Digital technology has fundamentally transformed healthcare delivery, exerting profound influence on patient outcomes. This paper delves into the roles played by telemedicine, electronic health records (EHRs), and mobile health applications in augmenting healthcare services. The objective is to scrutinize the ways in which these digital innovations enhance healthcare delivery and patient outcomes, while also identifying the attendant challenges in their adoption. To achieve this, a rigorous literature review encompassing peer-reviewed articles, reports, and case studies that examine the impact of digital technology in healthcare settings was conducted. The findings underscore that digital technology significantly bolsters patient care by enhancing access, operational efficiency, and diagnostic accuracy. Nonetheless, persistent challenges such as safeguarding data privacy, ensuring interoperability across systems, and managing implementation costs continue to pose significant hurdles.
In an attempt to assess the Kenyan healthcare system, this study looks at the current efforts that are already in place, what challenges they face, and what strategies can be put into practice to foster interoperability. By reviewing a variety of literature and using statistics, the paper ascertains notable impediments such as the absence of standard protocols, lack of adequate technological infrastructure, and weak regulatory frameworks. Resultant effects from these challenges regarding health provision target enhanced data sharing and merging for better patient outcomes and allocation of resources. It also highlights several opportunities that include the adoption of emerging technologies, and the establishment of public-private partnerships to strengthen the healthcare framework among others. In this regard, the article provides recommendations based on stakeholder views and global best practices addressed to policymakers, medical practitioners, and IT specialists concerned with achieving effective interoperability within Kenya's health system. This research is relevant because it adds knowledge to the existing literature on how healthcare quality can be improved to make it more patient-centered especially in Kenya.
Intimate Partner Violence (IPV) is a form of Gender Base Violence (GBV) where an intimate partner perpetrates violence.In the HIV care continua which has the aim of achieving epidemic control based on the goals defined by UNAIDS, 95% of people living with HIV (PLHIV) have to know their HIV status, 95% initiated ARV treatment and 95% are virally suppressed in order to achieve epidemic control.One of the evidence-based strategies used for achieving an optimal number of PLHIV who know their HIV status is the Index Case Testing Strategy (ICT).While the ICT strategy helps the achievement of epidemic control, its implementation increases the incidence of IPV among either serodiscordant or concordant couples.Tackling information about IPV is very sensitive.A review of the literature on the management of HIV patient information has shown that shifting from paper-based management of HIV patient information to computerized Electronic Medical Records (EMR) systems, using software such as OPEN MRS has significantly improved the management of HIV patient information with high-level confidentiality of patient information.The reviews showed that the EMR systems put in place to manage HIV patient information need to integrate the stages used for the management of IPV among PLHIV.
During the pandemic, technological innovation provided a platform with a range of uses, including in the healthcare industry. Technology is currently being used in vaccination drives run by many governments across the world to help spread vaccines quickly and efficiently. The technology makes healthcare personnel more effective at their professions and greatly raises the standard of service in the industry. The researchers undertook this study to create a suitable and long-lasting immunization database with a mapping method to give a better perspective of the immunization status. To gather essential information for this study, the researchers spoke with the local health officer in the targeted area. The obtained data then served as the basis for the system’s capabilities and features, becoming the target problems addressed by the developers. The investigation found that the majority of procedures and interactions are carried out manually and recorded on an unprotected, antiquated Excel spreadsheet. The researchers’ technology also shows to be a superior way to deal with the problems and difficulties while making their health-related transactions and operations quicker, safer, and much more effective.
Background: This review delves into the effects of artificial intelligence (AI) on healthcare, which is a crucial aspect considering the increasing costs of healthcare worldwide. While there is potential for AI to enhance healthcare delivery and efficiency, there are still uncertainties surrounding its effectiveness, value, and broader adoption. This comprehensive literature review aims to explore and synthesize existing knowledge on the economic impact of AI in healthcare. The primary objective of this review is to understand the potential cost savings and efficiency improvements associated with the deployment of AI in healthcare settings. By highlighting the economic implications of AI, this review seeks to offer insights into the value proposition of investing in AI technologies for stakeholders such as healthcare providers, payers, and policymakers. Methods: To conduct this review, we conducted a search of literature from 2020 to 2023 across three databases: PubMed, Scopus and Google Scholar. We specifically focused on studies that discuss the impacts of AI in healthcare and include cost evaluations, using combinations of keywords related to AI, economics, healthcare, and cost evaluation. The inclusion criteria were studies that conducted some form of economic evaluation related to AI in healthcare settings, while exclusion criteria were studies without a cost evaluation component. Data extraction and quality assessment using the CASP checklist were undertaken on the final set of included studies. Results: After screening studies, we identified 10 out of a total of 28 studies and reports that met our criteria of outlining any form of economic impact and evaluation of AI in healthcare settings. Based on our findings, implementing AI in healthcare could potentially lead to cost savings. Several studies suggest savings ranging from $200 billion to $360 billion in the United States alone. The use of AI in healthcare sectors such as ophthalmology, radiology and disease screening has shown positive economic impacts. Conclusion: While AI has potential for cost savings and efficiency improvements, in healthcare settings, it’s crucial to conduct detailed context specific cost evaluations to optimize the adoption and implementation strategies of AI.
This study analyzed the concept of time efficiency in the data management process associated with the personnel training and competence assessments in one of the quality control (QC) laboratories of Nigeria's Foods and Drugs Authority (NAFDAC).The laboratory administrators were burdened with a lot of mental and paper-based record keeping because the personnel training's data were managed manually, hence not efficiently processed.The Excel spreadsheet provided by a Purdue doctoral dissertation as a remedial to this challenge was found to be deficient in handling operations in database tables, and therefore did not appropriately address the inefficiencies.Purpose: This study aimed to reduce the time it essentially takes to generate, obtain, manipulate, exchange, and securely store data that are associated with personnel competence training and assessments.Method: The study developed a software system that was integrated with a relational database management system (RDBMS) to improve manual/Excel-based data management procedures.To validate the efficiency of the software the mean operational times in using the Excel-based format were compared with that of the "New" software system.The data were obtained by performing four predefined core tasks for five hypothetical subjects using Excel and the "New" system (the model system) respectively.Results: It was verified that the average time to accomplish the specified tasks using the "New" system (37.08 seconds) was significantly (p = 0.00191, α = 0.05) lower than the time measurements for the Excel system (77.39 seconds) in the ANACHEM laboratory.The RDBMS-based "New" system provided operational (time) efficiency in the personnel training and com-How to cite this paper:
