On implementing clinical decision support: achieving scalability and maintainability by combining business rules and ontologies.
We present an approach and architecture for implementing scalable and maintainable clinical decision support at the Partners HealthCare System. The architecture integrates a business rules engine that executes declarative if-then rules stored in a rule-base referencing objects and methods in a business object model. The rules engine executes object methods by invoking services implemented on the clinical data repository. Specialized inferences that support classification of data and instances into classes are identified and an approach to implement these inferences using an OWL based ontology engine is presented. Alternative representations of these specialized inferences as if-then rules or OWL axioms are explored and their impact on the scalability and maintenance of the system is presented. Architectural alternatives for integration of clinical decision support functionality with the invoking application and the underlying clinical data repository; and their associated trade-offs are discussed and presented.
Kashyap
Vipul
V
Clinical Informatics, R&D Partners HealthCare System, Wellesley, MA, USA. vkashyap1@partners.org
Morales
Alfredo
A
Hongsermeier
Tonya
T
D003658:Decision Making, Computer-Assisted; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D017410:Practice Guidelines as Topic; D012984:Software; D018875:Vocabulary, Controlled