Adoption of electronic medical records in developing countries-A multi-state study of the Nigerian healthcare system

Building genomic capacity for precision health in Africa

The African continent is poised to have a pivotal role in the global population landscape, with the United Nations projecting a population of 2.5 billion (more than 25% of the global population) by 2050. Amid this demographic shift, Africa faces a unique healthcare challenge-navigating a complex landscape of infectious and non-communicable diseases. This necessitates a departure from the conventional 'one-size-fits-all' medical model toward precision approaches that are efficient and sustainable. Genomic capacity is a pillar of precision health; however, access to up-to-date genetic testing in African countries is limited, compounded by a startling lack of representation of data from populations of African descent in gene discovery studies. In this Review, we delve into the challenges impeding the development of genomic capacity in Africa, such as the lack of electronic clinical and epidemiological records, infrastructural challenges, high supply chain costs and the 'dependency trap' that jeopardizes long-term sustainability. We emphasize the need for strategies hinged on true partnerships, robust infrastructure, workforce development and well-crafted policies. Finally, we outline recent progress and existing initiatives that should be considered as role models for future capacity-building initiatives.

EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes

The adoption of electronic health records (EHRs) has created opportunities to analyse historical data for predicting clinical outcomes and improving patient care. However, non-standardised data representations and anomalies pose major challenges to the use of EHRs in digital health research. To address these challenges, we have developed EHR-QC, a tool comprising two modules: the data standardisation module and the preprocessing module. The data standardisation module migrates source EHR data to a standard format using advanced concept mapping techniques, surpassing expert curation in benchmarking analysis. The preprocessing module includes several functions designed specifically to handle healthcare data subtleties. We provide automated detection of data anomalies and solutions to handle those anomalies. We believe that the development and adoption of tools like EHR-QC is critical for advancing digital health. Our ultimate goal is to accelerate clinical research by enabling rapid experimentation with data-driven observational research to generate robust, generalisable biomedical knowledge.

Acceptance of a Text Messaging Vaccination Reminder and Recall System in Malaysia's Healthcare Sector: Extending the Technology Acceptance Model

Malaysian healthcare institutions still use ineffective paper-based vaccination systems to manage childhood immunization schedules. This may lead to missed appointments, incomplete vaccinations, and outbreaks of preventable diseases among infants. To address this issue, a text messaging vaccination reminder and recall system named Virtual Health Connect (VHC) was studied. VHC simplifies and accelerates immunization administration for nurses, which may result in improving the completion and timeliness of immunizations among infants. Considering the limited research on the acceptance of these systems in the healthcare sector, we examined the factors influencing nurses' attitudes and intentions to use VHC using the extended technology acceptance model (TAM). The novelty of the conceptual model is the incorporation of new predictors of attitude, namely, perceived compatibility and perceived privacy and security issues. We conducted a survey among 121 nurses in Malaysian government hospitals and clinics to test the model. We analyzed the collected data using partial least squares structural equation modeling (PLS-SEM) to examine the significant factors influencing nurses' attitudes and intentions to use VHC. Moreover, we applied an artificial neural network (ANN) to determine the most significant factors of acceptance with higher accuracy. Therefore, we could offer more accurate insights to decision-makers in the healthcare sector for the advancement of health services. Our results highlighted that the compatibility of VHC with the current work setting of nurses developed their positive perspectives on the system. Moreover, the nurses felt optimistic about the system when they considered it useful and easy to use in the workplace. Finally, their attitude toward using VHC played a pivotal role in increasing their intention to use it. Based on the ANN models, we also found that perceived compatibility was the most significant factor influencing nurses' attitudes towards using VHC, followed by perceived ease of use and perceived usefulness.