Optimizing MRI utilization in resource-limited settings: A study of referral patterns at a tertiary center in Zimbabwe.

Abstract

Introduction: Magnetic Resonance Imaging (MRI) is indispensable in modern diagnostics, yet its optimal use in resource-limited settings remains understudied. Zimbabwe's healthcare system faces unique challenges, including centralized MRI access and a privatized imaging sector, which may exacerbate utilization disparities. This study aimed to evaluate MRI referral patterns at a major Zimbabwean tertiary center to identify inefficiencies and inform policy improvements.

Methods: A retrospective cross-sectional analysis was conducted on 430 MRI requests (January 2024-March 2025) at Zimbabwe's largest tertiary hospital. Data included demographics, requesting specialties, anatomical regions, clinical indications, and diagnoses. Descriptive statistics and chi-square tests analyzed utilization trends.

Results: Brain (36.28 %, n = 156) and spine (32.79 %, n = 141) MRIs were most frequent. Notably, 30.47 % (n = 131) of requests lacked clinical indications, and 33.95 % (n = 146) of scans were normal, which could suggest overuse. Age and gender disparities emerged: peak utilization occurred in 41-65-year-olds (39.07 %, n = 168). Males dominated brain MRIs (57.05 %, n = 227), and females had more abdominal/spine requests, and these observed differences were statistically significant (χ²_(4df) = 11.15, p = 0.03).

Conclusion: This study highlights systemic inefficiencies in Zimbabwe's MRI use, including unjustified referrals and demographic disparities. Urgent interventions are needed to ensure strict adherence to standardized referral protocols (e.g., ACR criteria), clinician training, and equitable service expansion. Future research should assess cost-effectiveness, appropriateness criteria, and multicenter patterns to optimize resource allocation in low-resource settings. The findings of this study fill a crucial knowledge gap in African radiology literature and provide actionable recommendations for optimizing imaging services in resource-limited settings.