Lancet Digital Health最新文献

{"title":"Artificial intelligence-assisted detection of nasopharyngeal carcinoma on endoscopic images: a national, multicentre, model development and validation study.","authors":"Yuxuan Shi, Zhen Li, Li Wang, Hong Wang, Xiaofeng Liu, Dantong Gu, Xiao Chen, Xueli Liu, Wentao Gong, Xiaowen Jiang, Wenquan Li, Yongdong Lin, Ke Liu, Deyan Luo, Tao Peng, Xuemei Peng, Meimei Tong, Huizhen Zheng, Xuanchen Zhou, Jianrong Wu, Georges El Fakhri, Mingzhang Chang, Jun Liao, Jie'en Li, Desheng Wang, Jing Ye, Shenhong Qu, Weihong Jiang, Quan Liu, Xicai Sun, Yefeng Zheng, Hongmeng Yu","doi":"10.1016/j.landig.2025.03.001","DOIUrl":"https://doi.org/10.1016/j.landig.2025.03.001","url":null,"abstract":"<p><strong>Background: </strong>Nasopharyngeal carcinoma is highly curable when diagnosed early. However, the nasopharynx's obscure anatomical position and the similarity of local imaging manifestations with those of other nasopharyngeal diseases often lead to diagnostic challenges, resulting in delayed or missed diagnoses. Our aim was to develop a deep learning algorithm to enhance an otolaryngologist's diagnostic capabilities by differentiating between nasopharyngeal carcinoma, benign hyperplasia, and normal nasopharynx during endoscopic examination.</p><p><strong>Methods: </strong>In this national, multicentre, model development and validation study, we developed a Swin Transformer-based Nasopharyngeal Diagnostic (STND) system to identify nasopharyngeal carcinoma, benign hyperplasia, and normal nasopharynx. STND was developed with 27 362 nasopharyngeal endoscopic images (10 693 biopsy-proven nasopharyngeal carcinoma, 7073 biopsy-proven benign hyperplasia, and 9596 normal nasopharynx) sourced from eight prominent nasopharyngeal carcinoma centres (stage 1), and externally validated with 1885 prospectively acquired images from ten comprehensive hospitals with a high incidence of nasopharyngeal carcinoma (stage 2). Furthermore, we did a fully crossed, multireader, multicase study involving four expert otolaryngologists from four regional leading nasopharyngeal carcinoma centres, and 24 general otolaryngologists from 24 geographically diverse primary hospitals. This study included 400 images to evaluate the diagnostic capabilities of the experts and general otolaryngologists both with and without the aid of the STND system in a real-world environment.</p><p><strong>Findings: </strong>Endoscopic images used in the internal study (Jan 1, 2017, to Jan 31, 2023) were from 15 521 individuals (9033 [58·2%] men and 6488 [41·8%] women; mean age 47·6 years [IQR 38·4-56·8]). Images from 945 participants (538 [56·9%] men and 407 [43·1%] women; mean age 45·2 years [IQR 35·2- 55·2]) were used in the external validation. STND in the internal dataset discriminated normal nasopharynx images from abnormalities (benign hyperplasia and nasopharyngeal carcinoma) with an area under the curve (AUC) of 0·99 (95% CI 0·99-0·99) and malignant images (ie, nasopharyngeal carcinoma) from non-malignant images (ie, benign hyperplasia and normal nasopharynx) with an AUC of 0·99 (95% CI 0·98-0·99). In the external validation, the system had an AUC for the detection of nasopharyngeal carcinoma of 0·95 (95% CI 0·94-0·96), a sensitivity of 91·6% (95% CI 89·3-93·5), and a specificity of 86·1% (95% CI 84·1-87·9). In the multireader, multicase study, the artificial intelligence (AI)-assisted strategy enhanced otolaryngologists' diagnostic accuracy by 7·9%, increasing from 83·4% (95% CI 80·1-86·7, without AI assistance) to 91·2% (95% CI 88·6-93·9, with AI assistance; p<0·0001) for primary care otolaryngologists. Reading time per image decreased with the aid of the AI model (mean 5·0 s [S","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100869"},"PeriodicalIF":23.8,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340497","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":"Medical digital twins: enabling precision medicine and medical artificial intelligence.","authors":"Christoph Sadée, Stefano Testa, Thomas Barba, Katherine Hartmann, Maximilian Schuessler, Alexander Thieme, George M Church, Ifeoma Okoye, Tina Hernandez-Boussard, Leroy Hood, Ilya Shmulevich, Ellen Kuhl, Olivier Gevaert","doi":"10.1016/j.landig.2025.02.004","DOIUrl":"https://doi.org/10.1016/j.landig.2025.02.004","url":null,"abstract":"<p><p>The notion of medical digital twins is gaining popularity both within the scientific community and among the general public; however, much of the recent enthusiasm has occurred in the absence of a consensus on their fundamental make-up. Digital twins originate in the field of engineering, in which a constantly updating virtual copy enables analysis, simulation, and prediction of a real-world object or process. In this Health Policy paper, we evaluate this concept in the context of medicine and outline five key components of the medical digital twin: the patient, data connection, patient-in-silico, interface, and twin synchronisation. We consider how various enabling technologies in multimodal data, artificial intelligence, and mechanistic modelling will pave the way for clinical adoption and provide examples pertaining to oncology and diabetes. We highlight the role of data fusion and the potential of merging artificial intelligence and mechanistic modelling to address the limitations of either the AI or the mechanistic modelling approach used independently. In particular, we highlight how the digital twin concept can support the performance of large language models applied in medicine and its potential to address health-care challenges. We believe that this Health Policy paper will help to guide scientists, clinicians, and policy makers in creating medical digital twins in the future and translating this promising new paradigm from theory into clinical practice.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100864"},"PeriodicalIF":23.8,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303357","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":"Health insights from face photographs.","authors":"The Lancet Digital Health","doi":"10.1016/j.landig.2025.100889","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100889","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100889"},"PeriodicalIF":23.8,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250373","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":"Online video versus face-to-face preoperative consultation for major abdominal surgery (VIDEOGO): a multicentre, open-label, randomised, controlled, non-inferiority trial.","authors":"Britte H E A Ten Haaft, Boris V Janssen, Esther Z Barsom, Wouter J K Hehenkamp, Mark I van Berge Henegouwen, Olivier R Busch, Susan van Dieren, Joris I Erdmann, Wietse J Eshuis, Suzanne S Gisbertz, Misha D P Luyer, Olga C Damman, Martine C de Bruijne, Geert Kazemier, Marlies P Schijven, Marc G Besselink","doi":"10.1016/j.landig.2025.02.007","DOIUrl":"https://doi.org/10.1016/j.landig.2025.02.007","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Online video consultation between patients and health-care providers rapidly gained popularity during the COVID-19 pandemic. However, to our knowledge, there is no high-quality comparative evidence regarding patient satisfaction and quality of information recall with online video consultation and traditional face-to-face consultation. This lack of evidence is especially concerning in the most demanding consultations. We aimed to assess whether online video consultation between patients and surgeons before major abdominal surgery was non-inferior to face-to-face consultation in terms of patient satisfaction, and to assess effects on patient information recall.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This open-label, randomised, controlled, non-inferiority trial (VIDEOGO) was conducted at two hospitals (one academic and one regional) in the Netherlands. Adult patients (aged ≥18 years) who required consultation with a surgeon to discuss major abdominal surgery and were able and willing to interact via both online video and face-to-face consultation were eligible for inclusion; patients were excluded if they were unable or unwilling to start or maintain online video consultation. Eligible patients were randomly allocated (1:1) to online video or face-to-face consultation by the study coordinator, using a computer-generated, concealed, permuted-block randomisation method with varying block sizes (two, four, and six patients), stratified by study site. Masking of patients and health-care providers was not possible owing to the nature of the study. The primary outcomes were patient satisfaction (score 0-100; assessed for non-inferiority with a predefined margin of -10%) and information recall (score 0-11), both of which were assessed with online questionnaires and analysed in the intention-to-treat population for whom outcome data were available. Technical adverse events were assessed directly after the consultation as part of the satisfaction questionnaire. This trial is registered with the International Clinical Trial Registry Platform and the Central Committee on Research Involving Human Subjects registry, NL-OMON20092, and is complete.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;Between Feb 13, 2021, and Oct 2, 2023, 120 patients were randomly assigned: 60 to online video consultation and 60 to face-to-face consultation. Outcome data were available for 57 patients in the online video consultation group (20 [35%] female and 37 [65%] male; median age 64·0 [54·5-72·5] years) and 55 patients in the face-to-face group (22 [40%] female and 33 [60%] male; median age 62·0 [56·0-70·0] years). The mean patient satisfaction score was 85·4 out of 100 (SD 12·3) in the online video consultation group and 85·2 (14·2) in the face-to-face group (mean difference 0·2, 95% CI -4·8 to 5·1), which was within the non-inferiority margin of -10% (p&lt;sub&gt;non-inferiority&lt;/sub&gt;&lt;0·0001). The mean information recall score was 7·30 out of 11 (SD 1·60) in the online vi","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100867"},"PeriodicalIF":23.8,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310552","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":"External validation of a digital pathology-based multimodal artificial intelligence-derived prognostic model in patients with advanced prostate cancer starting long-term androgen deprivation therapy: a post-hoc ancillary biomarker study of four phase 3 randomised controlled trials of the STAMPEDE platform protocol.","authors":"Charles T A Parker, Larissa Mendes, Vinnie Y T Liu, Emily Grist, Songwan Joun, Rikiya Yamashita, Akinori Mitani, Emmalyn Chen, Marina A Parry, Ashwin Sachdeva, Laura Murphy, Huei-Chung Huang, Jacqueline Griffin, Douwe van der Wal, Tamara Todorovic, Sharanpreet Lall, Sara Santos Vidal, Miriam Goncalves, Suparna Thakali, Anna Wingate, Leila Zakka, Mick Brown, Daniel Wetterskog, Claire L Amos, Nafisah B Atako, Robert J Jones, William R Cross, Silke Gillessen, Chris C Parker, Daniel M Berney, Phuoc T Tran, Daniel E Spratt, Matthew R Sydes, Mahesh K B Parmar, Noel W Clarke, Louise C Brown, Felix Y Feng, Andre Esteva, Nicholas D James, Gerhardt Attard","doi":"10.1016/j.landig.2025.100885","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100885","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Effective prognostication improves selection of patients with prostate cancer for treatment combinations. We aimed to evaluate whether a previously developed multimodal artificial intelligence (MMAI) algorithm was prognostic in very advanced prostate cancer using data from four phase 3 trials of the STAMPEDE platform protocol.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We included patients starting androgen-deprivation therapy in the docetaxel, docetaxel plus zoledronic acid, abiraterone, or abiraterone plus enzalutamide trials. Patients were recruited at 112 sites. We combined all standard-of-care control patients (including those allocated to standard of care [SOC-ADT] consisting of testosterone suppression with luteinising hormone-releasing hormone agonists or antagonists, and radiotherapy when indicated), and we combined the rest of the patients into docetaxel-treated or abiraterone-treated groups. Patients had either metastatic disease or were at very high-risk of metastatic disease, determined by node-positivity or, if node-negative, by T stage, serum prostate-specific antigen (PSA) level, and Gleason score. We used the locked ArteraAI Prostate MMAI algorithm that combined these clinical variables, age, and digitised prostate biopsy pathology images. We performed Fine-Gray and Cox regression adjusted for treatment allocation and cumulative incidence analyses at 5 years to evaluate associations with prostate cancer-specific mortality (PCSM) for continuous (per SD increase) and categorical (quartile-Q) scores. The STAMPEDE platform protocol is registered with ClinicalTrials.gov, NCT00268476.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Findings: &lt;/strong&gt;Of 5213 eligible patients recruited from Oct 5, 2005, to March 31, 2016, 3167 were included in this analysis (1575 [49·7%] with non-metastatic disease, 1592 [50·3%] with metastatic disease; median follow-up 6·9 years [IQR 5·9-8·0]) with all datapoints available for score generation. The MMAI algorithm (per SD increase) was strongly associated with PCSM (hazard ratio [HR] 1·40, 95% CI 1·30-1·51, p&lt;0·0001). On ad-hoc inspection, the highest scoring quartile of patients in each disease and treatment allocation group (MMAI Q4; vs the bottom three quartiles, Q1-3) had the highest PCSM risk in both patients with non-metastatic disease (HR 2·12, 1·61-2·81, p&lt;0·0001) and those with metastatic disease (HR 1·62, 1·39-1·88, p&lt;0·0001). MMAI quartile stratification split patients categorised by disease burden into groups with notably different risks of 5-year PCSM: patients with non-metastatic disease that were node-negative could be further stratified by MMAI score quartile Q1-3 (3%, 2-4) versus Q4 (11%, 7-15), those with non-metastatic disease that were node-positive could be stratified by Q1-3 (11%, 8-14) versus Q4 (20%, 13-26), those with metastatic disease with low-volume could be stratified by Q1-3 (27%, 23-31) versus Q4 (43%, 36-51), and those with metastatic disease with high-volume could be stratified by Q","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100885"},"PeriodicalIF":23.8,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144227290","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":"Video in the clinic: advancing care for patients, professionals, and the planet.","authors":"Lars Henrik Jensen","doi":"10.1016/j.landig.2025.100875","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100875","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100875"},"PeriodicalIF":23.8,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310553","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":"Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model.","authors":"Shishir Rao, Yikuan Li, Mohammad Mamouei, Gholamreza Salimi-Khorshidi, Malgorzata Wamil, Milad Nazarzadeh, Christopher Yau, Gary S Collins, Rod Jackson, Andrew Vickers, Goodarz Danaei, Kazem Rahimi","doi":"10.1016/j.landig.2025.03.005","DOIUrl":"https://doi.org/10.1016/j.landig.2025.03.005","url":null,"abstract":"<p><strong>Background: </strong>Although statistical models have been commonly used to identify patients at risk of cardiovascular disease for preventive therapy, these models tend to over-recommend therapy. Moreover, in populations with pre-existing diseases, the current approach is to indiscriminately treat all, as modelling in this context is currently inadequate. This study aimed to develop and validate the Transformer-based Risk assessment survival (TRisk) model, a novel deep learning model, for predicting 10-year risk of cardiovascular disease in both the primary prevention population and individuals with diabetes.</p><p><strong>Methods: </strong>An open cohort of 3 million adults aged 25-84 years was identified using linked electronic health records from 291 general practices, for model development, and 98 general practices, for validation, across England from 1998 to 2015. Comparison against the QRISK3 score and a deep learning derivation of it was done. Additional analyses compared discriminatory performance in other age groups, by sex, and across categories of socioeconomic status.</p><p><strong>Findings: </strong>TRisk showed superior discrimination (C index in the primary prevention population 0·910; 95% CI 0·906-0·913). TRisk's performance was found to be less sensitive to population age range than the benchmark models and outperformed other models also in analyses stratified by age, sex, or socioeconomic status. All models were overall well calibrated. In decision curve analyses, TRisk showed a greater net benefit than benchmark models across the range of relevant thresholds. At the widely recommended 10% risk threshold and the higher 15% threshold, TRisk reduced both the total number of patients classified at high risk (by 20·6% and 34·6%, respectively) and the number of false negatives as compared with recommended strategies. TRisk similarly outperformed other models in patients with diabetes. Compared with the widely recommended treat-all policy approach for patients with diabetes, TRisk at a 10% risk threshold would lead to deselection of 24·3% of individuals, with a small fraction of false negatives (0·2% of the cohort).</p><p><strong>Interpretation: </strong>TRisk enabled a more targeted selection of individuals at risk of cardiovascular disease in both the primary prevention population and cohorts with diabetes, compared with benchmark approaches. Incorporation of TRisk into routine care could potentially reduce the number of treatment-eligible patients by approximately one-third while preventing at least as many events as with currently adopted approaches.</p><p><strong>Funding: </strong>None.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100873"},"PeriodicalIF":23.8,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217272","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":"Importance of sample size on the quality and utility of AI-based prediction models for healthcare.","authors":"Richard D Riley, Joie Ensor, Kym I E Snell, Lucinda Archer, Rebecca Whittle, Paula Dhiman, Joseph Alderman, Xiaoxuan Liu, Laura Kirton, Jay Manson-Whitton, Maarten van Smeden, Karel G Moons, Krishnarajah Nirantharakumar, Jean-Baptiste Cazier, Alastair K Denniston, Ben Van Calster, Gary S Collins","doi":"10.1016/j.landig.2025.01.013","DOIUrl":"https://doi.org/10.1016/j.landig.2025.01.013","url":null,"abstract":"<p><p>Rigorous study design and analytical standards are required to generate reliable findings in healthcare from artificial intelligence (AI) research. One crucial but often overlooked aspect is the determination of appropriate sample sizes for studies developing AI-based prediction models for individual diagnosis or prognosis. Specifically, the number of participants and outcome events required in datasets for model training and evaluation remains inadequately addressed. Most AI studies do not provide a rationale for their chosen sample sizes and frequently rely on datasets that are inadequate for training or evaluating a clinical prediction model. Among the ten principles of Good Machine Learning Practice established by the US Food and Drug Administration, the UK Medicines and Healthcare products Regulatory Agency, and Health Canada, guidance on sample size is directly relevant to at least three principles. To reinforce this recommendation, we outline seven reasons why inadequate sample size negatively affects model training, evaluation, and performance. Using a range of examples, we illustrate these issues and discuss the potentially harmful consequences for patient care and clinical adoption. Additionally, we address challenges associated with increasing sample sizes in AI research and highlight existing approaches and software for calculating the minimum sample sizes required for model training and evaluation.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100857"},"PeriodicalIF":23.8,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217271","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":"Overlooked and under-reported: the impact of cyberattacks on primary care in the UK National Health Service.","authors":"Kunal Rajput, Ara Darzi, Saira Ghafur","doi":"10.1016/j.landig.2025.100879","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100879","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100879"},"PeriodicalIF":23.8,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144144069","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":"When data disappear: public health pays as US policy strays.","authors":"Thomas McAndrew, Andrew A Lover, Garrik Hoyt, Maimuna S Majumder","doi":"10.1016/j.landig.2025.100874","DOIUrl":"https://doi.org/10.1016/j.landig.2025.100874","url":null,"abstract":"<p><p>Presidential actions on Jan 20, 2025, by President Donald Trump, including executive orders, have delayed access to or led to the removal of crucial public health data sources in the USA. The continuous collection and maintenance of health data support public health, safety, and security associated with diseases such as seasonal influenza. To show how public health data surveillance enhances public health practice, we analysed data from seven US Government-maintained sources associated with seasonal influenza. We fit two models that forecast the number of national incident influenza hospitalisations in the USA: (1) a data-rich model incorporating data from all seven Government data sources; and (2) a data-poor model built using a single Government hospitalisation data source, representing the minimal required information to produce a forecast of influenza hospitalisations. The data-rich model generated reliable forecasts useful for public health decision making, whereas the predictions using the data-poor model were highly uncertain, rendering them impractical. Thus, health data can serve as a transparent and standardised foundation to improve domestic and global health. Therefore, a plan should be developed to safeguard public health data as a public good.</p>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":" ","pages":"100874"},"PeriodicalIF":23.8,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144129093","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}