{"title":"Attitudes of healthcare professionals and researchers toward wearable and app derived patient generated health data","authors":"Stefanie Brückner, Olamide Sadare, Sabrina Fesl, Madlen Scheibe, Caroline Lang, Stephen Gilbert","doi":"10.1038/s41746-025-01568-4","DOIUrl":"https://doi.org/10.1038/s41746-025-01568-4","url":null,"abstract":"<p>Patient-generated health data (PGHD) from apps and wearables hold significant potential for enhancing personalised care and medical research. Healthcare professionals (HCPs) are key to its successful adoption, as their attitudes can either support or hinder its integration into clinical practice. This review systematically analysed studies on HCPs’ and researchers’ perspectives on PGHD for clinical and research use. Three databases were searched for articles published between January 2013 and April 2023. Of 246 articles screened, 33 met the inclusion criteria. While most participants viewed PGHD positively, concerns around data security, reliability, and workflow integration persist. Addressing these barriers is essential to maximising PGHD’s benefits for participatory medicine and improved clinical outcomes. The included studies presented medium methodological quality, particularly among quantitative and mixed methods with risks of sampling and nonresponse bias, and often low sample sizes in qualitative studies. However, recurring themes across studies allow a valuable interpretation of the findings.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"58 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shiqi Hui, Jing Xie, Li Dong, Li Wei, Weiwei Dai, Dongmei Li
{"title":"Deep learning-based mobile application for efficient eyelid tumor recognition in clinical images","authors":"Shiqi Hui, Jing Xie, Li Dong, Li Wei, Weiwei Dai, Dongmei Li","doi":"10.1038/s41746-025-01539-9","DOIUrl":"https://doi.org/10.1038/s41746-025-01539-9","url":null,"abstract":"<p>Early detection, regular monitoring of eyelid tumors and post-surgery recurrence monitoring are crucial for patients. However, frequent hospital visits are burdensome for patients with poor medical conditions. This study validates a novel deep learning-based mobile application, based on YOLOv5 and Efficient-Net v2-B architectures, for self-diagnosing eyelid tumors, enabling improved health support systems for such patients. 1195 preprocessed clinical ocular photographs and biopsy results were collected for model training. The best-performing model was chosen and converted into a smartphone-based application, then further evaluated based on external validation dataset, achieved 0.921 accuracy for triple classification outcomes (benign/malignant eyelid tumors or normal eye), generally superior to that of general physicians, resident doctors, and ophthalmology specialists. Intelligent Eyelid Tumor Screening application exhibited a straightforward detection process, user-friendly interface and treatment recommendation scheme, provides preliminary evidence for recognizing eyelid tumors and could be used by healthcare professionals, patients and caregivers for detection and monitoring purposes.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"236 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Digital health for environmentally sustainable cancer screening","authors":"Vera Benedetto, Vanessa Mainardi, Francesca Pennucci, Angelo Damone, Gastone Ciuti, Sabina Nuti","doi":"10.1038/s41746-025-01561-x","DOIUrl":"https://doi.org/10.1038/s41746-025-01561-x","url":null,"abstract":"<p>This study explores the role of digital health in promoting environmental sustainability through a pilot study performed in rural areas in Tuscany (Italy). A multi-screening model, based on a mobile multi-screening unit (MMSU) and digital health tools, was deployed to reduce the travelled distances required for cancer screening. Serving 19 municipalities in a mountainous area, the MMSU reduced patient and caregivers’ travel, cutting CO<sub>2</sub>-equivalent emissions by over 90% compared to conventional healthcare models. The project demonstrates how integrating digital health technologies, such as telemedicine for data transmission and centralised reporting, can enhance healthcare accessibility and environmental sustainability. By consolidating multiple screenings into one visit, the MMSU model offers a scalable solution for reducing healthcare’s carbon footprint while addressing barriers to care in underserved areas. This pilot study highlights the potential of digital health to align service delivery with environmental objectives, contributing to the broader discourse on sustainable healthcare innovation.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"69 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuval Yaron, Vered Ofen Glassner, Michal Berkenstadt, Nurit Goldstein, Haike Reznik Wolf, Liat Ries Levavi, Liat Abo Gutstein, Yael Furman, Mori Anouchi, Galit Delmar, Doron M. Behar, Elon Pras, Adi Reches, Alina Kurolap, Hagit Baris Feldman
{"title":"Evaluation of the digital genetic assistant in technology assisted genetic counseling for genetic carrier screening","authors":"Yuval Yaron, Vered Ofen Glassner, Michal Berkenstadt, Nurit Goldstein, Haike Reznik Wolf, Liat Ries Levavi, Liat Abo Gutstein, Yael Furman, Mori Anouchi, Galit Delmar, Doron M. Behar, Elon Pras, Adi Reches, Alina Kurolap, Hagit Baris Feldman","doi":"10.1038/s41746-025-01573-7","DOIUrl":"https://doi.org/10.1038/s41746-025-01573-7","url":null,"abstract":"<p>Rapid growth in genetic testing usage resulted in declining availability of genetic counselors (GCs) per ordered tests, prolonging the waiting times for face-to-face (F2F) counseling. We evaluated the digital genetic assistant (DGA) for reproductive genetic carrier screening (RGCS) in a real-life clinical setting using a “couple-based” paradigm. The platform provides digital patient intake and automated counseling for low-risk individuals, as well as GC-facing tools that reduce administrative burden in patient-related activities. Among 225 couples undergoing RGCS during the study period, 4% had high-risk results requiring F2F counseling and an additional 4% of low-risk couples requested F2F counseling, suggesting that DGA use reduced GC-participant F2F interactions by 69.3%. GC evaluations revealed that the DGA triaging algorithm was accurate and surveys demonstrated high degrees of user comprehension and satisfaction. These results highlight the utility of digital platforms for patient intake and delivery of low-risk results in settings with limited genetic counseling resources.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"41 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ximing Zhu, ShihChun Chu, Jie Fu, Siyu Ao, Tingting Liu, Ziming Liao, Jiamei Song, Yuhong Wang, Yanping Guo, Qinghong Song, Pengfei Liu, Shuang Li, Bing Chen, Bin Yao
{"title":"A clinically interpretable model derived from skin conductance for assessing microangiopathy of the skin surface","authors":"Ximing Zhu, ShihChun Chu, Jie Fu, Siyu Ao, Tingting Liu, Ziming Liao, Jiamei Song, Yuhong Wang, Yanping Guo, Qinghong Song, Pengfei Liu, Shuang Li, Bing Chen, Bin Yao","doi":"10.1038/s41746-025-01562-w","DOIUrl":"https://doi.org/10.1038/s41746-025-01562-w","url":null,"abstract":"<p>The electrical properties of the skin can reflect changes in its structure and physiological state, and bioimpedance analysis has been widely used to distinguish specific components of the human body, such as water, fat, or muscle tissue, instead of the traditional examinations with bulky equipment gradually. For the assessment of microangiopathy, a low-cost, simple, effective, and clinically interpretable model was proposed that relies on individual skin conductance data, collected from 28 patients with lower extremity arterial occlusion. The model demonstrated a specificity of 67.4% and a sensitivity of 82.9% in classifying healthy-affected sides. The severity estimates were consistent with the patient’s laser speckle and ankle-brachial index, with intra-class correlation coefficients of 0.43 and 0.55. Further, patient record data was combined to improve accuracy by about 15% in multimodal ensemble learning, indicating the potential for using the electrical properties of the skin to characterize surface microcirculation disorders.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"59 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julia Palm, Ssuhir Alaid, Danny Ammon, Julian Brandes, Andreas Dürschmid, Claudia Fischer, Jonas Fortmann, Kristin Friebel, Sarah Geihs, Anne-Kathrin Hartig, Donghui He, Andrew J. Heidel, Petra Hetfeld, Roland Ihle, Suzanne Kahle, Verena Koi, Margarethe Konik, Frauke Kretzschmann, Henner Kruse, Norman Lippmann, Christoph Lübbert, Gernot Marx, Rafael Mikolajczyk, Anne Mlocek, Stefan Moritz, Christoph Müller, Susanne Müller, Ariadna Pérez Garriga, Lo An Phan-Vogtmann, Diana Pietzner, Mathias W. Pletz, Mario Popp, Maike Rebenstorff, Jonas Renz, Florian Rißner, Rainer Röhrig, Kutaiba Saleh, Sebastian G. Schönherr, Cord Spreckelsen, Anja Stempel, Abel Stolz, Eric Thomas, Susanne Thon, Daniel Tiller, Sebastian Uschmann, Sebastian Wendt, Thomas Wendt, Philipp Winnekens, Oliver Witzke, Stefan Hagel, André Scherag
{"title":"Leveraging electronic medical records to evaluate a computerized decision support system for staphylococcus bacteremia","authors":"Julia Palm, Ssuhir Alaid, Danny Ammon, Julian Brandes, Andreas Dürschmid, Claudia Fischer, Jonas Fortmann, Kristin Friebel, Sarah Geihs, Anne-Kathrin Hartig, Donghui He, Andrew J. Heidel, Petra Hetfeld, Roland Ihle, Suzanne Kahle, Verena Koi, Margarethe Konik, Frauke Kretzschmann, Henner Kruse, Norman Lippmann, Christoph Lübbert, Gernot Marx, Rafael Mikolajczyk, Anne Mlocek, Stefan Moritz, Christoph Müller, Susanne Müller, Ariadna Pérez Garriga, Lo An Phan-Vogtmann, Diana Pietzner, Mathias W. Pletz, Mario Popp, Maike Rebenstorff, Jonas Renz, Florian Rißner, Rainer Röhrig, Kutaiba Saleh, Sebastian G. Schönherr, Cord Spreckelsen, Anja Stempel, Abel Stolz, Eric Thomas, Susanne Thon, Daniel Tiller, Sebastian Uschmann, Sebastian Wendt, Thomas Wendt, Philipp Winnekens, Oliver Witzke, Stefan Hagel, André Scherag","doi":"10.1038/s41746-025-01569-3","DOIUrl":"https://doi.org/10.1038/s41746-025-01569-3","url":null,"abstract":"<p>Infectious disease specialists (IDS) improve outcomes of patients with Staphylococcus bacteremia, but immediate IDS access is not always guaranteed. We investigated whether a care-integrated computerized decision support system (CDSS) can safely enhance the standard of care (SOC) for these patients. We conducted a multicenter, noninferiority, interventional stepped-wedge cluster randomized controlled trial relying on the data integration centers at five university hospitals. By this means, electronic medical records can be used for part of the trial documentation. We analyzed 5056 patients from 134 wards (<i>Staphylococcus</i> <i>aureus</i> (SAB): <i>n</i> = 812, coagulase-negative staphylococci (CoNS): <i>n</i> = 4244) and found that the CDSS was noninferior to the SOC for hospital mortality in all patients. Noninferiority regarding the 90-day mortality/relapse in SAB patients was not observed and there was no evidence for differences in vancomycin usage among CoNS patients. Despite low reported usage, physicians rated the CDSS’s usability favorably. <b>Trial registration</b>: drks.de; Identifier: DRKS00014320; Registration Date: 2019-05-06.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"29 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Effect of video conferencing between primary and secondary care specialists on type 2 diabetes medication","authors":"Thim Prætorius, Anne Sofie Baymler Lundberg, Eskild Klausen Fredslund, Niklas Blach Rossen, Søren Gregersen, Anders Prior, Esben Søndergaard, Søren Tang Knudsen, Annelli Sandbæk","doi":"10.1038/s41746-025-01570-w","DOIUrl":"https://doi.org/10.1038/s41746-025-01570-w","url":null,"abstract":"<p>Although promising, the clinical impact of video conferencing between primary and secondary care specialists lacks trial evidence. We conducted a two-arm RCT (clinicaltrials.gov: NCT05268081) to evaluate whether four video conferences over 12 months between endocrinologists and general practitioners improved medication for people with type 2 diabetes (T2D). Twenty-seven of 100 general practices in Aarhus, Denmark were matched and randomized. Primary outcomes: the proportion of people with T2D and 1) ischemic heart disease treated with glucagon-like peptide 1 receptor agonist (GLP1-RA) and/or sodium glucose cotransporter 2 inhibitor (SGLT2I), 2) micro/macro-albuminuria treated with angiotensin-converting-enzyme-inhibitor (ACE) or angiotensin-2-receptorantagonist (AT2), 3) low-density lipoprotein >2.5 mmol/L treated with statins. Results showed a 17.6% difference [95% CI 4.6%; 30.7%] in GLP1-RA/SGLT2I prescriptions and minimal differences for ACE/AT2 (−1.1% [95% CI −2.8%; 0.6%]) and statins (0.0% [95% CI −3.5%; 3.6%]), attributed to a ceiling effect. Video conferencing can help bridge treatment gaps, particularly for recently updated guidelines.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"72 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jasmine Chiat Ling Ong, Michael Hao Chen, Ning Ng, Kabilan Elangovan, Nichole Yue Ting Tan, Liyuan Jin, Qihuang Xie, Daniel Shu Wei Ting, Rosa Rodriguez-Monguio, David W. Bates, Nan Liu
{"title":"A scoping review on generative AI and large language models in mitigating medication related harm","authors":"Jasmine Chiat Ling Ong, Michael Hao Chen, Ning Ng, Kabilan Elangovan, Nichole Yue Ting Tan, Liyuan Jin, Qihuang Xie, Daniel Shu Wei Ting, Rosa Rodriguez-Monguio, David W. Bates, Nan Liu","doi":"10.1038/s41746-025-01565-7","DOIUrl":"https://doi.org/10.1038/s41746-025-01565-7","url":null,"abstract":"<p>Medication-related harm has a significant impact on global healthcare costs and patient outcomes. Generative artificial intelligence (GenAI) and large language models (LLM) have emerged as a promising tool in mitigating risks of medication-related harm. This review evaluates the scope and effectiveness of GenAI and LLM in reducing medication-related harm. We screened 4 databases for literature published from 1st January 2012 to 15th October 2024. A total of 3988 articles were identified, and 30 met the criteria for inclusion into the final review. Generative AI and LLMs were applied in three key applications: drug-drug interaction identification and prediction, clinical decision support, and pharmacovigilance. While the performance and utility of these models varied, they generally showed promise in early identification, classification of adverse drug events, and supporting decision-making for medication management. However, no studies tested these models prospectively, suggesting a need for further investigation into integration and real-world application.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"4 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marvin Kopka, Niklas von Kalckreuth, Markus A. Feufel
{"title":"Accuracy of online symptom assessment applications, large language models, and laypeople for self–triage decisions","authors":"Marvin Kopka, Niklas von Kalckreuth, Markus A. Feufel","doi":"10.1038/s41746-025-01566-6","DOIUrl":"https://doi.org/10.1038/s41746-025-01566-6","url":null,"abstract":"<p>Symptom-Assessment Application (SAAs, e.g., NHS 111 online) that assist laypeople in deciding if and where to seek care (<i>self-triage</i>) are gaining popularity and Large Language Models (LLMs) are increasingly used too. However, there is no evidence synthesis on the accuracy of LLMs, and no review has contextualized the accuracy of SAAs and LLMs. This systematic review evaluates the self-triage accuracy of both SAAs and LLMs and compares them to the accuracy of laypeople. A total of 1549 studies were screened and 19 included. The self-triage accuracy of SAAs was moderate but highly variable (11.5–90.0%), while the accuracy of LLMs (57.8–76.0%) and laypeople (47.3–62.4%) was moderate with low variability. Based on the available evidence, the use of SAAs or LLMs should neither be universally recommended nor discouraged; rather, we suggest that their utility should be assessed based on the specific use case and user group under consideration.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"99 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143703283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mariska van Vliet, Jan J. J. Aalberts, Cora Hamelinck, Arnaud D. Hauer, Dieke Hoftijzer, Stefan H. J. Monnink, Jurjan C. Schipper, Jan C. Constandse, Nicholas S. Peters, Gregory Y. H. Lip, Steven R. Steinhubl, Eelko Ronner
{"title":"Ambulatory atrial fibrillation detection and quantification by wristworn AI device compared to standard holter monitoring","authors":"Mariska van Vliet, Jan J. J. Aalberts, Cora Hamelinck, Arnaud D. Hauer, Dieke Hoftijzer, Stefan H. J. Monnink, Jurjan C. Schipper, Jan C. Constandse, Nicholas S. Peters, Gregory Y. H. Lip, Steven R. Steinhubl, Eelko Ronner","doi":"10.1038/s41746-025-01555-9","DOIUrl":"https://doi.org/10.1038/s41746-025-01555-9","url":null,"abstract":"<p>Timely detection of atrial fibrillation (AF) is crucial for the prevention of serious consequences such as stroke and heart failure, yet it remains challenging due to its often asymptomatic or paroxysmal nature. Wearable devices with artificial intelligence algorithms offer promising solutions. AF detection by the CardioWatch 287-2 (CW2), a wrist-worn photoplethysmography (PPG) and single-lead ECG device, was compared to 24-h Holter. Patient compliance, AF prevalence and AF burden were evaluated for 27 additional days. Data from 150 participants (mean age 64 ± 12 SD; 41% female) were analysed. The CW2’s PPG and single-lead ECG algorithms achieved a specificity ≥98% and sensitivity ≥95% for AF detection, and 99% correlation for AF burden, compared to 24-h Holter. AF prevalence increased from 14.7% (24-h Holter) to 26.7% (28-day CW2). Thus, the wrist-worn device showed promising performance in detecting AF and determining AF burden. The trial was registered on ClinicalTrials.gov (NCT05899959) on June 2, 2023.</p>","PeriodicalId":19349,"journal":{"name":"NPJ Digital Medicine","volume":"93 1","pages":""},"PeriodicalIF":15.2,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143702977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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