Objectives: Digital tools for decision-support and health records can address the protracted process of guideline adoption at local levels and accelerate countries' implementation of new health policies and programmes. World Health Organization (WHO) launched the SMART Guidelines approach to support the uptake of clinical, public health, and data recommendations within digital systems. SMART guidelines are a package of tools that include Digital Adaptation Kits (DAKs), which distill WHO guidelines into a format that facilitates translation into digital systems. SMART Guidelines also include reference software applications known as digital modules.
Methods: This paper details the structured process to inform the adaptation of the WHO antenatal care (ANC) digital module to align with country-specific ANC packages for Zambia and Rwanda using the DAK. Digital landscape assessments were conducted to determine potential integrations between the ANC digital module and existing systems. A multi-stakeholder team consisting of Ministry of Health technical officers representing maternal health, HIV, digital health, and monitoring and evaluation at district and national levels was assembled to review existing guidelines to adapt the DAK.
Results: The landscape analysis resulted in considerations for integrating the ANC module into the broader digital ecosystems of both countries. Adaptations to the DAK included adding national services not reflected in the generic DAK and modification of decision support logic and indicators. Over 80% of the generic DAK content was consistent with processes for both countries. The adapted DAK will inform the customization of country-specific ANC digital modules.
Conclusion: Both countries found that coordination between maternal and digital health leads was critical to ensuring requirements were accurately reflected within the ANC digital module. Additionally, DAKs provided a structured process for gathering requirements, reviewing and addressing gaps within existing systems, and aligning clinical content.
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http://dx.doi.org/10.1177/20552076221076256 | DOI Listing |
BMC Med Inform Decis Mak
December 2024
Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
Background: Digital health has emerged as a promising solution for enhancing health system in the recent years, showing significant potential in improving service outcomes, particularly in low and middle-income countries where accessing essential health service is challenging. This review aimed to determine the effectiveness of short message services on focused antenatal care, skilled birth attendance, and postnatal care improvement in low and middle-income countries.
Method: Electronic databases such as PubMed, EMBASE, Scopus, Cochrane, and Google and Google Scholar were searched.
BMC Infect Dis
December 2024
National Cancer Institute, Maharagama, Sri Lanka.
Background: Mucormycosis, is a rare yet potentially life-threatening fungal infection common in immunocompromised patients. Despite optimal care, mucormycosis in haemato-oncological patients often results in poor outcomes. This case series details the presentations and unique challenges faced during the management of patients with acute myeloid leukemia who developed rhino-cerebral mucormycosis.
View Article and Find Full Text PDFJ Acoust Soc Am
November 2024
STEM College, RMIT University, Melbourne, Victoria 3000, Australia.
Recently, distributed active noise control (DANC) algorithms have been explored as a way to reduce computational complexity while ensuring system stability, thereby outperforming conventional centralized and decentralized algorithms. Most existing DANC algorithms assume that each node has only one pair of loudspeaker and microphone, limiting their flexibility in practical applications. In contrast, this paper proposes a DANC algorithm with general multi-device nodes based on the recently developed augmented diffusion strategy, allowing flexible and scalable ANC applications.
View Article and Find Full Text PDFNeural Netw
December 2024
Digital Signal Processing Lab, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
The recent Generative Fixed-filter Active Noise Control (GFANC) method achieves a good trade-off between noise reduction performance and system stability. However, labelling noise data for training the Convolutional Neural Network (CNN) in GFANC is typically resource-consuming. Even worse, labelling errors will degrade the CNN's filter-generation accuracy.
View Article and Find Full Text PDFJMIR Mhealth Uhealth
September 2024
Athena Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
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