PLOS digital health最新文献

{"title":"Performance of Publicly Available Large Language Models on Internal Medicine Board-style Questions.","authors":"Constantine Tarabanis, Sohail Zahid, Marios Mamalis, Kevin Zhang, Evangelos Kalampokis, Lior Jankelson","doi":"10.1371/journal.pdig.0000604","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000604","url":null,"abstract":"<p><p>Ongoing research attempts to benchmark large language models (LLM) against physicians' fund of knowledge by assessing LLM performance on medical examinations. No prior study has assessed LLM performance on internal medicine (IM) board examination questions. Limited data exists on how knowledge supplied to the models, derived from medical texts improves LLM performance. The performance of GPT-3.5, GPT-4.0, LaMDA and Llama 2, with and without additional model input augmentation, was assessed on 240 randomly selected IM board-style questions. Questions were sourced from the Medical Knowledge Self-Assessment Program released by the American College of Physicians with each question serving as part of the LLM prompt. When available, LLMs were accessed both through their application programming interface (API) and their corresponding chatbot. Mode inputs were augmented with Harrison's Principles of Internal Medicine using the method of Retrieval Augmented Generation. LLM-generated explanations to 25 correctly answered questions were presented in a blinded fashion alongside the MKSAP explanation to an IM board-certified physician tasked with selecting the human generated response. GPT-4.0, accessed either through Bing Chat or its API, scored 77.5-80.7% outperforming GPT-3.5, human respondents, LaMDA and Llama 2 in that order. GPT-4.0 outperformed human MKSAP users on every tested IM subject with its highest and lowest percentile scores in Infectious Disease (80th) and Rheumatology (99.7th), respectively. There is a 3.2-5.3% decrease in performance of both GPT-3.5 and GPT-4.0 when accessing the LLM through its API instead of its online chatbot. There is 4.5-7.5% increase in performance of both GPT-3.5 and GPT-4.0 accessed through their APIs after additional input augmentation. The blinded reviewer correctly identified the human generated MKSAP response in 72% of the 25-question sample set. GPT-4.0 performed best on IM board-style questions outperforming human respondents. Augmenting with domain-specific information improved performance rendering Retrieval Augmented Generation a possible technique for improving accuracy in medical examination LLM responses.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 9","pages":"e0000604"},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11407633/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302860","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":"Six-year (2016-2022) longitudinal patterns of mental health service utilization rates among children developmentally vulnerable in kindergarten and the COVID-19 pandemic disruption.","authors":"Fernanda Talarico, Dan Metes, Mengzhe Wang, Jake Hayward, Yang S Liu, Julie Tian, Yanbo Zhang, Andrew J Greenshaw, Ashley Gaskin, Magdalena Janus, Bo Cao","doi":"10.1371/journal.pdig.0000611","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000611","url":null,"abstract":"<p><strong>Introduction: </strong>In the context of the COVID-19 pandemic, it becomes important to comprehend service utilization patterns and evaluate disparities in mental health-related service access among children.</p><p><strong>Objective: </strong>This study uses administrative health records to investigate the association between early developmental vulnerability and healthcare utilization among children in Alberta, Canada from 2016 to 2022.</p><p><strong>Methods: </strong>Children who participated in the 2016 Early Development Instrument (EDI) assessment and were covered by public Alberta health insurance were included (N = 23 494). Linear regression models were employed to investigate the association between service utilization and vulnerability and biological sex. Separate models were used to assess vulnerability specific to each developmental domain and vulnerability across multiple domains. The service utilization was compared between pre- and post-pandemic onset periods.</p><p><strong>Results: </strong>The analysis reveals a significant decrease in all health services utilization from 2016 to 2019, followed by an increase until 2022. Vulnerable children had, on average, more events than non-vulnerable children. There was a consistent linear increase in mental health-related utilization from 2016 to 2022, with male children consistently experiencing higher utilization rates than females, particularly among vulnerable children. Specifically, there was a consistent linear increase in the utilization of anxiety-related services by children from 2016 to 2022, with females having, on average, 25 more events than males. The utilization of ADHD-related services showed different patterns for each group, with vulnerable male children having more utilization than their peers.</p><p><strong>Conclusion: </strong>Utilizing population-wide data, our study reveals sex specific developmental vulnerabilities and its impact on children's mental health service utilization during the COVID-19 pandemic, contributing to the existing literature. With data from kindergarten, we emphasize the need for early and targeted intervention strategies, especially for at-risk children, offering a path to reduce the burden of childhood mental health disorders.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 9","pages":"e0000611"},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11407640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302862","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":"The role of Open Access Data in democratizing healthcare AI: A pathway to research enhancement, patient well-being and treatment equity in Andalusia, Spain.","authors":"Álvaro Ritoré, Claudia M Jiménez, Juan Luis González, Juan Carlos Rejón-Parrilla, Pablo Hervás, Esteban Toro, Carlos Luis Parra-Calderón, Leo Anthony Celi, Isaac Túnez, Miguel Ángel Armengol de la Hoz","doi":"10.1371/journal.pdig.0000599","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000599","url":null,"abstract":"","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 9","pages":"e0000599"},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11404816/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302863","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":"Impact of electronic medical records on healthcare delivery in Nigeria: A review.","authors":"Sarah Oreoluwa Olukorode, Oluwakorede Joshua Adedeji, Adetayo Adetokun, Ajibola Ibraheem Abioye","doi":"10.1371/journal.pdig.0000420","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000420","url":null,"abstract":"<p><p>Electronic medical records (EMRs) have great potential to improve healthcare processes and outcomes. They are increasingly available in Nigeria, as in many developing countries. The impact of their introduction has not been well studied. We sought to synthesize the evidence from primary studies of the effect of EMRs on data quality, patient-relevant outcomes and patient satisfaction. We identified and examined five original research articles published up to May 2023 in the following medical literature databases: PUBMED/Medline, EMBASE, Web of Science, African Journals Online and Google Scholar. Four studies examined the influence of the introduction of or improvements in the EMR on data collection and documentation. The pooled percentage difference in data quality after introducing or improving the EMR was 142% (95% CI: 82% to 203%, p-value < 0.001). There was limited heterogeneity in the estimates (I2 = 0%, p-heterogeneity = 0.93) and no evidence suggestive of publication bias. The 5th study assessed patient satisfaction with pharmacy services following the introduction of the EMR but neither had a comparison group nor assessed patient satisfaction before EMR was introduced. We conclude that the introduction of EMR in Nigerian healthcare facilities meaningfully increased the quality of the data.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 9","pages":"e0000420"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11398640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302858","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":"Contextualised digital health communication infrastructure standards for resource-constrained settings: Perception of digital health stakeholders regarding suitability for Uganda's health system.","authors":"Andrew Egwar Alunyu, Mercy Rebekah Amiyo, Josephine Nabukenya","doi":"10.1371/journal.pdig.0000603","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000603","url":null,"abstract":"<p><p>Ignoring the need to contextualise international standards has caused low-resourced countries to implement digital health systems on the ad-hoc, thereby often failing to meet the local needs or scale up. Authors have recommended adapting standards to a country's context. However, to date, most resources constrained countries like Uganda have not done so, affecting their success in attaining the full benefits of using ICT to support their health systems. They apply the standards 'as is' with little regard for their fitness for potential use and ability to fulfil the country's digital health needs. A design science approach was followed to elicit digital health communication infrastructure (DHCI) requirements and develop the contextual DHCI standards for Uganda. The design science methodology's design cycle supported DHCI standards' construction and evaluation activities. Whereas two workgroup sessions were held to craft the standards, three cycles of evaluation and refinement were performed. The final refinement produces the contextualised DHCI standards approved by Uganda's DH stakeholders through summative evaluation. Results of the summative evaluation show that DH stakeholders agree that the statement of the standards and the requirements specification are suitable to guide DHCI standards implementation in Uganda. Stakeholders agreed that the standards are complete, have the potential to realise DHCI requirements in Uganda, that have been well structured and follow international style for standards, and finally, that the standards are fit to realise their intended use in Uganda. Having been endorsed by DH stakeholders in Uganda's health system, the standards should be piloted to establish their potency to improve health information exchange and healthcare outcomes. Also, we recommend other low middle income countries (LMICs) with similar challenges to those in Uganda adopt the same set of contextualised DHCI standards.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 9","pages":"e0000603"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11392385/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302854","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 and social accountability in the Canadian health care landscape: A rapid literature review.","authors":"Alex Anawati, Holly Fleming, Megan Mertz, Jillian Bertrand, Jennifer Dumond, Sophia Myles, Joseph Leblanc, Brian Ross, Daniel Lamoureux, Div Patel, Renald Carrier, Erin Cameron","doi":"10.1371/journal.pdig.0000597","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000597","url":null,"abstract":"<p><strong>Background: </strong>Situated within a larger project entitled \"Exploring the Need for a Uniquely Different Approach in Northern Ontario: A Study of Socially Accountable Artificial Intelligence,\" this rapid review provides a broad look into how social accountability as an equity-oriented health policy strategy is guiding artificial intelligence (AI) across the Canadian health care landscape, particularly for marginalized regions and populations. This review synthesizes existing literature to answer the question: How is AI present and impacted by social accountability across the health care landscape in Canada?</p><p><strong>Methodology: </strong>A multidisciplinary expert panel with experience in diverse health care roles and computer sciences was assembled from multiple institutions in Northern Ontario to guide the study design and research team. A search strategy was developed that broadly reflected the concepts of social accountability, AI and health care in Canada. EMBASE and Medline databases were searched for articles, which were reviewed for inclusion by 2 independent reviewers. Search results, a description of the studies, and a thematic analysis of the included studies were reported as the primary outcome.</p><p><strong>Principal findings: </strong>The search strategy yielded 679 articles of which 36 relevant studies were included. There were no studies identified that were guided by a comprehensive, equity-oriented social accountability strategy. Three major themes emerged from the thematic analysis: (1) designing equity into AI; (2) policies and regulations for AI; and (3) the inclusion of community voices in the implementation of AI in health care. Across the 3 main themes, equity, marginalized populations, and the need for community and partner engagement were frequently referenced, which are key concepts of a social accountability strategy.</p><p><strong>Conclusion: </strong>The findings suggest that unless there is a course correction, AI in the Canadian health care landscape will worsen the digital divide and health inequity. Social accountability as an equity-oriented strategy for AI could catalyze many of the changes required to prevent a worsening of the digital divide caused by the AI revolution in health care in Canada and should raise concerns for other global contexts.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 9","pages":"e0000597"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11392241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302844","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":"Short-term vital parameter forecasting in the intensive care unit: A benchmark study leveraging data from patients after cardiothoracic surgery.","authors":"Nils Hinrichs, Tobias Roeschl, Pia Lanmueller, Felix Balzer, Carsten Eickhoff, Benjamin O'Brien, Volkmar Falk, Alexander Meyer","doi":"10.1371/journal.pdig.0000598","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000598","url":null,"abstract":"<p><p>Patients in an Intensive Care Unit (ICU) are closely and continuously monitored, and many machine learning (ML) solutions have been proposed to predict specific outcomes like death, bleeding, or organ failure. Forecasting of vital parameters is a more general approach to ML-based patient monitoring, but the literature on its feasibility and robust benchmarks of achievable accuracy are scarce. We implemented five univariate statistical models (the naïve model, the Theta method, exponential smoothing, the autoregressive integrated moving average model, and an autoregressive single-layer neural network), two univariate neural networks (N-BEATS and N-HiTS), and two multivariate neural networks designed for sequential data (a recurrent neural network with gated recurrent unit, GRU, and a Transformer network) to produce forecasts for six vital parameters recorded at five-minute intervals during intensive care monitoring. Vital parameters were the diastolic, systolic, and mean arterial blood pressure, central venous pressure, peripheral oxygen saturation (measured by non-invasive pulse oximetry) and heart rate, and forecasts were made for 5 through 120 minutes into the future. Patients used in this study recovered from cardiothoracic surgery in an ICU. The patient cohort used for model development (n = 22,348) and internal testing (n = 2,483) originated from a heart center in Germany, while a patient sub-set from the eICU collaborative research database, an American multicenter ICU cohort, was used for external testing (n = 7,477). The GRU was the predominant method in this study. Uni- and multivariate neural network models proved to be superior to univariate statistical models across vital parameters and forecast horizons, and their advantage steadily became more pronounced for increasing forecast horizons. With this study, we established an extensive set of benchmarks for forecast performance in the ICU. Our findings suggest that supplying physicians with short-term forecasts of vital parameters in the ICU is feasible, and that multivariate neural networks are most suited for the task due to their ability to learn patterns across thousands of patients.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 9","pages":"e0000598"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11392423/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302861","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":"Impact of a pilot mHealth intervention on treatment outcomes of TB patients seeking care in the private sector using Propensity Scores Matching-Evidence collated from New Delhi, India.","authors":"Ridhima Sodhi, Vindhya Vatsyayan, Vikas Panibatla, Khasim Sayyad, Jason Williams, Theresa Pattery, Arnab Pal","doi":"10.1371/journal.pdig.0000421","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000421","url":null,"abstract":"<p><p>Mobile health applications called Digital Adherence Technologies (DATs), are increasingly used for improving treatment adherence among Tuberculosis patients to attain cure, and/or other chronic diseases requiring long-term and complex medication regimens. These DATs are found to be useful in resource-limited settings because of their cost efficiency in reaching out to vulnerable groups (providing pill and clinic visit reminders, relevant health information, and motivational messages) or those staying in remote or rural areas. Despite their growing ubiquity, there is very limited evidence on how DATs improve healthcare outcomes. We analyzed the uptake of DATs in an urban setting (DS-DOST, powered by Connect for LifeTM, Johnson & Johnson) among different patient groups accessing TB services in New Delhi, India, and subsequently assessed its impact in improving patient engagement and treatment outcomes. This study aims to understand the uptake patterns of a digital adherence technology and its impact in improving follow-ups and treatment outcomes among TB patients. Propensity choice modelling was used to create balanced treated and untreated patient datasets, before applying simple ordinary least square and logistic regression methods to estimate the causal impact of the intervention on the number of follow-ups made with the patient and treatment outcomes. After controlling for potential confounders, it was found that patients who installed and utilized DS-DOST application received an average of 6.4 (95% C.I. [5.32 to 7.557]) additional follow-ups, relative to those who did not utilize the application. This translates to a 58% increase. They also had a 245% higher likelihood of treatment success (Odds ratio: 3.458; 95% C.I. [1.709 to 6.996]).</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 9","pages":"e0000421"},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11389929/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302857","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":"Economic impact of a vision-based patient monitoring system across five NHS mental health trusts.","authors":"Ciara Buckley, Robert Malcolm, Jo Hanlon","doi":"10.1371/journal.pdig.0000559","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000559","url":null,"abstract":"<p><p>A vision-based patient monitoring system (VBPMS), Oxevision, has been introduced in approximately half of National Health Service (NHS) mental health trusts in England. A VBPMS is an assistive tool that supports patient safety by enabling non-contact physiological and physical monitoring. The system aims to help staff deliver safer, higher-quality and more efficient care. This paper summarises the potential health economic impact of using a VBPMS to support clinical practice in two inpatient settings: acute mental health and older adult mental health services. The economic model used a cost calculator approach to evaluate the potential impact of introducing a VBPMS into clinical practice, compared with clinical practice without a VBPMS. The analysis captured the cost differences in night-time observations, one-to-one continuous observations, self-harm incidents, and bedroom falls at night, including those resulting in A&E visits and emergency service callouts. The analysis is based on before and after studies conducted at five mental health NHS trusts, including acute mental health and older adult mental health services. Our findings indicate that the use of a VBPMS results in more efficient night-time observations and reductions in one-to-one observations, self-harm incidents, bedroom falls at night, and A&E visits and emergency service callouts from night-time falls. Substantial staff time in acute mental health and older adult mental health services is spent performing night-time observations, one-to-one observations, and managing incidents. The use of a VBPMS could lead to cost savings and a positive return on investment for NHS mental health trusts. The results do not incorporate all of the potential benefits associated with the use of a VBPMS, such as reductions in medication and length of hospital stay, plus the potential to avoid adverse events which would otherwise have a detrimental impact on a patient's quality of life.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 9","pages":"e0000559"},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11389945/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302855","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":"Optimizing COVID-19 testing resources use with wearable sensors.","authors":"Giorgio Quer, Arinbjörn Kolbeinsson, Jennifer M Radin, Luca Foschini, Jay Pandit","doi":"10.1371/journal.pdig.0000584","DOIUrl":"10.1371/journal.pdig.0000584","url":null,"abstract":"<p><p>The timely identification of infectious pre-symptomatic and asymptomatic cases is key towards preventing the spread of a viral illness like COVID-19. Early identification has been done through routine testing programs, which are indeed costly and potentially burdensome for individuals who should be tested with high frequency. A supplemental tool is represented by wearable technology, that can passively monitor and identify individuals at high risk, alerting them to take a test. We designed a Markov chain model and simulated a routine testing and a wearable testing strategy to estimate the number of tests required and the average number of days in which an individual is infectious and undetected. According to our model, with 2 test per month available, we have that the number of infectious and undetected days is 4.1 in the case of routine testing, while it decreases by 46% and 27% with a wearable testing strategy in the presence or absence of self-reported symptoms. The proposed parametric model can be used for different viral illnesses by tuning its parameters. It shows that wearable technology informing a testing strategy can significantly reduce the number of infectious days in which an individuals can spread the virus. With the same number of infectious days, by using wearables we can potentially reduce the number of required tests and the cost of the testing strategy.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 9","pages":"e0000584"},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11376555/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142141931","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}