Lancet Digital Health最新文献

{"title":"Addressing the challenges of integrating digital health technologies to measure patient-centred outcomes in clinical registries","authors":"Caroline Marra PhD ,&nbsp;Prof Tim Chico MD ,&nbsp;April Alexandrow PhD ,&nbsp;Prof Will G Dixon PhD ,&nbsp;Norman Briffa MD ,&nbsp;Erin Rainaldi MS ,&nbsp;Max A Little PhD ,&nbsp;Kristin Size MS ,&nbsp;Athanasios Tsanas PhD ,&nbsp;Joseph B Franklin JD ,&nbsp;Prof Ritu Kapur PhD ,&nbsp;Helen Grice ,&nbsp;Anwar Gariban ,&nbsp;Joy Ellery ,&nbsp;Cathie Sudlow FRCP ,&nbsp;Amy P Abernethy MD PhD ,&nbsp;Prof Andrew Morris MD","doi":"10.1016/S2589-7500(24)00223-1","DOIUrl":"10.1016/S2589-7500(24)00223-1","url":null,"abstract":"<div><div>Longitudinal patient registries generate important evidence for advancing clinical care and the regulatory evaluation of health-care products. Most national registries rely on data collected as part of routine clinical encounters, an approach that does not capture real-world, patient-centred outcomes, such as physical activity, fatigue, ability to do daily tasks, and other indicators of quality of life. Digital health technologies that obtain such real-world data could greatly enhance patient registries but unresolved challenges have so far prevented their broad adoption. Based on our experience implementing digital health technologies in registries and observational studies, we propose potential solutions to three practical challenges we have repeatedly encountered: determining what to measure digitally, selecting the appropriate device, and ensuring representativeness and engagement over time. We describe the example of a hypothetical patient registry for valvular heart disease, a condition for which there is substantial variation in treatment selection and postintervention outcomes, and for which patient-centred outcome data are urgently needed to inform clinical care guidelines and health-service commissioning.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"7 3","pages":"Pages e225-e231"},"PeriodicalIF":23.8,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142822615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges of AI-based pulmonary function estimation from chest x-rays – Authors' reply","authors":"Daiju Ueda ,&nbsp;Shannon L Walston ,&nbsp;Yasuhito Mitsuyama ,&nbsp;Hirotaka Takita ,&nbsp;Yukio Miki","doi":"10.1016/S2589-7500(24)00248-6","DOIUrl":"10.1016/S2589-7500(24)00248-6","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Page e881"},"PeriodicalIF":23.8,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to Lancet Digit Health 2024; 6: e791–802","authors":"","doi":"10.1016/S2589-7500(24)00252-8","DOIUrl":"10.1016/S2589-7500(24)00252-8","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Page e882"},"PeriodicalIF":23.8,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges of AI-based pulmonary function estimation from chest x-rays","authors":"Xinyu Li ,&nbsp;Jiajie Lv ,&nbsp;Jiajia Xue ,&nbsp;Ruhong Zhang ,&nbsp;Datao Li","doi":"10.1016/S2589-7500(24)00247-4","DOIUrl":"10.1016/S2589-7500(24)00247-4","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Page e880"},"PeriodicalIF":23.8,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing the management of maternal, fetal, and neonatal infection through harnessing digital health innovations","authors":"Damien K Ming PhD ,&nbsp;Abi Merriel PhD ,&nbsp;David M E Freeman PhD ,&nbsp;Carol Kingdon PhD ,&nbsp;Yamikani Chimwaza MPH ,&nbsp;Mohammad S Islam PhD ,&nbsp;Prof Anthony Cass DPhil ,&nbsp;Benjamin Greenfield MBChB ,&nbsp;Prof Address Malata PhD ,&nbsp;Prof Mahbubul Hoque FCPS ,&nbsp;Senjuti Saha PhD ,&nbsp;Prof Alison H Holmes MD FMedSci","doi":"10.1016/S2589-7500(24)00217-6","DOIUrl":"10.1016/S2589-7500(24)00217-6","url":null,"abstract":"<div><div>Infections occurring in the mother and neonate exert a substantial health burden worldwide. Optimising infection management is crucial for improving individual outcomes and reducing the incidence of antimicrobial resistance. Digital health technologies, through their accessibility and scalability, hold promise in improving the quality of care across diverse health-care settings. In settings with poor access to laboratory services, innovative uses of existing data, point-of-care diagnostics, and wearables could allow for better recognition of host responses during infection and antimicrobial optimisation. The linkage and connectivity of information can support the coordinated delivery of care between health-care facilities and the community. Continuous real-time monitoring of infection markers in the mother and neonate through biosensing can provide notable opportunities for intervention and improvements in care. However, the development and implementation of these interventions should be respectful, prioritise safety, and emphasise sustainable, locally derived solutions. Addressing existing gender, economic, and health-care disparities will be essential for ensuring equitable implementation.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Pages e926-e933"},"PeriodicalIF":23.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using digital health to address antimicrobial resistance","authors":"The Lancet Digital Health","doi":"10.1016/S2589-7500(24)00251-6","DOIUrl":"10.1016/S2589-7500(24)00251-6","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Page e879"},"PeriodicalIF":23.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using digital health technologies to optimise antimicrobial use globally","authors":"Timothy M Rawson PhD ,&nbsp;Nina Zhu PhD ,&nbsp;Ronald Galiwango PhD ,&nbsp;Derek Cocker PhD ,&nbsp;Mohammad Shahidul Islam PhD ,&nbsp;Ashleigh Myall PhD ,&nbsp;Vasin Vasikasin MD ,&nbsp;Richard Wilson MPharm ,&nbsp;Prof Nusrat Shafiq PhD ,&nbsp;Prof Shampa Das PhD ,&nbsp;Prof Alison H Holmes MD","doi":"10.1016/S2589-7500(24)00198-5","DOIUrl":"10.1016/S2589-7500(24)00198-5","url":null,"abstract":"<div><div>Digital health technology (DHT) describes tools and devices that generate or process health data. The application of DHTs could improve the diagnosis, treatment, and surveillance of bacterial infection and the prevention of antimicrobial resistance (AMR). DHTs to optimise antimicrobial use are rapidly being developed. To support the global adoption of DHTs and the opportunities offered to optimise antimicrobial use consensus is needed on what data are required to support antimicrobial decision making. This Series paper will explore bacterial AMR in humans and the need to optimise antimicrobial use in response to this global threat. It will also describe state-of-the-art DHTs to optimise antimicrobial prescribing in high-income and low-income and middle-income countries, and consider what fundamental data are ideally required for and from such technologies to support optimised antimicrobial use.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Pages e914-e925"},"PeriodicalIF":23.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative diagnostic technologies: navigating regulatory frameworks through advances, challenges, and future prospects","authors":"Jesus Rodriguez-Manzano PhD ,&nbsp;Sumithra Subramaniam PhD ,&nbsp;Chibuzor Uchea PhD ,&nbsp;Katarzyna M Szostak-Lipowicz PhD ,&nbsp;Jane Freeman PhD ,&nbsp;Marcus Rauch PhD ,&nbsp;Prof Halidou Tinto PhD ,&nbsp;Prof Heather J Zar MD ,&nbsp;Prof Umberto D'Alessandro MD ,&nbsp;Prof Alison H Holmes MD ,&nbsp;Prof Gordon A Awandare PhD","doi":"10.1016/S2589-7500(24)00242-5","DOIUrl":"10.1016/S2589-7500(24)00242-5","url":null,"abstract":"<div><div>Diagnostic tools are key to guiding patient management and informing public health policies to control infectious diseases. However, many diseases still do not have effective diagnostics and much of the global population faces restricted access to reliable, affordable testing. This limitation underscores the urgent need for innovation to enhance diagnostic availability and effectiveness. Developing diagnostics presents distinct challenges, especially for innovators and regulators. Unlike medicines, regulatory pathways for diagnostics are often less defined and more complex due to their diverse risk profiles and wide range of products. These challenges are amplified in low-income and middle-income countries, which often do not have regulatory frameworks for this specific purpose. In the UK, initiatives aim to support innovation by providing clearer regulatory pathways and ensuring that diagnostics are safe and effective. Regulators are also collaborating internationally to expedite diagnostics for high-need regions. Harmonised standards, regulatory frameworks, and approval processes are essential to ensure consistent quality and safety across regions and facilitate faster development and global access. This Series paper explores the regulatory challenges in infectious disease and antimicrobial resistance diagnostics, focusing on the UK's response and the broader global efforts to address these issues.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Pages e934-e943"},"PeriodicalIF":23.8,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-19 testing and reporting behaviours in England across different sociodemographic groups: a population-based study using testing data and data from community prevalence surveillance surveys","authors":"Sumali Bajaj SM ,&nbsp;Siyu Chen DPhil ,&nbsp;Richard Creswell DPhil ,&nbsp;Reshania Naidoo MD ,&nbsp;Joseph L-H Tsui MSc ,&nbsp;Olumide Kolade BSc ,&nbsp;George Nicholson DPhil ,&nbsp;Brieuc Lehmann PhD ,&nbsp;James A Hay PhD ,&nbsp;Prof Moritz U G Kraemer DPhil ,&nbsp;Ricardo Aguas PhD ,&nbsp;Prof Christl A Donnelly ScD ,&nbsp;Tom Fowler FFPH ,&nbsp;Prof Susan Hopkins FMedSci ,&nbsp;Liberty Cantrell MSc ,&nbsp;Prabin Dahal DPhil ,&nbsp;Prof Lisa J White PhD ,&nbsp;Kasia Stepniewska PhD ,&nbsp;Merryn Voysey DPhil ,&nbsp;Ben Lambert DPhil ,&nbsp;Lisa J White","doi":"10.1016/S2589-7500(24)00169-9","DOIUrl":"10.1016/S2589-7500(24)00169-9","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Understanding underlying mechanisms of heterogeneity in test-seeking and reporting behaviour during an infectious disease outbreak can help to protect vulnerable populations and guide equity-driven interventions. The COVID-19 pandemic probably exerted different stresses on individuals in different sociodemographic groups and ensuring fair access to and usage of COVID-19 tests was a crucial element of England's testing programme. We aimed to investigate the relationship between sociodemographic factors and COVID-19 testing behaviours in England during the COVID-19 pandemic.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;We did a population-based study of COVID-19 testing behaviours with mass COVID-19 testing data for England and data from community prevalence surveillance surveys (REACT-1 and ONS-CIS) from Oct 1, 2020, to March 30, 2022. We used mass testing data for lateral flow device (LFD; data for approximately 290 million tests performed and reported) and PCR (data for approximately 107 million tests performed and returned from the laboratory) tests made available for the general public and provided by date and self-reported age and ethnicity at the lower tier local authority (LTLA) level. We also used publicly available data on mean population size estimates for individual LTLAs, and data on ethnic groups, age groups, and deprivation indices for LTLAs. We did not have access to REACT-1 or ONS-CIS prevalence data disaggregated by sex or gender. Using a mechanistic causal model to debias the PCR testing data, we obtained estimates of weekly SARS-CoV-2 prevalence by both self-reported ethnic groups and age groups for LTLAs in England. This approach to debiasing the PCR (or LFD) testing data also estimated a testing bias parameter defined as the odds of testing in infected versus not infected individuals, which would be close to zero if the likelihood of test seeking (or seeking and reporting) was the same regardless of infection status. With confirmatory PCR data, we estimated false positivity rates, sensitivity, specificity, and the rate of decline in detection probability subsequent to reporting a positive LFD for PCR tests by sociodemographic groups. We also estimated the daily incidence, allowing us to calculate the fraction of cases captured by the testing programme.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;From March, 2021 onwards, individuals in the most deprived regions reported approximately half as many LFD tests per capita as individuals in the least deprived areas (median ratio 0·50 [IQR 0·44–0·54]). During the period October, 2020, to June, 2021, PCR testing patterns showed the opposite trend, with individuals in the most deprived areas performing almost double the number of PCR tests per capita than those in the least deprived areas (1·8 [1·7–1·9]). Infection prevalences in Asian or Asian British individuals were considerably higher than those of other ethnic groups during the alpha (B.1.1.7) and omicron (B.1.1.529","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e778-e790"},"PeriodicalIF":23.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revealing transparency gaps in publicly available COVID-19 datasets used for medical artificial intelligence development—a systematic review","authors":"Joseph E Alderman MB ChB ,&nbsp;Maria Charalambides MB ChB ,&nbsp;Gagandeep Sachdeva MB ChB ,&nbsp;Elinor Laws MB BCh ,&nbsp;Joanne Palmer PhD ,&nbsp;Elsa Lee MSc ,&nbsp;Vaishnavi Menon MB ChB ,&nbsp;Qasim Malik MB ChB ,&nbsp;Sonam Vadera MB BS ,&nbsp;Prof Melanie Calvert PhD ,&nbsp;Marzyeh Ghassemi PhD ,&nbsp;Melissa D McCradden PhD ,&nbsp;Johan Ordish MA ,&nbsp;Bilal Mateen MBBS ,&nbsp;Prof Charlotte Summers PhD ,&nbsp;Jacqui Gath ,&nbsp;Rubeta N Matin PhD ,&nbsp;Prof Alastair K Denniston PhD ,&nbsp;Xiaoxuan Liu PhD","doi":"10.1016/S2589-7500(24)00146-8","DOIUrl":"10.1016/S2589-7500(24)00146-8","url":null,"abstract":"<div><div>During the COVID-19 pandemic, artificial intelligence (AI) models were created to address health-care resource constraints. Previous research shows that health-care datasets often have limitations, leading to biased AI technologies. This systematic review assessed datasets used for AI development during the pandemic, identifying several deficiencies. Datasets were identified by screening articles from MEDLINE and using Google Dataset Search. 192 datasets were analysed for metadata completeness, composition, data accessibility, and ethical considerations. Findings revealed substantial gaps: only 48% of datasets documented individuals’ country of origin, 43% reported age, and under 25% included sex, gender, race, or ethnicity. Information on data labelling, ethical review, or consent was frequently missing. Many datasets reused data with inadequate traceability. Notably, historical paediatric chest x-rays appeared in some datasets without acknowledgment. These deficiencies highlight the need for better data quality and transparent documentation to lessen the risk that biased AI models are developed in future health emergencies.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e827-e847"},"PeriodicalIF":23.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}