Transcript Temporal Reasoning and Planning in Medicine Automated Support to Guideline-Based Care Yuval Shahar, M.D., Ph.D.

Temporal Reasoning and Planning in Medicine
Automated Support to
Guideline-Based Care
Yuval Shahar, M.D., Ph.D.
Clinical Guidelines
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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
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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
Domain
knowledge
base
ORB
ESPR
ORB
Yenta
Other PSMs
Guideline-acquisition tool
Tzolkin controller
RÉSUMÉ
Chronus
Patient database
ORB
CORBA
BUS
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, GEODECM, 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:
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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