Health Care Science最新文献

{"title":"A systematic evaluation of the performance of GPT-4 and PaLM2 to diagnose comorbidities in MIMIC-IV patients","authors":"Peter Sarvari,&nbsp;Zaid Al-fagih,&nbsp;Abdullatif Ghuwel,&nbsp;Othman Al-fagih","doi":"10.1002/hcs2.79","DOIUrl":"10.1002/hcs2.79","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Given the strikingly high diagnostic error rate in hospitals, and the recent development of Large Language Models (LLMs), we set out to measure the diagnostic sensitivity of two popular LLMs: GPT-4 and PaLM2. Small-scale studies to evaluate the diagnostic ability of LLMs have shown promising results, with GPT-4 demonstrating high accuracy in diagnosing test cases. However, larger evaluations on real electronic patient data are needed to provide more reliable estimates.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To fill this gap in the literature, we used a deidentified Electronic Health Record (EHR) data set of about 300,000 patients admitted to the Beth Israel Deaconess Medical Center in Boston. This data set contained blood, imaging, microbiology and vital sign information as well as the patients' medical diagnostic codes. Based on the available EHR data, doctors curated a set of diagnoses for each patient, which we will refer to as ground truth diagnoses. We then designed carefully-written prompts to get patient diagnostic predictions from the LLMs and compared this to the ground truth diagnoses in a random sample of 1000 patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Based on the proportion of correctly predicted ground truth diagnoses, we estimated the diagnostic hit rate of GPT-4 to be 93.9%. PaLM2 achieved 84.7% on the same data set. On these 1000 randomly selected EHRs, GPT-4 correctly identified 1116 unique diagnoses.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The results suggest that artificial intelligence (AI) has the potential when working alongside clinicians to reduce cognitive errors which lead to hundreds of thousands of misdiagnoses every year. However, human oversight of AI remains essential: LLMs cannot replace clinicians, especially when it comes to human understanding and empathy. Furthermore, a significant number of challenges in incorporating AI into health care exist, including ethical, liability and regulatory barriers.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 1","pages":"3-18"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.79","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139686292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning from socially driven frugal innovation to design the future of healthcare: A case of mobile Primary Health Center","authors":"Md Haseen Akhtar,&nbsp;Janakarajan Ramkumar","doi":"10.1002/hcs2.80","DOIUrl":"10.1002/hcs2.80","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Aim</h3>\u0000 \u0000 <p>Despite their flaws, the low-cost but powerful economical solutions can ensure everyone has access to health. The main aim of this study is to extract characteristics of frugal innovation (FI) and social innovation (SI) for Primary Health Centers (PHCs) in low resource settings (LRS) for sustainable development. We will use the gained insights to design the mobile primary healthcare infrastructure using FI and SI strategies. There is a lack of methodology to design sustainable healthcare infrastructure for LRS. There is a gap in the literature about building sustainable infrastructure to provide basic healthcare facilities essential to the community. This article studies several factors necessary for designing sustainable infrastructure from the lens of FI, SI, and sustainability to develop a mobile healthcare infrastructure for last-mile people.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Started with purposive sampled case studies to find out factors and criteria that most affect the success for an innovation to be frugal, social, and sustainable. The established criteria were used to design, develop, and deploy the mobile Primary Health Center (mPHC). Moving forward, we tested the system designed with stakeholders to gather insights. At this stage we found the feedback loop from the stakeholders and the role of interdisciplinary discussions between experts, medical officers, nurses, patient, and other staff of PHCs during the design, development, deployment, and test stage to be useful in taking design decisions efficiently.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The designed healthcare infrastructure of mPHC through the aspects of FI and SI proves to be efficient in providing key healthcare services to LRS.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Focusing on essential capabilities and optimizing performance with technology, methodologies, and processes reduces costs in an innovation. Focus on socially inclusive and rebalancing power disparities, overcome societal challenges and improve human capabilities will create a sustainable and novel solution.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 1","pages":"19-31"},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.80","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139875881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constructing an effective evaluation system to identify doctors’ research capabilities","authors":"Xiaojing Hu","doi":"10.1002/hcs2.82","DOIUrl":"https://doi.org/10.1002/hcs2.82","url":null,"abstract":"","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"1 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139882578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning from socially driven frugal innovation to design the future of healthcare: A case of mobile Primary Health Center","authors":"Md Haseen Akhtar, Janakarajan Ramkumar","doi":"10.1002/hcs2.80","DOIUrl":"https://doi.org/10.1002/hcs2.80","url":null,"abstract":"Despite their flaws, the low‐cost but powerful economical solutions can ensure everyone has access to health. The main aim of this study is to extract characteristics of frugal innovation (FI) and social innovation (SI) for Primary Health Centers (PHCs) in low resource settings (LRS) for sustainable development. We will use the gained insights to design the mobile primary healthcare infrastructure using FI and SI strategies. There is a lack of methodology to design sustainable healthcare infrastructure for LRS. There is a gap in the literature about building sustainable infrastructure to provide basic healthcare facilities essential to the community. This article studies several factors necessary for designing sustainable infrastructure from the lens of FI, SI, and sustainability to develop a mobile healthcare infrastructure for last‐mile people.Started with purposive sampled case studies to find out factors and criteria that most affect the success for an innovation to be frugal, social, and sustainable. The established criteria were used to design, develop, and deploy the mobile Primary Health Center (mPHC). Moving forward, we tested the system designed with stakeholders to gather insights. At this stage we found the feedback loop from the stakeholders and the role of interdisciplinary discussions between experts, medical officers, nurses, patient, and other staff of PHCs during the design, development, deployment, and test stage to be useful in taking design decisions efficiently.The designed healthcare infrastructure of mPHC through the aspects of FI and SI proves to be efficient in providing key healthcare services to LRS.Focusing on essential capabilities and optimizing performance with technology, methodologies, and processes reduces costs in an innovation. Focus on socially inclusive and rebalancing power disparities, overcome societal challenges and improve human capabilities will create a sustainable and novel solution.","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"9 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139816050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Balancing medical innovation and affordability in the new healthcare ecosystem in China: Review of pharmaceutical pricing and reimbursement policies","authors":"Vivian Chen,&nbsp;Wenbin Shao","doi":"10.1002/hcs2.76","DOIUrl":"10.1002/hcs2.76","url":null,"abstract":"<p>The China Basic Medical Insurance Program was created in 1999 with three objectives: equal accessibility, affordability, and quality. Today, it has become the biggest medical insurance program in the world, covering 95% of China's population. Since 2015, China's healthcare ecosystem has been reshaped by increasing innovation, which has in turn been driven by regulatory reform, enhancement of research and development capability, and capital market development. There has also been improved regulatory efficiency to reduce lags in launching drugs. In 2022, nearly 20% of novel active substances launched globally were from China. China has also risen to become the second biggest contributor to innovation in terms of pipelines. Using a “fast-follow” strategy, many locally developed innovative drugs can compete with products from multinational companies in their quality and pricing. However, China's pharmaceutical and biotechnology industry will continue to face challenges in pricing and reimbursement, as well as a shortened product lifecycle with rapid price erosion. The government has already accelerated the timeline for updating the drug reimbursement list and is willing to create a high-quality medical insurance program. However, some obstacles are hard to overcome, including reimbursement for advanced therapies, limited funding and an increasing burden of disease due to an aging population. This article reviews the trajectory of medical innovation in China, including the challenges. Looking forward, balancing affordability and innovation will be critical for China to continue the trajectory of growth. The article also offers some suggestions for future policy reform, including optimizing reimbursement efficiency with a focus on high-quality solutions, enhancing the value assessment framework, payer repositioning from “value buyer” to “strategic buyer”, and developing alternative market access pathways for innovative drugs.</p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 6","pages":"381-391"},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.76","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138822414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating catastrophic health expenditure among people living with HIV and AIDS in South Western Nigeria","authors":"Adeyinka Adeniran,&nbsp;Omobola Y. Ojo,&nbsp;Florence C. Chieme,&nbsp;Yeside Shogbamimu,&nbsp;Helen O. Olowofeso,&nbsp;Imane Sidibé,&nbsp;Oladipupo Fisher,&nbsp;Monsurat Adeleke","doi":"10.1002/hcs2.77","DOIUrl":"10.1002/hcs2.77","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>This study aimed to determine the catastrophic healthcare expenditure (CHE) among people living with HIV (PLHIV) in Lagos and to identify factors associated with CHE among them.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The study was a descriptive cross-sectional survey conducted between January and March 2021 among 578 PLHIVs drawn from various healthcare facilities in Lagos where HIV care and treatment services should be provided free of charge. Data were collected through pretested questionnaires and analyzed using Stata SE 12.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The mean monthly expenditure on food was N29,282 ($53.2), while expenditure on healthcare averaged N8364 ($15.2). Nearly 60% of respondents experienced CHE, while around 30% had to borrow money to pay for some aspect of their medical treatment. Almost all (96%) had no health insurance plan. Respondents' group, personal income, perception of current health status, and the number of people in their households were significantly associated with catastrophic health expenditure <i>p</i> &lt; 0.05. PLHIV in the racial/ethnic minority/migrants' group and those who earned less than ₦30,000 ($55) were statistically significantly associated with CHE at <i>p</i> &lt; 0.001 with OR of 28.7 and 3.15, respectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The study, therefore, highlights the widespread financial hardship faced by PLHIV in accessing healthcare, and the need for policies to increase financial risk protection.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 6","pages":"370-380"},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.77","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138822415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction and application of standardized training effect evaluation system for new nurses in operating room","authors":"Xiaoli Liu,&nbsp;Yanshu Wei,&nbsp;Jin Pei,&nbsp;Xiaozhou Wu","doi":"10.1002/hcs2.75","DOIUrl":"10.1002/hcs2.75","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>This study aims to develop and validate a Structured Training Effectiveness Evaluation (STEE) tool based on the Kirkpatrick model for newly graduated registered nurses in the operating room in China.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The first phase will involve focus group and individual interviews with nursing educators and newly graduated registered nurses selected using purposive sampling. The data will be analyzed thematically to identify key components necessary to develop the STEE tool. The second phase will develop and validate the STEE tool through a panel of experts using the Delphi method. The item weights will be determined with the analytic hierarchy process technique. The third phase will involve implementation and evaluation of the STEE tool with an exploratory, nonexperimental, and comparative analysis. Descriptive and inferential statistical analyses will be performed with SPSS version 23.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The STEE tool for newly graduated registered nurses in the operating room will be useful for evaluating training effectiveness during standardized training. The results obtained with this tool will clarify the effectiveness of training, thereby helping transform nursing students into competent nurses.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>In this way, this study will provide practical guidance for improving standardized training programs and help newly graduated nurses manage their transition to the clinical work environment and remain in their posts.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 6","pages":"392-399"},"PeriodicalIF":0.0,"publicationDate":"2023-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.75","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138584858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-patient suicide after telephone delivery of bad news to a suspected COVID-19 patient: What could be done to improve communication quality?","authors":"Natalie Tin Yau So,&nbsp;Olivia Miu Yung Ngan","doi":"10.1002/hcs2.74","DOIUrl":"10.1002/hcs2.74","url":null,"abstract":"<p>Breaking bad news is a critical communication competency for healthcare professionals. Any disclosure of a life-threatening event, such as a malignancy diagnosis, often causes significant stress to patients. While some patients may respond with acceptance and a determination to fight their illness, research has consistently shown that cancer patients often respond to the disclosure of their diagnosis with a range of negative emotions, such as anxiety, distress, and depression [<span>1, 2</span>]. These reactions are often accompanied by feelings of fear, uncertainty, and a sense of loss of control over their lives. Patients may also experience denial, manifesting as reluctance to accept or discuss the diagnosis [<span>3</span>]. Avoidance is another common reaction, where patients may choose to avoid certain situations or people that remind them of their illness [<span>4</span>]. These reactions are not uncommon and are a natural response to the stress and uncertainty of cancer diagnosis.</p><p>A common ethical dilemma in breaking a cancer diagnosis is that patients have different preferences and coping mechanisms when dealing with difficult news, and it is important to explore their wish to know about their health condition. Some patients may want to be fully informed about their diagnosis, prognosis, and treatment options, as they believe it empowers them to make decisions and take control of their healthcare. They may also value the opportunity to prepare emotionally and practically for the challenges that lie ahead. However, other patients may prefer to shield themselves from the potentially distressing information [<span>5</span>]. They may prioritize maintaining hope, protecting their mental well-being, or focusing on the present moment rather than dwelling on the future. Previous students showed that different cultures or religions influence how patients perceive the disease, their desire to know about the health condition, or their willingness to accept a diagnosis. For example, in some cultures, cancer is seen as a death sentence, leading to denial or avoidance of diagnosis and treatment [<span>6</span>]. There is a social stigma and gender label attached to cancer, which can lead to shame and embarrassment about the diagnosis [<span>7-9</span>]. Patients may be reluctant to seek medical attention, disclose their diagnosis, or follow through with treatment due to fear of being ostracized or discriminated against.</p><p>Remote communication methods like video and phone calls are being used more frequently to prevent the spread of the virus during disease outbreaks, such as the COVID-19 pandemic. It has become more difficult for healthcare professionals to inform patients about their cancer diagnosis. However, giving a cancer diagnosis over the phone can be a challenge since it does not allow for in-person support, and can come across as impersonal and insensitive. Unfortunately, in some cases, delivering bad news can have tragic conseq","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 6","pages":"400-405"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.74","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134902559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Choices of medical institutions and associated factors in older patients with multimorbidity in stabilization period in China: A study based on logistic regression and decision tree model","authors":"Xiaoran Wang,&nbsp;Dan Zhang","doi":"10.1002/hcs2.73","DOIUrl":"10.1002/hcs2.73","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>As China's population ages, its disease spectrum is changing, and the coexistence of multiple chronic diseases has become the norm with respect to the health status of its elderly population. However, the health institution choices of older patients with multimorbidity in stabilization period remains underresearched. This study investigate the factors influencing the choices of older patients with multimorbidity to provide references for the rational allocation of healthcare resources.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A multistage, stratified, whole-group random-sampling method was used to select eligible older patients from September to December of 2022 who attended the Community Health Service Center of Guangdong Province. We adopted a self-designed questionnaire to collect patients' general, disease-related, social-support information, their intention to choose a healthcare provider. A binary logistic regression and decision tree model based on the Chi-squared automatic interaction detector algorithm were implemented to analyze the associated factors involved.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A total of 998 patients in stabilization period were included in the study, of which 593 (59.42%) chose hospital and 405 (40.58%) chose primary care. Our binary logistic regression results revealed that age, sex, individual average annual income, educational level, self-reported health status, activities of daily living, alcohol consumption, family doctor contracting, and family supervision of medication or exercise were the principal factors influencing the choice of medical institutions for older patients with multimorbidity (<i>p</i> &lt; 0.05). The decision-tree model reflected three levels and 11 nodes, and we screened a total of four influencing factors: activities of daily living, age, a family doctor contract, and patient sex. The data showed that the logistic regression model possessed an accuracy of 72.9% and that the decision tree model exhibited an accuracy of 68.7%. Prediction using the binary logistic regression was thus statistically superior to the categorical decision-tree model based on the Chi-squared automatic interaction detector algorithm (<i>Z</i> = 3.238, <i>p</i> = 0.001).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>More than half of older patients with multimorbidity in stabilization period chose hospitals for healthcare. Efforts should be made to improve the quality of healthcare services and increase the medical contracting rate and recognition of family docto","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"2 6","pages":"359-369"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.73","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136079032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for emergency medical care","authors":"Shivam Rajput, Pramod Kumar Sharma, Rishabha Malviya","doi":"10.1002/hcs2.72","DOIUrl":"https://doi.org/10.1002/hcs2.72","url":null,"abstract":"Abstract There is increasing research into the potential benefits of incorporating artificial intelligence (AI) and machine learning algorithms into emergency medical services. AI is finding new applications across a wide range of sectors, one of which is healthcare, where it is being used to enhance clinical diagnostics. AI solutions have enormous untapped potential to improve healthcare efficiency and quality, thus researchers have focused heavily on emergency medicine (EM). Many individuals without prior experience with any physician often receive their initial medical care in the emergency room. Two areas that could benefit from the implementation of AI are reducing waiting times and enhancing diagnostic capabilities. This study provides further explanation of how AI is used in emergency rooms. Several machine learning‐based algorithms are also addressed. In this research, we summarise recent developments in the use of AI in EM. This research tries to summarise the usefulness of AI in EM by looking at recent developments in emergency department operations and clinical patient management.","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135917876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}