Implementing tutoring strategies into a patient simulator for clinical reasoning learning.

OBJECTIVE: This paper describes an approach for developing intelligent tutoring systems (ITS) for teaching clinical reasoning. MATERIALS AND METHODS: Our approach to ITS for clinical reasoning uses a novel hybrid knowledge representation for the pedagogic model, combining finite state machines to model different phases in the diagnostic process, production rules to model triggering conditions for feedback in different phases, temporal logic to express triggering conditions based upon past states of the student's problem solving trace, and finite state machines to model feedback dialogues between the student and TeachMed. The expert model is represented by an influence diagram capturing the relationship between evidence and hypotheses related to a clinical case. RESULTS: This approach is implemented into TeachMed, a patient simulator we are developing to support clinical reasoning learning for a problem-based learning medical curriculum at our institution; we demonstrate some scenarios of tutoring feedback generated using this approach. CONCLUSION: Each of the knowledge representation formalisms that we use has already been proven successful in different applications of artificial intelligence and software engineering, but their integration into a coherent pedagogic model as we propose is unique. The examples we discuss illustrate the effectiveness of this approach, making it promising for the development of complex ITS, not only for clinical reasoning learning, but potentially for other domains as well.

Integrating feedback from a clinical data warehouse into practice organisation.

A patient oriented hospital information system (ARIANE) was inaugurated at the Sherbrooke University hospital (CHUS) in 1990 and a clinical data warehouse (CDW) completed 2004. The CDW is updated from ARIANE every 24h and includes ICD discharge diagnosis data, visit DRG and SNOMED encoding. The data is encrypted on storage. Data is accessed according to institutional approval. To facilitate data access two levels of tool have been made accessible using a web-browser. The first level consists of a 'dashboard' that has a defined design and enables a set of pre-determined dynamic queries about a patient population. This level can be operated with minimal training. The second level uses a convivial database query tool, which requires some prior training. Two prototype dashboards have been designed and evaluated for acceptability. The first for the emergency department enables analysis of patient occupancy. The second for the biochemistry department enables quality assurance evaluation. In most cases worldwide the clinical data warehouse is only beginning to be exploited, often impeded by lack of connection between different enterprise databases. Our CDW is expected rapidly to create a culture change so that clinical practice can be continuously evaluated using compiled data readily available from the electronic health record/hospital information system.