A Cognitive Approach to Refine and Enhance Use of a Dental Diagnostic Terminology

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DESCRIPTION (provided by applicant): The advent of the electronic health record (EHR) affords unprecedented clinical research opportunities to improve the nation's health. Dentistry is uniquely positioned to leverage the EHR's power because nearly all U.S. dental schools use the same EHR, axiUm. Twenty dental schools that use axiUm have formed the Consortium for Oral-Health- Related Informatics (COHRI) and have agreed to deposit their data in a common registry. This registry will become the largest ever oral health research database. An important impediment to the consistent integration of these data is the lack of uniformly accepted dental diagnosis terms. COHRI schools have agreed upon a standardized diagnosis terminology, EZcodes, which encompass and expand upon existing diagnosis terminologies. However, to be useful, these terms must be consistently and correctly entered into the EHR. Our preliminary studies show that this is not currently the case and that the diagnostic term entry process in axiUm is inefficient and error-conducive. OBJECTIVE: The goals of this proposal are to increase utilization of diagnosis terms, decrease error rates of term entry, increase provider satisfaction with the entry process, and increase the number of COHRI schools that adopt axiUm's dental diagnosis module. SPECIFIC AIMS: (1) We will identify cognitive and functional impediments to diagnosis entry in the EHR workflow and interface to the dental diagnostic terms. (2) We will make refinements to the EHR workflow and interface to the diagnostic terms, as well as the diagnostic terminology at pilot sites to reduce the cognitive and functional impediments to diagnosis entry in the EHR. (3) We will disseminate and evaluate the adoption of the finalized diagnostic terminology and EHR interface to the diagnostic terms. DESIGN AND METHODS: Headed by the Harvard School of Dental Medicine, four COHRI schools will participate in this effort, two test and two control schools. After an initial evaluation, the two test schools will undergo an iterative cognitively-based EHR and diagnostic term refinement process until a final diagnosis module is created. We will implement the module at the test and control schools and will evaluate the impact of the refinements in terms of achieving our objectives. At a minimum, the four schools within this proposal will adopt the module, where over 80,000 patients make over 250,000 visits per year. Finally, we will disseminate the diagnosis module to all 50 schools that use axiUm, empowering these schools to make efficient diagnosis-based evaluations. PUBLIC HEALTH RELEVANCE: Twenty dental schools have agreed to combine data from their electronic health records (EHRs), which will result in the largest oral health research database ever created. The scope of this effort is limited by the fact that, though they provide the correct treatment, dental clinicians do not enter diagnostic data into the EHR consistently or correctly. In this proposal, we will use human-computer interaction methods to increase dentist satisfaction with the dental diagnosis entry into the EHR, increase how often diagnoses are entered, decrease mistakes made while entering the diagnosis, and increase the number of dental schools that adopt entering dental diagnoses in their EHR.