An adaptive two-tier menu approach to support on-line entry of diagnoses

Abstract

We demonstrate how a task model derived from data mining techniques could be used to anticipate diagnosis codes from patient complaints for improved data entry efficiency. The anticipated diagnoses are presented to the user in a two-tier fashion with the first tier presenting the most likely diagnoses based upon the complaints which the second tier refines. A success rate of 70% was obtained in anticipating patient diagnosis.