AI to predict clinical outcomes is a dynamic and rapidly evolving arena, with new methods and applications emerging. Extraction or accession of electronic health care records and combining these with patient genetic data is an area of present attention, with tremendous potential for future growth. Machine learning approaches, including decision tree methods of Random Forest and XGBoost, and deep learning techniques including deep multi-layer and recurrent neural networks, afford unique capabilities to accurately create predictions from high dimensional, multimodal data. Furthermore, AI methods are increasing our ability to accurately predict clinical outcomes that previously were difficult to model, including time-dependent and multi-class outcomes. Barriers to robust AI-based clinical outcome model deployment include changing AI product development interfaces, the specificity of regulation requirements, and limitations in ensuring model interpretability, generalizability, and adaptability over time.
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Affiliation(s)
- Rowland W. Pettit
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
| | - Robert Fullem
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, U.S.A
| | - Chao Cheng
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, U.S.A
| | - Christopher I. Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, U.S.A
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, U.S.A
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, U.S.A
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27
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Glock H, Milos Nymberg V, Borgström Bolmsjö B, Holm J, Calling S, Wolff M, Pikkemaat M. Attitudes, Barriers, and Concerns Regarding Telemedicine Among Swedish Primary Care Physicians: A Qualitative Study. Int J Gen Med 2021; 14:9237-9246. [PMID: 34880663 PMCID: PMC8646113 DOI: 10.2147/ijgm.s334782] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 10/12/2021] [Indexed: 01/08/2023] Open
Abstract
Purpose The primary care physician’s traditional patient contacts are challenged by the rapidly accelerating digital transformation. In a quantitative survey analysis based on the theory of planned behavior, we found high behavioral intention to use telemedicine among Swedish primary care physicians, but low reported use. The aim of this study was to further examine the physicians’ experiences regarding telemedicine, with a focus on possible explanations for the gap between intention and use, through analysis of the free-text comments supplied in the survey. Material and Methods The material was collected through a web-based survey which was sent out to physicians at 160 primary health care centers in southern Sweden from May to August 2019. The survey covered four areas: general experiences of telemedicine, digital contacts, chronic disease monitoring with digital tools, and artificial intelligence. A total of 100 physicians submitted one or more free-text comments. These were analyzed using qualitative content analysis with an inductive approach. Results The primary care physicians expressed attitudes towards telemedicine that focused on clinical usefulness. Barriers to use were the loss of personal contact with patients and a deficient technological infrastructure. The major concerns were that these factors would result in patient harm and an increased workload. The connection between intention and use postulated by the theory of planned behavior was not applicable in this context, as external factors in the form of availability and clinical usefulness of the specific technology were major impediments to use despite a generally positive attitude. Conclusion All telemedicine tools must be evaluated regarding clinical usefulness, patient safety, and effects on staff workload, and end users should be included in this process. Utmost consideration is needed regarding how to retain the benefits of personal contact between patient and provider when digital solutions are introduced.
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Affiliation(s)
- Hanna Glock
- Center for Primary Health Care Research, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Veronica Milos Nymberg
- Center for Primary Health Care Research, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Beata Borgström Bolmsjö
- Center for Primary Health Care Research, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Jonas Holm
- Center for Primary Health Care Research, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Susanna Calling
- Center for Primary Health Care Research, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Moa Wolff
- Center for Primary Health Care Research, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
| | - Miriam Pikkemaat
- Center for Primary Health Care Research, Department of Clinical Sciences in Malmö, Lund University, Malmö, Sweden
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28
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Ellertsson S, Loftsson H, Sigurdsson EL. Artificial intelligence in the GPs office: a retrospective study on diagnostic accuracy. Scand J Prim Health Care 2021; 39:448-458. [PMID: 34585629 PMCID: PMC8725823 DOI: 10.1080/02813432.2021.1973255] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVE Machine learning (ML) is expected to play an increasing role within primary health care (PHC) in coming years. No peer-reviewed studies exist that evaluate the diagnostic accuracy of ML models compared to general practitioners (GPs). The aim of this study was to evaluate the diagnostic accuracy of an ML classifier on primary headache diagnoses in PHC, compare its performance to GPs, and examine the most impactful signs and symptoms when making a prediction. DESIGN A retrospective study on diagnostic accuracy, using electronic health records from the database of the Primary Health Care Service of the Capital Area (PHCCA) in Iceland. SETTING Fifteen primary health care centers of the PHCCA. SUBJECTS All patients that consulted a physician, from 1 January 2006 to 30 April 2020, and received one of the selected diagnoses. MAIN OUTCOME MEASURES Sensitivity, Specificity, Positive Predictive Value, Matthews Correlation Coefficient, Receiver Operating Characteristic (ROC) curve, and Area under the ROC curve (AUROC) score for primary headache diagnoses, as well as Shapley Additive Explanations (SHAP) values of the ML classifier. RESULTS The classifier outperformed the GPs on all metrics except specificity. The SHAP values indicate that the classifier uses the same signs and symptoms (features) as a physician would, when distinguishing between headache diagnoses. CONCLUSION In a retrospective comparison, the diagnostic accuracy of the ML classifier for primary headache diagnoses is superior to GPs. According to SHAP values, the ML classifier relies on the same signs and symptoms as a physician when making a diagnostic prediction.KeypointsLittle is known about the diagnostic accuracy of machine learning (ML) in the context of primary health care, despite its considerable potential to aid in clinical work. This novel research sheds light on the diagnostic accuracy of ML in a clinical context, as well as the interpretation of its predictions. If the vast potential of ML is to be utilized in primary health care, its performance, safety, and inner workings need to be understood by clinicians.
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Affiliation(s)
- Steindor Ellertsson
- Primary Health Care Service of the Capital Area, Reykjavik, Iceland
- CONTACT Steindor Ellertsson Primary Health Care Service of the Capital Area, Grenimelur 44, 107, Reykjavik, Iceland
| | - Hrafn Loftsson
- Department of Computer Science, Reykjavik University, Reykjavik, Iceland
| | - Emil L. Sigurdsson
- Primary Health Care Service of the Capital Area, Reykjavik, Iceland
- Development Centre for Primary Health Care in Iceland, Reykjavik, Iceland
- Department of Family Medicine, University of Iceland, Reykjavik, Iceland
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29
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Kueper JK. [Not Available]. CANADIAN FAMILY PHYSICIAN MEDECIN DE FAMILLE CANADIEN 2021; 67:e317-e322. [PMID: 34906948 PMCID: PMC8670659 DOI: 10.46747/cfp.6712e317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Jacqueline K Kueper
- Fellow TechForward du CMFC et de l'AMC au Collège des médecins de famille du Canada; elle est candidate au doctorat au Département d'épidémiologie et de biostatistique et au Département de sciences informatiques à l'Université Western à London (Ontario).
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30
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Kueper JK. Primer for artificial intelligence in primary care. CANADIAN FAMILY PHYSICIAN MEDECIN DE FAMILLE CANADIEN 2021; 67:889-893. [PMID: 34906934 DOI: 10.46747/cfp.6712889] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Affiliation(s)
- Jacqueline K Kueper
- CFPC-AMS TechForward Fellow at the College of Family Physicians of Canada and a PhD candidate in the Department of Epidemiology and Biostatistics and the Department of Computer Science at Western University in London, Ont.
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31
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Schorr MT, Quadors Dos Santos BTM, Feiten JG, Sordi AO, Pessi C, Von Diemen L, Passos IC, Telles LEDB, Hauck S. Association between childhood trauma, parental bonding and antisocial personality disorder in adulthood: A machine learning approach. Psychiatry Res 2021; 304:114082. [PMID: 34303948 DOI: 10.1016/j.psychres.2021.114082] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 06/08/2021] [Accepted: 06/26/2021] [Indexed: 11/30/2022]
Abstract
Childhood trauma (CT) and parental bonding (PB) have been correlated with later antisocial personality disorder (ASPD). Aiming to better understand this complex interaction we analyzed the data from a cross-sectional study that evaluated 346 male inpatient cocaine users, using both traditional statistical analysis and machine learning (ML) approaches. Childhood Trauma Questionnaire (CTQ), Parental Bonding Instrument (PBI), and Mini International Neuropsychiatric Interview (MINI) were applied. We found a markedly higher prevalence of mental illness in the ASPD group. The ML method and the traditional analysis showed that emotional and physical abuse were the factors with the strongest relationship with ASPD. Also, there were discrepancies between the findings of both methods regarding physical neglect and paternal care. Although this study does not allow definitive answers in this matter, we do propose that these two methods can aid in better comprehending how multiple variables interact with each other in the development of psychological disorders.
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Affiliation(s)
- Manuela Teixeira Schorr
- Department of Psychiatry and Legal Medicine, School of Medicine, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Department of Psychiatry(,) Graduate Program in Psychiatry and Behavioral Sciences, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Research Laboratory in Psychodynamic Psychiatry, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil.
| | - Barbara Tietbohl Martins Quadors Dos Santos
- Department of Psychiatry and Legal Medicine, School of Medicine, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Department of Psychiatry(,) Graduate Program in Psychiatry and Behavioral Sciences, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Research Laboratory in Psychodynamic Psychiatry, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
| | - Jacson Gabriel Feiten
- Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) and Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre, RS, Brazil
| | - Anne Orgler Sordi
- Center for Drug and Alcohol Research (CPAD), Hospital de Clínicas de Porto Alegre (HCPA), Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Cristina Pessi
- Research Laboratory in Psychodynamic Psychiatry, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
| | - Lisia Von Diemen
- Department of Psychiatry and Legal Medicine, School of Medicine, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Center for Drug and Alcohol Research (CPAD), Hospital de Clínicas de Porto Alegre (HCPA), Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Ives Cavalcante Passos
- Laboratory of Molecular Psychiatry, Centro de Pesquisa Experimental (CPE) and Centro de Pesquisa Clínica (CPC), Hospital de Clínicas de Porto Alegre (HCPA), Instituto Nacional de Ciência e Tecnologia Translacional em Medicina (INCT-TM), Porto Alegre, RS, Brazil
| | - Lisieux Elaine de Borba Telles
- Department of Psychiatry and Legal Medicine, School of Medicine, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Forensic Psychiatric Institute 'Doutor Maurício Cardoso', Porto Alegre, RS, Brazil
| | - Simone Hauck
- Department of Psychiatry and Legal Medicine, School of Medicine, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Department of Psychiatry(,) Graduate Program in Psychiatry and Behavioral Sciences, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil; Research Laboratory in Psychodynamic Psychiatry, Hospital de Clínicas de Porto Alegre (HCPA), Porto Alegre, RS, Brazil
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32
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Wang JX, Somani S, Chen JH, Murray S, Sarkar U. Health Equity in Artificial Intelligence and Primary Care Research: Protocol for a Scoping Review. JMIR Res Protoc 2021; 10:e27799. [PMID: 34533458 PMCID: PMC8486995 DOI: 10.2196/27799] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 05/09/2021] [Accepted: 06/16/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Though artificial intelligence (AI) has the potential to augment the patient-physician relationship in primary care, bias in intelligent health care systems has the potential to differentially impact vulnerable patient populations. OBJECTIVE The purpose of this scoping review is to summarize the extent to which AI systems in primary care examine the inherent bias toward or against vulnerable populations and appraise how these systems have mitigated the impact of such biases during their development. METHODS We will conduct a search update from an existing scoping review to identify studies on AI and primary care in the following databases: Medline-OVID, Embase, CINAHL, Cochrane Library, Web of Science, Scopus, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI, and arXiv. Two screeners will independently review all abstracts, titles, and full-text articles. The team will extract data using a structured data extraction form and synthesize the results in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. RESULTS This review will provide an assessment of the current state of health care equity within AI for primary care. Specifically, we will identify the degree to which vulnerable patients have been included, assess how bias is interpreted and documented, and understand the extent to which harmful biases are addressed. As of October 2020, the scoping review is in the title- and abstract-screening stage. The results are expected to be submitted for publication in fall 2021. CONCLUSIONS AI applications in primary care are becoming an increasingly common tool in health care delivery and in preventative care efforts for underserved populations. This scoping review would potentially show the extent to which studies on AI in primary care employ a health equity lens and take steps to mitigate bias. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/27799.
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Affiliation(s)
- Jonathan Xin Wang
- Center for Vulnerable Populations at San Francisco General Hospital, University of California San Francisco, San Francisco, CA, United States
- Division of General Internal Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Sulaiman Somani
- Icahn School of Medicine at Mount Sinai, New York City, NY, United States
| | - Jonathan H Chen
- Center for Biomedical Informatics Research, Division of Hospital Medicine, Stanford Department of Medicine, Stanford, CA, United States
| | - Sara Murray
- Division of General Internal Medicine, University of California San Francisco, San Francisco, CA, United States
| | - Urmimala Sarkar
- Center for Vulnerable Populations at San Francisco General Hospital, University of California San Francisco, San Francisco, CA, United States
- Division of General Internal Medicine, University of California San Francisco, San Francisco, CA, United States
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Holdsworth LM, Park C, Asch SM, Lin S. Technology-Enabled and Artificial Intelligence Support for Pre-Visit Planning in Ambulatory Care: Findings From an Environmental Scan. Ann Fam Med 2021; 19:419-426. [PMID: 34546948 PMCID: PMC8437572 DOI: 10.1370/afm.2716] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 01/15/2021] [Accepted: 03/15/2021] [Indexed: 01/16/2023] Open
Abstract
PURPOSE Pre-visit planning (PVP) is believed to improve effectiveness, efficiency, and experience of care, yet numerous implementation barriers exist. There are opportunities for technology-enabled and artificial intelligence (AI) support to augment existing human-driven PVP processes-from appointment reminders and pre-visit questionnaires to pre-visit order sets and care gap closures. This study aimed to explore the current state of PVP, barriers to implementation, evidence of impact, and potential use of non-AI and AI tools to support PVP. METHODS We used an environmental scan approach involving: (1) literature review; (2) key informant interviews with PVP experts in ambulatory care; and (3) a search of the public domain for technology-enabled and AI solutions that support PVP. We then synthesized the findings using a qualitative matrix analysis. RESULTS We found 26 unique PVP implementations in the literature and conducted 16 key informant interviews. Demonstration of impact is typically limited to process outcomes, with improved patient outcomes remaining elusive. Our key informants reported that many PVP barriers are human effort-related and see potential for non-AI and AI technologies to support certain aspects of PVP. We identified 8 examples of commercially available technology-enabled tools that support PVP, some with AI capabilities; however, few of these have been independently evaluated. CONCLUSIONS As health systems transition toward value-based payment models in a world where the coronavirus disease 2019 pandemic has shifted patient care into the virtual space, PVP activities-driven by humans and supported by technology-may become more important and powerful and should be rigorously evaluated.
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Affiliation(s)
- Laura M Holdsworth
- Division of Primary Care and Population Health, Stanford School of Medicine, Stanford, California
| | - Chance Park
- University of British Columbia, Vancouver, British Columbia, Canada
| | - Steven M Asch
- Division of Primary Care and Population Health, Stanford School of Medicine, Stanford, California.,Center for Innovation to Implementation, Veterans Affairs, Menlo Park, California
| | - Steven Lin
- Division of Primary Care and Population Health, Stanford School of Medicine, Stanford, California
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Ford E, Edelman N, Somers L, Shrewsbury D, Lopez Levy M, van Marwijk H, Curcin V, Porat T. Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners. BMC Med Inform Decis Mak 2021; 21:193. [PMID: 34154580 PMCID: PMC8215812 DOI: 10.1186/s12911-021-01557-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Accepted: 05/31/2021] [Indexed: 11/29/2022] Open
Abstract
Background Well-established electronic data capture in UK general practice means that algorithms, developed on patient data, can be used for automated clinical decision support systems (CDSSs). These can predict patient risk, help with prescribing safety, improve diagnosis and prompt clinicians to record extra data. However, there is persistent evidence of low uptake of CDSSs in the clinic. We interviewed UK General Practitioners (GPs) to understand what features of CDSSs, and the contexts of their use, facilitate or present barriers to their use. Methods We interviewed 11 practicing GPs in London and South England using a semi-structured interview schedule and discussed a hypothetical CDSS that could detect early signs of dementia. We applied thematic analysis to the anonymised interview transcripts. Results We identified three overarching themes: trust in individual CDSSs; usability of individual CDSSs; and usability of CDSSs in the broader practice context, to which nine subthemes contributed. Trust was affected by CDSS provenance, perceived threat to autonomy and clear management guidance. Usability was influenced by sensitivity to the patient context, CDSS flexibility, ease of control, and non-intrusiveness. CDSSs were more likely to be used by GPs if they did not contribute to alert proliferation and subsequent fatigue, or if GPs were provided with training in their use. Conclusions Building on these findings we make a number of recommendations for CDSS developers to consider when bringing a new CDSS into GP patient records systems. These include co-producing CDSS with GPs to improve fit within clinic workflow and wider practice systems, ensuring a high level of accuracy and a clear clinical pathway, and providing CDSS training for practice staff. These recommendations may reduce the proliferation of unhelpful alerts that can result in important decision-support being ignored. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-021-01557-z.
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Affiliation(s)
- Elizabeth Ford
- Department of Primary Care and Public Health, Brighton and Sussex Medical School, Watson Building, Village Way, Falmer, Brighton, BN1 9PH, UK.
| | - Natalie Edelman
- Department of Primary Care and Public Health, Brighton and Sussex Medical School, Watson Building, Village Way, Falmer, Brighton, BN1 9PH, UK.,School of Sport and Health Sciences, University of Brighton, Brighton, UK
| | - Laura Somers
- Department of Primary Care and Public Health, Brighton and Sussex Medical School, Watson Building, Village Way, Falmer, Brighton, BN1 9PH, UK
| | - Duncan Shrewsbury
- Department of Primary Care and Public Health, Brighton and Sussex Medical School, Watson Building, Village Way, Falmer, Brighton, BN1 9PH, UK
| | - Marcela Lopez Levy
- Psychosocial Department, Centre for Researching and Embedding Human Rights (CREHR), Birkbeck College, London, UK
| | - Harm van Marwijk
- Department of Primary Care and Public Health, Brighton and Sussex Medical School, Watson Building, Village Way, Falmer, Brighton, BN1 9PH, UK
| | - Vasa Curcin
- School of Population Health and Environmental Sciences, King's College London, London, UK
| | - Talya Porat
- Dyson School of Design Engineering, Imperial College London, London, UK
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He Q, Du F, Simonse LWL. A Patient Journey Map to Improve the Home Isolation Experience of Persons With Mild COVID-19: Design Research for Service Touchpoints of Artificial Intelligence in eHealth. JMIR Med Inform 2021; 9:e23238. [PMID: 33444156 PMCID: PMC8043148 DOI: 10.2196/23238] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 12/18/2020] [Accepted: 01/10/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND In the context of the COVID-19 outbreak, 80% of the persons who are infected have mild symptoms and are required to self-recover at home. They have a strong demand for remote health care that, despite the great potential of artificial intelligence (AI), is not met by the current services of eHealth. Understanding the real needs of these persons is lacking. OBJECTIVE The aim of this paper is to contribute a fine-grained understanding of the home isolation experience of persons with mild COVID-19 symptoms to enhance AI in eHealth services. METHODS A design research method with a qualitative approach was used to map the patient journey. Data on the home isolation experiences of persons with mild COVID-19 symptoms was collected from the top-viewed personal video stories on YouTube and their comment threads. For the analysis, this data was transcribed, coded, and mapped into the patient journey map. RESULTS The key findings on the home isolation experience of persons with mild COVID-19 symptoms concerned (1) an awareness period before testing positive, (2) less typical and more personal symptoms, (3) a negative mood experience curve, (5) inadequate home health care service support for patients, and (6) benefits and drawbacks of social media support. CONCLUSIONS The design of the patient journey map and underlying insights on the home isolation experience of persons with mild COVID-19 symptoms serves health and information technology professionals in more effectively applying AI technology into eHealth services, for which three main service concepts are proposed: (1) trustworthy public health information to relieve stress, (2) personal COVID-19 health monitoring, and (3) community support.
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Affiliation(s)
- Qian He
- Department of Design Organisation & Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Fei Du
- Department of Design Organisation & Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
| | - Lianne W L Simonse
- Department of Design Organisation & Strategy, Faculty of Industrial Design Engineering, Delft University of Technology, Delft, Netherlands
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Jones OT, Calanzani N, Saji S, Duffy SW, Emery J, Hamilton W, Singh H, de Wit NJ, Walter FM. Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review. J Med Internet Res 2021; 23:e23483. [PMID: 33656443 PMCID: PMC7970165 DOI: 10.2196/23483] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 11/05/2020] [Accepted: 11/30/2020] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND More than 17 million people worldwide, including 360,000 people in the United Kingdom, were diagnosed with cancer in 2018. Cancer prognosis and disease burden are highly dependent on the disease stage at diagnosis. Most people diagnosed with cancer first present in primary care settings, where improved assessment of the (often vague) presenting symptoms of cancer could lead to earlier detection and improved outcomes for patients. There is accumulating evidence that artificial intelligence (AI) can assist clinicians in making better clinical decisions in some areas of health care. OBJECTIVE This study aimed to systematically review AI techniques that may facilitate earlier diagnosis of cancer and could be applied to primary care electronic health record (EHR) data. The quality of the evidence, the phase of development the AI techniques have reached, the gaps that exist in the evidence, and the potential for use in primary care were evaluated. METHODS We searched MEDLINE, Embase, SCOPUS, and Web of Science databases from January 01, 2000, to June 11, 2019, and included all studies providing evidence for the accuracy or effectiveness of applying AI techniques for the early detection of cancer, which may be applicable to primary care EHRs. We included all study designs in all settings and languages. These searches were extended through a scoping review of AI-based commercial technologies. The main outcomes assessed were measures of diagnostic accuracy for cancer. RESULTS We identified 10,456 studies; 16 studies met the inclusion criteria, representing the data of 3,862,910 patients. A total of 13 studies described the initial development and testing of AI algorithms, and 3 studies described the validation of an AI algorithm in independent data sets. One study was based on prospectively collected data; only 3 studies were based on primary care data. We found no data on implementation barriers or cost-effectiveness. Risk of bias assessment highlighted a wide range of study quality. The additional scoping review of commercial AI technologies identified 21 technologies, only 1 meeting our inclusion criteria. Meta-analysis was not undertaken because of the heterogeneity of AI modalities, data set characteristics, and outcome measures. CONCLUSIONS AI techniques have been applied to EHR-type data to facilitate early diagnosis of cancer, but their use in primary care settings is still at an early stage of maturity. Further evidence is needed on their performance using primary care data, implementation barriers, and cost-effectiveness before widespread adoption into routine primary care clinical practice can be recommended.
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Affiliation(s)
- Owain T Jones
- Primary Care Unit, Department of Public Health & Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Natalia Calanzani
- Primary Care Unit, Department of Public Health & Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Smiji Saji
- Primary Care Unit, Department of Public Health & Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Stephen W Duffy
- Wolfson Institute for Preventive Medicine, Queen Mary University of London, London, United Kingdom
| | - Jon Emery
- Centre for Cancer Research and Department of General Practice, University of Melbourne, Victoria, Australia
| | - Willie Hamilton
- College of Medicine and Health, University of Exeter, Exeter, United Kingdom
| | - Hardeep Singh
- Center for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, TX, United States
| | - Niek J de Wit
- Julius Center for Health Sciences and Primary Care, UMC Utrecht, Utrecht, Netherlands
| | - Fiona M Walter
- Primary Care Unit, Department of Public Health & Primary Care, University of Cambridge, Cambridge, United Kingdom
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