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Methods for Computer-Aided Design and Execution of Clinical Protocols Mark A. Musen, M.D., Ph.D. Stanford Medical Informatics Stanford University Research problems in medical informatics involve Formulation of models of clinical tasks and application areas Representation of those models in machine-understandable form Development of new algorithms that process domain models Implementation of computer programs that use models to automate clinically important tasks Protocol-based care is everywhere Algorithms for mid-level practitioners Clinical-trial protocols Clinical alerts and reminders Clinical practice guidelines Some basic beliefs Computer-based patient records eventually will become ubiquitous Clinical protocols can—and should—be authored from the beginning as machine-interpretable documents Electronic protocol knowledge bases will allow computer-based patient records to enhance all components of patient care and clinical research Work in protocol-based care ONCOCIN (1979–1988) Clinical Therapy Helper (1989–1995) Clinical trials in oncology trials for HIV infection EON (1989–) Reusable components for automation of protocols and guidelines in a variety of domains Our research addresses Development of computational models of Planning medical therapy Determining when therapy is applicable Reasoning about time-ordered data New approaches for acquisition, representation, and use of medical knowledge within computers EON: Components for automation of clinical protocols Models of protocol concepts Programs to plan patient therapy in accordance with protocol requirements Programs to match patients to potentially applicable protocols and guidelines Use of an explicit model to guide knowledge entry Model of protocol concepts Clinicians receive expert advice CustomKnowledge-base authors create protocol tailored descriptions protocol-entry tool EON Protocol knowledge base Therapyplanning program Eligibilitydetermination program Model (ontology) of protocol concepts Components of the protocol model (ontology) Guideline ontology Defines abstract structure of clinical protocols and guidelines Is independent of any medical specialty Medical-specialty ontology Defines clinical interventions, patient findings, and patient problems relevant in a given specialty Provides primitive concepts used to construct specialty-specific protocols An ontology Provides a domain of discourse for talking about some application area Defines concepts, attributes of concepts, and relationships among concepts Defines constraints on values of attributes of concepts Model (ontology) of protocol concepts Custom-tailored protocol-entry tool Details of CAF chemotherapy Details of CTX prescription Custom-tailored protocol-entry tool: Top level Specifying eligibility criteria Use of an explicit model to guide knowledge entry Model of protocol concepts Clinicians receive expert advice CustomKnowledge-base authors create protocol tailored descriptions protocol-entry tool EON Protocol knowledge base Therapyplanning program Eligibilitydetermination program Automation of protocol-based care requires Ability to deal with complexity of patient data (e.g., time dependencies, abstractions, missing data) Ability to deal with complexity of protocol actions (e.g., actions which are themselves protocols) A scalable and maintainable computational architecture The EON Architecture comprises Problem-solving components that have task-specific functions (e.g., planning, classification) A central database system for queries of both Primitive patient data Temporal abstractions of patient data A shared knowledge base of protocols and general medical concepts EON is “middleware” Software components designed for incorporation within other software systems (e.g., hospital information systems) reuse in different applications of protocolbased care Components of the EON architecture Therapyplanning component Clinical information system Eligibilitydetermination component RÉSUMÉ temporalabstraction system Chronus temporal database query system Tzolkin database mediator Protocol knowledge base Domain model Patient database Therapy-planning component Takes as input Data from computer-based patient record Knowledge of clinical protocol Generates as output Therapeutic interventions to make Laboratory tests to order Time for next patient visit Episodic skeletal-plan refinement 1. Flesh out standard plan from skeletal plan elements 2. Query database for presence of relevant patient problems Protocol Regimen A Regimen B Drug 1 ? Protocol Drug 2 3. Revise plan based on problems identified Regimen B Drug 1 Drug 2 Domain knowledge derives from knowledge base Problem-solving knowledge automates specific tasks Domain knowledge + Problem-solving method Intelligent behavior Problem-solving methods Are reusable, domain-independent software components that solve abstract tasks (e.g., planning, classification, constraint satisfaction) Represent data on which they operate as a method ontology (model), which must be mapped to the domain ontology that characterizes the application area Mapping domain ontologies to problem-solving methods Problem-Solving Method Method Input Ontology Method Output Ontology Domain Ontology (e.g., clinical protocols) Problem-solving methods can automate a variety of tasks Some skeletal planning tasks Therapy planning for protocol-based care (EON) Administration of digoxin in the presence of possible toxicity (Dig Advisor) Designing experiments in molecular genetics (MOLGEN) Each application entails mapping a different domain ontology to the same, reusable problem-solving method Components of the EON architecture Therapyplanning component Clinical information system Eligibilitydetermination component RÉSUMÉ temporalabstraction system Chronus temporal database query system Tzolkin database mediator Protocol knowledge base Domain ontology Patient database Our goals for eligibility determination Automated clinical-trial screening from institutional and regional databases Identification of specific actions that providers can take to enhance patient eligibility for guidelines and protocols Minimization of inappropriate enrollment of patients who are not eligible EON eligibility-determination component (Yenta) Takes as input Computer-based patient record data Knowledge of eligibility criteria of applicable protocols Generates as output List of patients potentially eligible for given protocols List of protocols for which given patients potentially are eligible Classification of eligibility criteria for clinical trials Stable (e.g., having received prior therapy) Variable (e.g., routine lab data) Controllable (e.g., use of a given drug) Subjective (e.g., likelihood of compliance) Special (e.g., lab data requiring invasive or expensive tests) Qualitative eligibility scores For each eligibility criterion, for each point in time, the computer assigns a score: P PP N FP F meets the criterion probably meets the criterion no assumption can be made probably fails the criterion fails the criterion Eligibility criteria derive from the electronic knowledge base Use of an explicit model to guide knowledge entry Model of protocol concepts Clinicians receive expert advice CustomKnowledge-base authors create protocol tailored descriptions protocol-entry tool EON Protocol knowledge base Therapyplanning program Eligibilitydetermination program Components of the EON architecture Therapyplanning component Clinical information system Eligibilitydetermination component RÉSUMÉ temporalabstraction system Chronus temporal database query system Tzolkin database mediator Protocol knowledge base Domain model Patient database Tzolkin database mediator Serves as a common conduit for all problem solvers that must access patient data Embodies components that address significant problems in temporal reasoning RÉSUMÉ—Temporal abstraction Chronus—Data query and manipulation RÉSUMÉ temporal-abstraction method Takes as input primary patient data and previously determined abstractions of those data Generates as output further abstractions of the input Requires a separate knowledge base of clinical parameters and their properties The temporal-abstraction task PAZ protocol BM T Expected CGVHD M[0] Platelet counts (• ) ² • 150K ² • ² • ² • ² ² • • ² • M[1] M[2] M[3] M[0] ² • ² ² • • 100K 0 50 100 . ² ² • ² ² • 200 Time (days) • • ² • M[0] ² • ² • Granulocyte counts ² (² ) • 2000 1000 400 Knowledge required for temporal abstraction Structural knowledge (e.g., definitional relationships among lab tests and clinical states) Classification knowledge (e.g., how numeric values map into qualitative ranges) Temporal-semantic knowledge (e.g., whether intervals are concatenable or downward heriditary) Temporal-dynamic knowledge (e.g., minimal values for a significant change, functions to predict persistence of a value over time) Acquiring temporal-abstraction knowledge for RÉSUMÉ Model of clinical parameters Tool for entry of temporalabstraction knowledge TZOLKIN Abstractions of relevant clinical parameters Knowledge-base authors enter knowledge required for temporal abstraction Parameter knowledge base RÉSUMÉ temporalabstraction system The EON Architecture Problem-solving components that have task-specific functions A central database system for queries of both Primitive patient data Temporal abstractions of patient data A shared knowledge base of protocols and general medical concepts A protocol model shared among all components Makes explicit relevant assumptions about the application domain—we know what our programs know Consolidates the task of maintaining the domain knowledge—all the knowledge is in one place and can be examined in a coherent fashion Planned applications of EON Hypertension guidelines at Palo Alto VA Health Care System Fast Track Systems, Inc., plans to develop systems for automation of clinical trials EON’s component-based approach allows Developers to create new problemsolving modules that “plug and play” Clinicians to create new guideline knowledge bases that can interoperate immediately with existing components System architects to integrate components with other software modules using standard communication methods Some implications of our work Enhanced authoring, maintenance, and execution of clinical protocols and guidelines Incorporation of guideline-based practice into routine patient care Increased participation of communitybased practitioners in clinical research