Pub Date : 2024-05-16DOI: 10.1007/s10916-024-02070-2
H Abedian Kalkhoran, J Zwaveling, F van Hunsel, A Kant
Reports from spontaneous reporting systems (SRS) are hypothesis generating. Additional evidence such as more reports is required to determine whether the generated drug-event associations are in fact safety signals. However, underreporting of adverse drug reactions (ADRs) delays signal detection. Through the use of natural language processing, different sources of real-world data can be used to proactively collect additional evidence for potential safety signals. This study aims to explore the feasibility of using Electronic Health Records (EHRs) to identify additional cases based on initial indications from spontaneous ADR reports, with the goal of strengthening the evidence base for potential safety signals. For two confirmed and two potential signals generated by the SRS of the Netherlands Pharmacovigilance Centre Lareb, targeted searches in the EHR of the Leiden University Medical Centre were performed using a text-mining based tool, CTcue. The search for additional cases was done by constructing and running queries in the structured and free-text fields of the EHRs. We identified at least five additional cases for the confirmed signals and one additional case for each potential safety signal. The majority of the identified cases for the confirmed signals were documented in the EHRs before signal detection by the Dutch Medicines Evaluation Board. The identified cases for the potential signals were reported to Lareb as further evidence for signal detection. Our findings highlight the feasibility of performing targeted searches in the EHR based on an underlying hypothesis to provide further evidence for signal generation.
{"title":"An innovative method to strengthen evidence for potential drug safety signals using Electronic Health Records.","authors":"H Abedian Kalkhoran, J Zwaveling, F van Hunsel, A Kant","doi":"10.1007/s10916-024-02070-2","DOIUrl":"10.1007/s10916-024-02070-2","url":null,"abstract":"<p><p>Reports from spontaneous reporting systems (SRS) are hypothesis generating. Additional evidence such as more reports is required to determine whether the generated drug-event associations are in fact safety signals. However, underreporting of adverse drug reactions (ADRs) delays signal detection. Through the use of natural language processing, different sources of real-world data can be used to proactively collect additional evidence for potential safety signals. This study aims to explore the feasibility of using Electronic Health Records (EHRs) to identify additional cases based on initial indications from spontaneous ADR reports, with the goal of strengthening the evidence base for potential safety signals. For two confirmed and two potential signals generated by the SRS of the Netherlands Pharmacovigilance Centre Lareb, targeted searches in the EHR of the Leiden University Medical Centre were performed using a text-mining based tool, CTcue. The search for additional cases was done by constructing and running queries in the structured and free-text fields of the EHRs. We identified at least five additional cases for the confirmed signals and one additional case for each potential safety signal. The majority of the identified cases for the confirmed signals were documented in the EHRs before signal detection by the Dutch Medicines Evaluation Board. The identified cases for the potential signals were reported to Lareb as further evidence for signal detection. Our findings highlight the feasibility of performing targeted searches in the EHR based on an underlying hypothesis to provide further evidence for signal generation.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11098892/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140945297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction: Virtual reality (VR) is becoming increasingly popular to train health-care professionals (HCPs) to acquire and/or maintain cardiopulmonary resuscitation (CPR) basic or advanced skills.
Aim: To understand whether VR in CPR training or retraining courses can have benefits for patients (neonatal, pediatric, and adult), HCPs and health-care organizations as compared to traditional CPR training.
Methods: A systematic review (PROSPERO: CRD42023431768) following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. In June 2023, the PubMed, Cochrane Library, Scopus and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases were searched and included studies evaluated in their methodological quality with Joanna Briggs Institute checklists. Data were narratively summarized.
Results: Fifteen studies published between 2013 and 2023 with overall fair quality were included. No studies investigated patients' outcomes. At the HCP level, the virtual learning environment was perceived to be engaging, realistic and facilitated the memorization of the procedures; however, limited decision-making, team building, psychological pressure and frenetic environment were underlined as disadvantages. Moreover, a general improvement in performance was reported in the use of the defibrillator and carrying out the chest compressions. At the organizational level, one study performed a cost/benefit evaluation in favor of VR as compared to traditional CPR training.
Conclusions: The use of VR for CPR training and retraining is in an early stage of development. Some benefits at the HCP level are promising. However, more research is needed with standardized approaches to ensure a progressive accumulation of the evidence and inform decisions regarding the best training methodology in this field.
导言:目的:了解与传统心肺复苏术培训相比,虚拟现实技术在心肺复苏术培训或再培训课程中的应用是否能为患者(新生儿、儿童和成人)、医护人员和医疗机构带来益处:按照系统综述和元分析首选报告项目(PRISMA)指南进行系统综述(PROSPERO:CRD42023431768)。2023 年 6 月,对 PubMed、Cochrane Library、Scopus 和 Cumulative Index to Nursing and Allied Health Literature (CINAHL) 数据库进行了检索,并根据 Joanna Briggs Institute 的检查表对纳入研究的方法学质量进行了评估。对数据进行了叙述性总结:结果:共纳入 15 项研究,这些研究发表于 2013 年至 2023 年之间,总体质量尚可。没有研究调查了患者的治疗效果。在高级保健人员层面,虚拟学习环境被认为是吸引人、逼真的,并有助于记忆程序;然而,有限的决策、团队建设、心理压力和狂热的环境被强调为缺点。此外,据报告,使用除颤器和进行胸外按压的成绩普遍有所提高。在组织层面,一项研究进行了成本/效益评估,结果显示,与传统心肺复苏术培训相比,VR 更受青睐:结论:将 VR 用于心肺复苏术培训和再培训尚处于早期发展阶段。在心肺复苏术培训和再培训中使用 VR 尚处于早期发展阶段。然而,还需要进行更多标准化方法的研究,以确保逐步积累证据,并为该领域最佳培训方法的决策提供依据。
{"title":"Virtual Reality for Cardiopulmonary Resuscitation Healthcare Professionals Training: A Systematic Review.","authors":"Roberto Trevi, Stefania Chiappinotto, Alvisa Palese, Alessandro Galazzi","doi":"10.1007/s10916-024-02063-1","DOIUrl":"10.1007/s10916-024-02063-1","url":null,"abstract":"<p><strong>Introduction: </strong>Virtual reality (VR) is becoming increasingly popular to train health-care professionals (HCPs) to acquire and/or maintain cardiopulmonary resuscitation (CPR) basic or advanced skills.</p><p><strong>Aim: </strong>To understand whether VR in CPR training or retraining courses can have benefits for patients (neonatal, pediatric, and adult), HCPs and health-care organizations as compared to traditional CPR training.</p><p><strong>Methods: </strong>A systematic review (PROSPERO: CRD42023431768) following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. In June 2023, the PubMed, Cochrane Library, Scopus and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases were searched and included studies evaluated in their methodological quality with Joanna Briggs Institute checklists. Data were narratively summarized.</p><p><strong>Results: </strong>Fifteen studies published between 2013 and 2023 with overall fair quality were included. No studies investigated patients' outcomes. At the HCP level, the virtual learning environment was perceived to be engaging, realistic and facilitated the memorization of the procedures; however, limited decision-making, team building, psychological pressure and frenetic environment were underlined as disadvantages. Moreover, a general improvement in performance was reported in the use of the defibrillator and carrying out the chest compressions. At the organizational level, one study performed a cost/benefit evaluation in favor of VR as compared to traditional CPR training.</p><p><strong>Conclusions: </strong>The use of VR for CPR training and retraining is in an early stage of development. Some benefits at the HCP level are promising. However, more research is needed with standardized approaches to ensure a progressive accumulation of the evidence and inform decisions regarding the best training methodology in this field.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11096216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140920678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-13DOI: 10.1007/s10916-024-02061-3
Widana Kankanamge Darsha Jayamini, Farhaan Mirza, M Asif Naeem, Amy Hai Yan Chan
Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.
{"title":"Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.","authors":"Widana Kankanamge Darsha Jayamini, Farhaan Mirza, M Asif Naeem, Amy Hai Yan Chan","doi":"10.1007/s10916-024-02061-3","DOIUrl":"10.1007/s10916-024-02061-3","url":null,"abstract":"<p><p>Asthma, a common chronic respiratory disease among children and adults, affects more than 200 million people worldwide and causes about 450,000 deaths each year. Machine learning is increasingly applied in healthcare to assist health practitioners in decision-making. In asthma management, machine learning excels in performing well-defined tasks, such as diagnosis, prediction, medication, and management. However, there remain uncertainties about how machine learning can be applied to predict asthma exacerbation. This study aimed to systematically review recent applications of machine learning techniques in predicting the risk of asthma attacks to assist asthma control and management. A total of 860 studies were initially identified from five databases. After the screening and full-text review, 20 studies were selected for inclusion in this review. The review considered recent studies published from January 2010 to February 2023. The 20 studies used machine learning techniques to support future asthma risk prediction by using various data sources such as clinical, medical, biological, and socio-demographic data sources, as well as environmental and meteorological data. While some studies considered prediction as a category, other studies predicted the probability of exacerbation. Only a group of studies applied prediction windows. The paper proposes a conceptual model to summarise how machine learning and available data sources can be leveraged to produce effective models for the early detection of asthma attacks. The review also generated a list of data sources that other researchers may use in similar work. Furthermore, we present opportunities for further research and the limitations of the preceding studies.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11090925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140912014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-10DOI: 10.1007/s10916-024-02068-w
Benjamin Friedrichson, Markus Ketomaeki, Thomas Jasny, Oliver Old, Lea Grebe, Elina Nürenberg-Goloub, Elisabeth H Adam, Kai Zacharowski, Jan Andreas Kloka
In Germany, a comprehensive reimbursement policy for extracorporeal membrane oxygenation (ECMO) results in the highest per capita use worldwide, although benefits remain controversial. Public ECMO data is unstructured and poorly accessible to healthcare professionals, researchers, and policymakers. In addition, there are no uniform policies for ECMO allocation which confronts medical personnel with ethical considerations during health crises such as respiratory virus outbreaks.Retrospective information on adult and pediatric ECMO support performed in German hospitals was extracted from publicly available reimbursement data and hospital quality reports and processed to create the web-based ECMO Dashboard built on Open-Source software. Patient-level and hospital-level data were merged resulting in a solid base for ECMO use analysis and ECMO demand forecasting with high spatial granularity at the level of 413 county and city districts in Germany.The ECMO Dashboard ( https://www.ecmo-dash.de/ ), an innovative visual platform, presents the retrospective utilization patterns of ECMO support in Germany. It features interactive maps, comprehensive charts, and tables, providing insights at the hospital, district, and national levels. This tool also highlights the high prevalence of ECMO support in Germany and emphasizes districts with ECMO surplus - where patients from other regions are treated, or deficit - origins from which ECMO patients are transferred to other regions. The dashboard will evolve iteratively to provide stakeholders with vital information for informed and transparent resource allocation and decision-making.Accessible public routine data could support evidence-informed, forward-looking resource management policies, which are urgently needed to increase the quality and prepare the critical care infrastructure for future pandemics.
{"title":"Web-based Dashboard on ECMO Utilization in Germany: An Interactive Visualization, Analyses, and Prediction Based on Real-life Data.","authors":"Benjamin Friedrichson, Markus Ketomaeki, Thomas Jasny, Oliver Old, Lea Grebe, Elina Nürenberg-Goloub, Elisabeth H Adam, Kai Zacharowski, Jan Andreas Kloka","doi":"10.1007/s10916-024-02068-w","DOIUrl":"10.1007/s10916-024-02068-w","url":null,"abstract":"<p><p>In Germany, a comprehensive reimbursement policy for extracorporeal membrane oxygenation (ECMO) results in the highest per capita use worldwide, although benefits remain controversial. Public ECMO data is unstructured and poorly accessible to healthcare professionals, researchers, and policymakers. In addition, there are no uniform policies for ECMO allocation which confronts medical personnel with ethical considerations during health crises such as respiratory virus outbreaks.Retrospective information on adult and pediatric ECMO support performed in German hospitals was extracted from publicly available reimbursement data and hospital quality reports and processed to create the web-based ECMO Dashboard built on Open-Source software. Patient-level and hospital-level data were merged resulting in a solid base for ECMO use analysis and ECMO demand forecasting with high spatial granularity at the level of 413 county and city districts in Germany.The ECMO Dashboard ( https://www.ecmo-dash.de/ ), an innovative visual platform, presents the retrospective utilization patterns of ECMO support in Germany. It features interactive maps, comprehensive charts, and tables, providing insights at the hospital, district, and national levels. This tool also highlights the high prevalence of ECMO support in Germany and emphasizes districts with ECMO surplus - where patients from other regions are treated, or deficit - origins from which ECMO patients are transferred to other regions. The dashboard will evolve iteratively to provide stakeholders with vital information for informed and transparent resource allocation and decision-making.Accessible public routine data could support evidence-informed, forward-looking resource management policies, which are urgently needed to increase the quality and prepare the critical care infrastructure for future pandemics.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11087321/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140898384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-25DOI: 10.1007/s10916-024-02062-2
Fatimah Altuhaifa, Dalal Al Tuhaifa
{"title":"Developing an Ontology Representing Fall Risk Management Domain Knowledge","authors":"Fatimah Altuhaifa, Dalal Al Tuhaifa","doi":"10.1007/s10916-024-02062-2","DOIUrl":"https://doi.org/10.1007/s10916-024-02062-2","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140654853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-24DOI: 10.1007/s10916-024-02064-0
J. A. Poppe, R. S. Smorenburg, T. G. Goos, H. R. Taal, I. K. M. Reiss, S. Simons
{"title":"Development of a Web-Based Oxygenation Dashboard for Preterm Neonates: A Quality Improvement Initiative","authors":"J. A. Poppe, R. S. Smorenburg, T. G. Goos, H. R. Taal, I. K. M. Reiss, S. Simons","doi":"10.1007/s10916-024-02064-0","DOIUrl":"https://doi.org/10.1007/s10916-024-02064-0","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140664516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-23DOI: 10.1007/s10916-024-02069-9
Thiago C. Moulin
{"title":"Learning with AI Language Models: Guidelines for the Development and Scoring of Medical Questions for Higher Education","authors":"Thiago C. Moulin","doi":"10.1007/s10916-024-02069-9","DOIUrl":"https://doi.org/10.1007/s10916-024-02069-9","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140668367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-22DOI: 10.1007/s10916-024-02067-x
Joelle Yan Xin Chua, Enci Mary Kan, Phin Peng Lee, Shefaly Shorey
The Stanford Biodesign needs-centric framework can guide healthcare innovators to successfully adopt the ‘Identify, Invent and Implement’ framework and develop new healthcare innovations products to address patients’ needs. This scoping review explored the application of the Stanford Biodesign framework for healthcare innovation training and the development of novel healthcare innovative products. Seven electronic databases were searched from their respective inception dates till April 2023: PubMed, Embase, CINAHL, PsycINFO, Web of Science, Scopus, ProQuest Dissertations, and Theses Global. This review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews and was guided by the Arksey and O’Malley’s scoping review framework. Findings were analyzed using Braun and Clarke’s thematic analysis framework. Three themes and eight subthemes were identified from the 26 included articles. The main themes are: (1) Making a mark on healthcare innovation, (2) Secrets behind success, and (3) The next steps. The Stanford Biodesign framework guided healthcare innovation teams to develop new medical products and achieve better patient health outcomes through the induction of training programs and the development of novel products. Training programs adopting the Stanford Biodesign approach were found to be successful in improving trainees’ entrepreneurship, innovation, and leadership skills and should continue to be promoted. To aid innovators in commercializing their newly developed medical products, additional support such as securing funds for early start-up companies, involving clinicians and users in product testing and validation, and establishing new guidelines and protocols for the new healthcare products would be needed.
斯坦福生物设计以需求为中心的框架可以指导医疗创新者成功采用 "发现、发明和实施 "框架,开发新的医疗创新产品,以满足患者的需求。本范围综述探讨了斯坦福生物设计框架在医疗创新培训和新型医疗创新产品开发中的应用。研究人员检索了七个电子数据库,检索时间从各自的开始日期起至 2023 年 4 月:PubMed, Embase, CINAHL, PsycINFO, Web of Science, Scopus, ProQuest Dissertations, and Theses Global。本综述根据《系统综述和元分析首选报告项目》(Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews)进行报告,并以 Arksey 和 O'Malley 的范围界定综述框架为指导。研究结果采用布劳恩和克拉克的主题分析框架进行分析。从纳入的 26 篇文章中确定了三个主题和八个次主题。主要主题包括(1) 在医疗保健创新方面有所建树,(2) 成功背后的秘密,以及 (3) 下一步。斯坦福生物设计框架指导医疗创新团队开发新的医疗产品,并通过诱导培训计划和开发新产品来实现更好的患者健康结果。研究发现,采用斯坦福生物设计方法的培训计划能成功提高学员的创业、创新和领导能力,应继续推广。为了帮助创新者将其新开发的医疗产品商业化,还需要更多的支持,如为早期创业公司提供资金,让临床医生和用户参与产品测试和验证,以及为新的医疗产品制定新的指导方针和协议。
{"title":"Application of the Stanford Biodesign Framework in Healthcare Innovation Training and Commercialization of Market Appropriate Products: A Scoping Review","authors":"Joelle Yan Xin Chua, Enci Mary Kan, Phin Peng Lee, Shefaly Shorey","doi":"10.1007/s10916-024-02067-x","DOIUrl":"https://doi.org/10.1007/s10916-024-02067-x","url":null,"abstract":"<p>The Stanford Biodesign needs-centric framework can guide healthcare innovators to successfully adopt the ‘Identify, Invent and Implement’ framework and develop new healthcare innovations products to address patients’ needs. This scoping review explored the application of the Stanford Biodesign framework for healthcare innovation training and the development of novel healthcare innovative products. Seven electronic databases were searched from their respective inception dates till April 2023: PubMed, Embase, CINAHL, PsycINFO, Web of Science, Scopus, ProQuest Dissertations, and Theses Global. This review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews and was guided by the Arksey and O’Malley’s scoping review framework. Findings were analyzed using Braun and Clarke’s thematic analysis framework. Three themes and eight subthemes were identified from the 26 included articles. The main themes are: (1) Making a mark on healthcare innovation, (2) Secrets behind success, and (3) The next steps. The Stanford Biodesign framework guided healthcare innovation teams to develop new medical products and achieve better patient health outcomes through the induction of training programs and the development of novel products. Training programs adopting the Stanford Biodesign approach were found to be successful in improving trainees’ entrepreneurship, innovation, and leadership skills and should continue to be promoted. To aid innovators in commercializing their newly developed medical products, additional support such as securing funds for early start-up companies, involving clinicians and users in product testing and validation, and establishing new guidelines and protocols for the new healthcare products would be needed.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140634741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-18DOI: 10.1007/s10916-024-02058-y
Arya Rao, John Kim, Winston Lie, Michael Pang, Lanting Fuh, Keith J. Dreyer, Marc D. Succi
Polypharmacy remains an important challenge for patients with extensive medical complexity. Given the primary care shortage and the increasing aging population, effective polypharmacy management is crucial to manage the increasing burden of care. The capacity of large language model (LLM)-based artificial intelligence to aid in polypharmacy management has yet to be evaluated. Here, we evaluate ChatGPT’s performance in polypharmacy management via its deprescribing decisions in standardized clinical vignettes. We inputted several clinical vignettes originally from a study of general practicioners’ deprescribing decisions into ChatGPT 3.5, a publicly available LLM, and evaluated its capacity for yes/no binary deprescribing decisions as well as list-based prompts in which the model was prompted to choose which of several medications to deprescribe. We recorded ChatGPT responses to yes/no binary deprescribing prompts and the number and types of medications deprescribed. In yes/no binary deprescribing decisions, ChatGPT universally recommended deprescribing medications regardless of ADL status in patients with no overlying CVD history; in patients with CVD history, ChatGPT’s answers varied by technical replicate. Total number of medications deprescribed ranged from 2.67 to 3.67 (out of 7) and did not vary with CVD status, but increased linearly with severity of ADL impairment. Among medication types, ChatGPT preferentially deprescribed pain medications. ChatGPT’s deprescribing decisions vary along the axes of ADL status, CVD history, and medication type, indicating some concordance of internal logic between general practitioners and the model. These results indicate that specifically trained LLMs may provide useful clinical support in polypharmacy management for primary care physicians.
{"title":"Proactive Polypharmacy Management Using Large Language Models: Opportunities to Enhance Geriatric Care","authors":"Arya Rao, John Kim, Winston Lie, Michael Pang, Lanting Fuh, Keith J. Dreyer, Marc D. Succi","doi":"10.1007/s10916-024-02058-y","DOIUrl":"https://doi.org/10.1007/s10916-024-02058-y","url":null,"abstract":"<p>Polypharmacy remains an important challenge for patients with extensive medical complexity. Given the primary care shortage and the increasing aging population, effective polypharmacy management is crucial to manage the increasing burden of care. The capacity of large language model (LLM)-based artificial intelligence to aid in polypharmacy management has yet to be evaluated. Here, we evaluate ChatGPT’s performance in polypharmacy management via its deprescribing decisions in standardized clinical vignettes. We inputted several clinical vignettes originally from a study of general practicioners’ deprescribing decisions into ChatGPT 3.5, a publicly available LLM, and evaluated its capacity for yes/no binary deprescribing decisions as well as list-based prompts in which the model was prompted to choose which of several medications to deprescribe. We recorded ChatGPT responses to yes/no binary deprescribing prompts and the number and types of medications deprescribed. In yes/no binary deprescribing decisions, ChatGPT universally recommended deprescribing medications regardless of ADL status in patients with no overlying CVD history; in patients with CVD history, ChatGPT’s answers varied by technical replicate. Total number of medications deprescribed ranged from 2.67 to 3.67 (out of 7) and did not vary with CVD status, but increased linearly with severity of ADL impairment. Among medication types, ChatGPT preferentially deprescribed pain medications. ChatGPT’s deprescribing decisions vary along the axes of ADL status, CVD history, and medication type, indicating some concordance of internal logic between general practitioners and the model. These results indicate that specifically trained LLMs may provide useful clinical support in polypharmacy management for primary care physicians.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140616059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-17DOI: 10.1007/s10916-024-02065-z
Giacomo Scaioli, Manuela Martella, Giuseppina Lo Moro, Alessandro Prinzivalli, Laura Guastavigna, Alessandro Scacchi, Andreea Mihaela Butnaru, Fabrizio Bert, Roberta Siliquini
The Electronic Personal Health Record (EPHR) provides an innovative service for citizens and professionals to manage health data, promoting patient-centred care. It enhances communication between patients and physicians and improves accessibility to documents for remote medical information management. The study aims to assess the prevalence of awareness and acceptance of the EPHR in northern Italy and define determinants and barriers to its implementation. In 2022, a region-wide cross-sectional study was carried out through a paper-based and online survey shared among adult citizens. Univariable and multivariable regression models analysed the association between the outcome variables (knowledge and attitudes toward the EPHR) and selected independent variables. Overall, 1634 people were surveyed, and two-thirds were aware of the EPHR. Among those unaware of the EPHR, a high prevalence of specific socio-demographic groups, such as foreign-born individuals and those with lower educational levels, was highlighted. Multivariable regression models showed a positive association between being aware of the EPHR and educational level, health literacy, and perceived poor health status, whereas age was negatively associated. A higher knowledge of the EPHR was associated with a higher attitude towards the EPHR. The current analysis confirms a lack of awareness regarding the existence of the EPHR, especially among certain disadvantaged demographic groups. This should serve as a driving force for a powerful campaign tailored to specific categories of citizens for enhancing knowledge and usage of the EPHR. Involving professionals in promoting this tool is crucial for helping patients and managing health data.
{"title":"Knowledge, Attitudes, and Practices about Electronic Personal Health Records: A Cross-Sectional Study in a Region of Northern Italy","authors":"Giacomo Scaioli, Manuela Martella, Giuseppina Lo Moro, Alessandro Prinzivalli, Laura Guastavigna, Alessandro Scacchi, Andreea Mihaela Butnaru, Fabrizio Bert, Roberta Siliquini","doi":"10.1007/s10916-024-02065-z","DOIUrl":"https://doi.org/10.1007/s10916-024-02065-z","url":null,"abstract":"<p>The Electronic Personal Health Record (EPHR) provides an innovative service for citizens and professionals to manage health data, promoting patient-centred care. It enhances communication between patients and physicians and improves accessibility to documents for remote medical information management. The study aims to assess the prevalence of awareness and acceptance of the EPHR in northern Italy and define determinants and barriers to its implementation. In 2022, a region-wide cross-sectional study was carried out through a paper-based and online survey shared among adult citizens. Univariable and multivariable regression models analysed the association between the outcome variables (knowledge and attitudes toward the EPHR) and selected independent variables. Overall, 1634 people were surveyed, and two-thirds were aware of the EPHR. Among those unaware of the EPHR, a high prevalence of specific socio-demographic groups, such as foreign-born individuals and those with lower educational levels, was highlighted. Multivariable regression models showed a positive association between being aware of the EPHR and educational level, health literacy, and perceived poor health status, whereas age was negatively associated. A higher knowledge of the EPHR was associated with a higher attitude towards the EPHR. The current analysis confirms a lack of awareness regarding the existence of the EPHR, especially among certain disadvantaged demographic groups. This should serve as a driving force for a powerful campaign tailored to specific categories of citizens for enhancing knowledge and usage of the EPHR. Involving professionals in promoting this tool is crucial for helping patients and managing health data.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140617904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}