Designing a framework of intelligent information processing for dentistry administration data.
OBJECTIVES: This study was designed to test a cumulative view of current data in the clinical database at the Faculty of Dentistry, Dalhousie University. We planned to examine associations among demographic factors and treatments. METHODS: Three tables were selected from the database of the faculty: patient, treatment and procedures. All fields and record numbers in each table were documented. Data was explored using SQL server and Visual Basic and then cleaned by removing incongruent fields. After transformation, a data warehouse was created. This was imported to SQL analysis services manager to create an OLAP (Online Analytic Process) cube. RESULTS: The multidimensional model used for access to data was created using a star schema. Treatment count was the measurement variable. Five dimensions--date, postal code, gender, age group and treatment categories--were used to detect associations. Another data warehouse of 8 tables (international tooth code # 1-8) was created and imported to SAS enterprise miner to complete data mining. Association nodes were used for each table to find sequential associations and minimum criteria were set to 2% of cases. Findings of this study confirmed most assumptions of treatment planning procedures. There were some small unexpected patterns of clinical interest. Further developments are recommended to create predictive models. CONCLUSIONS: Recent improvements in information technology offer numerous advantages for conversion of raw data from faculty databases to information and subsequently to knowledge. This knowledge can be used by decision makers, managers, and researchers to answer clinical questions, affect policy change and determine future research needs.