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

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 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 ?

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-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:

Eligibility criteria derive from the electronic knowledge base

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

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 community-based practitioners in clinical research