Abstract
A prototype decision support system has been designed for managing dental caries using a risk assessment model. Caries is a multifactorial disease with risk prediction models having low sensitivity (65%) and moderate specificity (80%) for 2 or more new lesions. These models are inaccurate for targeting resources at high risk people. However, low risk individuals can be more accurately identified. If the activity of early tooth decay lesions, in low risk people, are monitored over time and only lesions beyond 1/3 of the dentin depth are filled, the number of annual fillings may be reduced by 50%. Currently, most US dental schools do not teach risk assessment for caries and encourage early treatment of lesions leading to a repair destruction cycle. The combination of a decision support system with a moderate accuracy specificity risk model for predicting low risk individuals may produce a significant improvement in caries management.
| Original language | English |
|---|---|
| Pages (from-to) | 577-581 |
| Number of pages | 5 |
| Journal | Proceedings / AMIA ... Annual Symposium. AMIA Symposium |
| State | Published - 1998 |
| Externally published | Yes |
Fingerprint
Cite this
Can low accuracy disease risk predictor models improve health care using decision support systems? / Benn, Douglas; Dankel, D. D.; Kostewicz, S. H.
In: Proceedings / AMIA ... Annual Symposium. AMIA Symposium, 1998, p. 577-581.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Can low accuracy disease risk predictor models improve health care using decision support systems?
AU - Benn, Douglas
AU - Dankel, D. D.
AU - Kostewicz, S. H.
PY - 1998
Y1 - 1998
N2 - A prototype decision support system has been designed for managing dental caries using a risk assessment model. Caries is a multifactorial disease with risk prediction models having low sensitivity (65%) and moderate specificity (80%) for 2 or more new lesions. These models are inaccurate for targeting resources at high risk people. However, low risk individuals can be more accurately identified. If the activity of early tooth decay lesions, in low risk people, are monitored over time and only lesions beyond 1/3 of the dentin depth are filled, the number of annual fillings may be reduced by 50%. Currently, most US dental schools do not teach risk assessment for caries and encourage early treatment of lesions leading to a repair destruction cycle. The combination of a decision support system with a moderate accuracy specificity risk model for predicting low risk individuals may produce a significant improvement in caries management.
AB - A prototype decision support system has been designed for managing dental caries using a risk assessment model. Caries is a multifactorial disease with risk prediction models having low sensitivity (65%) and moderate specificity (80%) for 2 or more new lesions. These models are inaccurate for targeting resources at high risk people. However, low risk individuals can be more accurately identified. If the activity of early tooth decay lesions, in low risk people, are monitored over time and only lesions beyond 1/3 of the dentin depth are filled, the number of annual fillings may be reduced by 50%. Currently, most US dental schools do not teach risk assessment for caries and encourage early treatment of lesions leading to a repair destruction cycle. The combination of a decision support system with a moderate accuracy specificity risk model for predicting low risk individuals may produce a significant improvement in caries management.
UR - http://www.scopus.com/inward/record.url?scp=0032239217&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0032239217&partnerID=8YFLogxK
M3 - Article
C2 - 9929285
AN - SCOPUS:0032239217
SP - 577
EP - 581
JO - Proceedings / AMIA . Annual Symposium. AMIA Symposium
JF - Proceedings / AMIA . Annual Symposium. AMIA Symposium
SN - 1531-605X
ER -