Clinical management of sepsis can be improved by artificial intelligence: yes

Komorowski, Matthieu

doi:10.1007/s00134-019-05898-2

Cited by 38 publications

(20 citation statements)

References 9 publications

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“…With an increasing flood of information for medical staff, there are already data reporting staff to be overwhelmed by alarms and notifications [ 31 ]. Intelligent systems may filter these and other digital information, making them better usable for patient care, which is more urgent than ever in some medical disciplines [ 32 ]. Furthermore, many processes could be simplified through the implementation of telemedicine in health care, thereby optimizing patient care [ 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

Undergraduate Medical Competencies in Digital Health and Curricular Module Development: Mixed Methods Study

Poncette¹,

Glauert²,

Mosch³

et al. 2020

J Med Internet Res

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Background Owing to an increase in digital technologies in health care, recently leveraged by the COVID-19 pandemic, physicians are required to use these technologies appropriately and to be familiar with their implications on patient care, the health system, and society. Therefore, medical students should be confronted with digital health during their medical education. However, corresponding teaching formats and concepts are still largely lacking in the medical curricula. Objective This study aims to introduce digital health as a curricular module at a German medical school and to identify undergraduate medical competencies in digital health and their suitable teaching methods. Methods We developed a 3-week curricular module on digital health for third-year medical students at a large German medical school, taking place for the first time in January 2020. Semistructured interviews with 5 digital health experts were recorded, transcribed, and analyzed using an abductive approach. We obtained feedback from the participating students and lecturers of the module through a 17-item survey questionnaire. Results The module received overall positive feedback from both students and lecturers who expressed the need for further digital health education and stated that the field is very important for clinical care and is underrepresented in the current medical curriculum. We extracted a detailed overview of digital health competencies, skills, and knowledge to teach the students from the expert interviews. They also contained suggestions for teaching methods and statements supporting the urgency of the implementation of digital health education in the mandatory curriculum. Conclusions An elective class seems to be a suitable format for the timely introduction of digital health education. However, a longitudinal implementation in the mandatory curriculum should be the goal. Beyond training future physicians in digital skills and teaching them digital health’s ethical, legal, and social implications, the experience-based development of a critical digital health mindset with openness to innovation and the ability to assess ever-changing health technologies through a broad transdisciplinary approach to translate research into clinical routine seem more important. Therefore, the teaching of digital health should be as practice-based as possible and involve the educational cooperation of different institutions and academic disciplines.

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Section: Discussionmentioning

confidence: 99%

Undergraduate Medical Competencies in Digital Health and Curricular Module Development: Mixed Methods Study

Poncette¹,

Glauert²,

Mosch³

et al. 2020

J Med Internet Res

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show abstract

“…Artificial intelligence (AI) has the potential to deliver timely and accurate sepsis detection [24,25], potentially outperforming current clinical warning scores, which are not based on sophisticated mathematical models. Early prediction of sepsis could be achieved by developing a decision support system based on machine learning (ML) algorithms trained on patient data, usually based on electronic medical records, biomedical signals, and/or laboratory results [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Early detection of sepsis using artificial intelligence: a scoping review protocol

et al. 2021

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Background Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence. Methods The overall aim of this scoping review is to summarize the literature on existing methods for early detection of sepsis using artificial intelligence. The review will be performed using the framework formulated by Arksey and O’Malley and further developed by Levac and colleagues. To identify primary studies and reviews that are suitable to answer our research questions, a comprehensive literature collection will be compiled by searching several sources. Constrictions regarding time and language will have to be implemented. Therefore, only studies published between 1 January 1990 and 31 December 2020 will be taken into consideration, and foreign language publications will not be considered, i.e., only papers with full text in English will be included. Databases/web search engines that will be used are PubMed, Web of Science Platform, Scopus, IEEE Xplore, Google Scholar, Cochrane Library, and ACM Digital Library. Furthermore, clinical studies that have completed patient recruitment and reported results found in the database ClinicalTrials.gov will be considered. The term artificial intelligence is viewed broadly, and a wide range of machine learning and mathematical models suitable as base for decision support will be evaluated. Two members of the team will test the framework on a sample of included studies to ensure that the coding framework is suitable and can be consistently applied. Analysis of collected data will provide a descriptive summary and thematic analysis. The reported results will convey knowledge about the state of current research and innovation for using artificial intelligence to detect sepsis in early phases of the medical care chain. Ethics and dissemination The methodology used here is based on the use of publicly available information and does not need ethical approval. It aims at aiding further research towards digital solutions for disease detection and health innovation. Results will be extracted into a review report for submission to a peer-reviewed scientific journal. Results will be shared with relevant local and national authorities and disseminated in additional appropriate formats such as conferences, lectures, and press releases.

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Section: Introductionmentioning

confidence: 99%

Early Detection of Sepsis Using Artificial Intelligence: A Scoping Review Protocol

Pepic

Feldt

Ljungström

et al. 2020

Preprint

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Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. To decrease the high case fatality rates and morbidity for sepsis and septic shock, there is a need to increase the accuracy of early detection of suspected sepsis in prehospital and emergency department settings. This may be achieved by developing risk prediction decision support systems based on artificial intelligence. Methods: The overall aim of this scoping review is to summarize the literature on existing methods for early detection of sepsis using artificial intelligence. The review will be performed using the framework formulated by Arksey and O’Malley and further developed by Levac and colleagues. To identify primary studies and reviews that are suitable to answer our research questions, a comprehensive literature collection will be compiled by searching several sources. Constrictions regarding time and language will have to be implemented. Therefore, only studies published between 1 January 1990 and 30 September 2020 will be taken into consideration and foreign language publications will not be considered, only papers with full text in English will be included. Databases/web search engines that will be used are PubMed, Web of Science Platform, Scopus, IEEE Xplore, Google Scholar, Cochrane Library and ACM Digital Library. Furthermore, clinical studies that have completed patient recruitment and reported results found in the database ClinicalTrials.gov will be considered. The term artificial intelligence is viewed broadly and a wide range of machine learning and mathematical models suitable as base for decision support will be evaluated. Two members of the team will test the framework on a sample of included studies to ensure that the coding framework is suitable and can be consistently applied. Analysis of collected data will provide a descriptive summary and thematic analysis. The reported results will convey knowledge about the state of current research and innovation for using artificial intelligence to detect sepsis in early phases of the medical care chain.Ethics and dissemination: The methodology used here is based on the use of publicly available information and does not need ethical approval. It aims at aiding further research towards digital solutions for disease detection and health innovation. Results will be extracted into a review report for submission to a peer-reviewed scientific journal. Results will be shared with relevant local and national authorities and disseminated in additional appropriate formats such as conferences, lectures, and press releases.

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Clinical management of sepsis can be improved by artificial intelligence: yes

Cited by 38 publications

References 9 publications

Undergraduate Medical Competencies in Digital Health and Curricular Module Development: Mixed Methods Study

Undergraduate Medical Competencies in Digital Health and Curricular Module Development: Mixed Methods Study

Early detection of sepsis using artificial intelligence: a scoping review protocol

Early Detection of Sepsis Using Artificial Intelligence: A Scoping Review Protocol

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