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Klug K, Beckh K, Antweiler D, Chakraborty N, Baldini G, Laue K, Hosch R, Nensa F, Schuler M, Giesselbach S. From admission to discharge: a systematic review of clinical natural language processing along the patient journey. BMC Med Inform Decis Mak 2024; 24:238. [PMID: 39210370 PMCID: PMC11360876 DOI: 10.1186/s12911-024-02641-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 08/20/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Medical text, as part of an electronic health record, is an essential information source in healthcare. Although natural language processing (NLP) techniques for medical text are developing fast, successful transfer into clinical practice has been rare. Especially the hospital domain offers great potential while facing several challenges including many documents per patient, multiple departments and complex interrelated processes. METHODS In this work, we survey relevant literature to identify and classify approaches which exploit NLP in the clinical context. Our contribution involves a systematic mapping of related research onto a prototypical patient journey in the hospital, along which medical documents are created, processed and consumed by hospital staff and patients themselves. Specifically, we reviewed which dataset types, dataset languages, model architectures and tasks are researched in current clinical NLP research. Additionally, we extract and analyze major obstacles during development and implementation. We discuss options to address them and argue for a focus on bias mitigation and model explainability. RESULTS While a patient's hospital journey produces a significant amount of structured and unstructured documents, certain steps and documents receive more research attention than others. Diagnosis, Admission and Discharge are clinical patient steps that are researched often across the surveyed paper. In contrast, our findings reveal significant under-researched areas such as Treatment, Billing, After Care, and Smart Home. Leveraging NLP in these stages can greatly enhance clinical decision-making and patient outcomes. Additionally, clinical NLP models are mostly based on radiology reports, discharge letters and admission notes, even though we have shown that many other documents are produced throughout the patient journey. There is a significant opportunity in analyzing a wider range of medical documents produced throughout the patient journey to improve the applicability and impact of NLP in healthcare. CONCLUSIONS Our findings suggest that there is a significant opportunity to leverage NLP approaches to advance clinical decision-making systems, as there remains a considerable understudied potential for the analysis of patient journey data.
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Grants
- 5-2011-0041/2 Ministry for Economic Affairs, Industry, Climate Action and Energy of the State of North-Rhine-Westphalia, Germany
- 5-2011-0041/2 Ministry for Economic Affairs, Industry, Climate Action and Energy of the State of North-Rhine-Westphalia, Germany
- 5-2011-0041/2 Ministry for Economic Affairs, Industry, Climate Action and Energy of the State of North-Rhine-Westphalia, Germany
- 5-2011-0041/2 Ministry for Economic Affairs, Industry, Climate Action and Energy of the State of North-Rhine-Westphalia, Germany
- 5-2011-0041/2 Ministry for Economic Affairs, Industry, Climate Action and Energy of the State of North-Rhine-Westphalia, Germany
- 5-2011-0041/2 Ministry for Economic Affairs, Industry, Climate Action and Energy of the State of North-Rhine-Westphalia, Germany
- 5-2011-0041/2 Ministry for Economic Affairs, Industry, Climate Action and Energy of the State of North-Rhine-Westphalia, Germany
- 5-2011-0041/2 Ministry for Economic Affairs, Industry, Climate Action and Energy of the State of North-Rhine-Westphalia, Germany
- 5-2011-0041/2 Ministry for Economic Affairs, Industry, Climate Action and Energy of the State of North-Rhine-Westphalia, Germany
- 5-2011-0041/2 Ministry for Economic Affairs, Industry, Climate Action and Energy of the State of North-Rhine-Westphalia, Germany
- Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS (1050)
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Affiliation(s)
| | | | | | | | - Giulia Baldini
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Katharina Laue
- West German Cancer Centre, University Hospital Essen, Essen, Germany
| | - René Hosch
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Felix Nensa
- Institute of Interventional and Diagnostic Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
- Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
| | - Martin Schuler
- West German Cancer Centre, University Hospital Essen, Essen, Germany
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Ruksakulpiwat S, Thorngthip S, Niyomyart A, Benjasirisan C, Phianhasin L, Aldossary H, Ahmed BH, Samai T. A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next? J Multidiscip Healthc 2024; 17:1603-1616. [PMID: 38628616 PMCID: PMC11020344 DOI: 10.2147/jmdh.s459946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 03/05/2024] [Indexed: 04/19/2024] Open
Abstract
Background Integrating Artificial Intelligence (AI) into healthcare has transformed the landscape of patient care and healthcare delivery. Despite this, there remains a notable gap in the existing literature synthesizing the comprehensive understanding of AI's utilization in nursing care. Objective This systematic review aims to synthesize the available evidence to comprehensively understand the application of AI in nursing care. Methods Studies published between January 2019 and December 2023, identified through CINAHL Plus with Full Text, Web of Science, PubMed, and Medline, were included in this review. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines guided the identification, screening, exclusion, and inclusion of articles. The convergent integrated analysis framework, as proposed by the Joanna Briggs Institute, was employed to synthesize data from the included studies for theme generation. Results A total of 337 records were identified from databases. Among them, 35 duplicates were removed, and 302 records underwent eligibility screening. After applying inclusion and exclusion criteria, eleven studies were deemed eligible and included in this review. Through data synthesis of these studies, six themes pertaining to the use of AI in nursing care were identified: 1) Risk Identification, 2) Health Assessment, 3) Patient Classification, 4) Research Development, 5) Improved Care Delivery and Medical Records, and 6) Developing a Nursing Care Plan. Conclusion This systematic review contributes valuable insights into the multifaceted applications of AI in nursing care. Through the synthesis of data from the included studies, six distinct themes emerged. These findings not only consolidate the current knowledge base but also underscore the diverse ways in which AI is shaping and improving nursing care practices.
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Affiliation(s)
- Suebsarn Ruksakulpiwat
- Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand
| | - Sutthinee Thorngthip
- Department of Nursing Siriraj Hospital, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Atsadaporn Niyomyart
- Ramathibodi School of Nursing, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | | | - Lalipat Phianhasin
- Department of Medical Nursing, Faculty of Nursing, Mahidol University, Bangkok, Thailand
| | - Heba Aldossary
- Department of Nursing, Prince Sultan Military College of Health Sciences, Dammam, Saudi Arabia
| | - Bootan Hasan Ahmed
- Frances Payne Bolton School of Nursing, Case Western Reserve University, Cleveland, OH, USA
| | - Thanistha Samai
- Department of Public Health Nursing, Faculty of Nursing, Mahidol University, Nakhon Pathom, Thailand
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Jacobs SM, Lundy NN, Issenberg SB, Chandran L. Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence. JMIR MEDICAL EDUCATION 2023; 9:e50903. [PMID: 38052721 PMCID: PMC10762622 DOI: 10.2196/50903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/15/2023] [Accepted: 12/05/2023] [Indexed: 12/07/2023]
Abstract
The proliferation of generative artificial intelligence (AI) and its extensive potential for integration into many aspects of health care signal a transformational shift within the health care environment. In this context, medical education must evolve to ensure that medical trainees are adequately prepared to navigate the rapidly changing health care landscape. Medical education has moved toward a competency-based education paradigm, leading the Association of American Medical Colleges (AAMC) to define a set of Entrustable Professional Activities (EPAs) as its practical operational framework in undergraduate medical education. The AAMC's 13 core EPAs for entering residencies have been implemented with varying levels of success across medical schools. In this paper, we critically assess the existing core EPAs in the context of rapid AI integration in medicine. We identify EPAs that require refinement, redefinition, or comprehensive change to align with the emerging trends in health care. Moreover, this perspective proposes a set of "emerging" EPAs, informed by the changing landscape and capabilities presented by generative AI technologies. We provide a practical evaluation of the EPAs, alongside actionable recommendations on how medical education, viewed through the lens of the AAMC EPAs, can adapt and remain relevant amid rapid technological advancements. By leveraging the transformative potential of AI, we can reshape medical education to align with an AI-integrated future of medicine. This approach will help equip future health care professionals with technological competence and adaptive skills to meet the dynamic and evolving demands in health care.
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Affiliation(s)
- Sarah Marie Jacobs
- Department of Medical Education, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Neva Nicole Lundy
- Department of Medical Education, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Saul Barry Issenberg
- Department of Medical Education, University of Miami Miller School of Medicine, Miami, FL, United States
| | - Latha Chandran
- Department of Medical Education, University of Miami Miller School of Medicine, Miami, FL, United States
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Ahun E, Demir A, Yiğit Y, Tulgar YK, Doğan M, Thomas DT, Tulgar S. Perceptions and concerns of emergency medicine practitioners about artificial intelligence in emergency triage management during the pandemic: a national survey-based study. Front Public Health 2023; 11:1285390. [PMID: 37965502 PMCID: PMC10640989 DOI: 10.3389/fpubh.2023.1285390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 10/05/2023] [Indexed: 11/16/2023] Open
Abstract
Objective There have been continuous discussions over the ethics of using AI in healthcare. We sought to identify the ethical issues and viewpoints of Turkish emergency care doctors about the use of AI during epidemic triage. Materials and methods Ten emergency specialists were initially enlisted for this project, and their responses to open-ended questions about the ethical issues surrounding AI in the emergency room provided valuable information. A 15-question survey was created based on their input and was refined through a pilot test with 15 emergency specialty doctors. Following that, the updated survey was sent to emergency specialists via email, social media, and private email distribution. Results 167 emergency medicine specialists participated in the study, with an average age of 38.22 years and 6.79 years of professional experience. The majority agreed that AI could benefit patients (54.50%) and healthcare professionals (70.06%) in emergency department triage during pandemics. Regarding responsibility, 63.47% believed in shared responsibility between emergency medicine specialists and AI manufacturers/programmers for complications. Additionally, 79.04% of participants agreed that the responsibility for complications in AI applications varies depending on the nature of the complication. Concerns about privacy were expressed by 20.36% regarding deep learning-based applications, while 61.68% believed that anonymity protected privacy. Additionally, 70.66% of participants believed that AI systems would be as sensitive as humans in terms of non-discrimination. Conclusion The potential advantages of deploying AI programs in emergency department triage during pandemics for patients and healthcare providers were acknowledged by emergency medicine doctors in Turkey. Nevertheless, they expressed notable ethical concerns related to the responsibility and accountability aspects of utilizing AI systems in this context.
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Affiliation(s)
- Erhan Ahun
- Department of Emergency Medicine, Sabuncuoglu Serefeddin Training and Research Hospital, Amasya, Türkiye
| | - Ahmet Demir
- Department of Emergency Medicine, Faculty of Medicine, Mugla Sitki Kocman University, Mugla, Türkiye
| | - Yavuz Yiğit
- Department of Emergency Medicine, Hamad Medical Corporation, Doha, Qatar
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom
| | - Yasemin Koçer Tulgar
- Department of Medical History and Ethics, Samsun University Faculty of Medicine, Samsun, Türkiye
- Department of Medical History and Ethics, Kocaeli University Faculty of Medicine, Kocaeli, Türkiye
| | - Meltem Doğan
- Department of Medical History and Ethics, Kocaeli University Faculty of Medicine, Kocaeli, Türkiye
| | - David Terence Thomas
- Department of Medical Education, Maltepe University Faculty of Medicine, Istanbul, Türkiye
- Department of Pediatric Surgery, Maltepe University Faculty of Medicine, Istanbul, Türkiye
| | - Serkan Tulgar
- Department of Anesthesiology, Samsun University Faculty of Medicine, Samsun Training and Research Hospital, Samsun, Türkiye
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Ahmed A. Pro: Can We Use Artificial Intelligence-Derived Algorithms to Guide Patient Blood Management Decision-Making? J Cardiothorac Vasc Anesth 2023; 37:2141-2144. [PMID: 37365072 DOI: 10.1053/j.jvca.2023.05.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/12/2023] [Accepted: 05/30/2023] [Indexed: 06/28/2023]
Affiliation(s)
- Aamer Ahmed
- Department of Anaesthesia and Critical Care, University Hospitals of Leicester National Health Services Trust, Leicester, UK; Department of Cardiovascular Sciences, University of Leicester, Leicester, UK.
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