PLOS digital health最新文献

{"title":"Determinants of photoplethysmography signal quality at the wrist.","authors":"Peter H Charlton, Vaidotas Marozas, Elisa Mejía-Mejía, Panicos A Kyriacou, Jonathan Mant","doi":"10.1371/journal.pdig.0000585","DOIUrl":"10.1371/journal.pdig.0000585","url":null,"abstract":"<p><p>Wrist photoplethysmogram (PPG) signals are widely used for physiological monitoring in consumer devices. However, the PPG is highly susceptible to noise, which can reduce the accuracy of monitored parameters. The aim of this study was to identify factors which influence PPG signal quality. Data from the Aurora-BP dataset were used, consisting of reflectance wrist PPG signals measured from 1,142 subjects of varying ages and health statuses. Measurements were acquired in supine, sitting, and standing postures, and with the sensor held at different heights. Three signal quality metrics were calculated: the signal-to-noise ratio (SNR), the perfusion index (PI), and the template-matching correlation coefficient (TMCC). When comparing between postures with the sensor held at a natural height, quality was greatest in the supine position (SNR: 18.6 dB), followed by sitting with the arm resting in the lap (13.7 dB), and lowest whilst standing with the arm hanging alongside (9.0 dB) (p < 0.001). Signal quality increased as the arm was raised to heart height: whilst sitting, quality was lowest with the arm alongside the body (10.5 dB), and increased when the sensor was held in the lap (13.7 dB) and at heart height (15.5 dB) (p < 0.001). Similar trends were observed for the TMCC and PI. Findings were mixed for the influence of participant characteristics on signal quality. The SNR and TMCC, but not the PI, increased with age. The SNR either decreased or remained constant at darker skin tones when controlling for PPG DC amplitude, compared to constant or increased when allowing DC amplitude to vary. In conclusion, this study identified the impacts of posture and sensor height on signal quality, with highest qualities observed in the supine posture and with the sensor at heart height. It also highlights the importance of adjusting LED light intensity to maintain signal quality across skin tones.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000585"},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12204546/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144512909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nursing education research in Sub-Saharan Africa: A systematic review and bibliometric analysis.","authors":"Beth Waweru, Peter Gatiti, Serah Wachira","doi":"10.1371/journal.pdig.0000900","DOIUrl":"10.1371/journal.pdig.0000900","url":null,"abstract":"<p><p>Nursing education is pivotal for ensuring competent healthcare professionals, and its improvement is essential for enhancing the quality of health care systems globally. This study focuses on nursing education research in Sub-Saharan Africa (SSA) over the last decade, employing both bibliometric analysis and systematic review methodologies. The bibliometric analysis reveals an evolving landscape of nursing education research in SSA, offering insights into trends, key countries, journals, and predominant research themes. Notably, the study identifies a scarcity of literature using bibliometric approaches in nursing research, addressing this gap by providing a comprehensive overview of the field.The systematic review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, explores 1359 articles published in the last ten years, focusing on nursing education in SSA. The analysis of 1288 selected articles emphasize experiences and challenges faced by nursing and midwifery students during their education and clinical training. The emerging themes cuts across classroom teaching, clinical learning environments, and overall clinical practice. The findings highlight the need for attention to educational support, effective communication, professionalism, inclusivity, and innovative teaching methods. Limitations include the exclusive focus on SSA, restricting generalizability to other regions. Nonetheless, the study offers valuable insights for educators, policymakers, and institutions to enhance the quality of nursing education. By addressing identified challenges, fostering innovation, and promoting inclusivity, stakeholders can better prepare students to meet the dynamic demands of the healthcare profession in SSA and potentially other regions, especially Low- and Middle-income Countries. The research contributes to the ongoing efforts to bridge the gap between nursing education theory and practice, ultimately improving healthcare outcomes in the region.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000900"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12200722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144509888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Challenges in detecting and predicting adverse drug events via distributed analysis of electronic health record data from German university hospitals.","authors":"Anna Maria Wermund, Torsten Thalheim, André Medek, Florian Schmidt, Thomas Peschel, Alexander Strübing, Daniel Neumann, André Scherag, Markus Loeffler, Miriam Kesselmeier, Ulrich Jaehde","doi":"10.1371/journal.pdig.0000892","DOIUrl":"10.1371/journal.pdig.0000892","url":null,"abstract":"<p><p>The Medical Informatics Initiative Germany (MII) aims to facilitate the interoperability and exchange of electronic health record data from all German university hospitals. The MII use case \"POLyphamacy, drug interActions and Risks\" (POLAR_MI) was designed to retrospectively detect medication-related risks in adult inpatients. As part of POLAR_MI, we aimed to build predictive models for specific adverse events. Here, using the two adverse events gastrointestinal bleeding and drug-related hypoglycaemia as examples, we present our initial investigation to determine whether these adverse events and their associations with potential risk factors can be detected. We applied a two-step distributed analysis approach to electronic health record data from 2018 to 2021. This approach consisted of a local statistical data analysis at ten participating centres, followed by a mixed-effects meta-analysis. For each adverse event, two multivariable logistic regression models were constructed: (1) including only demographics, diagnoses and medications, and (2) extended by laboratory values. As numerically stable estimations of both models were not possible at each centre, we constructed different centre subgroups for meta-analyses. We received prevalence estimates of around 1.2% for GI bleeding and around 3.0% for drug-related hypoglycaemia. Although unavailability of laboratory values was a common reason hindering model estimation, multivariable regression models were obtained for both adverse events from several centres. Regarding our original intention to build predictive models, the median area under the receiver operating characteristic curve was above 0.70 for all multivariable regression models, indicating feasibility. In conclusion, plausible estimates for prevalence and regression modelling odds ratios were received when using a distributed analysis approach on inpatient treatment data from diverse German university hospitals. Our results suggest that the development of predictive models in a distributed setting is possible if the research question is adapted to the infrastructure and the available data.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000892"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12200832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144509887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a dynamic model to estimate evolving risk of major bleeding after percutaneous coronary intervention.","authors":"Nathan C Hurley, Nihar Desai, Sanket S Dhruva, Rohan Khera, Wade Schulz, Chenxi Huang, Jeptha Curtis, Frederick Masoudi, John Rumsfeld, Sahand Negahban, Harlan M Krumholz, Bobak J Mortazavi","doi":"10.1371/journal.pdig.0000906","DOIUrl":"10.1371/journal.pdig.0000906","url":null,"abstract":"<p><p>While static risk models may identify key driving risk factors, the dynamic nature of risk requires up-to-date risk information to guide treatment decision making. Bleeding is a complication of percutaneous coronary intervention (PCI), and existing risk models produce only a single risk estimate anchored at a single point in time, despite the dynamic nature of this risk. Using data available from the National Cardiovascular Data Registry (NCDR) CathPCI, we trained 6 different tree-based machine learning models to estimate the risk of bleeding at key decision points: 1) choice of access site, 2) prescription of medication before PCI, and 3) choice of closure device. We began with 3,423,170 PCIs performed between July 2009 through April 2015. We included only index PCIs and removed anyone who had missing data regarding bleeding events or underwent coronary artery bypass grafting during the index admission. We included 2,868,808 PCIs; 2,314,446 (80.7%) before 2014 for training and 554,362 (19.3%) remaining for validation. This study considered all data available from the Registry prior to patient discharge: patient characteristics, coronary anatomy and lesion characterization, laboratory data, past medical history, anti-coagulation, stent type, and closure method categories. The primary outcome was any in-hospital bleeding event within 72 hours after the start of the PCI procedure. Discrimination improved from an area under the receiver operating characteristic curve (AUROC) of 0.812 using only presentation variables to 0.845 using all variables. Among 123,712 patients classified as low risk by the initial model, 14,441 were reclassified as moderate risk (1.4% experienced bleeds), while 723 were reclassified as high risk (12.5% experienced bleeds). Static risk prediction models have more predictive error than those that update risk prediction with newly available data, which provides up-to-date risk prediction for individualized care throughout a hospitalization.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000906"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12193038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An evaluation of telehealth services at New York City tuberculosis clinics throughout the COVID-19 pandemic.","authors":"Grace E Gao, Alice V Easton, Marco M Salerno, Matthew Angulo, Claudia Buchanan, Deandra J Ingram, Erica Humphrey, Marci Whitehead, Errol Robinson, Christine Chuck, Joseph Burzynski, Felicia Dworkin, Diana Nilsen, Michelle Macaraig","doi":"10.1371/journal.pdig.0000898","DOIUrl":"10.1371/journal.pdig.0000898","url":null,"abstract":"<p><p>In March 2020, three New York City (NYC) Department of Health and Mental Hygiene Tuberculosis (TB) clinics suspended most in-person services due to the COVID-19 pandemic and rapidly implemented telehealth to provide remote TB care. We conducted a prospective cohort study of patients with TB or latent TB infection (LTBI), who received treatment from TB clinics between April 2020 and December 2022, to compare telehealth and in-clinic services. To evaluate the success and breadth of the telehealth program, we compared patients who utilized telehealth with those who did not, analyzing differences in demographic characteristics and key outcomes, including utilization of telehealth, appointment completion, and treatment completion. \"Telehealth patients\" completed at least one scheduled telehealth visit during the study period. We conducted bivariate analyses comparing telehealth versus in-clinic patients. 56% (497/885) of patients with TB and 45% (954/2127) of patients with LTBI had a telehealth visit. Among patients with TB, no disparities in proportions of telehealth and in-clinic patients were observed for age (p = 0.31) or primary language spoken (p = 0.37). Among patients with LTBI, younger patients were more likely to use telehealth (p < 0.001). Using mixed-effects logistic regression models, the AOR of completing a telehealth visit was lower compared to in-clinic for patients with TB (0.77, CI:0.65-0.91). However, excluding April to June 2020, the AORs of completing a telehealth visit were comparable to an in-clinic visit for patients with TB (0.94, CI:0.77-1.14) and for patients with LTBI (0.96, CI:0.82-1.13). Among 641 patients with drug-susceptible TB, 95% (333/352) of telehealth patients completed treatment within one year compared to 88% (254/289) of in-clinic patients (p = 0.002). This result is limited to the descriptive summary of this study population. During the COVID-19 pandemic, NYC Health Department provided telehealth to many patients with TB and LTBI of diverse demographics, and telehealth services were mostly comparable to in-clinic services.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000898"},"PeriodicalIF":0.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12186896/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144487392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based hybrid risk estimation system (ERES) in cardiac surgery: Supplementary insights from the ASA score analysis.","authors":"Ayşe Banu Birlik, Hakan Tozan, Kevser Banu Köse","doi":"10.1371/journal.pdig.0000889","DOIUrl":"10.1371/journal.pdig.0000889","url":null,"abstract":"<p><p>Accurate prediction of postoperative mortality risk after cardiac surgery is essential to improve patient outcomes. Traditional models, such as EuroSCORE I, often struggle to capture the complex interactions among clinical variables, leading to suboptimal performance in specific populations. In this study, we developed and validated the Ensemble-Based Risk Estimation System (ERES), a machine learning model designed to enhance mortality prediction in patients undergoing coronary artery bypass grafting and/or valve surgery. A retrospective analysis of 543 patients was performed using six machine learning algorithms applied to preoperative clinical data to assess predictive accuracy and clinical outcomes. Feature selection techniques, including Gini importance, Recursive Feature Elimination, and Adaptive Synthetic Sampling, were employed to improve accuracy and address class imbalance. ERES, which utilizes 15 key features, demonstrated superior predictive performance compared to EuroSCORE I. Calibration plots indicated more accurate probability estimates, whereas SHAP analysis identified creatinine, age, and left ventricular ejection fraction as the most significant predictors. The decision curve analysis further confirmed the superior clinical utility of ERES across a range of decision thresholds. Additionally, although the American Society of Anesthesiologists (ASA PS) score had limited predictive power independently, its combination with EuroSCORE I enhanced the predictive performance. Integrating machine learning models like ERES into clinical practice can improve decision making and patient outcomes although external validation is warranted for broader implementation.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000889"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12184902/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144478109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based equations for improved body composition estimation in Indian adults.","authors":"Nick Birk, Bharati Kulkarni, Santhi Bhogadi, Aastha Aggarwal, Gagandeep Kaur Walia, Vipin Gupta, Usha Rani, Hemant Mahajan, Sanjay Kinra, Poppy A C Mallinson","doi":"10.1371/journal.pdig.0000671","DOIUrl":"10.1371/journal.pdig.0000671","url":null,"abstract":"<p><p>Bioelectrical impedance analysis (BIA) is commonly used as a lower-cost measurement of body composition as compared to dual-energy X-ray absorptiometry (DXA) in large-scale epidemiological studies. However, existing equations for body composition based on BIA measures may not generalize well to all populations. We combined BIA measurements (TANITA BC-418) with skinfold thickness, body circumferences, and grip strength to develop equations to predict six DXA-measured body composition parameters in a cohort of Indian adults using machine learning techniques. The participants were split into training (80%, 1297 males and 1133 females) and testing (20%, 318 males and 289 females) data to develop and validate the performance of equations for total body fat mass (kg), total body lean mass (kg), total body fat percentage (%), trunk fat percentage (%), L1-L4 fat percentage (%), and total appendicular lean mass (kg), separately for males and females. Our novel equations outperformed existing equations for each of these body composition parameters. For example, the mean absolute error for total body fat mass was 1.808 kg for males and 2.054 kg for females using the TANITA's built-in estimation algorithm, 2.105 kg for males and 2.995 kg for females using Durnin-Womersley equations, and 0.935 kg for males and 0.976 kg for females using our novel equations. Our findings demonstrate that supplementing body composition estimates from BIA devices with simple anthropometric measures can greatly improve the validity of BIA-measured body composition in South Asians. This approach could be extended to other BIA devices and populations to improve the performance of BIA devices. Our equations are made available for use by other researchers.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000671"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12185013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144478108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Augmenting electronic health record data with social and environmental determinant of health measures to understand regional factors associated with asthma exacerbations.","authors":"Alana Schreibman, Kimberly Lactaoen, Jaehyun Joo, Patrick K Gleeson, Gary E Weissman, Andrea J Apter, Rebecca A Hubbard, Blanca E Himes","doi":"10.1371/journal.pdig.0000677","DOIUrl":"10.1371/journal.pdig.0000677","url":null,"abstract":"<p><p>Electronic health records (EHRs) provide rich data for diverse populations but often lack information on social and environmental determinants of health (SEDH) that are important for the study of complex conditions such as asthma, a chronic inflammatory lung disease. We integrated EHR data with seven SEDH datasets to conduct a retrospective cohort study of 6,656 adults with asthma. Using Penn Medicine encounter data from January 1, 2017 to December 31, 2020, we identified individual-level and spatially-varying factors associated with asthma exacerbations. Black race and prescription of an inhaled corticosteroid were strong risk factors for asthma exacerbations according to a logistic regression model of individual-level risk. A spatial generalized additive model (GAM) identified a hotspot of increased exacerbation risk (mean OR = 1.41, SD 0.14, p < 0.001), and inclusion of EHR-derived variables in the model attenuated the spatial variance in exacerbation odds by 34.0%, while additionally adjusting for the SEDH variables attenuated the spatial variance in exacerbation odds by 66.9%. Additional spatial GAMs adjusted one variable at a time revealed that neighborhood deprivation (OR = 1.05, 95% CI: 1.03, 1.07), Black race (OR = 1.66, 95% CI: 1.44, 1.91), and Medicaid health insurance (OR = 1.30, 95% CI: 1.15, 1.46) contributed most to the spatial variation in exacerbation odds. In spatial GAMs stratified by race, adjusting for neighborhood deprivation and health insurance type did not change the spatial distribution of exacerbation odds. Thus, while some EHR-derived and SEDH variables explained a large proportion of the spatial variance in asthma exacerbations across Philadelphia, a more detailed understanding of SEDH variables that vary by race is necessary to address asthma disparities. More broadly, our findings demonstrate how integration of information on SEDH with EHR data can improve understanding of the combination of risk factors that contribute to complex diseases.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000677"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12184914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144478107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of digital therapeutics in decentralized clinical trials: A scoping review.","authors":"Cinja Koller, Marc Blanchard, Thomas Hügle","doi":"10.1371/journal.pdig.0000905","DOIUrl":"10.1371/journal.pdig.0000905","url":null,"abstract":"<p><p>This scoping review aims to identify the necessary and practical considerations for the design, conduct and safety of decentralized clinical trials (DCTs) that test digital therapeutics (DTx) or software as a medical device (SaMD). The review follows the framework of Arksey & O'Malley. A search strategy with the keywords \"Digital therapeutics\" or \"Software as Medical Device\" AND \"decentralized clinical trial\" or synonyms was applied to Cochrane CENTRAL, EMBASE, MEDLINE and Web of Science databases with the latest search on the 25th of April 2025. We selected peer-reviewed articles reporting about fully or partly DCTs using apps or devices that were classified as DTx or SaMD. Studies using general health software or not focusing on the design or experiences of the DCT were excluded. Main study characteristics were extracted and the articles thematically coded with the qualitative software Atlas.ti. 335 results were assessed for title and abstract screening and 113 articles were identified for full-text screening, of those 41 fulfilled inclusion criteria. DTx used in the trials were mainly targeting depression. The clinical trial design differed significantly in the number of study arms (1-16), participants (11─5602) and blinding. E-recruitment (78%), e-eligibility screening (73%), e-informed consent (68%), inclusion of electronic-patient reported outcomes (e-PROs) (88%), passive data collection (59%) and use of reminders (59%) were key reoccurring features of the studies. Effective access and inclusion of participants, but low adherence and engagement is highlighted in most studies. In some cases, only 40% of participants installed the app and significant drop-out rates of about 50% are reported. A framework for DCTs evaluating DTx is provided. In summary, DCTs for DTx are unstandardized, heterogenous and characterized by low adherence. Further research on how to tackle the engagement problem, along with clearer guidance and regulatory frameworks, is required to standardize this trial type in the future.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000905"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12185025/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144478106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adapting the WHO ANC digital module for the NAMAI study: Formative research to inform implementation science interventions for enhanced quality service delivery following the WHO SMART guidelines approach.","authors":"Nachela Chelwa, Bernard R Ngabo, Muyereka Nyirenda, Musange F Sabine, María Barreix, Tigest Tamrat, Natasha Okpara, Chifundo Phiri, Nathalie K Murindahabi, David Nzeyimana, Tobias Makai, Gilbert Uwayezu, Gladys Yabalwazi, Mwamba Kangwa, Rosemary K Muliokela, Hedieh Mehrtash, Caren Chizuni, Vincent Mutabazi, Felix Sayinzoga, Michael T Mbizvo, Maurice Bucagu, Özge Tunçalp","doi":"10.1371/journal.pdig.0000910","DOIUrl":"10.1371/journal.pdig.0000910","url":null,"abstract":"<p><p>The Ministries of Health in Zambia and Rwanda have adapted and validated their national antenatal care (ANC) guidelines in line with WHO 2016 recommendations. Both countries conducted implementation research, composed of five implementation strategies to support the adapted ANC package service delivery. One implementation strategy deploys a digital module, a point of service digital tool that encompasses clinical decision support and person-centric record management to support health workers in implementing the adapted ANC packages. The formative phase of the study, included countries' adaptation of the WHO digital ANC module to their contexts in three steps: (i) the reference module was tailored to create Rwanda and Zambia ANC digital modules and training materials; (ii) health workers were trained to use the module and provide feedback; (iii) country research teams conducted qualitative assessments to understand the health worker experience using the adapted ANC module. ANC health workers at selected facilities completed a three-day training on the use of the module. Qualitative methods were used to understand health worker's perspectives on the module's use for service provision and feedback for its refinement. The three major themes emerged: i) experiences using digital interventions in the health profession; ii) strengths and challenges related to the use of digital interventions; iii) considerations for improving the use of digital interventions within health systems. Rwanda and Zambia ANC modules were modified to improve their use for ANC services delivery. Initial testing led to the identification and fixing of bugs in the system. The module was updated to include dashboards to support facility-based monitoring of ANC indicators. Training materials were also improved based on feedback from interviews of health workers. The iterative process in developing country-adapted digital ANC modules is key to their deployments for routine use and a key proof of concept for the WHO SMART guideline approach.</p>","PeriodicalId":74465,"journal":{"name":"PLOS digital health","volume":"4 6","pages":"e0000910"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12184926/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144478105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}