Journal of Medical Systems最新文献

{"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":"48 1","pages":"50"},"PeriodicalIF":3.5,"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}

{"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":"48 1","pages":"49"},"PeriodicalIF":3.5,"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}

{"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":"48 1","pages":"48"},"PeriodicalIF":3.5,"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}

{"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":"23 1","pages":""},"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}

{"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":"1 1","pages":""},"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}

{"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":"4 1","pages":""},"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}

{"title":"Comparing the Effectiveness of a Clinical Decision Support Tool in Reducing Pediatric Opioid Dose Calculation Errors: PediPain App vs. Traditional Calculators – A Simulation-Based Randomised Controlled Study","authors":"Clyde T. Matava, Martina Bordini, Amanda Jasudavisius, Carmina Santos, Monica Caldeira-Kulbakas","doi":"10.1007/s10916-024-02060-4","DOIUrl":"https://doi.org/10.1007/s10916-024-02060-4","url":null,"abstract":"<p>Wrong dose calculation medication errors are widespread in pediatric patients mainly due to weight-based dosing. PediPain app is a clinical decision support tool that provides weight- and age- based dosages for various analgesics. We hypothesized that the use of a clinical decision support tool, the PediPain app versus pocket calculators for calculating pain medication dosages in children reduces the incidence of wrong dosage calculations and shortens the time taken for calculations. The study was a randomised controlled trial comparing the PediPain app vs. pocket calculator for performing eight weight-based calculations for opioids and other analgesics. Participants were healthcare providers routinely administering opioids and other analgesics in their practice. The primary outcome was the incidence of wrong dose calculations. Secondary outcomes were the incidence of wrong dose calculations in simple versus complex calculations; time taken to complete calculations; the occurrence of tenfold; hundredfold errors; and wrong-key presses. A total of 140 residents, fellows and nurses were recruited between June 2018 and November 2019; 70 participants were randomized to control group (pocket calculator) and 70 to the intervention group (PediPain App). After randomization two participants assigned to PediPain group completed the simulation in the control group by mistake. Analysis was by intention-to-treat (PediPain app = 68 participants, pocket calculator = 72 participants). The overall incidence of wrong dose calculation was 178/576 (30.9%) for the control and 23/544 (4.23%) for PediPain App, <i>P</i> &lt; 0·001. The risk difference was − 32.8% [-38.7%, -26.9%] for complex and − 20.5% [-26.3%, -14.8%] for simple calculations. Calculations took longer within control group (median of 69 Sects. [50, 96]) compared to PediPain app group, (median 48 Sects. [38, 63]), <i>P</i> &lt; 0.001. There were no differences in other secondary outcomes. A weight-based clinical decision support tool, the PediPain app reduced the incidence of wrong doses calculation. Clinical decision support tools calculating medications may be valuable instruments for reducing medication errors, especially in the pediatric population.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"197 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140617575","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}

{"title":"Patient Engagement with Conversational Agents in Health Applications 2016–2022: A Systematic Review and Meta-Analysis","authors":"Kevin E. Cevasco, Rachel E. Morrison Brown, Rediet Woldeselassie, Seth Kaplan","doi":"10.1007/s10916-024-02059-x","DOIUrl":"https://doi.org/10.1007/s10916-024-02059-x","url":null,"abstract":"<p>Clinicians and patients seeking electronic health applications face challenges in selecting effective solutions due to a high market failure rate. Conversational agent applications (“chatbots”) show promise in increasing healthcare user engagement by creating bonds between the applications and users. It is unclear if chatbots improve patient adherence or if past trends to include chatbots in electronic health applications were due to technology hype dynamics and competitive pressure to innovate. We conducted a systematic literature review using Preferred Reporting Items for Systematic reviews and Meta-Analyses methodology on health chatbot randomized control trials. The goal of this review was to identify if user engagement indicators are published in eHealth chatbot studies. A meta-analysis examined patient clinical trial retention of chatbot apps. The results showed no chatbot arm patient retention effect. The small number of studies suggests a need for ongoing eHealth chatbot research, especially given the claims regarding their effectiveness made outside the scientific literatures.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"30 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140589994","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}

{"title":"Variations of the Relative Parasympathetic Tone Assessed by ANI During Oocyte Retrieval Under Local Anaesthesia with Virtual Reality : A Randomized, Controlled, Monocentric, Open Study","authors":"Florent Malard, Ludovic Moy, Vincent Denoual, Helene Beloeil, Emilie Leblong","doi":"10.1007/s10916-024-02057-z","DOIUrl":"https://doi.org/10.1007/s10916-024-02057-z","url":null,"abstract":"<p>Transvaginal oocyte retrieval is an outpatient procedure performed under local anaesthesia. Hypno-analgesia could be effective in managing comfort during this procedure. This study aimed to assess the effectiveness of a virtual reality headset as an adjunct to local anaesthesia in managing nociception during oocyte retrieval. This was a prospective, randomized single-centre study including patients undergoing oocyte retrieval under local anaesthesia. Patients were randomly assigned to the intervention group (virtual reality headset + local anaesthesia) or the control group (local anaesthesia). The primary outcome was the efficacy on the ANI^®, which reflects the relative parasympathetic tone. Secondary outcomes included pain, anxiety, conversion to general anaesthesia rate, procedural duration, patient’s and gynaecologist’s satisfaction and virtual reality headset tolerance. ANI was significantly lower in the virtual reality group during the whole procedure <i>(mean ANI: 79 95 CI [77; 81] vs 74 95 CI [72; 76]; p</i> &lt; <i>0.001; effect size Cohen’s d -0.53 [-0.83, -0.23])</i>, and during the two most painful moments: infiltration (mean ANI: 81 +/- 11 vs 74 +/- 13; p &lt; 0.001; <i>effect size Cohen’s d -0.54[-0.85, -0.24]</i>) and oocytes retrieval <i>(mean ANI: 78 </i>+/- <i>11 vs 74.40 </i>+/- <i>11; p</i> = <i>0.020; effect size Cohen’s d -0.37 [-0.67, -0.07]).</i>There was no significant difference in pain measured by VAS. No serious adverse events related were reported. The integration of virtual reality as an hypnotic tool during oocyte retrieval under local anaesthesia in assisted reproductive techniques could improve patient’s comfort and experience.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"41 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140589989","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}

{"title":"Responses of Five Different Artificial Intelligence Chatbots to the Top Searched Queries About Erectile Dysfunction: A Comparative Analysis","authors":"Mehmet Fatih Şahin, Hüseyin Ateş, Anıl Keleş, Rıdvan Özcan, Çağrı Doğan, Murat Akgül, Cenk Murat Yazıcı","doi":"10.1007/s10916-024-02056-0","DOIUrl":"https://doi.org/10.1007/s10916-024-02056-0","url":null,"abstract":"<p>The aim of the study is to evaluate and compare the quality and readability of responses generated by five different artificial intelligence (AI) chatbots—ChatGPT, Bard, Bing, Ernie, and Copilot—to the top searched queries of erectile dysfunction (ED). Google Trends was used to identify ED-related relevant phrases. Each AI chatbot received a specific sequence of 25 frequently searched terms as input. Responses were evaluated using DISCERN, Ensuring Quality Information for Patients (EQIP), and Flesch-Kincaid Grade Level (FKGL) and Reading Ease (FKRE) metrics. The top three most frequently searched phrases were “erectile dysfunction cause”, “how to erectile dysfunction,” and “erectile dysfunction treatment.” Zimbabwe, Zambia, and Ghana exhibited the highest level of interest in ED. None of the AI chatbots achieved the necessary degree of readability. However, Bard exhibited significantly higher FKRE and FKGL ratings (<i>p</i> = 0.001), and Copilot achieved better EQIP and DISCERN ratings than the other chatbots (<i>p</i> = 0.001). Bard exhibited the simplest linguistic framework and posed the least challenge in terms of readability and comprehension, and Copilot’s text quality on ED was superior to the other chatbots. As new chatbots are introduced, their understandability and text quality increase, providing better guidance to patients.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":"36 1","pages":""},"PeriodicalIF":5.3,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140589996","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}