XXXX resource usage prediction.

@entity4064 @entity1
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 @entity4064 clinical assessment, is involved because of the heterogeneity of the @entity1 populations seen by such clinics. PURPOSE. This research attempts to discover if these diverse @entity1 populations can be identified and clustered into groups based upon similarity of clinical resources use. Specifically, the inquiry examines the potential for a @entity4064 @entity1 resource utilization classification scheme at the @entity4064 Clinic (LVC) in the Centre for Sight Enhancement (CSE), University of Waterloo. METHODS. From a sample of 99 @entity1 consulting the LVC in a 3-month period, retrospective data collection involved abstracting and coding medical records containing information detailing each @entity1 '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 @entity1 visiting the LVC 1 year later. RESULTS. @entity1 can be classified into five iso-resource groups, hereby termed @entity4064 @entity1 resource groups (LVPRGs). The clusters represent a resource consistent and clinically coherent scheme for classifying @entity4064 @entity1 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.
