Practical approach to evidence-based management of caries.

This paper discusses evidence-based management of dental caries with regard to: (1) need to adopt new office methods, (2) potential barriers to change, and (3) possible practical solutions to aid change. The need for classifying individual patients into low-, medium-, and high-risk caries groups is justified from a review of the epidemiological characteristics of caries. In addition, a deficiency is identified in traditional caries recording methods since they are unable to grade the severity and activity of individual lesions. The traditional basis of six-monthly recall examinations for all patients is shown from the literature to have no scientific support. It is suggested a three-twelve month recall interval be used, depending on a patient's risk group classification. Some barriers to change are identified as: (1) the collection of more comprehensive history and clinical caries data, (2) the complexity of evidence-based decision-making, and (3) dentists' difficulty in standardizing decision-making. A new pictorial classification for caries severity and activity is described. A demonstration decision-support system is presented in terms of assisting collection of data, automatic identification of risk factors, patient risk classification, and generation of a suggested treatment plan. Evidence-based management may result in change of professional manpower levels.

Can low accuracy disease risk predictor models improve health care using decision support systems?

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.

Standardizing data collection and decision making with an expert system.

The software for the pilot system has been completed. The appropriateness of the risk factor weights needs to be evaluated by clinical testing. However, this does not prevent the system from being used to teach the philosophy of risk group identification and selection of different management strategies according to disease activity. The current system does demonstrate a dynamic relationship between caries risk assessment/activity and different management strategies. A formal scientific evaluation of the effectiveness of the system as a teaching tool is being developed.

XNEOr: development and evaluation of an expert system to improve the quality and cost of decision-making in neuro-oncology.

The treatment of brain tumors requires a large team of medical experts. However, the process of medical decision-making for these patients is hampered by the frequent inaccessibility of the experts because of conflicting scheduling, inconsistencies in the management of different patients, and the fact that multiple experts often yield multiple opinions. The goals of this work were (1) to develop and validate an expert system to assist the medical team deliver efficient, quality care to children with recurrent medulloblastoma, a common type of pediatric brain tumor, and (2) to determine if the expert system can be used as an educational tool. The results of our study indicate that residents enjoy learning by using XNEOr, the brain tumor expert system. XNEOr enabled residents to order appropriate ancillary tests for patients and to make fewer incorrect treatment decisions. The potential net effect of residents using XNEOr may be increased patient and family satisfaction and decreased probability of medical liability. At a time of important changes in our health care system, novel expert systems hold promise as tools to reduce medical costs, improve the quality of multi-expert medical care, and advance health care education.