ABSTRACT
Background: As people living with HIV experience increased life expectancy, there is a growing concern about the burden of comorbid non-communicable diseases, particularly hypertension. This policy brief describes the current policy landscape in Akwa Ibom State, Nigeria, the research activities, and five policy recommendations rooted in an ongoing research study designed to integrate hypertension management into HIV care across primary health centers in the state. Analysis: The policy brief was developed in four steps: review of existing policies, using the reviewed policies to inform research activities, solicitation of stakeholder recommendations via focus group discussions, and formulation of the resulting five policy recommendations for integrating hypertension management into HIV care programs in Akwa Ibom. The key analysis for this brief emerged from the thematic analyses of stakeholder responses. Policy Implications: The five policy recommendations for integrating hypertension management in HIV care in Akwa Ibom State, Nigeria are: 1) build capacity by leveraging retired community nurses as mentors; 2) emphasize community engagement; 3) develop consistent training programs on hypertension management for health workers; 4) expand health insurance accessibility; and 5) formally integrate hypertension management into primary healthcare centers in Akwa Ibom State.
ABSTRACT
Nanosensors are nanoscale devices that measure physical attributes and convert these signals into analyzable information. In preparation, for the impending reality of nanosensors in clinical practice, we confront important questions regarding the evidence supporting widespread device use. Our objectives are to demonstrate the value and implications for new nanosensors as they relate to the next phase of remote patient monitoring and to apply lessons learned from digital health devices through real-world examples.
Subject(s)
Delivery of Health Care , Technology , HumansABSTRACT
The application of artificial intelligence (AI) to healthcare has garnered significant enthusiasm in recent years. Despite the adoption of new analytic approaches, medical education on AI is lacking. We aim to create a usable AI primer for medical education. We discuss how to generate a clinical question involving AI, what data are suitable for AI research, how to prepare a dataset for training and how to determine if the output has clinical utility. To illustrate this process, we focused on an example of how medical imaging is employed in designing a machine learning model. Our proposed medical education curriculum addresses AI's potential and limitations for enhancing clinicians' skills in research, applied statistics and care delivery.
The application of artificial intelligence (AI) to healthcare has generated increasing interest in recent years; however, medical education on AI is lacking. With this primer, we provide an overview on how to understand AI, gain exposure to machine learning (ML) and how to develop research questions utilizing ML. Using an example of a ML application in imaging, we provide a practical approach to understanding and executing a ML analysis. Our proposed medical education curriculum provides a framework for healthcare education which we hope will propel healthcare institutions to implement ML laboratories and training environments and improve access to this transformative paradigm.
