Clinical low vision resource usage prediction.

Abstract

In an era of increased demands and constrained budgets, it is necessary to make the best use of all available resources. This is difficult when specialized vision care, such as low vision clinical assessment, is involved because of the heterogeneity of the patient populations seen by such clinics. PURPOSE. This research attempts to discover if these diverse patient populations can be identified and clustered into groups based upon similarity of clinical resources use. Specifically, the inquiry examines the potential for a low vision patient resource utilization classification scheme at the Low Vision Clinic (LVC) in the Centre for Sight Enhancement (CSE), University of Waterloo. METHODS. From a sample of 99 patients consulting the LVC in a 3-month period, retrospective data collection involved abstracting and coding medical records containing information detailing each patient's demographic, diagnostic, therapeutic, and resource utilization characteristics. Cluster analysis using Hartigan's block clustering algorithm was then applied to the data. A replication study was completed using a sample of 99 patients visiting the LVC 1 year later. RESULTS. Patients can be classified into five iso-resource groups, hereby termed low vision patient resource groups (LVPRGs). The clusters represent a resource consistent and clinically coherent scheme for classifying low vision patients based upon resource requirements. As a measure of repeatability, the groups reemerged in the replication study. CONCLUSIONS. If the groupings demonstrate robustness in a field test, clustering algorithms in general, and LVPRGs in specific, may offer useful tools to enhance resource utilization in the LVC setting.