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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 trials in oncology
Therapy Helper (1989–1995) Clinical 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 ofprotocol concepts
Custom-tailored
protocol-entrytool
Protocolknowledge base
Therapy-planningprogram
Eligibility-determination
program
Knowledge-baseauthors create protocoldescriptions
Cliniciansreceive expertadvice
EON
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
Use of an explicit model to guide knowledge entry
Model ofprotocol concepts
Custom-tailored
protocol-entrytool
Protocolknowledge base
Therapy-planningprogram
Eligibility-determination
program
Knowledge-baseauthors create protocoldescriptions
Cliniciansreceive expertadvice
EON
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 protocol-based care
Components of the EON architecture
Tzolkin database mediator
RÉSUMÉtemporal-
abstractionsystem
Chronustemporaldatabase
query system
Patientdatabase
Therapy-planning
component
Eligibility-determination
component
Protocol knowledge base
Domainmodel
Clinicalinformationsystem
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
Protocol
Drug 2Drug 1
Regimen BRegimen A
Protocol
Drug 2Drug 1
Regimen B
1. Flesh out standard planfrom skeletal plan elements
3. Revise plan based onproblems identified
2. Query database forpresence of relevantpatient problems ?
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-SolvingMethod
Domain Ontology(e.g., clinical protocols)
MethodInput Ontology
MethodOutput Ontology
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
Tzolkin database mediator
RÉSUMÉtemporal-
abstractionsystem
Chronustemporaldatabase
query system
Patientdatabase
Therapy-planning
component
Eligibility-determination
component
Protocol knowledge base
Domainontology
Clinicalinformationsystem
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
P meets the criterion PP probably meets the criterion N no assumption can be made FP probably fails the criterion F fails the criterion
For each eligibility criterion, for each point in time,the computer assigns a score:
Use of an explicit model to guide knowledge entry
Model ofprotocol concepts
Custom-tailored
protocol-entrytool
Protocolknowledge base
Therapy-planningprogram
Eligibility-determination
program
Knowledge-baseauthors create protocoldescriptions
Cliniciansreceive expertadvice
EON
Components of the EON architecture
Tzolkin database mediator
RÉSUMÉtemporal-
abstractionsystem
Chronustemporaldatabase
query system
Patientdatabase
Therapy-planning
component
Eligibility-determination
component
Protocol knowledge base
Domainmodel
Clinicalinformationsystem
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
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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 ofclinical parameters
Tool for entryof temporal-abstraction knowledge
Parameterknowledge base
RÉSUMÉ temporal-
abstraction system
Knowledge-baseauthors enter knowledgerequired for temporalabstraction
Abstractionsof relevantclinicalparameters
TZOLKIN
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 problem-
solving 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
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