Temporal Reasoning and Planning in Medicine Automated Support toGuideline-Based Care

Yuval Shahar, M.D., Ph.D.

Clinical Guidelines

• A standard of care, typically an experts’ consensus• Usually specifies diagnostic and therapeutic procedures• Also known as clinical protocols (e.g., in oncology); care plans• A powerful method to standardize and improve the quality of

medical care [Grimshaw and Russel, 1993]. • Increasingly widespread use, to spur best practices in medical care

and to incorporate evidence-based medicine• Computer-based techniques needed to automated the support of

guideline-oriented medical care. • Example tasks to be supported: determining the applicability of a

guideline for a given patient, monitoring the application of the guideline, assessing the effectiveness of the guideline

Characteristics of Automated Support to

Guideline-Based Care• Dialog: Care provider automated support system• Both have relative strengths:

– Care provider: Better access to patient data and to medical knowledge– Automated system: Better access to guidelines and to temporal patterns

• The aim is synergy

Automated Support for Clinical Guidelines:

Examples of Prescriptive approaches– DILEMMA, PRESTIGE, Proforma, Prodigy (UK/EU)

– Oncocin, T-Helper, EON, ATHENA (Stanford)– Arden Syntax/MLMs (Columbia/LDS)– GLIF (Columbia, Harvard, Stanford)– ActiveGuidelines (Epic Systems Co., USA)– The Pavia web-based diabetes-therapy project (Italy)

Automated Support for Clinical Guidelines:

Critiquing Approaches– VT-Attending (Miller, Yale)– HyperCritique (Van der Lei and Musen, Rotterdam)– The Asgaard project (Stanford, Vienna, London)

integrates both prescriptive and critiquing approaches by representing both the prescribed default actions and the underlying process and outcome intentions

Requirements for Automated Protocol-

Based Care

• 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 Arden Syntax

(Hripcsak et al., SCAMC 1990)• Named after the Arden Homestead in NY, in which representatives

from ten universities discussed sharing of medical knowledge• Represents medical knowledge as independent units called Medical

Logical Modules (MLMs)• Uses a Pascal-like programming language to encode highly specific

rules, grounded in the local institution’s database schema• General medical logic (encoded in the Arden syntax) separated

from institution-specific component (encoded in the local query language and terms)

• An ASTM standard

The Arden Syntax: An Example• Maintenance:

– title: Agranulocytosis and trimethoprim/sulfamethoxazole– author: Dr. Bonzo

• Library:– keywords: granulocytopenia; agranulocytosis ; trimethoprim; sulfamethoxazole– citations: 1. Anti-infective drug use ... Archives of Internal Medicine 1989; 149(5): 1036-40

• Knowledge– type: data driven;– data:

• anc:= read last 2 from ({query for ANC} where it occurred within the past 1 week);• pt_taking_tms := read exist {query for TMS order};• evoke: on storage of {ANC};

– logic: • if pt_taking_tms and last anc < 1000 and decrease of anc > 0 then conclude true else conclude false;

– action: • store “Caution: The patient’s relative granulocytopenia may be exacerbated by

trimethoprim/sulfamethoxazole.”;

The Arden Syntax: Issues

• Difficulty in reuse of general clinical knowledge within different contexts, even within a single system (e.g., what is “mild anemia”) leading to difficulties in maintenance (Shwe et al., SCAMC 1992)

• Sharing problems encountered when MLMs were transported from Columbia to LDS (Utah); most difficulties due to local query and vocabulary differences as well as local practices (Pryor and Hripcsak, SCAMC 1993)

• Difficulty in representation of continuous therapy plans (each MLM represents a well-defined, independent rule)

• Lack of ability to represent and reuse higher, meta-level problem-solving knowledge

The EON Project

(Musen et al, JAMIA 1996)• A general, client–server architecture that developers can use to build systems that support automated reasoning about guideline-directed care

• Includes reusable components, such as – A therapy planner (the episodic skeletal-plan–refinement method)– A temporal mediator (Tzolkin) to the patient database, which includes

• the RÉSUMÉ temporal-abstraction system• the Chronus temporal-maintenance system

– An eligibility-determination module (Yenta)– A domain knowledge base server

• Uses the Common Object Request Broker Architecture (CORBA) as a communication protocol

The EON Architecture: A Conceptual View

• 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 as “Middleware”

• Software components designed for– incorporation within other software systems (e.g., hospital

information systems)– reuse in different applications of protocol-based care

The EON Architecture: A Graphical View

ESPR

RÉSUMÉ Chronus

Tzolkin controller

Patient database

CORBABUS

Domainknowledgebase

ORB

ORB

ORB

Yenta

Guideline-acquisition toolOther PSMs

The EON Protégé-Based Guideline-Acquisition Tool

The ATHENA/EON Hypertension-Management

System

GLIF(Machado et al., JAMIA

1998)

• Guideline Interchange Format: A specification language• Resulted from the InterMed multiple-center collaboration

effort (Columbia, Harvard, Stanford) • Attempts to integrate key lessons from MLMs, GEODE-

CM, MTBA, EON • Intended to enable representation of complex plans with

branching logic as well as simpler alerts, and therapeutic as well as diagnostic guidelines

GLIF: Necessary Extensions

• A formal syntax for conditions (currently strings)• Ability to represent complex temporal expressions

and to query patient records for these expressions• Ability to handle uncertainty regarding patient data• Clarification of the application semantics • As in other frameworks: ability to ground the medical

concepts within an established, standard vocabulary

GLIF: Current Status

• Several guidelines are encoded in paper (breast cancer workup, breast cancer therapy, cholesterol screening, influenza)

• A formal Arden-like syntax for conditions is being developed• An interpreter for the conditions is being developed in BWH

(Harvard), an expression evaluator (EV), that can be used for determination of conditions such as eligibility

• The EV has been integrated into a WWW-based front-end, that "drives" a user through a guideline

A GLIF3 Flowchart

The ActiveGuideline Architecture in EpicCareTM

(Tang & Young, Proc. AMIA 2000)

Using A Depression ActiveGuideline Within EpicCareTM

The Prodigy III Scenarios: A High-Level View of a Hypertension

Guideline(Johnson et al., Proc. AMIA 2000)

The Asgaard Project(Shahar, Miksch, and Johnson, AIM

1998)

• A task-specific framework for the representation, application, critiquing and quality assessment of time-oriented clinical guidelines

• Uses the Asbru guideline-specification language, which includes expressive semantics for sequential, parallel, and periodic actions

• Enables explicit representation of intentions as temporal patterns to achieve, avoid, or maintain

• Focuses on the critiquing and quality-assessment tasks • Develops algorithms for recognizing and explaining care-provider

intentions given their actions, the intentions of the guideline they are applying, and a domain-specific knowledge base

The BGU/Stanford/Vienna/UK Asgaard Project(Shahar, Miksch, and Johnson, AIM 1998)

• A task-specific framework for the representation, application, and quality assessment of time-oriented clinical guidelines

• Uses the highly expressive Asbru guideline-specification language• Enables explicit representation of process and outcome intentions• The quality-assessment algorithms try to explain care-provider

intentions given their actions, the intentions of the guideline they are applying, and a domain-specific knowledge base

• Includes a Web-based guideline server at BGU on which an Asbru-based guideline library resides

Summary• Multiple approaches to guideline representation

• Prescriptive versus critiquing approaches • major issues:

– Grounding of guidelines in the terms of shared vocabularies– Clear semantics– Authoring and maintenance: Knowledge reuse and sharing– Site-specific instantiation, sensitive to local constraints– Improved temporal representations (both for EMRs and for

guideline specification languages)– Sufficient expressiveness to capture the intentions of the

guideline designers in a machine-readable fashion