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 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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 422-436 |
| Number of pages | 15 |
| Journal | Optometry and Vision Science |
| Volume | 71 |
| Issue number | 7 |
| State | Published - 1994 |
| Externally published | Yes |
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Keywords
- Classification schemes
- Low vision
- Resource usage
ASJC Scopus subject areas
- Ophthalmology
- Optometry
Cite this
Clinical low vision resource usage prediction. / Dilts, David; Khamalah, Joseph; Plotkin, Ann.
In: Optometry and Vision Science, Vol. 71, No. 7, 1994, p. 422-436.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Clinical low vision resource usage prediction
AU - Dilts, David
AU - Khamalah, Joseph
AU - Plotkin, Ann
PY - 1994
Y1 - 1994
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
UR - http://www.scopus.com/inward/record.url?scp=0027964815&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0027964815&partnerID=8YFLogxK
M3 - Article
C2 - 7970557
AN - SCOPUS:0027964815
VL - 71
SP - 422
EP - 436
JO - Optometry and Vision Science
JF - Optometry and Vision Science
SN - 1040-5488
IS - 7
ER -