Lancet Digital Health最新文献

{"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 MD , Boris V Janssen BSc , Esther Z Barsom MD PhD , Prof Wouter J K Hehenkamp MD PhD , Prof Mark I van Berge Henegouwen MD PhD , Prof Olivier R Busch MD PhD , Susan van Dieren PhD , Joris I Erdmann MD PhD , Wietse J Eshuis MD PhD , Suzanne S Gisbertz MD PhD , Prof Misha D P Luyer MD PhD , Olga C Damman PhD , Prof Martine C de Bruijne MD PhD , Prof Geert Kazemier MD PhD , Prof Marlies P Schijven MD PhD , Prof Marc G Besselink MD PhD","doi":"10.1016/j.landig.2025.02.007","DOIUrl":"10.1016/j.landig.2025.02.007","url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Findings</h3><div>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<sub>non-inferiority</sub><0·0001). The mean information recall score was 7·30 out of 11 (SD 1·60) in the","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 6","pages":"Article 100867"},"PeriodicalIF":23.8,"publicationDate":"2025-06-01","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":"Video in the clinic: advancing care for patients, professionals, and the planet","authors":"Lars Henrik Jensen","doi":"10.1016/j.landig.2025.100875","DOIUrl":"10.1016/j.landig.2025.100875","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 6","pages":"Article 100875"},"PeriodicalIF":23.8,"publicationDate":"2025-06-01","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":"Importance of sample size on the quality and utility of AI-based prediction models for healthcare","authors":"Prof Richard D Riley PhD ,&nbsp;Joie Ensor PhD ,&nbsp;Kym I E Snell PhD ,&nbsp;Lucinda Archer PhD ,&nbsp;Rebecca Whittle PhD ,&nbsp;Paula Dhiman PhD ,&nbsp;Joseph Alderman MBChB ,&nbsp;Xiaoxuan Liu PhD ,&nbsp;Laura Kirton MSc ,&nbsp;Jay Manson-Whitton ,&nbsp;Maarten van Smeden PhD ,&nbsp;Prof Karel G Moons PhD ,&nbsp;Prof Krishnarajah Nirantharakumar MD ,&nbsp;Prof Jean-Baptiste Cazier PhD ,&nbsp;Prof Alastair K Denniston PhD ,&nbsp;Prof Ben Van Calster PhD ,&nbsp;Prof Gary S Collins PhD","doi":"10.1016/j.landig.2025.01.013","DOIUrl":"10.1016/j.landig.2025.01.013","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 6","pages":"Article 100857"},"PeriodicalIF":23.8,"publicationDate":"2025-06-01","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}

{"title":"Correction to Lancet Digit Health 2024; 6: e386–95","authors":"","doi":"10.1016/j.landig.2025.100877","DOIUrl":"10.1016/j.landig.2025.100877","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 5","pages":"Article 100877"},"PeriodicalIF":23.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144054723","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":"Efficacy of digital health technologies in the management of inflammatory bowel disease: an umbrella review","authors":"Marco Gasparetto MD ,&nbsp;Priya Narula MD ,&nbsp;Charlotte Wong MBBS MSc ,&nbsp;James Ashton MD PhD ,&nbsp;Jochen Kammermeier MD PhD ,&nbsp;Prof Marieke Pierik MD PhD ,&nbsp;Prof Uri Kopylov MD ,&nbsp;Prof Naila Arebi MD PhD","doi":"10.1016/j.landig.2024.12.007","DOIUrl":"10.1016/j.landig.2024.12.007","url":null,"abstract":"<div><div>The use of digital health technology (DHT) is increasing worldwide. Clinical trials assessing available health tools for the management of patients with inflammatory bowel disease (IBD) are sparse, with limited evidence-based outcome data. In this umbrella review, we investigated the effectiveness of DHT in the care of patients with IBD and identified areas for future research following the Joanna Briggs Institute methodology. Systematic reviews published between January, 2012, and September, 2024, were identified through searches across nine databases (Ovid Embase, Ovid MEDLINE, ProQuest PsycINFO, Epistemonikos, Cochrane, Health Evidence, DoPHER, PROSPERO, and CINAHL via EBSCO), and the results were imported into Covidence software. Inclusion criteria included systematic reviews of randomised controlled trials (RCTs) involving patients of all ages with Crohn’s disease or ulcerative colitis, using DHT for diagnostics, treatment support, monitoring, self-management, or increasing participation in research studies, compared with standard care or alternative interventions. Outcomes included the efficacy and effectiveness of digital interventions, as reported in the studies. The primary outcome was clinical efficacy reported as one or more of the following: clinical response or remission, disease activity, flare-ups or relapses, and quality of life. Secondary outcomes included medication adherence, number of health-care visits, patient engagement (satisfaction and adherence or compliance with interventions), attendance for all terms of engagement, rate of interactions, knowledge improvement, psychological outcomes, and cost or cost–time effectiveness. The review protocol was registered in PROSPERO (registration number: CRD42023417525). AMSTAR-2 was used for methodological quality assessment. Nine relevant reviews were included, including five with meta-analyses comprising 13–19 RCTs in each review; four reviews were rated as high quality and five as critically low quality. DHT was not directly beneficial in achieving or maintaining clinical remission in IBD. In four trials, DHT use was associated with a reduced number of hospital attendances and increased treatment adherence, supporting its role as an adjuvant to standard clinical practice in IBD. Although current evidence from several RCTs and systematic reviews does not indicate better clinical outcomes with DHT in maintaining IBD remission and reducing relapse rates, DHT could be used as an adjuvant resource contributing towards treatment adherence and reducing hospital visits.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 5","pages":"Article 100843"},"PeriodicalIF":23.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081271","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":"Utilising the Benefit Risk Assessment of Vaccines (BRAVE) toolkit to evaluate the benefits and risks of Vaxzevria in the EU: a population-based study","authors":"Hector Gonzalez Dorta MSc ,&nbsp;Johan Verbeeck PhD ,&nbsp;Jonas Crevecoeur PhD ,&nbsp;Daniel R Morales PhD ,&nbsp;Neilshan Loedy MSc ,&nbsp;Catherine Cohet PhD ,&nbsp;Lander Willem PhD ,&nbsp;Prof Geert Molenberghs PhD ,&nbsp;Prof Niel Hens PhD ,&nbsp;Xavier Kurz PhD ,&nbsp;Chantal Quinten PhD ,&nbsp;Steven Abrams PhD","doi":"10.1016/j.landig.2025.02.001","DOIUrl":"10.1016/j.landig.2025.02.001","url":null,"abstract":"<div><h3>Background</h3><div>Several COVID-19 vaccines have been licensed. To support the assessment of safety signals, we developed a toolkit to support COVID-19 vaccine monitoring and benefit–risk assessment. We aim to show the application of our toolkit in the EU using thrombosis with thrombocytopenia syndrome (TTS) associated with the Vaxzevria (AstraZeneca) vaccine as a use case.</div></div><div><h3>Methods</h3><div>In this population-based study, we used a model incorporating data from multiple EU sources such as The European Surveillance System and EudraVigilance, and estimated the benefits of COVID-19 vaccines by comparing the observed COVID-19 confirmed cases, hospitalisations, intensive care unit (ICU) admissions, and deaths across Europe to the expected numbers in the absence of Vaxzevria vaccination. Risks of TTS associated with Vaxzevria were calculated by comparing the observed number of TTS events in individuals who received Vaxzevria to the expected number of events based on background incidence rates. To visualise the results, we developed a toolkit with an interactive web application.</div></div><div><h3>Findings</h3><div>62 598 505 Vaxzevria vaccines (32 763 183 to females and 29 835 322 to males) had been administered in Europe by Feb 10, 2021. Our results showed that a first dose of Vaxzevria provided benefits across all age groups. Based on vaccine effectiveness estimates and reported coverage in Europe, from Dec 13, 2020 to Dec 31, 2021, vaccination with Vaxzevria was estimated to prevent (per 100 000 doses) 12 113 COVID-19 cases, 1140 hospitalisations, 184 ICU admissions, and 261 deaths. Women aged 30–59 years and males aged 20–29 years had the highest frequency of TTS events. The benefits of vaccination outweighed the risks of TTS in all age groups, with the highest benefits and risks observed in individuals aged 60–69 years.</div></div><div><h3>Interpretation</h3><div>Our toolkit and underlying model contextualised the risk of TTS associated with Vaxzevria relative to its benefits. The methodology employed could be applied to other serious adverse events related to COVID-19 or other vaccines. The adaptability and versatility of such toolkits might contribute to strengthening preparedness for future public health emergencies.</div></div><div><h3>Funding</h3><div>European Medicines Agency.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 5","pages":"Article 100861"},"PeriodicalIF":23.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144019610","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":"Technology for global immunisation","authors":"The Lancet Digital Health","doi":"10.1016/j.landig.2025.100881","DOIUrl":"10.1016/j.landig.2025.100881","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 5","pages":"Article 100881"},"PeriodicalIF":23.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144095799","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":"Generating evidence to support the role of AI in diabetic eye screening: considerations from the UK National Screening Committee","authors":"Trystan Macdonald MBChB ,&nbsp;Zhivko Zhelev PhD ,&nbsp;Xiaoxuan Liu PhD ,&nbsp;Prof Christopher Hyde PhD ,&nbsp;Jiri Fajtl PhD ,&nbsp;Catherine Egan PhD ,&nbsp;Prof Adnan Tufail MD ,&nbsp;Alicja R Rudnicka PhD ,&nbsp;Prof Bethany Shinkins PhD ,&nbsp;Rosalind Given-Wilson FRCR ,&nbsp;J Kevin Dunbar MBChB ,&nbsp;Prof Steve Halligan PhD ,&nbsp;Prof Peter Scanlon MD ,&nbsp;Prof Anne Mackie PhD ,&nbsp;Prof Sian Taylor-Philips PhD ,&nbsp;Prof Alastair K Denniston PhD","doi":"10.1016/j.landig.2024.12.004","DOIUrl":"10.1016/j.landig.2024.12.004","url":null,"abstract":"<div><div>Screening for diabetic retinopathy has been shown to reduce the risk of sight loss in people with diabetes, because of early detection and treatment of sight-threatening disease. There is long-standing interest in the possibility of automating parts of this process through artificial intelligence, commonly known as automated retinal imaging analysis software (ARIAS). A number of such products are now on the market. In the UK, Scotland has used a rules-based autograder since 2011, but the diabetic eye screening programmes in the rest of the UK rely solely on human graders. With more sophisticated machine learning-based ARIAS now available and greater challenges in terms of human grader capacity, in 2019 the UK's National Screening Committee (NSC) was asked to consider the modification of diabetic eye screening in England with ARIAS. Following up on a review of ARIAS research highlighting the strengths and limitations of existing evidence, the NSC here sets out their considerations for evaluating evidence to support the introduction of ARIAS into the diabetic eye screening programme.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 5","pages":"Article 100840"},"PeriodicalIF":23.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789117","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}