Pub Date : 2025-06-01DOI: 10.1016/j.landig.2025.01.014
Prof Israel Júnior Borges do Nascimento MD ClinPath , Hebatullah Mohamed Abdulazeem MD MSc , Ishanka Weerasekara PhD , Prof Jodie Marquez PhD , Lenny T Vasanthan PhD , Genevieve Deeken MSc , Prof Rosemary Morgan PhD , Heang-Lee Tan MPH , Isabel Yordi Aguirre PhD , Lasse Østeengaard MSc , Indunil Kularathne BSc , Natasha Azzopardi-Muscat PhD , Prof Robin van Kessel PhD , Edson Zangiacomi Martinez PhD , Govin Permanand PhD , David Novillo-Ortiz PhD MLIS
We evaluated the effects of digital health technologies (DHTs) on women's health, empowerment, and gender equality, using the scoping review method. Following a search across five databases and grey literature, we analysed 80 studies published up to Aug 18, 2023. The thematic appraisal and quantitative analysis found that DHTs positively affect women's access to health-care services, self-care, and tailored self-monitoring enabling the acquisition of health-related interventions. Use of these technologies is beneficial across various medical fields, including gynaecology, endocrinology, and psychiatry. DHTs also improve women's empowerment and gender equality by facilitating skills acquisition, health education, and social interaction, while allowing cost-effective health services. Overall, DHTs contribute to better health outcomes for women and support the UN Sustainable Development Goals by improving access to health care and financial literacy.
{"title":"Transforming women's health, empowerment, and gender equality with digital health: evidence-based policy and practice","authors":"Prof Israel Júnior Borges do Nascimento MD ClinPath , Hebatullah Mohamed Abdulazeem MD MSc , Ishanka Weerasekara PhD , Prof Jodie Marquez PhD , Lenny T Vasanthan PhD , Genevieve Deeken MSc , Prof Rosemary Morgan PhD , Heang-Lee Tan MPH , Isabel Yordi Aguirre PhD , Lasse Østeengaard MSc , Indunil Kularathne BSc , Natasha Azzopardi-Muscat PhD , Prof Robin van Kessel PhD , Edson Zangiacomi Martinez PhD , Govin Permanand PhD , David Novillo-Ortiz PhD MLIS","doi":"10.1016/j.landig.2025.01.014","DOIUrl":"10.1016/j.landig.2025.01.014","url":null,"abstract":"<div><div>We evaluated the effects of digital health technologies (DHTs) on women's health, empowerment, and gender equality, using the scoping review method. Following a search across five databases and grey literature, we analysed 80 studies published up to Aug 18, 2023. The thematic appraisal and quantitative analysis found that DHTs positively affect women's access to health-care services, self-care, and tailored self-monitoring enabling the acquisition of health-related interventions. Use of these technologies is beneficial across various medical fields, including gynaecology, endocrinology, and psychiatry. DHTs also improve women's empowerment and gender equality by facilitating skills acquisition, health education, and social interaction, while allowing cost-effective health services. Overall, DHTs contribute to better health outcomes for women and support the UN Sustainable Development Goals by improving access to health care and financial literacy.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 6","pages":"Article 100858"},"PeriodicalIF":23.8,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081422","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}
Pub Date : 2025-06-01DOI: 10.1016/j.landig.2025.03.001
Yuxuan Shi PhD , Zhen Li PhD , Li Wang PhD , Hong Wang PhD , Prof Xiaofeng Liu PhD , Dantong Gu MS , Xiao Chen MS , Xueli Liu PhD , Wentao Gong MS , Xiaowen Jiang MD , Wenquan Li MD , Yongdong Lin BS , Ke Liu MD , Deyan Luo MD , Tao Peng PhD , Xuemei Peng BS , Meimei Tong BS , Huizhen Zheng MD , Xuanchen Zhou MD , Jianrong Wu PhD , Prof Hongmeng Yu PhD
<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Findings</h3><div>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 (mea
{"title":"Artificial intelligence-assisted detection of nasopharyngeal carcinoma on endoscopic images: a national, multicentre, model development and validation study","authors":"Yuxuan Shi PhD , Zhen Li PhD , Li Wang PhD , Hong Wang PhD , Prof Xiaofeng Liu PhD , Dantong Gu MS , Xiao Chen MS , Xueli Liu PhD , Wentao Gong MS , Xiaowen Jiang MD , Wenquan Li MD , Yongdong Lin BS , Ke Liu MD , Deyan Luo MD , Tao Peng PhD , Xuemei Peng BS , Meimei Tong BS , Huizhen Zheng MD , Xuanchen Zhou MD , Jianrong Wu PhD , Prof Hongmeng Yu PhD","doi":"10.1016/j.landig.2025.03.001","DOIUrl":"10.1016/j.landig.2025.03.001","url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Findings</h3><div>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 (mea","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 6","pages":"Article 100869"},"PeriodicalIF":23.8,"publicationDate":"2025-06-01","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}
Pub Date : 2025-06-01DOI: 10.1016/j.landig.2025.100889
The Lancet Digital Health
{"title":"Health insights from face photographs","authors":"The Lancet Digital Health","doi":"10.1016/j.landig.2025.100889","DOIUrl":"10.1016/j.landig.2025.100889","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 6","pages":"Article 100889"},"PeriodicalIF":23.8,"publicationDate":"2025-06-01","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}
Pub Date : 2025-06-01DOI: 10.1016/j.landig.2025.02.007
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
Background
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.
Methods
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.
Findings
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% (pnon-inferiority<0·0001). The mean information recall score was 7·30 out of 11 (SD 1·60) in the
{"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}
Pub Date : 2025-06-01DOI: 10.1016/j.landig.2025.100875
Lars Henrik Jensen
{"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}
Pub Date : 2025-06-01DOI: 10.1016/j.landig.2025.01.013
Prof Richard D Riley PhD , Joie Ensor PhD , Kym I E Snell PhD , Lucinda Archer PhD , Rebecca Whittle PhD , Paula Dhiman PhD , Joseph Alderman MBChB , Xiaoxuan Liu PhD , Laura Kirton MSc , Jay Manson-Whitton , Maarten van Smeden PhD , Prof Karel G Moons PhD , Prof Krishnarajah Nirantharakumar MD , Prof Jean-Baptiste Cazier PhD , Prof Alastair K Denniston PhD , Prof Ben Van Calster PhD , Prof Gary S Collins PhD
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.
{"title":"Importance of sample size on the quality and utility of AI-based prediction models for healthcare","authors":"Prof Richard D Riley PhD , Joie Ensor PhD , Kym I E Snell PhD , Lucinda Archer PhD , Rebecca Whittle PhD , Paula Dhiman PhD , Joseph Alderman MBChB , Xiaoxuan Liu PhD , Laura Kirton MSc , Jay Manson-Whitton , Maarten van Smeden PhD , Prof Karel G Moons PhD , Prof Krishnarajah Nirantharakumar MD , Prof Jean-Baptiste Cazier PhD , Prof Alastair K Denniston PhD , Prof Ben Van Calster PhD , 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}
Pub Date : 2025-05-01DOI: 10.1016/j.landig.2025.100877
{"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}
Pub Date : 2025-05-01DOI: 10.1016/j.landig.2024.12.007
Marco Gasparetto MD , Priya Narula MD , Charlotte Wong MBBS MSc , James Ashton MD PhD , Jochen Kammermeier MD PhD , Prof Marieke Pierik MD PhD , Prof Uri Kopylov MD , Prof Naila Arebi MD PhD
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.
{"title":"Efficacy of digital health technologies in the management of inflammatory bowel disease: an umbrella review","authors":"Marco Gasparetto MD , Priya Narula MD , Charlotte Wong MBBS MSc , James Ashton MD PhD , Jochen Kammermeier MD PhD , Prof Marieke Pierik MD PhD , Prof Uri Kopylov MD , 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}
Pub Date : 2025-05-01DOI: 10.1016/j.landig.2025.02.001
Hector Gonzalez Dorta MSc , Johan Verbeeck PhD , Jonas Crevecoeur PhD , Daniel R Morales PhD , Neilshan Loedy MSc , Catherine Cohet PhD , Lander Willem PhD , Prof Geert Molenberghs PhD , Prof Niel Hens PhD , Xavier Kurz PhD , Chantal Quinten PhD , Steven Abrams PhD
Background
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.
Methods
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.
Findings
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.
Interpretation
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.
{"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 , Johan Verbeeck PhD , Jonas Crevecoeur PhD , Daniel R Morales PhD , Neilshan Loedy MSc , Catherine Cohet PhD , Lander Willem PhD , Prof Geert Molenberghs PhD , Prof Niel Hens PhD , Xavier Kurz PhD , Chantal Quinten PhD , 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}
Pub Date : 2025-05-01DOI: 10.1016/j.landig.2025.100881
The Lancet Digital Health
{"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}
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