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Background: iDeliver, a digital clinical support system for maternal and neonatal care, was developed to support quality of care improvements in Kenya.
Objective: Taking an implementation research approach, we evaluated the adoption and fidelity of iDeliver over time and assessed the feasibility of its use to provide routine Ministry of Health (MOH) reports.
Methods: We analyzed routinely collected data from iDeliver, which was implemented at the Transmara West Sub-County Hospital from December 2018 to September 2020. To evaluate its adoption, we assessed the proportion of actual facility deliveries that was recorded in iDeliver over time. We evaluated the fidelity of iDeliver use by studying the completeness of data entry by care providers during each stage of the labor and delivery workflow and whether the use reflected iDeliver's envisioned function. We also examined the data completeness of the maternal and neonatal indicators prioritized by the Kenya MOH.
Results: A total of 1164 deliveries were registered in iDeliver, capturing 45.31% (1164/2569) of the facility's deliveries over 22 months. This uptake of registration improved significantly over time by 6.7% (SE 2.1) on average in each quarter-year (P=.005), from 9.6% (15/157) in the fourth quarter of 2018 to 64% (235/367) in the third quarter of 2020. Across iDeliver's workflow, the overall completion rate of all variables improved significantly by 2.9% (SE 0.4) on average in each quarter-year (P<.001), from 22.25% (257/1155) in the fourth quarter of 2018 to 49.21% (8905/18,095) in the third quarter of 2020. Data completion was highest for the discharge-labor summary stage (16,796/23,280, 72.15%) and lowest for the labor signs stage (848/5820, 14.57%). The completion rate of the key MOH indicators also improved significantly by 4.6% (SE 0.5) on average in each quarter-year (P<.001), from 27.1% (69/255) in the fourth quarter of 2018 to 83.75% (3346/3995) in the third quarter of 2020.
Conclusions: iDeliver's adoption and data completeness improved significantly over time. The assessment of iDeliver' use fidelity suggested that some features were more easily used because providers had time to enter data; however, there was low use during active childbirth, which is when providers are necessarily engaged with the woman and newborn. These insights on the adoption and fidelity of iDeliver use prompted the team to adapt the application to reflect the users' culture of use and further improve the implementation of iDeliver.
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http://dx.doi.org/10.2196/34741 | DOI Listing |
Regen Biomater
August 2025
Institute of Stomatology & Oral Maxilla Facial Key Laboratory, First Medical Center of Chinese PLA General Hospital, Beijing 100853, China.
Reconstructing bone defects remains a significant challenge in clinical practice, driving the urgent need for advanced artificial grafts that simultaneously promote vascularization and osteogenesis. Addressing the critical trade-off between achieving high porosity/strength and effective bioactivity at safe ion doses, we incorporated strontium (Sr) into β-tricalcium phosphate (β-TCP) scaffolds with a triply periodic minimal surface (TPMS) structure using digital light processing (DLP)-based three-dimensional (3D) printing. Systematically screening Sr concentrations (0-10 mol%), we identified 10 mol% as optimal, leveraging the synergy between the biomimetic TPMS architecture, providing exceptional mechanical strength (up to 1.
View Article and Find Full Text PDFJ Clin Exp Hepatol
August 2025
Dept of Histopathology, PGIMER, Chandigarh, 160012, India.
Artificial intelligence (AI) is a technique or tool to simulate or emulate human "intelligence." Precision medicine or precision histology refers to the subpopulation-tailored diagnosis, therapeutics, and management of diseases with its sociocultural, behavioral, genomic, transcriptomic, and pharmaco-omic implications. The modern decade experiences a quantum leap in AI-based models in various aspects of daily routines including practice of precision medicine and histology.
View Article and Find Full Text PDFBackground: Transforming Clinical Practice Guideline (CPG) recommendations into computer readable language is a complex and ongoing process that requires significant resources, including time, expertise, and funds. The objective is to provide an extension of the widely used GIN-McMaster Guideline Development Checklist (GDC) and Tool for the development of computable guidelines (CGs).
Methods: Based on an outcome from the Human Centered Design (HCD) workshop hosted by the Guidelines International Network North America (GIN-NA), a team was formed to develop the checklist extension.
Radiol Adv
September 2024
Department of Radiology, Northwestern University and Northwestern Medicine, Chicago, IL, 60611, United States.
Background: In clinical practice, digital subtraction angiography (DSA) often suffers from misregistration artifact resulting from voluntary, respiratory, and cardiac motion during acquisition. Most prior efforts to register the background DSA mask to subsequent postcontrast images rely on key point registration using iterative optimization, which has limited real-time application.
Purpose: Leveraging state-of-the-art, unsupervised deep learning, we aim to develop a fast, deformable registration model to substantially reduce DSA misregistration in craniocervical angiography without compromising spatial resolution or introducing new artifacts.
Background: Acupuncture is a widely recognized complementary therapy with proven therapeutic benefits; however, concerns regarding patient safety persist due to adverse events ranging from minor complications to severe outcomes like pneumothorax and nerve injury.
Objective: This study aims to identify common adverse events in acupuncture, propose innovative risk mitigation strategies, establish standardized best practices, enhance practitioner training, and examine global disparities in safety protocols.
Design: Comprehensive review of existing literature, clinical case studies, and international safety guidelines.