TY - JOUR
T1 - Clinical low vision resource usage prediction
AU - Dilts, David M.
AU - Khamalah, Joseph
AU - Plotkin, Ann
PY - 1994/7
Y1 - 1994/7
N2 - 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 isoresource groups, hereby termed low vision patient re-source groups (LVPRGs). The clusters represent a re-source 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.
AB - 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 isoresource groups, hereby termed low vision patient re-source groups (LVPRGs). The clusters represent a re-source 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.
KW - Classification schemes
KW - Low vision
KW - Resource usage
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U2 - 10.1097/00006324-199407000-00002
DO - 10.1097/00006324-199407000-00002
M3 - Article
C2 - 7970557
AN - SCOPUS:0027964815
VL - 71
SP - 422
EP - 436
JO - American Journal of Optometry and Physiological Optics
JF - American Journal of Optometry and Physiological Optics
SN - 1040-5488
IS - 7
ER -