BMC Medical Informatics and Decision Making最新文献

{"title":"Development and evaluation of a whole-chain management system for critical value reporting.","authors":"Dongdong Wu, Feng Zhu, Yifan Sheng, Weiwei Zhang, Hanbo Le, Guoqiang Zhang, Lei Wang, Boer Yan","doi":"10.1186/s12911-025-02936-6","DOIUrl":"10.1186/s12911-025-02936-6","url":null,"abstract":"<p><strong>Background: </strong>Critical value (CV) management is vital for patient safety and shows the quality of critical care. This study aimed to develop a whole-chain management system (WCMS) for CV reporting and evaluate its impact on clinical practice.</p><p><strong>Methods: </strong>A WCMS for CV reporting, considering sample, process and patient population, was developed. A quasi-experimental study was conducted at Zhoushan Hospital. 591 CVs were divided into two groups: the postapplication group (n = 298) and the preapplication group (n = 293). CV quality-related indicators were compared between the two groups, including the timely reporting rate, timely receiving rate, timely treatment rate, completeness of treatment records and closed-loop rate.</p><p><strong>Results: </strong>Before system implementation, the timely treatment rate (93.17%), completeness of treatment records (78.16%), and closed-loop rate (88.05%) were lower than the timely reporting rate (94.54%). After implementation, there were significant differences between the two groups in timely reporting rate (94.54% vs. 97.99%, P < 0.05), timely treatment rate (93.17% vs. 97.65%, P < 0.01), completeness of treatment records (78.16% vs. 94.97%, P < 0.01), and closed-loop rate (88.05% vs. 97.32%, P < 0.01).</p><p><strong>Conclusion: </strong>Implementing the WCMS from sample, process and patient population has improved patient safety. The system's successful integration also shows its potential for use in health information systems of various healthcare facilities.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"104"},"PeriodicalIF":3.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11869711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143522733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and evaluation of an electronic follow-up questionnaire for patients after percutaneous coronary intervention.","authors":"Hassan Rajabi Moghadam, Parsa Rabbani, Majid Mazouchi, Hossein Akbari, Ehsan Nabovati, Soroosh Rabbani, Parissa Bagheri Toolaroud","doi":"10.1186/s12911-025-02931-x","DOIUrl":"10.1186/s12911-025-02931-x","url":null,"abstract":"<p><strong>Background: </strong>Patient-centered, measurable, and transparent care is essential for improving healthcare outcomes, particularly for patients undergoing percutaneous coronary intervention (PCI) procedures. Electronic follow-up questionnaires offer the potential for efficient and accurate data collection, enhancing the monitoring of patient experiences and outcomes. This study aimed to design and evaluate an electronic follow-up questionnaire tailored for post-PCI patients, focusing on real-time symptom monitoring and data collection.</p><p><strong>Methods: </strong>This developmental study was conducted in 2020 in three phases. In the first phase, a follow-up questionnaire was developed through a needs assessment and expert consultations. Each item's content validity ratio (CVR) and content validity index (CVI) were evaluated to ensure content validity. The finalized questionnaire elements were then reviewed and refined by a panel of ten cardiologists using the Delphi technique. In the second phase, an electronic platform was designed to host the follow-up questionnaire. The tool's effectiveness for post-PCI follow-up was evaluated in the third phase.</p><p><strong>Results: </strong>Cardiologists confirmed all items in the Delphi technique's first round, validating the follow-up questionnaire's content. A total of 41 patients undergoing PCI were enrolled in the study. The most frequently reported symptoms included issues at the catheter insertion site, chest discomfort, digestive complications, and shortness of breath. Of these patients, 21 (51.2%) utilized the electronic follow-up tool. The primary reasons for non-participation were busy schedules, forgetfulness, and perceived recovery. Among the participants, 16 (76.2%) expressed high or very high satisfaction with the tool.</p><p><strong>Conclusion: </strong>The findings suggest that this electronic follow-up questionnaire has the potential to effectively collect clinical data, support academic research, and improve the quality of post-PCI care. However, addressing barriers to patient participation and involving patients in the tool's iterative development will be critical for enhancing its adoption and impact.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"103"},"PeriodicalIF":3.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11866665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143514801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: FHIR PIT: a geospatial and spatiotemporal data integration pipeline to support subject-level clinical research.","authors":"Karamarie Fecho, Juan J Garcia, Hong Yi, Griffin Roupe, Ashok Krishnamurthy","doi":"10.1186/s12911-025-02940-w","DOIUrl":"10.1186/s12911-025-02940-w","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"102"},"PeriodicalIF":3.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11863691/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143499429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection of COPD exacerbations with continuous monitoring of breathing rate and inspiratory amplitude under oxygen therapy.","authors":"Juliana Alves Pegoraro, Antoine Guerder, Thomas Similowski, Philippe Salamitou, Jesus Gonzalez-Bermejo, Etienne Birmelé","doi":"10.1186/s12911-025-02939-3","DOIUrl":"10.1186/s12911-025-02939-3","url":null,"abstract":"<p><strong>Background: </strong>Chronic Obstructive Pulmonary Disease (COPD) is one of the main causes of morbidity and mortality worldwide. Its management represents real economic and public health burdens, accentuated by periods of acute disease deterioration, called exacerbations. Some researchers have studied the interest of monitoring patients' breathing rate as an indicator of exacerbation, although achieving limited sensitivity and/or specificity. In this study, we look to improve the previously described method, by combining breathing variables, using multiple daily measures, and using an artificial intelligence-based novelty detection approach.</p><p><strong>Methods: </strong>Patients with COPD were monitored with a telemedicine device during their stay in a rehabilitation care center. Daily measures are compared to individually trained reference models based on: i. oxygen therapy duration ii. mean breathing rate, iii. mean inspiratory amplitude, iv. mean breathing rate and mean inspiratory amplitude, v. average distribution of breathing rate and inspiratory amplitude, vi. hidden Markov model (HMM) from a time series of breathing rate and inspiratory amplitude.</p><p><strong>Results: </strong>A set of 16 recordings with exacerbation and 23 recordings without exacerbation was obtained. When using a daily measure of breathing rate, pre-exacerbation periods were identified with a specificity of 50% and a sensitivity of 55.6%. The method based on daily oxygen therapy usage and the method based on time series obtain a sensitivity of 76.8% and 73.2%, respectively, for a fixed specificity of 50%.</p><p><strong>Conclusion: </strong>A single daily measure of breathing rate alone is not sufficient for the detection of pre-exacerbation periods. More complete models also achieve limited performance, equivalent to models based on changes in the duration of therapy usage.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"101"},"PeriodicalIF":3.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11863910/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143498251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Machine learning to predict virological failure among HIV patients on antiretroviral therapy in the University of Gondar Comprehensive and Specialized Hospital, in Amhara Region, Ethiopia, 2022.","authors":"Daniel Niguse Mamo, Tesfahun Melese Yilma, Makda Fekadie Tewelgne, Yakub Sebastian, Tilahun Bizuayehu, Mequannent Sharew Melaku, Agmasie Damtew Walle","doi":"10.1186/s12911-025-02908-w","DOIUrl":"10.1186/s12911-025-02908-w","url":null,"abstract":"","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"100"},"PeriodicalIF":3.3,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11853309/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143499431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based prediction model for patients with recurrent Staphylococcus aureus bacteremia.","authors":"Yuan Li, Shuang Song, Liying Zhu, Xiaorun Zhang, Yijiao Mou, Maoxing Lei, Wenjing Wang, Zhen Tao","doi":"10.1186/s12911-025-02878-z","DOIUrl":"10.1186/s12911-025-02878-z","url":null,"abstract":"<p><strong>Background: </strong>Staphylococcus aureus bacteremia (SAB) remains a significant contributor to both community-acquired and healthcare-associated bloodstream infections. SAB exhibits a high recurrence rate and mortality rate, leading to numerous clinical treatment challenges. Particularly, since the outbreak of COVID-19, there has been a gradual increase in SAB patients, with a growing proportion of (Methicillin-resistant Staphylococcus aureus) MRSA infections. Therefore, we have constructed and validated a pediction model for recurrent SAB using machine learning. This model aids physicians in promptly assessing the condition and intervening proactively.</p><p><strong>Methods: </strong>The patients data is sourced from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database version 2.2. The patients were divided into training and testing datasets using a 7:3 random sampling ratio. The process of feature selection employed two methods: Recursive Feature Elimination (RFE) and Least Absolute Shrinkage and Selection Operator (LASSO). Prediction models were built using Extreme Gradient Boosting (XGBoost), Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), and Artificial Neural Network (ANN). Model validation included Receiver Operating Characteristic (ROC) analysis, Decision Curve Analysis (DCA), and Precision-Recall Curve (PRC). We utilized SHAP (SHapley Additive exPlanations) values to demonstrate the significance of each feature and explain the XGBoost model.</p><p><strong>Results: </strong>After screening, MRSA, PTT, RBC, RDW, Neutrophils_abs, Sodium, Calcium, Vancomycin concentration, MCHC, MCV, and Prognostic Nutritional Index(PNI) were selected as features for constructing the model. Through combined evaluation using ROC、 DCA and PRC, XGBoost demonstrated the best predictive performance, achieving an AUC value of 0.76 (95% CI: 0.66-0.85) in ROC and 0.56 (95% CI: 0.37-0.75) in PRC. Building a website based on the Xgboost model. SHAP illustrated the feature importance ranking in the XGBoost model and provided examples to explain the XGBoost model.</p><p><strong>Conclusions: </strong>The adoption of XGBoost for model development holds widespread acceptance in the medical domain. The prediction model for recurrent SAB, developed by our team, aids physicians in timely diagnosis and treatment of patients.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"99"},"PeriodicalIF":3.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11853511/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143490854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transforming liver transplant allocation with artificial intelligence and machine learning: a systematic review.","authors":"Lisiane Pruinelli, Kiruthika Balakrishnan, Sisi Ma, Zhigang Li, Anji Wall, Jennifer C Lai, Jesse D Schold, Timothy Pruett, Gyorgy Simon","doi":"10.1186/s12911-025-02890-3","DOIUrl":"10.1186/s12911-025-02890-3","url":null,"abstract":"<p><strong>Background: </strong>The principles of urgency, utility, and benefit are fundamental concepts guiding the ethical and practical decision-making process for organ allocation; however, LT allocation still follows an urgency model.</p><p><strong>Aim: </strong>To identify and analyze data elements used in Machine Learning (ML) and Artificial Intelligence (AI) methods, data sources, and their focus on urgency, utility, or benefit in LT.</p><p><strong>Methods: </strong>A comprehensive search across Ovid Medline and Scopus was conducted for studies published from 2002 to June 2023. Inclusion criteria targeted quantitative studies using ML/AI for candidates, donors, or recipients. Two reviewers assessed eligibility and extracted data, following PRISMA guidelines.</p><p><strong>Results: </strong>A total of 20 papers were included, synthesizing results into five major categories. Eight studies were led by a Spanish team, focusing on donor-recipient matching and proposing machine learning models to predict post- LT survival. Other international studies addressed organ supply-demand issues and developed predictive models to optimize LT outcomes. The studies highlight the potential of ML/AI to enhance LT allocation and outcomes. Despite advancements, limitations included the lack of robust transplant-related benefit models and improvements in urgency models compared to MELD.</p><p><strong>Discussion: </strong>This review highlighted the potential of AI and ML to enhance liver transplant allocation and outcomes. Significant advancements were noted, but limitations such as the need for better urgency models and the absence of a transplant-related benefit model remain. Most studies emphasized utility, focusing on survival outcomes. Future research should address the interpretability and generalizability of these models to improve organ allocation and post-LT survival predictions.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"98"},"PeriodicalIF":3.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11852809/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143490855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolution of clinical Health Information Exchanges to population health resources: a case study of the Indiana network for patient care.","authors":"Karmen S Williams, Saurabh Rahurkar, Shaun J Grannis, Titus K Schleyer, Brian E Dixon","doi":"10.1186/s12911-025-02933-9","DOIUrl":"10.1186/s12911-025-02933-9","url":null,"abstract":"<p><strong>Background: </strong>Motivated by the Triple Aim, US health care policy is expanding its focus from individual patient care to include population health management. Health Information Exchanges are positioned to play an important role in that expansion.</p><p><strong>Objective: </strong>The objective is to describe the evolution of the Indiana Network for Patient Care (INPC) and discuss examples of its innovations that support both population health and clinical applications.</p><p><strong>Methods: </strong>A descriptive analytical approach was used to gather information on the INPC. This included a literature review of recent systematic and scoping reviews, collection of research that used INPC data as a resource, and data abstracted by Regenstrief Data Services to understand the breadth of uses for the INPC as a data resource.</p><p><strong>Results: </strong>Although INPC data are primarily gathered from and used in healthcare settings, their use for population health management and research has increased. By December 2023, the INPC contained nearly 25 million patients, a significant growth from 3.5 million in 2004. This growth was a result of the use of INPC data for population health surveillance, clinical applications for data, disease registries, Patient-Centered Data Homes, non-clinical population health advancements, and accountable care organization connections with Health Information Exchanges.</p><p><strong>Conclusion: </strong>By structuring services on the fundamental building blocks, expanding the focus to population health, and ensuring value in the services provided to the stakeholders, Health Information Exchanges are uniquely positioned to support both population health and clinical applications.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"97"},"PeriodicalIF":3.3,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11849299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143490841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based prediction of post-induction hypotension: identifying risk factors and enhancing anesthesia management.","authors":"Ming Chen, Dingyu Zhang","doi":"10.1186/s12911-025-02930-y","DOIUrl":"10.1186/s12911-025-02930-y","url":null,"abstract":"<p><strong>Background: </strong>Post-induction hypotension (PIH) increases surgical complications including myocardial injury, acute kidney injury, delirium, stroke, prolonged hospitalization, and endangerment of the patient's life. Machine learning is an effective tool to analyze large amounts of data and identify perioperative complication factors. This study aims to identify risk factors for PIH and develop predictive models to support anesthesia management.</p><p><strong>Methods: </strong>A dataset of 5406 patients was analyzed using machine learning methods. Logistic regression, random forest, XGBoost, and neural network models were compared. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), calibration curves, and decision curve analysis (DCA).</p><p><strong>Results: </strong>The logistic regression model achieved an AUROC of 0.74 (95% CI: 0.71-0.77), outperforming the random forest (AUROC: 0.71), XGBoost (AUROC: 0.72), and neural network (AUROC: 0.72) models. In terms of calibration, logistic regression demonstrated superior performance, as reflected by Brier Scores and calibration curves, followed by XGBoost, random forest, and neural network. Decision curve analysis indicated that the logistic regression model provided the greatest clinical utility among all models. Baseline blood pressure, age, sex, type of surgery, platelet count, and certain anesthesia-inducing drugs were identified as important features.</p><p><strong>Conclusions: </strong>This study provides a valuable tool for personalized preoperative risk assessment and customized anesthesia management, allowing for early intervention and improved patient outcomes. Integration of machine learning models into electronic medical record systems can facilitate real-time risk assessment and prediction.</p>","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"96"},"PeriodicalIF":3.3,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11846375/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143476269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"User acceptability and perceived impact of a mobile interactive education and support group intervention to improve postnatal health care in northern India: a qualitative study.","authors":"Valentina Cox, Preetika Sharma, Garima Singh Verma, Navneet Gill, Nadia G Diamond-Smith, Mona Duggal, Vijay Kumar, Rashmi Bagga, Jasmeet Kaur, Pushpendra Singh, Alison M El Ayadi","doi":"10.1186/s12911-025-02935-7","DOIUrl":"10.1186/s12911-025-02935-7","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Postnatal care, crucial for preventing and assessing complications after birth, remains low in India. An interactive mHealth community-based postnatal intervention was implemented to promote healthy maternal behaviors through knowledge and social support in rural Northern India. However, there is limited information on how virtual health interventions in resource-constrained settings are perceived by the users and which elements influence their engagement and sustained participation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;We explored the user perceptions of acceptability and impact of a virtual interactive maternal and child health intervention pilot tested in Punjab State, India, including their perspectives on barriers and facilitators to engage with this intervention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;This qualitative study was embedded within extensive mixed-method research, and oriented by the Realist Evaluation approach. Sixteen participants were recruited from the parent study. They were identified by purposive sampling to cover diverse levels of attendance and engagement with the intervention. In-depth interviews were conducted by phone. Following translation, a framework analysis was completed to search for the main themes. Feedback was requested from intervention moderators during the process to prioritize local interpretation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Study participants reported overall satisfaction with the intervention. The mothers appreciated the educational material provided and the communication with other participants and health professionals. Across context, intervention, and actor domains, the barriers most commented on were network and connectivity challenges, lack of time due to household responsibilities, and feeling uncomfortable sharing personal experiences. Family buy-in and support were fundamental for overcoming the high domestic workload and baby care. Another facilitator mentioned was moderators' guidance on using the different intervention modalities. Regarding perceived impact, participants shared that MeSSSSage increased their capability and motivation to breastfeed, seek care as needed, and use contraception according to their preferences. Finally, participants suggested adding more topics to the educational content and adjusting the dynamics within the group calls to improve the intervention.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This study identifies the high acceptability and perceived impact of a novel postnatal care program in a rural setting, including the users' perceived barriers to engaging with the intervention and possible solutions to overcome them. These findings enable refinement of the ongoing intervention, providing a more robust framing for its scalability and long-term sustainability. On a larger scale, conclusions from this research provide new insights and encouragement to global stakeholders who aspire to improve maternal and neonatal outcomes in low-income and mi","PeriodicalId":9340,"journal":{"name":"BMC Medical Informatics and Decision Making","volume":"25 1","pages":"93"},"PeriodicalIF":3.3,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11844055/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143467125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}