Artificial Intelligence-Driven Clinical Decision Support in Pakistan: Implications for Primary Care Practice
TL;DR
This research proposes a framework for the sustainable integration of AI-CDSS into Pakistan's healthcare system, with a focus on scaling solutions to primary care environments such as Basic Health Units and rural health centers.
Abstract
AI-integrated Clinical Decision Support Systems (AI-CDSS) consolidate patient data with medical expertise to aid healthcare practitioners in deciding their approach. They promote guideline compliance, enhance treatment safety, and lead to more accurate diagnoses. This narrative review examines the potential role and emerging applications of AI-CDSS in strengthening primary healthcare systems by reviewing existing literature and evidence to evaluate current implementations, benefits, and limitations of AI-CDSS in the local healthcare context, with a particular focus on gaps to be addressed in the Pakistani healthcare landscape. Currently, although Pakistan is only utilizing AI-CDSS at a small scale and limited to tertiary care settings, these efforts encompass multiple domains such as drug safety, diagnostic help, and treatment planning. Findings have shown positive results and advocate for implementing AI-CDSS systems on a larger scale, which can reduce the burden on the healthcare system in Pakistan. Unfortunately, multiple concerns, such as user adoption and warning fatigue, must be dealt with before implementing a large-scale application of this system. Our research proposes a framework for the sustainable integration of AI-CDSS into Pakistan's healthcare system, with a focus on scaling solutions to primary care environments such as Basic Health Units and rural health centers. Overall, AI-CDSS holds significant potential to enhance accessibility, quality, and capacity of primary healthcare in Pakistan, provided that existing systemic and operational barriers are effectively addressed.