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Sariköse S, Şenol Çelik S. The Effect of Clinical Decision Support Systems on Patients, Nurses, and Work Environment in ICUs: A Systematic Review. Comput Inform Nurs 2024; 42:298-304. [PMID: 38376391 DOI: 10.1097/cin.0000000000001107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
This study aimed to examine the impact of clinical decision support systems on patient outcomes, working environment outcomes, and decision-making processes in nursing. The authors conducted a systematic literature review to obtain evidence on studies about clinical decision support systems and the practices of ICU nurses. For this purpose, the authors searched 10 electronic databases, including PubMed, CINAHL, Web of Science, Scopus, Cochrane Library, Ovid MEDLINE, Science Direct, Tr-Dizin, Harman, and DergiPark. Search terms included "clinical decision support systems," "decision making," "intensive care," "nurse/nursing," "patient outcome," and "working environment" to identify relevant studies published during the period from the year 2007 to October 2022. Our search yielded 619 articles, of which 39 met the inclusion criteria. A higher percentage of studies compared with others were descriptive (20%), conducted through a qualitative (18%), and carried out in the United States (41%). According to the results of the narrative analysis, the authors identified three main themes: "patient care outcomes," "work environment outcomes," and the "decision-making process in nursing." Clinical decision support systems, which target practices of ICU nurses and patient care outcomes, have positive effects on outcomes and show promise in improving the quality of care; however, available studies are limited.
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Affiliation(s)
- Seda Sariköse
- Author Affiliation: Koç University School of Nursing, Istanbul, Turkey
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Teele SA, Tremoulet P, Laussen PC, Danaher-Garcia N, Salvin JW, White BAA. Complex decision making in an intensive care environment: Perceived practice versus observed reality. J Eval Clin Pract 2024; 30:337-345. [PMID: 37767761 DOI: 10.1111/jep.13930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 09/01/2023] [Accepted: 09/14/2023] [Indexed: 09/29/2023]
Abstract
RATIONALE Advancing our understanding of how decisions are made in cognitively, socially and technologically complex hospital environments may reveal opportunities to improve healthcare delivery, medical education and the experience of patients, families and clinicians. AIMS AND OBJECTIVES Explore factors impacting clinician decision making in the Boston Children's Hospital Cardiac Intensive Care Unit. METHODS A convergent mixed methods design was used. Quantitative and qualitative data sources consisted of a faculty survey, direct observations of clinical rounds in a specific patient population identified by a clinical decision support system (CDSS) and semistructured interviews (SSIs). Deductive and inductive coding was used for qualitative data. Qualitative data were translated into images using social network analysis which illustrate the frequency and connectivity of the codes in each data set. RESULTS A total of 25 observations of eight faculty-led interprofessional teams were performed between 12 February and 31 March 2021. Individual patient characteristics were noted by faculty in SSIs to be the most important factor in their decision making, yet ethnographic observations suggested faculty cognitive traits, team expertise and value-based decisions were more heavily weighted. The development of expertise was impacted by role modeling. Decisions were perceived to be influenced by the system and environment. CONCLUSIONS Clinician perception of decision making was not congruent with the observed behaviours in a complicated and dynamic system. This study identifies important considerations in clinical curricula as well as the design and implementation of CDSS. Our method of using social network analysis to visualize components of decision making could be adopted to explore other complex environments.
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Affiliation(s)
- Sarah A Teele
- Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Massachusetts General Hospital Institute of Health Professions, Boston, Massachusetts, USA
| | | | - Peter C Laussen
- Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Nicole Danaher-Garcia
- Massachusetts General Hospital Institute of Health Professions, Boston, Massachusetts, USA
| | - Joshua W Salvin
- Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Bobbie Ann A White
- Massachusetts General Hospital Institute of Health Professions, Boston, Massachusetts, USA
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Dai L, Wu Z, Pan X, Zheng D, Kang M, Zhou M, Chen G, Liu H, Tian X. Design and implementation of an automatic nursing assessment system based on CDSS technology. Int J Med Inform 2024; 183:105323. [PMID: 38141563 DOI: 10.1016/j.ijmedinf.2023.105323] [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: 07/30/2023] [Revised: 11/26/2023] [Accepted: 12/17/2023] [Indexed: 12/25/2023]
Abstract
BACKGROUND Various quantitative and quality assessment tools are currently used in nursing to evaluate a patient's physiological, psychological, and socioeconomic status. The results play important roles in evaluating the efficiency of healthcare, improving the treatment plans, and lowing relevant clinical risks. However, the manual process of the assessment imposes a substantial burden and can lead to errors in digitalization. To fill these gaps, we proposed an automatic nursing assessment system based on clinical decision support system (CDSS). The framework underlying the CDSS included experts, evaluation criteria, and voting roles for selecting electronic assessment sheets over paper ones. METHODS We developed the framework based on an expert voting flow to choose electronic assessment sheets. The CDSS was constructed based on a nursing process workflow model. A multilayer architecture with independent modules was used. The performance of the proposed system was evaluated by comparing the adverse events' incidence and the average time for regular daily assessment before and after the implementation. RESULTS After implementation of the system, the adverse nursing events' incidence decreased significantly from 0.43 % to 0.37 % in the first year and further to 0.27 % in the second year (p-value: 0.04). Meanwhile, the median time for regular daily assessments further decreased from 63 s to 51 s. CONCLUSIONS The automatic assessment system helps to reduce nurses' workload and the incidence of adverse nursing events.
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Affiliation(s)
- Ling Dai
- Hangzhou Children's Hospital, Hangzhou, Zhejiang, China
| | - Zhijun Wu
- ZhongWei Institute of Nursing Information, Beijing, China
| | - Xiaocheng Pan
- Hangzhou Children's Hospital, Hangzhou, Zhejiang, China
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare Coventry University, Coventry, UK
| | - Mengli Kang
- Hangzhou Children's Hospital, Hangzhou, Zhejiang, China
| | - Mingming Zhou
- Hangzhou Children's Hospital, Hangzhou, Zhejiang, China
| | - Guanyu Chen
- Ewell Technology Co., Ltd, Hangzhou, Zhejiang, China
| | - Haipeng Liu
- Research Centre for Intelligent Healthcare Coventry University, Coventry, UK.
| | - Xin Tian
- The Affiliated Hospital of Hangzhou Normal University, Hangzhou, Zhejiang, China
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Giddings R, Joseph A, Callender T, Janes SM, van der Schaar M, Sheringham J, Navani N. Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review. Lancet Digit Health 2024; 6:e131-e144. [PMID: 38278615 DOI: 10.1016/s2589-7500(23)00241-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 10/20/2023] [Accepted: 11/14/2023] [Indexed: 01/28/2024]
Abstract
Machine learning (ML)-based risk prediction models hold the potential to support the health-care setting in several ways; however, use of such models is scarce. We aimed to review health-care professional (HCP) and patient perceptions of ML risk prediction models in published literature, to inform future risk prediction model development. Following database and citation searches, we identified 41 articles suitable for inclusion. Article quality varied with qualitative studies performing strongest. Overall, perceptions of ML risk prediction models were positive. HCPs and patients considered that models have the potential to add benefit in the health-care setting. However, reservations remain; for example, concerns regarding data quality for model development and fears of unintended consequences following ML model use. We identified that public views regarding these models might be more negative than HCPs and that concerns (eg, extra demands on workload) were not always borne out in practice. Conclusions are tempered by the low number of patient and public studies, the absence of participant ethnic diversity, and variation in article quality. We identified gaps in knowledge (particularly views from under-represented groups) and optimum methods for model explanation and alerts, which require future research.
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Affiliation(s)
- Rebecca Giddings
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK.
| | - Anabel Joseph
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Thomas Callender
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Sam M Janes
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK; The Alan Turing Institute, London, UK
| | - Jessica Sheringham
- Department of Applied Health Research, University College London, London, UK
| | - Neal Navani
- Lungs for Living Research Centre, UCL Respiratory, University College London, London, UK
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Chen Z, Liang N, Zhang H, Li H, Yang Y, Zong X, Chen Y, Wang Y, Shi N. Harnessing the power of clinical decision support systems: challenges and opportunities. Open Heart 2023; 10:e002432. [PMID: 38016787 PMCID: PMC10685930 DOI: 10.1136/openhrt-2023-002432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/31/2023] [Indexed: 11/30/2023] Open
Abstract
Clinical decision support systems (CDSSs) are increasingly integrated into healthcare settings to improve patient outcomes, reduce medical errors and enhance clinical efficiency by providing clinicians with evidence-based recommendations at the point of care. However, the adoption and optimisation of these systems remain a challenge. This review aims to provide an overview of the current state of CDSS, discussing their development, implementation, benefits, limitations and future directions. We also explore the potential for enhancing their effectiveness and provide an outlook for future developments in this field. There are several challenges in CDSS implementation, including data privacy concerns, system integration and clinician acceptance. While CDSS have demonstrated significant potential, their adoption and optimisation remain a challenge.
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Affiliation(s)
- Zhao Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ning Liang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Haili Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Huizhen Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yijiu Yang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xingyu Zong
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yaxin Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanping Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Nannan Shi
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
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Bamgboje-Ayodele A, McPhail SM, Brain D, Taggart R, Burger M, Bruce L, Holtby C, Pradhan M, Simpson M, Shaw TJ, Baysari MT. How digital health translational research is prioritised: a qualitative stakeholder-driven approach to decision support evaluation. BMJ Open 2023; 13:e075009. [PMID: 37931965 PMCID: PMC10632864 DOI: 10.1136/bmjopen-2023-075009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 09/26/2023] [Indexed: 11/08/2023] Open
Abstract
OBJECTIVES Digital health is now routinely being applied in clinical care, and with a variety of clinician-facing systems available, healthcare organisations are increasingly required to make decisions about technology implementation and evaluation. However, few studies have examined how digital health research is prioritised, particularly research focused on clinician-facing decision support systems. This study aimed to identify criteria for prioritising digital health research, examine how these differ from criteria for prioritising traditional health research and determine priority decision support use cases for a collaborative implementation research programme. METHODS Drawing on an interpretive listening model for priority setting and a stakeholder-driven approach, our prioritisation process involved stakeholder identification, eliciting decision support use case priorities from stakeholders, generating initial use case priorities and finalising preferred use cases based on consultations. In this qualitative study, online focus group session(s) were held with stakeholders, audiorecorded, transcribed and analysed thematically. RESULTS Fifteen participants attended the online priority setting sessions. Criteria for prioritising digital health research fell into three themes, namely: public health benefit, health system-level factors and research process and feasibility. We identified criteria unique to digital health research as the availability of suitable governance frameworks, candidate technology's alignment with other technologies in use,and the possibility of data-driven insights from health technology data. The final selected use cases were remote monitoring of patients with pulmonary conditions, sepsis detection and automated breast screening. CONCLUSION The criteria for determining digital health research priority areas are more nuanced than that of traditional health condition focused research and can neither be viewed solely through a clinical lens nor technological lens. As digital health research relies heavily on health technology implementation, digital health prioritisation criteria comprised enablers of successful technology implementation. Our prioritisation process could be applied to other settings and collaborative projects where research institutions partner with healthcare delivery organisations.
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Affiliation(s)
- Adeola Bamgboje-Ayodele
- Biomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia
| | - Steven M McPhail
- Australian Centre for Health Service Innovation and Centre for Healthcare Transformation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - David Brain
- Australian Centre for Health Service Innovation and Centre for Healthcare Transformation, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Richard Taggart
- Sydney Local Health District, NSW Health, Camperdown, New South Wales, Australia
| | - Mitchell Burger
- Sydney Local Health District, NSW Health, Camperdown, New South Wales, Australia
| | - Lenert Bruce
- Murrumbidgee Local Health District, NSW Health, Wagga Wagga, New South Wales, Australia
| | - Caroline Holtby
- Murrumbidgee Local Health District, NSW Health, Wagga Wagga, New South Wales, Australia
| | | | - Mark Simpson
- eHealth NSW, Chatswood, New South Wales, Australia
| | - Tim J Shaw
- Biomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia
| | - Melissa T Baysari
- Biomedical Informatics and Digital Health, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Camperdown, New South Wales, Australia
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Alanazi A. Clinicians' Views on Using Artificial Intelligence in Healthcare: Opportunities, Challenges, and Beyond. Cureus 2023; 15:e45255. [PMID: 37842420 PMCID: PMC10576621 DOI: 10.7759/cureus.45255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/14/2023] [Indexed: 10/17/2023] Open
Abstract
INTRODUCTION The healthcare industry has made significant progress in information technology, which has improved healthcare procedures and brought about advancements in clinical care services. This includes gathering crucial clinical data and implementing intelligent health information management. Artificial Intelligence (AI) has the potential to bolster further existing health information systems, notably electronic health records (EHRs). With AI, EHRs can offer more customized and adaptable roles for patients. This study aims to delve into the current and potential uses of AI and examine the obstacles that come with it. METHOD In this study, we employed a qualitative methodology and purposive sampling to select participants. We sought out clinicians who were eager to share their professional insights. Our research involved conducting three focus group interviews, each lasting an hour. The moderator began each session by introducing the study's goals and assuring participants of confidentiality to foster a collaborative environment. The facilitator asked open-ended questions about EHR, including its applications, challenges, and AI-assisted features. RESULTS The research conducted by 26 participants has identified five crucial areas of using AI in healthcare delivery. These areas include predictive analysis, clinical decision support systems, data visualization, natural language processing (NLP), patient monitoring, mobile technology, and future and emerging trends. However, the hype surrounding AI and the fact that the technology is still in its early stages pose significant challenges. Technical limitations related to language processing and context-specific reasoning must be addressed. Furthermore, medico-legal challenges arise when AI supports or autonomously delivers healthcare services. Governments must develop strategies to ensure AI's responsible and transparent application in healthcare delivery. CONCLUSION AI technology has the potential to revolutionize healthcare through its integration with EHRs and other existing technologies. However, several challenges must be addressed before this potential can be fully realized. The development and testing of complex EHR systems that utilize AI must be approached with care to ensure their accuracy and trustworthiness in decision-making about patient treatment. Additionally, there is a need to navigate medico-legal obligations and ensure that benefits are equitably distributed.
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Affiliation(s)
- Abdullah Alanazi
- Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, SAU
- Research, King Abdullah International Medical Research Center, Riyadh, SAU
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Dhala A, Fusaro MV, Uddin F, Tuazon D, Klahn S, Schwartz R, Sasangohar F, Alegria J, Masud F. Integrating a Virtual ICU with Cardiac and Cardiovascular ICUs: Managing the Needs of a Complex and High-Acuity Specialty ICU Cohort. Methodist Debakey Cardiovasc J 2023; 19:4-16. [PMID: 37547898 PMCID: PMC10402825 DOI: 10.14797/mdcvj.1247] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 06/12/2023] [Indexed: 08/08/2023] Open
Abstract
A long-standing shortage of critical care intensivists and nurses, exacerbated by the coronavirus disease (COVID-19) pandemic, has led to an accelerated adoption of tele-critical care in the United States (US). Due to their complex and high-acuity nature, cardiac, cardiovascular, and cardiothoracic intensive care units (ICUs) have generally been limited in their ability to leverage tele-critical care resources. In early 2020, Houston Methodist Hospital (HMH) launched its tele-critical care program called Virtual ICU, or vICU, to improve its ICU staffing efficiency while providing high-quality, continuous access to in-person and virtual intensivists and critical care nurses. This article provides a roadmap with prescriptive specifications for planning, launching, and integrating vICU services within cardiac and cardiovascular ICUs-one of the first such integrations among the leading academic US hospitals. The success of integrating vICU depends upon the (1) recruitment of intensivists and RNs with expertise in managing cardiac and cardiovascular patients on the vICU staff as well as concerted efforts to promote mutual trust and confidence between in-person and virtual providers, (2) consultations with the bedside clinicians to secure their buy-in on the merits of vICU resources, and (3) collaborative approaches to improve workflow protocols and communications. Integration of vICU has resulted in the reduction of monthly night-call requirements for the in-person intensivists and an increase in work satisfaction. Data also show that support of the vICU is associated with a significant reduction in the rate of Code Blue events (denoting a situation where a patient requires immediate resuscitation, typically due to a cardiac or respiratory arrest). As the providers become more comfortable with the advances in artificial intelligence and big data-driven technology, the Cardiac ICU Cohort continues to improve methods to predict and track patient trends in the ICUs.
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Affiliation(s)
- Atiya Dhala
- Houston Methodist Hospital, Houston, Texas, US
| | | | - Faisal Uddin
- Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, Texas, US
| | - Divina Tuazon
- Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, Texas, US
| | - Steven Klahn
- Department of Virtual Medicine, Houston Methodist Hospital, Houston, Texas, US
| | | | - Farzan Sasangohar
- Houston Methodist Academic Institute, Houston Methodist Hospital, Houston, Texas, US
- Texas A&M University, College Station, Texas, US
| | | | - Faisal Masud
- Houston Methodist DeBakey Heart & Vascular Center, Houston Methodist Hospital, Houston, Texas, US
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Benzinger L, Ursin F, Balke WT, Kacprowski T, Salloch S. Should Artificial Intelligence be used to support clinical ethical decision-making? A systematic review of reasons. BMC Med Ethics 2023; 24:48. [PMID: 37415172 PMCID: PMC10327319 DOI: 10.1186/s12910-023-00929-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/28/2023] [Indexed: 07/08/2023] Open
Abstract
BACKGROUND Healthcare providers have to make ethically complex clinical decisions which may be a source of stress. Researchers have recently introduced Artificial Intelligence (AI)-based applications to assist in clinical ethical decision-making. However, the use of such tools is controversial. This review aims to provide a comprehensive overview of the reasons given in the academic literature for and against their use. METHODS PubMed, Web of Science, Philpapers.org and Google Scholar were searched for all relevant publications. The resulting set of publications was title and abstract screened according to defined inclusion and exclusion criteria, resulting in 44 papers whose full texts were analysed using the Kuckartz method of qualitative text analysis. RESULTS Artificial Intelligence might increase patient autonomy by improving the accuracy of predictions and allowing patients to receive their preferred treatment. It is thought to increase beneficence by providing reliable information, thereby, supporting surrogate decision-making. Some authors fear that reducing ethical decision-making to statistical correlations may limit autonomy. Others argue that AI may not be able to replicate the process of ethical deliberation because it lacks human characteristics. Concerns have been raised about issues of justice, as AI may replicate existing biases in the decision-making process. CONCLUSIONS The prospective benefits of using AI in clinical ethical decision-making are manifold, but its development and use should be undertaken carefully to avoid ethical pitfalls. Several issues that are central to the discussion of Clinical Decision Support Systems, such as justice, explicability or human-machine interaction, have been neglected in the debate on AI for clinical ethics so far. TRIAL REGISTRATION This review is registered at Open Science Framework ( https://osf.io/wvcs9 ).
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Affiliation(s)
- Lasse Benzinger
- Institute for Ethics, History and Philosophy of Medicine, Hannover Medical School (MHH), Carl-Neuberg-Str. 1, 30625, Hannover, Germany.
| | - Frank Ursin
- Institute for Ethics, History and Philosophy of Medicine, Hannover Medical School (MHH), Carl-Neuberg-Str. 1, 30625, Hannover, Germany
| | - Wolf-Tilo Balke
- Institute for Information Systems, TU Braunschweig, Braunschweig, Germany
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany
- Braunschweig Integrated Centre for Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
| | - Sabine Salloch
- Institute for Ethics, History and Philosophy of Medicine, Hannover Medical School (MHH), Carl-Neuberg-Str. 1, 30625, Hannover, Germany
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Mukherjee T, Pournik O, Lim Choi Keung SN, Arvanitis TN. Clinical Decision Support Systems for Brain Tumour Diagnosis and Prognosis: A Systematic Review. Cancers (Basel) 2023; 15:3523. [PMID: 37444633 DOI: 10.3390/cancers15133523] [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: 05/31/2023] [Revised: 07/02/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023] Open
Abstract
CDSSs are being continuously developed and integrated into routine clinical practice as they assist clinicians and radiologists in dealing with an enormous amount of medical data, reduce clinical errors, and improve diagnostic capabilities. They assist detection, classification, and grading of brain tumours as well as alert physicians of treatment change plans. The aim of this systematic review is to identify various CDSSs that are used in brain tumour diagnosis and prognosis and rely on data captured by any imaging modality. Based on the 2020 preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, the literature search was conducted in PubMed and Engineering Village Compendex databases. Different types of CDSSs identified through this review include Curiam BT, FASMA, MIROR, HealthAgents, and INTERPRET, among others. This review also examines various CDSS tool types, system features, techniques, accuracy, and outcomes, to provide the latest evidence available in the field of neuro-oncology. An overview of such CDSSs used to support clinical decision-making in the management and treatment of brain tumours, along with their benefits, challenges, and future perspectives has been provided. Although a CDSS improves diagnostic capabilities and healthcare delivery, there is lack of specific evidence to support these claims. The absence of empirical data slows down both user acceptance and evaluation of the actual impact of CDSS on brain tumour management. Instead of emphasizing the advantages of implementing CDSS, it is important to address its potential drawbacks and ethical implications. By doing so, it can promote the responsible use of CDSS and facilitate its faster adoption in clinical settings.
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Affiliation(s)
- Teesta Mukherjee
- Department of Electronic, Electrical and Systems Engineering, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Omid Pournik
- Department of Electronic, Electrical and Systems Engineering, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Sarah N Lim Choi Keung
- Department of Electronic, Electrical and Systems Engineering, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Theodoros N Arvanitis
- Department of Electronic, Electrical and Systems Engineering, School of Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
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Qudus MS, Tian M, Sirajuddin S, Liu S, Afaq U, Wali M, Liu J, Pan P, Luo Z, Zhang Q, Yang G, Wan P, Li Y, Wu J. The roles of critical pro-inflammatory cytokines in the drive of cytokine storm during SARS-CoV-2 infection. J Med Virol 2023; 95:e28751. [PMID: 37185833 DOI: 10.1002/jmv.28751] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 03/17/2023] [Accepted: 04/07/2023] [Indexed: 05/17/2023]
Abstract
In patients with severe COVID-19, acute respiratory distress syndrome (ARDS), multiple organ dysfunction syndrome (MODS), and even mortality can result from cytokine storm, which is a hyperinflammatory medical condition caused by the excessive and uncontrolled release of pro-inflammatory cytokines. High levels of numerous crucial pro-inflammatory cytokines, such as interleukin-1 (IL-1), IL-2, IL-6, tumor necrosis factor-α, interferon (IFN)-γ, IFN-induced protein 10 kDa, granulocyte-macrophage colony-stimulating factor, monocyte chemoattractant protein-1, and IL-10 and so on, have been found in severe COVID-19. They participate in cascade amplification pathways of pro-inflammatory responses through complex inflammatory networks. Here, we review the involvements of these critical inflammatory cytokines in SARS-CoV-2 infection and discuss their potential roles in triggering or regulating cytokine storm, which can help to understand the pathogenesis of severe COVID-19. So far, there is rarely effective therapeutic strategy for patients with cytokine storm besides using glucocorticoids, which is proved to result in fatal side effects. Clarifying the roles of key involved cytokines in the complex inflammatory network of cytokine storm will help to develop an ideal therapeutic intervention, such as neutralizing antibody of certain cytokine or inhibitor of some inflammatory signal pathways.
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Affiliation(s)
- Muhammad Suhaib Qudus
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, China
| | - Mingfu Tian
- Key Laboratory of Ministry of Education for Viral Pathogenesis & Infection Prevention and Control, Institute of Medical Microbiology, Jinan University, Guangzhou, China
| | - Summan Sirajuddin
- Department of Health and Biological Sciences, Abasyn University, Peshawar, Pakistan
| | - Siyu Liu
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, China
| | - Uzair Afaq
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, China
| | - Muneeba Wali
- Department of Allied Health Sciences, CECOS University of IT and Emerging Sciences, Peshawar, Pakistan
| | - Jinbiao Liu
- Key Laboratory of Ministry of Education for Viral Pathogenesis & Infection Prevention and Control, Institute of Medical Microbiology, Jinan University, Guangzhou, China
| | - Pan Pan
- Key Laboratory of Ministry of Education for Viral Pathogenesis & Infection Prevention and Control, Institute of Medical Microbiology, Jinan University, Guangzhou, China
- Foshan Institute of Medical Microbiology, Foshan, China
| | - Zhen Luo
- Key Laboratory of Ministry of Education for Viral Pathogenesis & Infection Prevention and Control, Institute of Medical Microbiology, Jinan University, Guangzhou, China
- Foshan Institute of Medical Microbiology, Foshan, China
| | - Qiwei Zhang
- Key Laboratory of Ministry of Education for Viral Pathogenesis & Infection Prevention and Control, Institute of Medical Microbiology, Jinan University, Guangzhou, China
- Foshan Institute of Medical Microbiology, Foshan, China
| | - Ge Yang
- Foshan Institute of Medical Microbiology, Foshan, China
| | - Pin Wan
- Foshan Institute of Medical Microbiology, Foshan, China
| | - Yongkui Li
- Key Laboratory of Ministry of Education for Viral Pathogenesis & Infection Prevention and Control, Institute of Medical Microbiology, Jinan University, Guangzhou, China
- Foshan Institute of Medical Microbiology, Foshan, China
| | - Jianguo Wu
- State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, China
- Key Laboratory of Ministry of Education for Viral Pathogenesis & Infection Prevention and Control, Institute of Medical Microbiology, Jinan University, Guangzhou, China
- Foshan Institute of Medical Microbiology, Foshan, China
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12
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Development of a Machine Learning-Based Prediction Model for Chemotherapy-Induced Myelosuppression in Children with Wilms' Tumor. Cancers (Basel) 2023; 15:cancers15041078. [PMID: 36831423 PMCID: PMC9954251 DOI: 10.3390/cancers15041078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 02/03/2023] [Accepted: 02/05/2023] [Indexed: 02/11/2023] Open
Abstract
Purpose: Develop and validate an accessible prediction model using machine learning (ML) to predict the risk of chemotherapy-induced myelosuppression (CIM) in children with Wilms' tumor (WT) before chemotherapy is administered, enabling early preventive management. Methods: A total of 1433 chemotherapy cycles in 437 children with WT who received chemotherapy in our hospital from January 2009 to March 2022 were retrospectively analyzed. Demographic data, clinicopathological characteristics, hematology and blood biochemistry baseline results, and medication information were collected. Six ML algorithms were used to construct prediction models, and the predictive efficacy of these models was evaluated to select the best model to predict the risk of grade ≥ 2 CIM in children with WT. A series of methods, such as the area under the receiver operating characteristic curve (AUROC), the calibration curve, and the decision curve analysis (DCA) were used to test the model's accuracy, discrimination, and clinical practicability. Results: Grade ≥ 2 CIM occurred in 58.5% (839/1433) of chemotherapy cycles. Based on the results of the training and validation cohorts, we finally identified that the extreme gradient boosting (XGB) model has the best predictive efficiency and stability, with an AUROC of up to 0.981 in the training set and up to 0.896 in the test set. In addition, the calibration curve and the DCA showed that the XGB model had the best discrimination and clinical practicability. The variables were ranked according to the feature importance, and the five variables contributing the most to the model were hemoglobin (Hgb), white blood cell count (WBC), alkaline phosphatase, coadministration of highly toxic chemotherapy drugs, and albumin. Conclusions: The incidence of grade ≥ 2 CIM was not low in children with WT, which needs attention. The XGB model was developed to predict the risk of grade ≥ 2 CIM in children with WT for the first time. The model has good predictive performance and stability and has the potential to be translated into clinical applications. Based on this modeling and application approach, the extension of CIM prediction models to other pediatric malignancies could be expected.
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Blanes-Selva V, Asensio-Cuesta S, Doñate-Martínez A, Pereira Mesquita F, García-Gómez JM. User-centred design of a clinical decision support system for palliative care: Insights from healthcare professionals. Digit Health 2023; 9:20552076221150735. [PMID: 36644661 PMCID: PMC9837281 DOI: 10.1177/20552076221150735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 12/26/2022] [Indexed: 01/13/2023] Open
Abstract
Objective Although clinical decision support systems (CDSS) have many benefits for clinical practice, they also have several barriers to their acceptance by professionals. Our objective in this study was to design and validate The Aleph palliative care (PC) CDSS through a user-centred method, considering the predictions of the artificial intelligence (AI) core, usability and user experience (UX). Methods We performed two rounds of individual evaluation sessions with potential users. Each session included a model evaluation, a task test and a usability and UX assessment. Results The machine learning (ML) predictive models outperformed the participants in the three predictive tasks. System Usability Scale (SUS) reported 62.7 ± 14.1 and 65 ± 26.2 on a 100-point rating scale for both rounds, respectively, while User Experience Questionnaire - Short Version (UEQ-S) scores were 1.42 and 1.5 on the -3 to 3 scale. Conclusions The think-aloud method and including the UX dimension helped us to identify most of the workflow implementation issues. The system has good UX hedonic qualities; participants were interested in the tool and responded positively to it. Performance regarding usability was modest but acceptable.
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Affiliation(s)
- Vicent Blanes-Selva
- Biomedical Data Science Lab, Instituto Universitarios de Tecnologías de La Información y Comunicaciones (ITACA), Universitat Politècnica de València, Valencia, Spain,Vicent Blanes-Selva, Biomedical Data Science Lab, Instituto Universitarios de Tecnologías de La Información y Comunicaciones (ITACA), Universitat Politècnica de València, Valencia, 46022, Spain.
| | - Sabina Asensio-Cuesta
- Biomedical Data Science Lab, Instituto Universitarios de Tecnologías de La Información y Comunicaciones (ITACA), Universitat Politècnica de València, Valencia, Spain
| | | | - Felipe Pereira Mesquita
- Divisão de Hematologia, departamento de Clínica Médica, da Universidade Federal de Juiz de Fora, Minas Gerais, Brasil
| | - Juan M. García-Gómez
- Biomedical Data Science Lab, Instituto Universitarios de Tecnologías de La Información y Comunicaciones (ITACA), Universitat Politècnica de València, Valencia, Spain
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14
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Moris D, Henao R, Hensman H, Stempora L, Chasse S, Schobel S, Dente CJ, Kirk AD, Elster E. Multidimensional machine learning models predicting outcomes after trauma. Surgery 2022; 172:1851-1859. [PMID: 36116976 DOI: 10.1016/j.surg.2022.08.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 08/01/2022] [Accepted: 08/04/2022] [Indexed: 01/07/2023]
Abstract
BACKGROUND An emerging body of literature supports the role of individualized prognostic tools to guide the management of patients after trauma. The aim of this study was to develop advanced modeling tools from multidimensional data sources, including immunological analytes and clinical and administrative data, to predict outcomes in trauma patients. METHODS This was a prospective study of trauma patients at Level 1 centers from 2015 to 2019. Clinical, flow cytometry, and serum cytokine data were collected within 48 hours of admission. Sparse logistic regression models were developed, jointly selecting predictors and estimating the risk of ventilator-associated pneumonia, acute kidney injury, complicated disposition (death, rehabilitation, or nursing facility), and return to the operating room. Model parameters (regularization controlling model sparsity) and performance estimation were obtained via nested leave-one-out cross-validation. RESULTS A total of 179 patients were included. The incidences of ventilator-associated pneumonia, acute kidney injury, complicated disposition, and return to the operating room were 17.7%, 28.8%, 22.5%, and 12.3%, respectively. Regarding extensive resource use, 30.7% of patients had prolonged intensive care unit stay, 73.2% had prolonged length of stay, and 23.5% had need for prolonged ventilatory support. The models were developed and cross-validated for ventilator-associated pneumonia, acute kidney injury, complicated dispositions, and return to the operating room, yielding predictive areas under the curve from 0.70 to 0.91. Each model derived its optimal predictive value by combining clinical, administrative, and immunological analyte data. CONCLUSION Clinical, immunological, and administrative data can be combined to predict post-traumatic outcomes and resource use. Multidimensional machine learning modeling can identify trauma patients with complicated clinical trajectories and high resource needs.
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Affiliation(s)
| | | | - Hannah Hensman
- DecisionQ, Arlington, VA; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD
| | - Linda Stempora
- Medical Center, Duke University Durham, NC; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD
| | - Scott Chasse
- Medical Center, Duke University Durham, NC; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD
| | - Seth Schobel
- Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD; Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc, Bethesda, MD
| | | | - Allan D Kirk
- Medical Center, Duke University Durham, NC; Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD
| | - Eric Elster
- Surgical Critical Care Initiative, Department of Surgery, Uniformed Services University of the Health Sciences; Bethesda, MD; Walter Reed National Military Medical Center, Bethesda, MD
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15
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Samitinjay A, Ramavath A, Kulakarni SC, Biswas R. Autoimmune haemolytic anaemia due to immunodeficiency. BMJ Case Rep 2022; 15:e250074. [PMID: 36414334 PMCID: PMC9685200 DOI: 10.1136/bcr-2022-250074] [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: 11/23/2022] Open
Abstract
Autoimmune disorders are common presenting manifestations of immunodeficiency syndromes. We present a case of a woman in her late teens, with a history of frequent sinopulmonary tract infections during her childhood, who presented to our hospital with anaemia, jaundice and fatigue. She also had significant physical growth retardation for her age and sex. With this case report, we intend to present the diagnostic and therapeutic challenges faced by the patient and our healthcare system and propose a few feasible solutions to tackle these challenges.
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Affiliation(s)
- Aditya Samitinjay
- General Medicine, Kamineni Institute of Medical Sciences, Chityala, Telangana, India
- General Medicine, Government General and Chest Hospital, Hyderabad, Telangana, India
| | - Arjun Ramavath
- General Medicine, Kamineni Institute of Medical Sciences, Chityala, Telangana, India
| | - Sai Charan Kulakarni
- General Medicine, Kamineni Institute of Medical Sciences, Chityala, Telangana, India
| | - Rakesh Biswas
- General Medicine, Kamineni Institute of Medical Sciences, Chityala, Telangana, India
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16
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Mathis MR, Engoren MC, Williams AM, Biesterveld BE, Croteau AJ, Cai L, Kim RB, Liu G, Ward KR, Najarian K, Gryak J. Prediction of Postoperative Deterioration in Cardiac Surgery Patients Using Electronic Health Record and Physiologic Waveform Data. Anesthesiology 2022; 137:586-601. [PMID: 35950802 PMCID: PMC10227693 DOI: 10.1097/aln.0000000000004345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
BACKGROUND Postoperative hemodynamic deterioration among cardiac surgical patients can indicate or lead to adverse outcomes. Whereas prediction models for such events using electronic health records or physiologic waveform data are previously described, their combined value remains incompletely defined. The authors hypothesized that models incorporating electronic health record and processed waveform signal data (electrocardiogram lead II, pulse plethysmography, arterial catheter tracing) would yield improved performance versus either modality alone. METHODS Intensive care unit data were reviewed after elective adult cardiac surgical procedures at an academic center between 2013 and 2020. Model features included electronic health record features and physiologic waveforms. Tensor decomposition was used for waveform feature reduction. Machine learning-based prediction models included a 2013 to 2017 training set and a 2017 to 2020 temporal holdout test set. The primary outcome was a postoperative deterioration event, defined as a composite of low cardiac index of less than 2.0 ml min-1 m-2, mean arterial pressure of less than 55 mmHg sustained for 120 min or longer, new or escalated inotrope/vasopressor infusion, epinephrine bolus of 1 mg or more, or intensive care unit mortality. Prediction models analyzed data 8 h before events. RESULTS Among 1,555 cases, 185 (12%) experienced 276 deterioration events, most commonly including low cardiac index (7.0% of patients), new inotrope (1.9%), and sustained hypotension (1.4%). The best performing model on the 2013 to 2017 training set yielded a C-statistic of 0.803 (95% CI, 0.799 to 0.807), although performance was substantially lower in the 2017 to 2020 test set (0.709, 0.705 to 0.712). Test set performance of the combined model was greater than corresponding models limited to solely electronic health record features (0.641; 95% CI, 0.637 to 0.646) or waveform features (0.697; 95% CI, 0.693 to 0.701). CONCLUSIONS Clinical deterioration prediction models combining electronic health record data and waveform data were superior to either modality alone, and performance of combined models was primarily driven by waveform data. Decreased performance of prediction models during temporal validation may be explained by data set shift, a core challenge of healthcare prediction modeling. EDITOR’S PERSPECTIVE
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Affiliation(s)
- Michael R Mathis
- Department of Anesthesiology, University of Michigan Health System, Ann Arbor, Michigan; Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan; Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan; and Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan
| | - Milo C Engoren
- Department of Anesthesiology, University of Michigan Health System, Ann Arbor, Michigan
| | - Aaron M Williams
- Department of General Surgery, University of Michigan Health System, Ann Arbor, Michigan
| | - Ben E Biesterveld
- Department of General Surgery, University of Michigan Health System, Ann Arbor, Michigan
| | - Alfred J Croteau
- Department of General Surgery, Hartford HealthCare Medical Group, Hartford, Connecticut
| | - Lingrui Cai
- Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan
| | - Renaid B Kim
- Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan
| | - Gang Liu
- Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan
| | - Kevin R Ward
- Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan; Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan; and Department of Emergency Medicine, University of Michigan Health System, Ann Arbor, Michigan
| | - Kayvan Najarian
- Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan; Michigan Integrated Center for Health Analytics and Medical Prediction, Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, Michigan; and Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan
| | - Jonathan Gryak
- Department of Computational Medicine and Bioinformatics, University of Michigan Health System, Ann Arbor, Michigan; and Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, Michigan
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Mumuni AN, Hasford F, Udeme NI, Dada MO, Awojoyogbe BO. A SWOT analysis of artificial intelligence in diagnostic imaging in the developing world: making a case for a paradigm shift. PHYSICAL SCIENCES REVIEWS 2022. [DOI: 10.1515/psr-2022-0121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Abstract
Diagnostic imaging (DI) refers to techniques and methods of creating images of the body’s internal parts and organs with or without the use of ionizing radiation, for purposes of diagnosing, monitoring and characterizing diseases. By default, DI equipment are technology based and in recent times, there has been widespread automation of DI operations in high-income countries while low and middle-income countries (LMICs) are yet to gain traction in automated DI. Advanced DI techniques employ artificial intelligence (AI) protocols to enable imaging equipment perceive data more accurately than humans do, and yet automatically or under expert evaluation, make clinical decisions such as diagnosis and characterization of diseases. In this narrative review, SWOT analysis is used to examine the strengths, weaknesses, opportunities and threats associated with the deployment of AI-based DI protocols in LMICs. Drawing from this analysis, a case is then made to justify the need for widespread AI applications in DI in resource-poor settings. Among other strengths discussed, AI-based DI systems could enhance accuracies in diagnosis, monitoring, characterization of diseases and offer efficient image acquisition, processing, segmentation and analysis procedures, but may have weaknesses regarding the need for big data, huge initial and maintenance costs, and inadequate technical expertise of professionals. They present opportunities for synthetic modality transfer, increased access to imaging services, and protocol optimization; and threats of input training data biases, lack of regulatory frameworks and perceived fear of job losses among DI professionals. The analysis showed that successful integration of AI in DI procedures could position LMICs towards achievement of universal health coverage by 2030/2035. LMICs will however have to learn from the experiences of advanced settings, train critical staff in relevant areas of AI and proceed to develop in-house AI systems with all relevant stakeholders onboard.
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Affiliation(s)
| | - Francis Hasford
- Department of Medical Physics , University of Ghana, Ghana Atomic Energy Commission , Accra , Ghana
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Abouzahra M, Guenter D, Tan J. Exploring physicians’ continuous use of clinical decision support systems. EUR J INFORM SYST 2022. [DOI: 10.1080/0960085x.2022.2119172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Affiliation(s)
| | - Dale Guenter
- Department of Family Medicine, McMaster University
| | - Joseph Tan
- DeGroote School of Medicine, McMaster University
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Abstract
OBJECTIVES To assess the current landscape of clinical decision support (CDS) tools in PICUs in order to identify priority areas of focus in this field. DESIGN International, quantitative, cross-sectional survey. SETTING Role-specific, web-based survey administered in November and December 2020. SUBJECTS Medical directors, bedside nurses, attending physicians, and residents/advanced practice providers at Pediatric Acute Lung Injury and Sepsis Network-affiliated PICUs. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS The survey was completed by 109 respondents from 45 institutions, primarily attending physicians from university-affiliated PICUs in the United States. The most commonly used CDS tools were people-based resources (93% used always or most of the time) and laboratory result highlighting (86%), with order sets, order-based alerts, and other electronic CDS tools also used frequently. The most important goal providers endorsed for CDS tools were a proven impact on patient safety and an evidence base for their use. Negative perceptions of CDS included concerns about diminished critical thinking and the burden of intrusive processes on providers. Routine assessment of existing CDS was rare, with infrequent reported use of observation to assess CDS impact on workflows or measures of individual alert burden. CONCLUSIONS Although providers share some consensus over CDS utility, we identified specific priority areas of research focus. Consensus across practitioners exists around the importance of evidence-based CDS tools having a proven impact on patient safety. Despite broad presence of CDS tools in PICUs, practitioners continue to view them as intrusive and with concern for diminished critical thinking. Deimplementing ineffective CDS may mitigate this burden, though postimplementation evaluation of CDS is rare.
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Feng X, Hua Y, Zou J, Jia S, Ji J, Xing Y, Zhou J, Liao J. Intelligible Models for HealthCare: Predicting the Probability of 6-Month Unfavorable Outcome in Patients with Ischemic Stroke. Neuroinformatics 2022; 20:575-585. [PMID: 34435319 DOI: 10.1007/s12021-021-09535-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/04/2021] [Indexed: 12/31/2022]
Abstract
Early prediction of unfavorable outcome after ischemic stroke is significant for clinical management. Machine learning as a novel computational modeling technique could help clinicians to address the challenge. We aim to investigate the applicability of machine learning models for individualized prediction in ischemic stroke patients and demonstrate the utility of various model-agnostic explanation techniques for machine learning predictions. A total of 499 consecutive patients with Unfavorable [modified Rankin Scale (mRS) score 3-6, n = 140] and favorable (mRS score 0-2, n = 359) outcome after 6-month from ischemic stroke were enrolled in this study. Four machine learning models, including Random Forest [RF], eXtreme Gradient Boosting [XGBoost], Adaptive Boosting [Adaboost] and Support Vector Machine [SVM] were performed with the area-under-the-curve (AUC): (90.20 ± 0.22)%, (86.91 ± 1.05)%, (86.49 ± 2.35)%, (81.89 ± 2.40)%, respectively. Three global interpretability techniques (Feature Importance shows the contribution of selected features, Partial Dependence Plot aims to visualize the average effect of a feature on the predicted probability of unfavorable outcome, Feature Interaction detects the change in the prediction that occurs by varying the features after considering the individual feature effects) and one local interpretability technique (Shapley Value indicates the probability of unfavorable outcome of different instances) have been applied to present the interpretability techniques via visualization. Thereby, the current study is important for better understanding intelligible healthcare analytics via explanations for the prediction of local and global levels, and potentially reduction of the mortality of patients with ischemic stroke by assisting clinicians in the decision-making process.
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Affiliation(s)
- Xiaobing Feng
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Yingrong Hua
- School of Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Jianjun Zou
- Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Shuopeng Jia
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Jiatong Ji
- School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Yan Xing
- School of Science, China Pharmaceutical University, #639 Longmian Avenue, Jiangning District, Nanjing, 211198, China
| | - Junshan Zhou
- Department of Neurology, Nanjing First Hospital, Nanjing, China
| | - Jun Liao
- School of Science, China Pharmaceutical University, #639 Longmian Avenue, Jiangning District, Nanjing, 211198, China.
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Ossai CI, Wickramasinghe N. A hybrid approach for risk stratification and predictive modelling of 30-days unplanned readmission of comorbid patients with diabetes. J Diabetes Complications 2022; 36:108200. [PMID: 35490078 DOI: 10.1016/j.jdiacomp.2022.108200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/02/2022] [Accepted: 04/13/2022] [Indexed: 10/18/2022]
Abstract
OBJECTIVES When comorbid patients with diabetes have 30-days Unplanned Readmission (URA), they attract more burdens to the healthcare system due to increased cost of treatment, insurance penalties to hospitals, and unavailable bed spaces for new patients. This paper, therefore, aims to develop a risk stratification and a predictive model for identifying patients at various risk severities of 30-days URA. METHODS Patients records of comorbid patients with diabetes treated with different medications were collected from different hospitals and analysed with Principal Component Analysis (PCA) and Multivariate Logistic Regression (MLR) to determine the probability of 30-days URA, which is classified into very low, low, moderate, high, and very high. The risk classes are later modelled using ANOVA feature selection to identify the optimal predictors and the best random forest (RF) hyperparameters for 30-days URA risk stratification. Synthetic Minority Oversampling Technique (SMOTE) was used to balance the risk classes while employing a10-fold cross-validation. RESULTS After analysing 17,933 episodes of comorbid diabetes patients' treatment, 10.71% are identified to have 30-days URA with 61.95% of patients at moderate risk, 35.5% at low risk, 2.25% at very low risk, 0.37% at high risk, and 0.08% at very high risk. The predictive accuracy of RF is: - recall: 0.947 ± 0.035, precision: 0.951 ± 0.033, F1-score: 0.947 ± 0.035, AUC: 0.994 ± 0.007 and Average Precision (AP) of 0.99. The predictive accuracies of the risk classes measured with F1-score are: - very low: 0.985 ± 0.019, low risk: 0.871 ± 0.079, moderate: 0.881 ± 0.093, high: 0.999 ± 0.003, and very high: 1.000 ± 0.00. CONCLUSION This study identified the risk severity of comorbid patients with diabetes treated with different medications, making it easier to identify those that will be prioritized on hospitalization to minimize 30-days URA. By relying on the technique developed, vulnerable patients to 30-days URA can be given better post-discharge monitoring to build critical self-management skills that will minimize the cost of diabetes care and improve the quality of life.
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Affiliation(s)
- Chinedu I Ossai
- School of Health Sciences, Department of Health and Biostatistics, Swinburne University, John Street Hawthorn, Victoria 3122, Australia.
| | - Nilmini Wickramasinghe
- School of Health Sciences, Department of Health and Biostatistics, Swinburne University, John Street Hawthorn, Victoria 3122, Australia
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22
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Wang K, Muennig PA. Realizing the promise of big data: how Taiwan can help the world reduce medical errors and advance precision medicine. APPLIED COMPUTING AND INFORMATICS 2022. [DOI: 10.1108/aci-11-2021-0298] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
PurposeThe study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.Design/methodology/approachThis study is a narrative review of the literature.FindingsThe body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.Originality/valueWhile research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.
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SCHNYDER JASON D A, KRİSHNAN V, VİNAYACHANDRAN D. Intelligent systems for precision dental diagnosis and treatment planning – A review. CUMHURIYET DENTAL JOURNAL 2022. [DOI: 10.7126/cumudj.991480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Machines have changed the course of mankind. Simple machines were the basis of human civilization. Today with humongous technological development, machines are intelligent enough to carry out very complex nerve-racking tasks. The ability of a machine to learn from algorithms changed eventually into, the machine learning by itself, which constitutes artificial intelligence. Literature has plausible evidence for the use of intelligent systems in medical field. Artificial intelligence has been used in the multiple denominations of dentistry. These machines are used in the precision diagnosis, interpretation of medical images, accumulation of data, classification and compilation of records, determination of treatment and construction of a personalized treatment plan. Artificial intelligence can help in timely diagnosis of complex dental diseases which would ultimately aid in rapid commencement of treatment. Research helps us understand the effectiveness and challenges in the use of this technology. The apt use of intelligent systems could transform the entire medical system for the better.
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Affiliation(s)
| | - Vidya KRİSHNAN
- SRM Kattankulathur Dental College, SRM Institute of Science and Technology
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A Clinical Decision Support System for the Prediction of Quality of Life in ALS. J Pers Med 2022; 12:jpm12030435. [PMID: 35330435 PMCID: PMC8955774 DOI: 10.3390/jpm12030435] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 03/04/2022] [Accepted: 03/07/2022] [Indexed: 11/17/2022] Open
Abstract
Amyotrophic Lateral Sclerosis (ALS), also known as Motor Neuron Disease (MND), is a rare and fatal neurodegenerative disease. As ALS is currently incurable, the aim of the treatment is mainly to alleviate symptoms and improve quality of life (QoL). We designed a prototype Clinical Decision Support System (CDSS) to alert clinicians when a person with ALS is experiencing low QoL in order to inform and personalise the support they receive. Explainability is important for the success of a CDSS and its acceptance by healthcare professionals. The aim of this work isto announce our prototype (C-ALS), supported by a first short evaluation of its explainability. Given the lack of similar studies and systems, this work is a valid proof-of-concept that will lead to future work. We developed a CDSS that was evaluated by members of the team of healthcare professionals that provide care to people with ALS in the ALS/MND Multidisciplinary Clinic in Dublin, Ireland. We conducted a user study where participants were asked to review the CDSS and complete a short survey with a focus on explainability. Healthcare professionals demonstrated some uncertainty in understanding the system’s output. Based on their feedback, we altered the explanation provided in the updated version of our CDSS. C-ALS provides local explanations of its predictions in a post-hoc manner, using SHAP (SHapley Additive exPlanations). The CDSS predicts the risk of low QoL in the form of a probability, a bar plot shows the feature importance for the specific prediction, along with some verbal guidelines on how to interpret the results. Additionally, we provide the option of a global explanation of the system’s function in the form of a bar plot showing the average importance of each feature. C-ALS is available online for academic use.
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Ryu S, Kim SC, Won DO, Bang CS, Koh JH, Jeong IC. iApp: An Autonomous Inspection, Auscultation, Percussion, and Palpation Platform. Front Physiol 2022; 13:825612. [PMID: 35237180 PMCID: PMC8883036 DOI: 10.3389/fphys.2022.825612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/21/2022] [Indexed: 11/20/2022] Open
Abstract
Disease symptoms often contain features that are not routinely recognized by patients but can be identified through indirect inspection or diagnosis by medical professionals. Telemedicine requires sufficient information for aiding doctors' diagnosis, and it has been primarily achieved by clinical decision support systems (CDSSs) utilizing visual information. However, additional medical diagnostic tools are needed for improving CDSSs. Moreover, since the COVID-19 pandemic, telemedicine has garnered increasing attention, and basic diagnostic tools (e.g., classical examination) have become the most important components of a comprehensive framework. This study proposes a conceptual system, iApp, that can collect and analyze quantified data based on an automatically performed inspection, auscultation, percussion, and palpation. The proposed iApp system consists of an auscultation sensor, camera for inspection, and custom-built hardware for automatic percussion and palpation. Experiments were designed to categorize the eight abdominal divisions of healthy subjects based on the system multi-modal data. A deep multi-modal learning model, yielding a single prediction from multi-modal inputs, was designed for learning distinctive features in eight abdominal divisions. The model's performance was evaluated in terms of the classification accuracy, sensitivity, positive predictive value, and F-measure, using epoch-wise and subject-wise methods. The results demonstrate that the iApp system can successfully categorize abdominal divisions, with the test accuracy of 89.46%. Through an automatic examination of the iApp system, this proof-of-concept study demonstrates a sophisticated classification by extracting distinct features of different abdominal divisions where different organs are located. In the future, we intend to capture the distinct features between normal and abnormal tissues while securing patient data and demonstrate the feasibility of a fully telediagnostic system that can support abnormality diagnosis.
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Affiliation(s)
- Semin Ryu
- School of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea
| | - Seung-Chan Kim
- Department of Sport Interaction Science, Sungkyunkwan University, Suwon, South Korea
| | - Dong-Ok Won
- School of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea
| | - Chang Seok Bang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, South Korea
| | - Jeong-Hwan Koh
- School of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea
| | - In cheol Jeong
- School of Artificial Intelligence Convergence, Hallym University, Chuncheon, South Korea
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- *Correspondence: In cheol Jeong
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El-Kareh R, Sittig DF. Enhancing Diagnosis Through Technology: Decision Support, Artificial Intelligence, and Beyond. Crit Care Clin 2022; 38:129-139. [PMID: 34794627 PMCID: PMC8608279 DOI: 10.1016/j.ccc.2021.08.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Patient care in intensive care environments is complex, time-sensitive, and data-rich, factors that make these settings particularly well-suited to clinical decision support (CDS). A wide range of CDS interventions have been used in intensive care unit environments. The field needs well-designed studies to identify the most effective CDS approaches. Evolving artificial intelligence and machine learning models may reduce information-overload and enable teams to take better advantage of the large volume of patient data available to them. It is vital to effectively integrate new CDS into clinical workflows and to align closely with the cognitive processes of frontline clinicians.
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Affiliation(s)
- Robert El-Kareh
- University of California, San Diego, 9500 Gilman Drive, #0881 La Jolla, CA 92093-0881, USA.
| | - Dean F Sittig
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, UT-Memorial Hermann Center for Healthcare Quality & Safety, Houston, TX 77030, USA. https://twitter.com/DeanSittig
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Dente CJ, Mina MJ, Morse BC, Hensman H, Schobel S, Gelbard RB, Belard A, Buchman TG, Kirk AD, Elster EA. Predicting the need for massive transfusion: Prospective validation of a smartphone-based clinical decision support tool. Surgery 2021; 170:1574-1580. [PMID: 34112517 DOI: 10.1016/j.surg.2021.04.034] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Revised: 04/14/2021] [Accepted: 04/25/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Improper or delayed activation of a massive transfusion protocol may have consequences to individuals and institutions. We designed a complex predictive algorithm that was packaged within a smartphone application. We hypothesized it would accurately assess the need for massive transfusion protocol activation and assist clinicians in that decision. METHODS We prospectively enrolled patients at an urban, level I trauma center. The application recorded the surgeon's initial opinion for activation and then prompted inputs for the model. The application provided a prediction and recorded the surgeon's final decision on activation. RESULTS Three hundred and twenty-one patients were enrolled (83% male; 59% penetrating; median Injury Severity Score 9; mean base deficit -4.11). Of 36 massive transfusion protocol activations, 26 had an app prediction of "high" or "moderate" probability. Of these, 4 (15%) patients received <10 u blood as a result of early hemorrhage control. Two hundred and eighty-five patients did not have massive transfusion protocol activated by the surgeon with 27 (9%) patients having "moderate" or "high" likelihood predicted by the application. Twenty-four of these did not require massive transfusion, and all patients had acidosis that unrelated to hemorrhagic shock. For 13 (50%) of the patients with "high" probability, the surgeon correctly altered their initial decision based on this information. The algorithm demonstrated an adjusted accuracy of 0.96 (95% confidence interval [0.93-0.98); P ≤ .001]), sensitivity = 0.99, specificity 0.72, positive predictive value 0.96, negative predictive value 0.99, and area under the receiver operating curve = 0.86. CONCLUSION A smartphone-based clinical decision tools can aid surgeons in the decision to active massive transfusion protocol in real time, although it does not completely replace clinician judgment.
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Affiliation(s)
- Christopher J Dente
- Emory University, Atlanta, GA; Department of Surgery, Grady Memorial Hospital, Atlanta, GA.
| | - Michael J Mina
- Emory University, Atlanta, GA; Department of Surgery, Grady Memorial Hospital, Atlanta, GA
| | - Bryan C Morse
- Emory University, Atlanta, GA; Department of Surgery, Grady Memorial Hospital, Atlanta, GA
| | | | - Seth Schobel
- Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD
| | - Rondi B Gelbard
- Emory University, Atlanta, GA; Department of Surgery, Grady Memorial Hospital, Atlanta, GA
| | - Arnaud Belard
- Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD
| | | | | | - Eric A Elster
- Uniformed Services University of the Health Sciences and Walter Reed National Military Medical Center, Bethesda, MD
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Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11115088] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Machine Learning and Artificial Intelligence (AI) more broadly have great immediate and future potential for transforming almost all aspects of medicine. However, in many applications, even outside medicine, a lack of transparency in AI applications has become increasingly problematic. This is particularly pronounced where users need to interpret the output of AI systems. Explainable AI (XAI) provides a rationale that allows users to understand why a system has produced a given output. The output can then be interpreted within a given context. One area that is in great need of XAI is that of Clinical Decision Support Systems (CDSSs). These systems support medical practitioners in their clinic decision-making and in the absence of explainability may lead to issues of under or over-reliance. Providing explanations for how recommendations are arrived at will allow practitioners to make more nuanced, and in some cases, life-saving decisions. The need for XAI in CDSS, and the medical field in general, is amplified by the need for ethical and fair decision-making and the fact that AI trained with historical data can be a reinforcement agent of historical actions and biases that should be uncovered. We performed a systematic literature review of work to-date in the application of XAI in CDSS. Tabular data processing XAI-enabled systems are the most common, while XAI-enabled CDSS for text analysis are the least common in literature. There is more interest in developers for the provision of local explanations, while there was almost a balance between post-hoc and ante-hoc explanations, as well as between model-specific and model-agnostic techniques. Studies reported benefits of the use of XAI such as the fact that it could enhance decision confidence for clinicians, or generate the hypothesis about causality, which ultimately leads to increased trustworthiness and acceptability of the system and potential for its incorporation in the clinical workflow. However, we found an overall distinct lack of application of XAI in the context of CDSS and, in particular, a lack of user studies exploring the needs of clinicians. We propose some guidelines for the implementation of XAI in CDSS and explore some opportunities, challenges, and future research needs.
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Jiang L, Wu Z, Xu X, Zhan Y, Jin X, Wang L, Qiu Y. Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies. J Int Med Res 2021; 49:3000605211000157. [PMID: 33771068 PMCID: PMC8165857 DOI: 10.1177/03000605211000157] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Recent advancements in the field of artificial intelligence have demonstrated
success in a variety of clinical tasks secondary to the development and
application of big data, supercomputing, sensor networks, brain science, and
other technologies. However, no projects can yet be used on a large scale in
real clinical practice because of the lack of standardized processes, lack of
ethical and legal supervision, and other issues. We analyzed the existing
problems in the field of artificial intelligence and herein propose possible
solutions. We call for the establishment of a process framework to ensure the
safety and orderly development of artificial intelligence in the medical
industry. This will facilitate the design and implementation of artificial
intelligence products, promote better management via regulatory authorities, and
ensure that reliable and safe artificial intelligence products are selected for
application.
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Affiliation(s)
- Lushun Jiang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Zhe Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Xiaolan Xu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Yaqiong Zhan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Xuehang Jin
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Li Wang
- Department of Rehabilitation Medicine, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, People's Republic of China.,Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, Hangzhou, Zhejiang, People's Republic of China
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Cognitive biases, environmental, patient and personal factors associated with critical care decision making: A scoping review. J Crit Care 2021; 64:144-153. [PMID: 33906103 DOI: 10.1016/j.jcrc.2021.04.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Revised: 03/31/2021] [Accepted: 04/15/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE Cognitive biases and factors affecting decision making in critical care can potentially lead to life-threatening errors. We aimed to examine the existing evidence on the influence of cognitive biases and factors on decision making in critical care. MATERIALS AND METHODS We conducted a scoping review by searching MEDLINE for articles from 2004 to November 2020. We included studies conducted in physicians that described cognitive biases or factors associated with decision making. During the study process we decided on the method to summarize the evidence, and based on the obtained studies a descriptive summary of findings was the best fit. RESULTS Thirty heterogenous studies were included. Four main biases or factors were observed, e.g. cognitive biases, personal factors, environmental factors, and patient factors. Six (20%) studies reported biases associated with decision making comprising omission-, status quo-, implicit-, explicit-, outcome-, and overconfidence bias. Nineteen (63%) studies described personal factors, twenty-two (73%) studies described environmental factors, and sixteen (53%) studies described patient factors. CONCLUSIONS The current evidence on cognitive biases and factors is heterogenous, but shows they influence clinical decision. Future studies should investigate the prevalence of cognitive biases and factors in clinical practice and their impact on clinical outcomes.
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Viana Dos Santos Santana Í, Cm da Silveira A, Sobrinho Á, Chaves E Silva L, Dias da Silva L, Santos DFS, Gurjão EC, Perkusich A. Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach. J Med Internet Res 2021; 23:e27293. [PMID: 33750734 PMCID: PMC8034680 DOI: 10.2196/27293] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 03/08/2021] [Accepted: 03/21/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Controlling the COVID-19 outbreak in Brazil is a challenge due to the population's size and urban density, inefficient maintenance of social distancing and testing strategies, and limited availability of testing resources. OBJECTIVE The purpose of this study is to effectively prioritize patients who are symptomatic for testing to assist early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies. METHODS Raw data from 55,676 Brazilians were preprocessed, and the chi-square test was used to confirm the relevance of the following features: gender, health professional, fever, sore throat, dyspnea, olfactory disorders, cough, coryza, taste disorders, and headache. Classification models were implemented relying on preprocessed data sets; supervised learning; and the algorithms multilayer perceptron (MLP), gradient boosting machine (GBM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbors (KNN), support vector machine (SVM), and logistic regression (LR). The models' performances were analyzed using 10-fold cross-validation, classification metrics, and the Friedman and Nemenyi statistical tests. The permutation feature importance method was applied for ranking the features used by the classification models with the highest performances. RESULTS Gender, fever, and dyspnea were among the highest-ranked features used by the classification models. The comparative analysis presents MLP, GBM, DT, RF, XGBoost, and SVM as the highest performance models with similar results. KNN and LR were outperformed by the other algorithms. Applying the easy interpretability as an additional comparison criterion, the DT was considered the most suitable model. CONCLUSIONS The DT classification model can effectively (with a mean accuracy≥89.12%) assist COVID-19 test prioritization in Brazil. The model can be applied to recommend the prioritizing of a patient who is symptomatic for COVID-19 testing.
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Affiliation(s)
| | | | - Álvaro Sobrinho
- Federal University of the Agreste of Pernambuco, Garanhuns, Brazil.,Federal University of Alagoas, Maceió, Brazil
| | | | | | | | - Edmar C Gurjão
- Federal University of Campina Grande, Campina Grande, Brazil
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Measures of success of computerized clinical decision support systems: An overview of systematic reviews. HEALTH POLICY AND TECHNOLOGY 2021. [DOI: 10.1016/j.hlpt.2020.11.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Laka M, Milazzo A, Merlin T. Factors That Impact the Adoption of Clinical Decision Support Systems (CDSS) for Antibiotic Management. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18041901. [PMID: 33669353 PMCID: PMC7920296 DOI: 10.3390/ijerph18041901] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/08/2021] [Accepted: 02/11/2021] [Indexed: 01/22/2023]
Abstract
The study evaluated individual and setting-specific factors that moderate clinicians’ perception regarding use of clinical decision support systems (CDSS) for antibiotic management. A cross-sectional online survey examined clinicians’ perceptions about CDSS implementation for antibiotic management in Australia. Multivariable logistic regression determined the association between drivers of CDSS adoption and different moderators. Clinical experience, CDSS use and care setting were important predictors of clinicians’ perception concerning CDSS adoption. Compared to nonusers, CDSS users were less likely to lack confidence in CDSS (OR = 0.63, 95%, CI = 0.32, 0.94) and consider it a threat to professional autonomy (OR = 0.47, 95%, CI = 0.08, 0.83). Conversely, there was higher likelihood in experienced clinicians (>20 years) to distrust CDSS (OR = 1.58, 95%, CI = 1.08, 2.23) due to fear of comprising their clinical judgement (OR = 1.68, 95%, CI = 1.27, 2.85). In primary care, clinicians were more likely to perceive time constraints (OR = 1.96, 95%, CI = 1.04, 3.70) and patient preference (OR = 1.84, 95%, CI = 1.19, 2.78) as barriers to CDSS adoption for antibiotic prescribing. Our findings provide differentiated understanding of the CDSS implementation landscape by identifying different individual, organisational and system-level factors that influence system adoption. The individual and setting characteristics can help understand the variability in CDSS adoption for antibiotic management in different clinicians.
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Affiliation(s)
- Mah Laka
- School of Public Health, University of Adelaide, Adelaide 5005, Australia; (M.L.); (A.M.)
| | - Adriana Milazzo
- School of Public Health, University of Adelaide, Adelaide 5005, Australia; (M.L.); (A.M.)
| | - Tracy Merlin
- Adelaide Health Technology Assessment (AHTA), School of Public Health, University of Adelaide, Adelaide 5005, Australia
- Correspondence: ; Tel.: +61-(8)-8313-3575
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Khanthong P, Chaiyasat C, Danuwong C. Lessons learnt from CBR practice at Hua Don Primary Health Care, Thailand. JOURNAL OF HEALTH RESEARCH 2021. [DOI: 10.1108/jhr-07-2020-0297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
PurposeThe purpose of this study is to determine the capacity map of professional learning community (PLC) practicing community-based research (CBR) in Ubon Ratchathani Rajabhat University, Thailand, and the implementation of the lessons learnt from the process and essential skills at Hua Don Primary Health Care (PHC).Design/methodology/approachParticipatory action research (PAR) design was conducted in two phases, one on campus and the other in the PHC. For gathering and validating the data, the snowball sampling technique, focus group, in-depth interviews and the triangulation method were used.FindingsThe PLC capacity map from the first phase provided the essential skills of CBR and the second phase revealed lessons learnt from the implementation in the Hua Don PHC. The shortcut in researching a new target area by a collaboration of the community leader and village health volunteers was prominent. The results could be interpreted in creating collaboration in health care with a new community.Originality/valueThe capacity map is a practical guideline for a beginner or CBR novice researcher, and the lessons learnt help the implementation in the health field, particularly in PHC, succeed smoothly.
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Wang P, Zhou L, Mu D, Zhang D, Shao Q. What makes clinical documents helpful and engaging? An empirical investigation of experience sharing in an online medical community. Int J Med Inform 2020; 143:104273. [PMID: 32979649 DOI: 10.1016/j.ijmedinf.2020.104273] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 08/19/2020] [Accepted: 09/14/2020] [Indexed: 11/28/2022]
Abstract
BACKGROUND Social media have emerged as a platform for experience and knowledge sharing in the medical community. The online medical community is garnering increasing research attention; however, there is a lack of understanding of what factors influence the helpfulness and engagement of experience sharing in the community. METHODS Clinical documents manifest physicians' experience and knowledge. This study fills the knowledge gap by investigating what elements of clinical documents contribute to the helpfulness of sharing clinical documents online and what influence member engagement. Clinical documents follow certain architecture to specify their structure and semantics for exchange (e.g., HL7 C-CDA). Accordingly, the structural elements of clinical documents may influence document helpfulness for the online community. Member engagement is one of the indicators of community success. We collected 6514 clinical documents from a real-world online medical community, and normalized them with the structural elements of HL7 C-CDA. We performed regression analyses to identify the structural elements that have significant impacts on document helpfulness and member engagement. RESULTS The results show that some structural elements of clinical documents such as assessment, chief complaints, medications, physical exams, procedures, results, and vital signs sections have positive effects whereas assessment and plan, general status, history and past illness of patients, instructions, problem and review of systems have negative effects on the helpfulness of clinical documents. The results also reveal that structural elements such as family history, history of past illness, medication, physical exam, review of systems, and vital signs positively; whereas assessment, assessment and plan, instruction, and result negatively; influence member engagement. CONCLUSIONS The findings provide guide on how to improve the effectiveness of sharing clinical experience online. The new and in-depth insights may contribute to the success of online medical communities and the quality of medical decisions.
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Affiliation(s)
- Ping Wang
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, 130021, China; Belk College of Business, The University of North Carolina at Charlotte, 28223, USA; School of Public Health, Jilin University, Changchun, 130021, China
| | - Lina Zhou
- Belk College of Business, The University of North Carolina at Charlotte, 28223, USA.
| | - Dongmei Mu
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, 130021, China.
| | - Dongsong Zhang
- Belk College of Business, The University of North Carolina at Charlotte, 28223, USA
| | - Qi Shao
- Division of Clinical Research, The First Hospital of Jilin University, Changchun, 130021, China; School of Public Health, Jilin University, Changchun, 130021, China
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Choi SH. A digital application for implementing the ICD-11 traditional medicine chapter. JOURNAL OF INTEGRATIVE MEDICINE-JIM 2020; 18:455-458. [PMID: 32891598 DOI: 10.1016/j.joim.2020.08.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 07/02/2020] [Indexed: 11/17/2022]
Abstract
On May 25, 2019, the World Health Assembly approved the eleventh revision of the International Statistical Classification of Diseases and Related Health Problems (ICD-11), containing a chapter on traditional medicine. This means that the traditional East Asian medicine (TEAM) is now officially recognized as a part of mainstream medical practice. However, the patterns presented in the ICD-11 traditional medicine chapter are only the tip of the iceberg of TEAM clinical practice, and it will be necessary to supplement and upgrade the contents. In order to implement this, objectification and standardization of TEAM must be premised, and grafting with proper modern science and technology is imperative. Pattern Identification and Prescription Expert-11 (PIPE-11), which is a TEAM clinical decision support system, adopts vastly from clinical literature on pattern identification and the prescription. By adopting the rule-based reasoning method, the way of diagnosis and prescription by a TEAM practitioner in actual clinical practice is implemented as it is. PIPE-11 could support to improve both the accuracy of medical diagnosis and the reliability of the medical treatment of TEAM in clinical practices. In the field of research, it might facilitate the usage for reliable reference for symptoms and signs retrieval and patient simulation. In the field of education, it can provide a high level of training for learning pattern identification and prescription, and further be used to reinforce skills of diagnosis and prescription by providing self-simulation methods. Therefore, PIPE-11 as a digital application is expected to support the traditional medicine chapter of ICD-11 to successfully contribute to the improvement of human health.
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Affiliation(s)
- Seung-Hoon Choi
- Department of Life Convergence, Graduate School, Dankook University, Yongin, Gyeonggi-do 16890, Republic of Korea.
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Gagalova KK, Leon Elizalde MA, Portales-Casamar E, Görges M. What You Need to Know Before Implementing a Clinical Research Data Warehouse: Comparative Review of Integrated Data Repositories in Health Care Institutions. JMIR Form Res 2020; 4:e17687. [PMID: 32852280 PMCID: PMC7484778 DOI: 10.2196/17687] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 06/09/2020] [Accepted: 07/17/2020] [Indexed: 12/23/2022] Open
Abstract
Background Integrated data repositories (IDRs), also referred to as clinical data warehouses, are platforms used for the integration of several data sources through specialized analytical tools that facilitate data processing and analysis. IDRs offer several opportunities for clinical data reuse, and the number of institutions implementing an IDR has grown steadily in the past decade. Objective The architectural choices of major IDRs are highly diverse and determining their differences can be overwhelming. This review aims to explore the underlying models and common features of IDRs, provide a high-level overview for those entering the field, and propose a set of guiding principles for small- to medium-sized health institutions embarking on IDR implementation. Methods We reviewed manuscripts published in peer-reviewed scientific literature between 2008 and 2020, and selected those that specifically describe IDR architectures. Of 255 shortlisted articles, we found 34 articles describing 29 different architectures. The different IDRs were analyzed for common features and classified according to their data processing and integration solution choices. Results Despite common trends in the selection of standard terminologies and data models, the IDRs examined showed heterogeneity in the underlying architecture design. We identified 4 common architecture models that use different approaches for data processing and integration. These different approaches were driven by a variety of features such as data sources, whether the IDR was for a single institution or a collaborative project, the intended primary data user, and purpose (research-only or including clinical or operational decision making). Conclusions IDR implementations are diverse and complex undertakings, which benefit from being preceded by an evaluation of requirements and definition of scope in the early planning stage. Factors such as data source diversity and intended users of the IDR influence data flow and synchronization, both of which are crucial factors in IDR architecture planning.
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Affiliation(s)
- Kristina K Gagalova
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.,Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC, Canada.,Research Institute, BC Children's Hospital, Vancouver, BC, Canada
| | - M Angelica Leon Elizalde
- Research Institute, BC Children's Hospital, Vancouver, BC, Canada.,School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada
| | - Elodie Portales-Casamar
- Research Institute, BC Children's Hospital, Vancouver, BC, Canada.,Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada
| | - Matthias Görges
- Research Institute, BC Children's Hospital, Vancouver, BC, Canada.,Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC, Canada
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Duan Y, Shan W, Liu L, Wang Q, Wu Z, Liu P, Ji J, Liu Y, He K, Wang Y. Primary Categorizing and Masking Cerebral Small Vessel Disease Based on "Deep Learning System". Front Neuroinform 2020; 14:17. [PMID: 32523523 PMCID: PMC7261942 DOI: 10.3389/fninf.2020.00017] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 03/31/2020] [Indexed: 12/20/2022] Open
Abstract
Objective To supply the attending doctor’s diagnosis of the persisting of cerebral small vessel disease and speed up their work effectively, we developed a “deep learning system (DLS)” for cerebral small vessel disease predication. The reliability and the disease area segmentation accuracy, of the proposed DLS, was also investigated. Methods A deep learning model based on the convolutional neural network was designed and trained on 1,010 DWI b1000 images from 1010 patients diagnosed with segmentation of subcortical infarction, 359 T2∗ images from 359 patients diagnosed with segmentation of cerebral microbleed, as well as 824 T1-weighted and T2-FLAIR images from 824 patients diagnosed with segmentation of lacune and WMH. Dicw accuracy, recall, and f1-score were calculated to evaluate the proposed deep learning model. Finally, we also compared the DLS prediction capability with that of 6 doctors with 3 to 18 years’ clinical experience (8 ± 6 years). Results The results support that an appropriately trained DLS can achieve a high-level dice accuracy, 0.598 in the training section over all these four classifications on 30 patients (0.576 for young neuroradiologists), validation accuracy is 0.496 in lacune, 0.666 in WMH, 0.728 in subcortical infarction, and 0.503 in cerebral microbleeds. It is comparable to attending doctor with a few years of experience, regardless of whether the emphasis is placed on the segmentation or detection of lesions with less time-spending compared with manual analysis, about 4.4 s/case, which is dramatically less than doctors about 634 s/case. Conclusion The results of our comparison lend support to the case that an appropriately trained DLS can be trusted to the same extent as one would trust an attending doctor with a few years of experience, regardless of whether the emphasis is placed on the segmentation or detection of lesions.
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Affiliation(s)
- Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Center for Clinical Medicine of Neurological Diseases, Beijing, China
| | - Wei Shan
- National Center for Clinical Medicine of Neurological Diseases, Beijing, China.,Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Institute for Brain Disorders, Beijing, China
| | - Liying Liu
- National Center for Clinical Medicine of Neurological Diseases, Beijing, China
| | - Qun Wang
- National Center for Clinical Medicine of Neurological Diseases, Beijing, China.,Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Institute for Brain Disorders, Beijing, China
| | - Zhenzhou Wu
- National Center for Clinical Medicine of Neurological Diseases, Beijing, China
| | - Pan Liu
- National Center for Clinical Medicine of Neurological Diseases, Beijing, China
| | - Jiahao Ji
- National Center for Clinical Medicine of Neurological Diseases, Beijing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,National Center for Clinical Medicine of Neurological Diseases, Beijing, China
| | - Kunlun He
- Laboratory of Translational Medicine, Chinese PLA General Hospital, Beijing, China.,Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital, Beijing, China
| | - Yongjun Wang
- National Center for Clinical Medicine of Neurological Diseases, Beijing, China.,Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Turner RS, Stubbs T, Davies DA, Albensi BC. Potential New Approaches for Diagnosis of Alzheimer's Disease and Related Dementias. Front Neurol 2020; 11:496. [PMID: 32582013 PMCID: PMC7290039 DOI: 10.3389/fneur.2020.00496] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Accepted: 05/06/2020] [Indexed: 12/21/2022] Open
Abstract
Dementia is an umbrella term-caused by a large number of specific diagnoses, including several neurodegenerative disorders. Alzheimer's disease (AD) is now the most common cause of dementia in advanced countries, while dementia due to neurosyphilis was the leading cause a century ago. Many challenges remain for diagnosing dementia definitively. Some of these include variability of early symptoms and overlap with similar disorders, as well as the possibility of combined, or mixed, etiologies in some cases. Newer technologies, including the incorporation of PET neuroimaging and other biomarkers (genomics and proteomics), are being incorporated into revised diagnostic criteria. However, the application of novel diagnostic methods at clinical sites is plagued by many caveats including availability and access. This review surveys new diagnostic methods as well as remaining challenges-for clinical care and clinical research.
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Affiliation(s)
- R Scott Turner
- Department of Neurology, Georgetown University, Washington, DC, United States
| | - Terry Stubbs
- ActivMed, Practices & Research, Methuen, MA, United States
| | - Don A Davies
- Division of Neurodegenerative Disorders, St Boniface Hospital Research, University of Manitoba, Winnipeg, MB, Canada
| | - Benedict C Albensi
- Division of Neurodegenerative Disorders, St Boniface Hospital Research, University of Manitoba, Winnipeg, MB, Canada.,Department of Pharmacology & Therapeutics, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB, Canada
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Tao L, Zhang C, Zeng L, Zhu S, Li N, Li W, Zhang H, Zhao Y, Zhan S, Ji H. Accuracy and Effects of Clinical Decision Support Systems Integrated With BMJ Best Practice-Aided Diagnosis: Interrupted Time Series Study. JMIR Med Inform 2020; 8:e16912. [PMID: 31958069 PMCID: PMC6997922 DOI: 10.2196/16912] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 12/02/2019] [Accepted: 12/15/2019] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Clinical decision support systems (CDSS) are an integral component of health information technologies and can assist disease interpretation, diagnosis, treatment, and prognosis. However, the utility of CDSS in the clinic remains controversial. OBJECTIVE The aim is to assess the effects of CDSS integrated with British Medical Journal (BMJ) Best Practice-aided diagnosis in real-world research. METHODS This was a retrospective, longitudinal observational study using routinely collected clinical diagnosis data from electronic medical records. A total of 34,113 hospitalized patient records were successively selected from December 2016 to February 2019 in six clinical departments. The diagnostic accuracy of the CDSS was verified before its implementation. A self-controlled comparison was then applied to detect the effects of CDSS implementation. Multivariable logistic regression and single-group interrupted time series analysis were used to explore the effects of CDSS. The sensitivity analysis was conducted using the subgroup data from January 2018 to February 2019. RESULTS The total accuracy rates of the recommended diagnosis from CDSS were 75.46% in the first-rank diagnosis, 83.94% in the top-2 diagnosis, and 87.53% in the top-3 diagnosis in the data before CDSS implementation. Higher consistency was observed between admission and discharge diagnoses, shorter confirmed diagnosis times, and shorter hospitalization days after the CDSS implementation (all P<.001). Multivariable logistic regression analysis showed that the consistency rates after CDSS implementation (OR 1.078, 95% CI 1.015-1.144) and the proportion of hospitalization time 7 days or less (OR 1.688, 95% CI 1.592-1.789) both increased. The interrupted time series analysis showed that the consistency rates significantly increased by 6.722% (95% CI 2.433%-11.012%, P=.002) after CDSS implementation. The proportion of hospitalization time 7 days or less significantly increased by 7.837% (95% CI 1.798%-13.876%, P=.01). Similar results were obtained in the subgroup analysis. CONCLUSIONS The CDSS integrated with BMJ Best Practice improved the accuracy of clinicians' diagnoses. Shorter confirmed diagnosis times and hospitalization days were also found to be associated with CDSS implementation in retrospective real-world studies. These findings highlight the utility of artificial intelligence-based CDSS to improve diagnosis efficiency, but these results require confirmation in future randomized controlled trials.
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Affiliation(s)
- Liyuan Tao
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
| | - Chen Zhang
- Information Management and Big Data Center, Peking University Third Hospital, Beijing, China
| | - Lin Zeng
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
| | - Shengrong Zhu
- Information Management and Big Data Center, Peking University Third Hospital, Beijing, China
| | - Nan Li
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
| | - Wei Li
- Information Management and Big Data Center, Peking University Third Hospital, Beijing, China
| | - Hua Zhang
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
| | - Yiming Zhao
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
| | - Siyan Zhan
- Research Center of Clinical Epidemiology, Peking University Third Hospital, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Hong Ji
- Information Management and Big Data Center, Peking University Third Hospital, Beijing, China
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Shaban-Nejad A, Michalowski M, Peek N, Brownstein JS, Buckeridge DL. Seven pillars of precision digital health and medicine. Artif Intell Med 2020; 103:101793. [PMID: 32143798 DOI: 10.1016/j.artmed.2020.101793] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 01/03/2020] [Indexed: 01/07/2023]
Affiliation(s)
- Arash Shaban-Nejad
- The University of Tennessee Health Science Center - Oak-Ridge National Lab (UTHSC-ORNL) Center for Biomedical Informatics, Department of Pediatrics, College of Medicine, R492-50 N. Dunlap Street, Memphis, TN 38103, USA.
| | - Martin Michalowski
- School of Nursing, University of Minnesota - Twin Cities, 5-140 Weaver-Densford Hall, 308 Harvard Street SE, Minneapolis, MN, 55455, United States
| | - Niels Peek
- Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK
| | - John S Brownstein
- Boston Children's Hospital and Harvard Medical School, Harvard University, Boston, MA, USA
| | - David L Buckeridge
- McGill Clinical and Health Informatics, School of Population and Global Health, McGill University, Montreal, Quebec H3A 1A3, Canada
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Fernandes M, Vieira SM, Leite F, Palos C, Finkelstein S, Sousa JM. Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review. Artif Intell Med 2020; 102:101762. [DOI: 10.1016/j.artmed.2019.101762] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 08/29/2019] [Accepted: 11/07/2019] [Indexed: 12/23/2022]
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Disrupting Deficiencies in Data Delivery and Decision-Making During Daily ICU Rounds. Crit Care Med 2019; 47:478-479. [PMID: 30768508 DOI: 10.1097/ccm.0000000000003605] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Stone EG. Unintended adverse consequences of a clinical decision support system: two cases. J Am Med Inform Assoc 2019; 25:564-567. [PMID: 29036296 DOI: 10.1093/jamia/ocx096] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2017] [Accepted: 08/21/2017] [Indexed: 12/19/2022] Open
Abstract
Many institutions have implemented clinical decision support systems (CDSSs). While CDSS research papers have focused on benefits of these systems, there is a smaller body of literature showing that CDSSs may also produce unintended adverse consequences (UACs). Detailed here are 2 cases of UACs resulting from a CDSS. Both of these cases were related to external systems that fed data into the CDSS. In the first case, lack of knowledge of data categorization in an external pharmacy system produced a UAC; in the second case, the change of a clinical laboratory instrument produced the UAC. CDSSs rely on data from many external systems. These systems are dynamic and may have changes in hardware, software, vendors, or processes. Such changes can affect the accuracy of CDSSs. These cases point to the need for the CDSS team to be familiar with these external systems. This team (manager and alert builders) should include members in specific clinical specialties with deep knowledge of these external systems.
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Affiliation(s)
- Erin G Stone
- Department of Hospital Medicine, Kaiser Permanente, Woodland Hills, CA, USA
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Shawahna R. Merits, features, and desiderata to be considered when developing electronic health records with embedded clinical decision support systems in Palestinian hospitals: a consensus study. BMC Med Inform Decis Mak 2019; 19:216. [PMID: 31703675 PMCID: PMC6842153 DOI: 10.1186/s12911-019-0928-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 10/14/2019] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Electronic health records (EHRs) with embedded clinical decision support systems (CDSSs) have the potential to improve healthcare delivery. This study was conducted to explore merits, features, and desiderata to be considered when planning for, designing, developing, implementing, piloting, evaluating, maintaining, upgrading, and/or using EHRs with CDSSs. METHODS A mixed-method combining the Delphi technique and Analytic Hierarchy Process was used. Potentially important items were collected after a thorough search of the literature and from interviews with key contact experts (n = 19). Opinions and views of the 76 panelists on the use of EHRs were also explored. Iterative Delphi rounds were conducted to achieve consensus on 122 potentially important items by a panel of 76 participants. Items on which consensus was achieved were ranked in the order of their importance using the Analytic Hierarchy Process. RESULTS Of the 122 potentially important items presented to the panelists in the Delphi rounds, consensus was achieved on 110 (90.2%) items. Of these, 16 (14.5%) items were related to the demographic characteristics of the patient, 16 (14.5%) were related to prescribing medications, 16 (14.5%) were related to checking prescriptions and alerts, 14 (12.7%) items were related to the patient's identity, 13 (11.8%) items were related to patient assessment, 12 (10.9%) items were related to the quality of alerts, 11 (10%) items were related to admission and discharge of the patient, 9 (8.2%) items were general features, and 3 (2.7%) items were related to diseases and making diagnosis. CONCLUSIONS In this study, merits, features, and desiderata to be considered when planning for, designing, developing, implementing, piloting, evaluating, maintaining, upgrading, and/or using EHRs with CDSSs were explored. Considering items on which consensus was achieved might promote congruence and safe use of EHRs. Further studies are still needed to determine if these recommendations can improve patient safety and outcomes in Palestinian hospitals.
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Affiliation(s)
- Ramzi Shawahna
- Department of Physiology, Pharmacology and Toxicology, Faculty of Medicine and Health Sciences, An-Najah National University, Nablus, Palestine.
- An-Najah BioSciences Unit, Centre for Poisons Control, Chemical and Biological Analyses, An-Najah National University, Nablus, Palestine.
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Yang L, Huang Y, Ho YC(C, Lin Z. Is online multiple-stores cooperative promotion better than single-store promotion? Misprediction from evaluation mode. INFORMATION & MANAGEMENT 2019. [DOI: 10.1016/j.im.2019.02.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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Potter BK, Forsberg JA, Silvius E, Wagner M, Khatri V, Schobel SA, Belard AJ, Weintrob AC, Tribble DR, Elster EA. Combat-Related Invasive Fungal Infections: Development of a Clinically Applicable Clinical Decision Support System for Early Risk Stratification. Mil Med 2019; 184:e235-e242. [PMID: 30124943 DOI: 10.1093/milmed/usy182] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Indexed: 11/14/2022] Open
Affiliation(s)
- Benjamin K Potter
- Department of Surgery, Uniformed Services University of the Health Sciences & Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD.,Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD
| | - Jonathan A Forsberg
- Department of Surgery, Uniformed Services University of the Health Sciences & Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD.,Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD.,Regenerative Medicine Department, Naval Medical Research Center, 503 Robert Grant Avenue, Silver Spring, MD
| | - Elizabeth Silvius
- Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD.,DecisionQ Corporation, 2500 Wilson Blvd #325, Arlington, VA
| | - Matthew Wagner
- Department of Surgery, Uniformed Services University of the Health Sciences & Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD.,Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD
| | - Vivek Khatri
- Department of Surgery, Uniformed Services University of the Health Sciences & Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD.,Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD
| | - Seth A Schobel
- Department of Surgery, Uniformed Services University of the Health Sciences & Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD.,Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD
| | - Arnaud J Belard
- Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD
| | - Amy C Weintrob
- Infectious Disease Clinical Research Program, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD.,Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., 6720A Rockledge Drive #100, Bethesda, MD.,Veterans Affairs Medical Center, 50 Irving St NW, Washington, DC
| | - David R Tribble
- Infectious Disease Clinical Research Program, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD
| | - Eric A Elster
- Department of Surgery, Uniformed Services University of the Health Sciences & Walter Reed National Military Medical Center, 4301 Jones Bridge Road, Bethesda, MD.,Surgical Critical Care Initiative (SC2i), 4301 Jones Bridge Road, Bethesda, MD
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Franssen FME, Alter P, Bar N, Benedikter BJ, Iurato S, Maier D, Maxheim M, Roessler FK, Spruit MA, Vogelmeier CF, Wouters EFM, Schmeck B. Personalized medicine for patients with COPD: where are we? Int J Chron Obstruct Pulmon Dis 2019; 14:1465-1484. [PMID: 31371934 PMCID: PMC6636434 DOI: 10.2147/copd.s175706] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Accepted: 06/05/2019] [Indexed: 12/19/2022] Open
Abstract
Chronic airflow limitation is the common denominator of patients with chronic obstructive pulmonary disease (COPD). However, it is not possible to predict morbidity and mortality of individual patients based on the degree of lung function impairment, nor does the degree of airflow limitation allow guidance regarding therapies. Over the last decades, understanding of the factors contributing to the heterogeneity of disease trajectories, clinical presentation, and response to existing therapies has greatly advanced. Indeed, diagnostic assessment and treatment algorithms for COPD have become more personalized. In addition to the pulmonary abnormalities and inhaler therapies, extra-pulmonary features and comorbidities have been studied and are considered essential components of comprehensive disease management, including lifestyle interventions. Despite these advances, predicting and/or modifying the course of the disease remains currently impossible, and selection of patients with a beneficial response to specific interventions is unsatisfactory. Consequently, non-response to pharmacologic and non-pharmacologic treatments is common, and many patients have refractory symptoms. Thus, there is an ongoing urgency for a more targeted and holistic management of the disease, incorporating the basic principles of P4 medicine (predictive, preventive, personalized, and participatory). This review describes the current status and unmet needs regarding personalized medicine for patients with COPD. Also, it proposes a systems medicine approach, integrating genetic, environmental, (micro)biological, and clinical factors in experimental and computational models in order to decipher the multilevel complexity of COPD. Ultimately, the acquired insights will enable the development of clinical decision support systems and advance personalized medicine for patients with COPD.
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Affiliation(s)
- Frits ME Franssen
- Department of Research and Education, CIRO, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht, The Netherlands
| | - Peter Alter
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
| | - Nadav Bar
- Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Birke J Benedikter
- Institute for Lung Research, Universities of Giessen and Marburg Lung Centre, Philipps-University Marburg, Member of the German Center for Lung Research (DZL), Marburg, Germany
- Department of Medical Microbiology, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
| | | | | | - Michael Maxheim
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
| | - Fabienne K Roessler
- Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Martijn A Spruit
- Department of Research and Education, CIRO, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht, The Netherlands
- REVAL - Rehabilitation Research Center, BIOMED - Biomedical Research Institute, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium
| | - Claus F Vogelmeier
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
| | - Emiel FM Wouters
- Department of Research and Education, CIRO, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht, The Netherlands
| | - Bernd Schmeck
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
- Institute for Lung Research, Universities of Giessen and Marburg Lung Centre, Philipps-University Marburg, Member of the German Center for Lung Research (DZL), Marburg, Germany
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