# Addressing health information inequities: making evidence-based clinical content more accessible in low- and middle-income primary care

## TL;DR

This paper explores ways to make clinical guidelines more accessible in low- and middle-income countries to reduce health information gaps and improve primary care.

## Contribution

The paper introduces a practical approach to maintain and update clinical content for low-resource settings using existing evidence synthesis and AI.

## Key findings

- The PACK Global guide uses established evidence synthesis processes to maintain up-to-date clinical recommendations.
- Generative AI is being explored to streamline guideline reviews and content management for LMIC primary care.
- Leveraging existing processes can help address health information inequities in resource-limited settings.

## Abstract

Disparity in access to trustworthy health information between high-income and low-income settings remains stark and contributes to global health inequity. The volume of new clinical practice guidelines a healthcare provider needs to digest to deliver up-to-date, evidence-based care is overwhelming, particularly in primary care, where the scope is comprehensive. However, many low- and middle-income countries (LMICs) lack the resources to tailor guidance for their realities. International standards for adaptation or adoption of existing guidelines tend to focus on a single clinical topic and still require considerable evidence synthesis expertise, slowing provision of up-to-date, relevant protocols for the primary care provider.

The Practical Approach to Care Kit (PACK) guide covers most conditions managed in primary care. It has been introduced to South Africa, Ethiopia, Brazil, Nigeria, Botswana and Indonesia to support primary care reforms. This paper describes the reference repository and updating mechanisms underpinning the PACK Global guide (that forms a template for local adaptation) so that it reflects latest international evidence and WHO guidance. The referencing and updating mechanism to curate its 3689 recommendations drew on the established evidence synthesis processes of the British Medical Journal’s Best Practice and the WHO. The challenges of maintaining this content set were largely funding and resource constraints in our small team. We are exploring how advances in generative artificial intelligence might expedite review of the large clinical guidelines and policies required for PACK updates as well as address limitations of current database software as a content management system, to facilitate editorial and publication processes.

Leveraging existing evidence synthesis processes appears to be a feasible approach to maintaining a comprehensive LMIC primary care clinical content set and may go some way to improving access to up-to-date health information, thus addressing global health inequities.