PLOS digital health最新文献

{"title":"Exploring the relationship between telehealth utilization and treatment burden among patients with chronic conditions: A cross-sectional study in Ontario, Canada.","authors":"Farah Tahsin, Carolyn Steele Gray, Jay Shaw, Aviv Shachak","doi":"10.1371/journal.pdig.0000610","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000610","url":null,"abstract":"<p><p>One in five Canadians lives with one or more chronic conditions. Patients with chronic conditions often experience a high treatment burden because of the work associated with managing care. Telehealth is considered a useful solution to reduce the treatment burden among patients with chronic conditions. However, telehealth can also increase the treatment burden by offloading responsibilities on patients. This cross-sectional study conducted in Ontario, Canada examines the association between telehealth utilization and treatment burden among patients with chronic conditions. This study aimed to explore whether and to what extent, telehealth use is associated with treatment burden among patients with chronic conditions. The secondary objective was to explore which sociodemographic variables are associated with patients' treatment burden. An online survey was administered to community-dwelling patients with one or more chronic conditions. The Treatment Burden Questionnaire (TBQ-15) was used to measure the patient's level of treatment burden, and a modified telehealth usage scale was developed and used to measure the frequency of telehealth use. Data was analyzed using descriptive statistics, correlations, analyses of variance, and hierarchical linear regression analysis. A total of 75 patients completed the survey. The participants' mean age was 64 (SD = 18.93) and 79% were female. The average reported treatment burden was 72.15 out of 150 (a higher score indicating a higher level of burden). When adjusted for demographic variables, a higher frequency of telehealth use was associated with experiencing a higher treatment burden, but the association was not statistically significant. Additionally, when adjusted for demographic variables, younger age, and the presence of an unpaid caregiver were positively related to a high treatment burden score. This finding demonstrates that some patient populations are more at risk of experiencing high treatment burden in the context of telehealth use; and hence, may require extra support to utilize telehealth technologies. The study highlights the need for further research to explore how to minimize the treatment burden among individuals with higher healthcare needs.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 10","pages":"e0000610"},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11478863/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482583","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":"Towards women's digital health equity: A qualitative inquiry into attitude and adoption of reproductive mHealth services in Bangladesh.","authors":"M Jonayed, Maruf Hasan Rumi","doi":"10.1371/journal.pdig.0000637","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000637","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Health equity in Bangladesh faces a large chasm over the economic conditions, socio-cultural factors and geographic location despite the push for digitalization of the health sector. While some research has been conducted assessing the viability of digital health solutions in Bangladesh, gender dynamics of digital healthcare have been absent. This study dived into healthcare equity for women with a focus on reproductive health services delivered through mobile devices. This paper reported the findings of a qualitative study employing in-depth interviews conducted among 26 women about their behavioral intention to use mHealth services for reproductive health and the underlying factors influencing this intention with the help of the Integrative Model of Planned Behavior (IMPB). A snowball sampling technique were used to interview those university educated women, aged 21-31, based on their familiarity and exposure of mHealth services from seven universities in Bangladesh. The findings suggested that users of mHealth services find it more convenient and secure compared to visiting healthcare facilities, especially for trivial issues and inquiries regarding their reproductive health. Although promoting such services is lagging behind traditional healthcare, the attitude toward reproductive health services in Bangladesh is generally favorable resulting increasing adoption and use. Because such information-related mobile services (apps, websites, and social media) served as a first base of knowledge on reproductive health among many young girls and women in Bangladesh, who are generally shy to share or talk about their menstruation or personal health problems with family members, peers, or even health professionals due to socio-cultural factors and stigmatization. Conversely, urban centric services, availability of experts, quality management, security of privacy, authenticity of the information, digital divide, lack of campaign initiatives, lack of equipment and technology, lack of sex education, and outdated apps and websites were identified as obstacles that constrain the widespread use of reproductive mHealth services in Bangladesh. This study also concluded that promotion will be crucial in reforming conservative norms, taboos, and misconceptions about women's health and recommended such endeavors to be initiated by the policy makers as there is a substantive need for a specific policy regulating emerging digital health market in Bangladesh. Notwithstanding, women-only sample, low sample size, narrow focus on mHealth users and absence of perspectives from healthcare providers were among shortcomings of this study which could be addressed in future research. Further quantitative explorations are must to determine the usage patterns of reproductive mHealth services and their effectiveness that would identify implementation challenges in terms of customization and personalization in reproductive healthcare in a developing country like Bangladesh","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 10","pages":"e0000637"},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11478865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482585","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":"Unsupervised clustering of longitudinal clinical measurements in electronic health records.","authors":"Arshiya Mariam, Hamed Javidi, Emily C Zabor, Ran Zhao, Tomas Radivoyevitch, Daniel M Rotroff","doi":"10.1371/journal.pdig.0000628","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000628","url":null,"abstract":"<p><p>Longitudinal electronic health records (EHR) can be utilized to identify patterns of disease development and progression in real-world settings. Unsupervised temporal matching algorithms are being repurposed to EHR from signal processing- and protein-sequence alignment tasks where they have shown immense promise for gaining insight into disease. The robustness of these algorithms for classifying EHR clinical data remains to be determined. Timeseries compiled from clinical measurements, such as blood pressure, have far more irregularity in sampling and missingness than the data for which these algorithms were developed, necessitating a systematic evaluation of these methods. We applied 30 state-of-the-art unsupervised machine learning algorithms to 6,912 systematically generated simulated clinical datasets across five parameters. These algorithms included eight temporal matching algorithms with fourteen partitional and eight fuzzy clustering methods. Nemenyi tests were used to determine differences in accuracy using the Adjusted Rand Index (ARI). Dynamic time warping and its lower-bound variants had the highest accuracies across all cohorts (median ARI>0.70). All 30 methods were better at discriminating classes with differences in magnitude compared to differences in trajectory shapes. Missingness impacted accuracies only when classes were different by trajectory shape. The method with the highest ARI was then used to cluster a large pediatric metabolic syndrome (MetS) cohort (N = 43,426). We identified three unique childhood BMI patterns with high average cluster consensus (>70%). The algorithm identified a cluster with consistently high BMI which had the greatest risk of MetS, consistent with prior literature (OR = 4.87, 95% CI: 3.93-6.12). While these algorithms have been shown to have similar accuracies for regular timeseries, their accuracies in clinical applications vary substantially in discriminating differences in shape and especially with moderate to high missingness (>10%). This systematic assessment also shows that the most robust algorithms tested here can derive meaningful insights from longitudinal clinical data.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 10","pages":"e0000628"},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11478862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482586","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":"Use of large language models as a scalable approach to understanding public health discourse.","authors":"Laura Espinosa, Marcel Salathé","doi":"10.1371/journal.pdig.0000631","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000631","url":null,"abstract":"<p><p>Online public health discourse is becoming more and more important in shaping public health dynamics. Large Language Models (LLMs) offer a scalable solution for analysing the vast amounts of unstructured text found on online platforms. Here, we explore the effectiveness of Large Language Models (LLMs), including GPT models and open-source alternatives, for extracting public stances towards vaccination from social media posts. Using an expert-annotated dataset of social media posts related to vaccination, we applied various LLMs and a rule-based sentiment analysis tool to classify the stance towards vaccination. We assessed the accuracy of these methods through comparisons with expert annotations and annotations obtained through crowdsourcing. Our results demonstrate that few-shot prompting of best-in-class LLMs are the best performing methods, and that all alternatives have significant risks of substantial misclassification. The study highlights the potential of LLMs as a scalable tool for public health professionals to quickly gauge public opinion on health policies and interventions, offering an efficient alternative to traditional data analysis methods. With the continuous advancement in LLM development, the integration of these models into public health surveillance systems could substantially improve our ability to monitor and respond to changing public health attitudes.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 10","pages":"e0000631"},"PeriodicalIF":0.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11472907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482587","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":"Machine learning approaches in non-contact autofluorescence spectrum classification.","authors":"Ashutosh P Raman, Tanner J Zachem, Sarah Plumlee, Christine Park, William Eward, Patrick J Codd, Weston Ross","doi":"10.1371/journal.pdig.0000602","DOIUrl":"10.1371/journal.pdig.0000602","url":null,"abstract":"<p><p>Manual surgical resection of soft tissue sarcoma tissue can involve many challenges, including the critical need for precise determination of tumor boundary with normal tissue and limitations of current surgical instrumentation, in addition to standard risks of infection or tissue healing difficulty. Substantial research has been conducted in the biomedical sensing landscape for development of non-human contact sensing devices. One such point-of-care platform, previously devised by our group, utilizes autofluorescence-based spectroscopic signatures to highlight important physiological differences in tumorous and healthy tissue. The following study builds on this work, implementing classification algorithms, including Artificial Neural Network, Support Vector Machine, Logistic Regression, and K-Nearest Neighbors, to diagnose freshly resected murine tissue as sarcoma or healthy. Classification accuracies of over 93% are achieved with Logistic Regression, and Area Under the Curve scores over 94% are achieved with Support Vector Machines, delineating a clear way to automate photonic diagnosis of ambiguous tissue in assistance of surgeons. These interpretable algorithms can also be linked to important physiological diagnostic indicators, unlike the black-box ANN architecture. This is the first known study to use machine learning to interpret data from a non-contact autofluorescence sensing device on sarcoma tissue, and has direct applications in rapid intraoperative sensing.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 10","pages":"e0000602"},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11463826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395809","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":"Attitudes towards digital health technology for the care of people with chronic kidney disease: A technology acceptance model exploration.","authors":"Daphne Kaklamanou, Le Nguyen, Miznah Al-Abbadey, Nick Sangala, Robert Lewis","doi":"10.1371/journal.pdig.0000614","DOIUrl":"10.1371/journal.pdig.0000614","url":null,"abstract":"<p><strong>Background: </strong>Chronic Kidney Disease (CKD) is a long-term condition and a major health problem, which affects over 3.5 million adults in the UK. Use of digital technology has been proposed as a means of improving patient management. It is important to understand the factors that affect the acceptability of this technology to people living with chronic kidney disease. This study used the Technology Acceptance Model 3 (TAM) to investigate whether perceived ease of use and perceived usefulness could predict intention behaviour. It then investigated if intention to use digital technology predicted actual use.</p><p><strong>Methodology: </strong>This was a cross-sectional study whereby the TAM3 questionnaire was sent online to people known to have chronic kidney disease via Kidney Care UK. The characteristics of the respondents (age, sex, CKD stage) were recorded.</p><p><strong>Principal findings: </strong>The questionnaire was sent to 12,399 people, of which 229 (39% drop out) completed it. The respondents' age ranged from 24-90 years and 45% (n = 102) were male. Thirty-five percent of participants had advanced kidney care, 33% (n = 76) had kidney transplant and 22% (n = 51) had CKD. A multiple regression analysis showed a perceived ease of use and perceived usefulness of the technology predicted behaviour intention to use digital health technology. Behaviour intention did not significantly predict actual use behaviour.</p><p><strong>Conclusion: </strong>Perceived usefulness and perceived ease of use are important factors in determining the intention of people with CKD to use digital healthcare. However, a gap exists between this intention and readiness to actually use the technology. This needs to be overcome if digital healthcare is to gain future traction in the clinical scenario.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 10","pages":"e0000614"},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11463739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395806","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":"Unmasking biases and navigating pitfalls in the ophthalmic artificial intelligence lifecycle: A narrative review.","authors":"Luis Filipe Nakayama, João Matos, Justin Quion, Frederico Novaes, William Greig Mitchell, Rogers Mwavu, Claudia Ju-Yi Ji Hung, Alvina Pauline Dy Santiago, Warachaya Phanphruk, Jaime S Cardoso, Leo Anthony Celi","doi":"10.1371/journal.pdig.0000618","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000618","url":null,"abstract":"<p><p>Over the past 2 decades, exponential growth in data availability, computational power, and newly available modeling techniques has led to an expansion in interest, investment, and research in Artificial Intelligence (AI) applications. Ophthalmology is one of many fields that seek to benefit from AI given the advent of telemedicine screening programs and the use of ancillary imaging. However, before AI can be widely deployed, further work must be done to avoid the pitfalls within the AI lifecycle. This review article breaks down the AI lifecycle into seven steps-data collection; defining the model task; data preprocessing and labeling; model development; model evaluation and validation; deployment; and finally, post-deployment evaluation, monitoring, and system recalibration-and delves into the risks for harm at each step and strategies for mitigating them.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 10","pages":"e0000618"},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11460710/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395811","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":"Patient experience and barriers of using a mHealth exercise app in musculoskeletal (MSK) Physiotherapy.","authors":"Jack Grodon, Christopher Tack, Laura Eccott, Mindy C Cairns","doi":"10.1371/journal.pdig.0000626","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000626","url":null,"abstract":"<p><p>Digital transformation has led to an abundance of digital health technologies (DHTs) readily available for Physiotherapists. In July 2020, the Physiotherapy department at a London NHS Trust implemented a mobile health (mHealth) exercise application (app), Physitrack. This service evaluation aims to evaluate patient experience and identify any barriers to using Physitrack/PhysiApp in musculoskeletal (MSK) Physiotherapy. An online experience survey was sent to 10,287 MSK Physiotherapy patients who had appointments between January 17th and April 9th 2022.The survey received 1,447 responses (response rate: 14.07%), with 954 (65.93%) respondents previously provided PhysiApp as part of their Physiotherapy management. Most participants used PhysiApp (83.06%), found it easy to use (82.20%) and had positive perceptions on how it added value to their Physiotherapy treatment through its functionality. However, negative impacts on patient-centred care and practical exercise demonstration were apparent in the qualitative results. Key barriers to use included suboptimal explanation, digital exclusion, registration/ login issues and opinion that PhysiApp was superfluous to Physiotherapy treatment. These differed to the main barriers of why participants stopped using/ used PhysiApp less: if they were confident exercising without it, their condition improved/ resolved, loss of motivation, their exercise programme ended or if they found their exercise programme was unsuitable. Despite multiple interdependent factors influencing patient experience and barriers of using PhysiApp, the survey results revealed the significant influence that is exerted by MSK Physiotherapists. The patient-physiotherapist interaction can positively or negatively impact upon many barriers of use and the subsequent potential added value of PhysiApp to MSK Physiotherapy treatment. Future research should focus on those at most risk of digital exclusion and health inequalities, exploring their barriers to using mHealth apps and other DHTs.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 10","pages":"e0000626"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11458024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395810","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":"Can eye-tracking help to create a new method for X-ray analysis of rheumatoid arthritis patients, including joint segmentation and scoring methods?","authors":"Baptiste Quéré, Léonie Méneur, Nathan Foulquier, Hugo Pensec, Valérie Devauchelle-Pensec, Florent Garrigues, Alain Saraux","doi":"10.1371/journal.pdig.0000616","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000616","url":null,"abstract":"<p><p>Reading hand and foot X-rays in rheumatoid arthritis patients is difficult and time-consuming. In research, physicians use the modified Sharp van der Heijde Sharp (mvdH) score by reading of hand and foot radiographs. The aim of this study was to create a new method of determining the mvdH via eye tracking and to study its concordance with the mvdH score. We created a new method of quantifying the mvdH score based on reading time of a reader monitored via eye tracking (Tobii Pro Lab software) after training with the aid of a metronome. Radiographs were read twice by the trained eye-tracking reader and once by an experienced reference radiologist. A total of 440 joints were selected; 416 could be interpreted for erosion, and 396 could be interpreted for joint space narrowing (JSN) when read by eye tracking (eye tracking could not measure the time spent when two pathological joints were too close together). The agreement between eye tracking mvdH Sharp score and classical mvdH Sharp score yes (at least one erosion or JSN) versus no (no erosion or no JSN) was excellent for both erosions (kappa 0.97; 95% CI: 0.96-0.99) and JSN (kappa: 0.95; 95% CI: 0.93-0.097). This agreement by class (0 to 10) remained excellent for both erosions (kappa 0.82; 95% CI: 0.79-0.0.85) and JSN (kappa: 0.68; 95% CI: 0.65-0.0.71). To conclude, eye-tracking reading correlates strongly with classical mvdH-Sharp and is useful for assessing severity, segmenting joints and establishing a rapid score for lesions.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 10","pages":"e0000616"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11458192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395807","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":"Diagnosing skin neglected tropical diseases with the aid of digital health tools: A scoping review.","authors":"Ewelina Julia Barnowska, Anil Fastenau, Srilekha Penna, Ann-Kristin Bonkass, Sophie Stuetzle, Ricky Janssen","doi":"10.1371/journal.pdig.0000629","DOIUrl":"https://doi.org/10.1371/journal.pdig.0000629","url":null,"abstract":"<p><p>Delays in diagnosis and detection of skin neglected tropical diseases (NTDs) pose obstacles to prompt treatment, which is crucial in preventing disability. Recent developments in digital health have given rise to approaches that could increase access to diagnosis in resource-poor areas affected by skin NTDs. This scoping review provides an overview of current digital health approaches that aim to aid in the diagnosis of skin NTDs and provides an insight into the diverse functionalities of current digital health tools, their feasibility, usability, and the current gaps in research around these digital health approaches. This scoping review included a comprehensive literature search on PubMed, EMBASE and SCOPUS, following the PRISMA guidelines. Eleven studies were included in the review and were analysed using a descriptive thematic approach. Most digital tools were found to be mobile-phone based, such as mobile Health (mHealth) apps, store-and-forward tele-dermatology, and Short Messaging Service (SMS) text-messaging. Other digital approaches were based on computer software, such as tele-dermatopathology, computer-based telemedicine, and real-time tele-dermatology. Digital health tools commonly facilitated provider-provider interactions, which helped support diagnoses of skin NTDs at the community level. Articles which focused on end-user user experience reported that users appreciated the usefulness and convenience of these digital tools. However, the results emphasized the existing lack of data regarding the diagnostic precision of these tools, and highlighted various hurdles to their effective implementation, including insufficient infrastructure, data security issues and low adherence to the routine use of digital health tools. Digital health tools can help ascertain diagnosis of skin NTDs through remote review or consultations with patients, and support health providers in the diagnostic process. However, further research is required to address the data security issues associated with digital health tools. Developers should consider adapting digital health tools to diverse socio-cultural and technical environments, where skin NTDs are endemic. Researchers are encouraged to assess the diagnostic accuracy of digital health tools and conduct further qualitative studies to inform end-user experience. Overall, future studies should consider expanding the geographical and disease scope of research on digital health tools which aid the diagnosis of skin NTDs.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"3 10","pages":"e0000629"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11458012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395808","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}