Reference: Musen, M.; Tu, S.; Das, A.; & Shahar, Y. A Component-Based Approach to Automation of Protocol-Directed Therapy. Knowledge Systems Laboratory, Medical Computer Science, 1996, 1996.
Abstract: Objective: Automating the task of planning protocol-directed therapy requires a computer program to take as input clinical data stored in an electronic patient-record system, and to generate as output recommendations for therapeutic interventions and laboratory testing that are defined by predefined protocols. The output must be tailored for the current patient situation and stage of protocol execution. Our goal has been to model the functional requirements of the therapy-planning task. Design: We constructed a computational model that includes components that (1) interpret abstract protocol specifications to construct appropriate patient-specific treatment plans, (2) infer from time-stamped patient data higher-level, interval-based, abstract concepts, (3) perform time-oriented queries on a time-oriented patient database, and (4) allow for acquisition and maintenance of protocol knowledge in a manner that facilitates efficient processing both by humans and computers. We have implemented these components in a computer system known as EON. Results: The EON architecture brings together (1) a therapy planner based on a reusable problem-solving method known as episodic skeletal-plan refinement, (2) the RESUME temporal-abstraction system, which implements the knowledge-based temporal-abstraction problem-solving method, (3) the Chronus database system, which processes complex temporal queries referred to a clinical, time-oriented relational database, and (4) special-purpose knowledge-acquisition tools that are generated automatically from descriptions of the relevant clinical domains by the PROTEGE-II system. We have evaluated the capabilities of the EON components by implementing T-Helper, a computer-based patient record system that uses EON to offer advice regarding the management of patients who have AIDS and HIV infection. Conclusion: Each of the modules that comprise the EON architecture has been developed as a self-contained, reusable component. The integration of these elements, however, leads to an aggregate component that is itself reusable for automating the task of planning protocol-based therapy in a variety of clinical domains. EON provides a comprehensive, yet flexible approach that facilitates development, maintenance, and execution of electronic knowledge bases that encode clinical protocols of substantial complexity. Objective: Automating the task of planning protocol-directed therapy requires a computer program to take as input clinical data stored in an electronic patient-record system, and to generate as output recommendations for therapeutic interventions and laboratory testing that are defined by predefined protocols. The output must be tailored for the current patient situation and stage of protocol execution. Our goal has been to model the functional requirements of the therapy-planning task. Design: We constructed a computational model that includes components that (1) interpret abstract protocol specifications to construct appropriate patient-specific treatment plans, (2) infer from time-stamped patient data higher-level, interval-based, abstract concepts, (3) perform time-oriented queries on a time-oriented patient database, and (4) allow for acquisition and maintenance of protocol knowledge in a manner that facilitates efficient processing both by humans and computers. We have implemented these components in a computer system known as EON. Results: The EON architecture brings together (1) a therapy planner based on a reusable problem-solving method known as episodic skeletal-plan refinement, (2) the RESUME temporal-abstraction system, which implements the knowledge-based temporal-abstraction problem-solving method, (3) the Chronus database system, which processes complex temporal queries referred to a clinical, time-oriented relational database, and (4) special-purpose knowledge-acquisition tools that are generated automatically from descriptions of the relevant clinical domains by the PROTEGE-II system. We have evaluated the capabilities of the EON components by implementing T-Helper, a computer-based patient record system that uses EON to offer advice regarding the management of patients who have AIDS and HIV infection. Conclusion: Each of the modules that comprise the EON architecture has been developed as a self-contained, reusable component. The integration of these elements, however, leads to an aggregate component that is itself reusable for automating the task of planning protocol-based therapy in a variety of clinical domains. EON provides a comprehensive, yet flexible approach that facilitates development, maintenance, and execution of electronic knowledge bases that encode clinical protocols of substantial complexity.