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Clinical Guidelines. Veli Bi ç er. Outl ine. Evidence-Based Medicine Clinical Guidelines Developing Guidelines Computerized Clinical Guidelines Arden Syntax GEM PRO forma & Arezzo. Outl ine cont’d. Asbru & DeGel GUIDE & NewGuide MyHeart EON & Athena GLIF Towards Standardization

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Clinical guidelines l.jpg

Clinical Guidelines

Veli Biçer


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Outline

  • Evidence-Based Medicine

  • Clinical Guidelines

  • Developing Guidelines

  • Computerized Clinical Guidelines

  • Arden Syntax

  • GEM

  • PROforma & Arezzo


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Outline cont’d

  • Asbru & DeGel

  • GUIDE & NewGuide

  • MyHeart

  • EON & Athena

  • GLIF

  • Towards Standardization

  • What is next?

  • References


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Evidence-Based Medicine

  • Advocates the use of up-to-date “best” scientific evidence from healthcare research as the basis of making decisions. It offers:

    • Objective way to determine high quality and safety standards

    • The process of transfering clinical findings into practice

    • Potential to reduce healthcare costs.


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Clinical Guidelines

  • “systematically developed statements to assist practitioners and patients on decisions about appropriate health care for specific circumstances" [Field and Lohr [1990] ]


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Developing Guidelines

  • Prioritizing Guideline Topic:

    • Major causes of mortality for a population

    • Uncertainty about the appropriateness of healthcare

    • Need to conserve resources in providing care

  • Cardiovascular Diseases is a major category.


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Developing Guidelines

  • The topic is usually refined since the task of developing a guideline for Cardiovascular diseases is considerable

  • Care Elements:

    • Primary (The initial and nonspecialized health care)

    • Secondary (Specialist care in a hospital setting )

    • Tertiary (Services provided by highly specialized providers and tech.)

  • Aspects of Management:

    • Screening

    • Diagnosis

    • Drug Therapy

    • Risk Factor Management


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Developing Guidelines

  • Setting

    • Inpatient

    • Outpatient

  • Time Frame

    • Emergency

    • Acute

    • Chronic


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Developing Guidelines

  • Identifying and Assessing the evidence

    • Best done by systematic review.

  • The Cochrane Library contains references to over 218000 clinical trials

    • http://www.cochrane.org/

  • Once gathered, the evidence is interpreted and translated into CPG.


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Computerized Clinical Guidelines

  • Most clinical guidelines are text-based

  • All of them is not accessible online

  • Physicians have difficulties in deciding which of multiple guidelines best pertains to their patient

  • A clear need for effective guideline-support tools at the point of care

  • To be effective, these tools:

    • need to be grounded in the patient's record

    • must use standard medical vocabularies

    • should have clear semantics

    • must facilitate knowledge sharing


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Computerized Clinical Guidelines

  • Approaches to Electronic Guideline Representation

    • Formal Representation Specification

    • Encoding logic into application-specific format

  • Guideline Modeling Methodologies:

    • Rule-based: Arden Syntax

    • Logic-based : PROforma

    • Workflow: GUIDE, GLIF


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Arden Syntax

  • HL7/ANSI standard

  • Current approved version is 2.1

  • Standard, formal procedural language that represents medical algorithms in clinical information systems as Medical Logic Modules (MLMs).

  • MLM: an independent unit in a health knowledge base. It contains:

    • Maintenance Information

    • Links to other sources

    • Logic to make a single decision


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Arden Syntax

maintenance:

title: Hepatitis B Surface Antigen in Women;;

mlmname: hepatitis_B_mlm;;

arden: version 2.1;;

...

library:

keywords: hepatitis B;

citations:

1. Goldman L, Cook EF, et al. A computer protocol to predict myocardial infarction. N Engl J Med 1988;318(13);;

...

knowledge:

data: penicillin_storage := event {store penicillin order} ;;

evoke: penicillin_storage;;

evoke: 3 days after time of creatinine_storage;…;;

var1 := call my_interface_function with param1, param2;

logic:

if last_creat is not present then

alert_text := "No recent creatinine available. Consider ordering creatinine before giving IV contrast.";

conclude true;

end if;;

end:


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Arden Syntax

  • Advantages:

    • Not a full-feature programming language; Suitable for Clinicians.

    • Provides explicit links to data, trigger events.

    • Defines how an MLM can be called (evoked) from a trigger event.

    • Brings particular support for time functions.

    • HL7/ANSI standard

    • Used by Commercial DSSs.


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Arden Syntax

  • The basic format is not appropriate for developing complete electronic guideline applications

  • Not as declarative as GLIF

  • In case of an interaction with a clinical database to provide alerts and reminders, the encoding of clinical knowledge (MLM) may vary due to database schema, clinical vocabulary.


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GEM

  • Guideline Elements Model

  • XML-based guideline markup model

  • International ASTM (American Society for Testing and Materials) standard.

  • The free-text is markup in XML.


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PROforma

  • A formal knowledge representation language

  • EU 4th Framework Health Telematics PROMPT project

  • Guideline is modeled as a set of:

    • Tasks

    • Data Items

  • Tasks are divided into:

    • Actions

    • Enquiries

    • Decisions

    • Plans

  • PROforma software consists of a graphical editor to support the authoring process, and an engine to execute the guideline specification.

  • Two major tools: AREZZO, TALLIS


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PROforma


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AREZZO

  • Software to create and run clinical guidelines based on PROforma

  • Commercial

  • Two main components: Composer, Performer

  • PROforma provides some rules supported by AREZZO

  • Performer has Microsoft COM Interface


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Asbru

  • The Asgaard project led by the Vienna University of Technology and Stanford Medical Informatics, 1998

  • A task-specific and intention-based plan representation language

  • Embody clinical guidelines and protocols as time-oriented skeletal plans

  • Regarding the timing, the plans can be Sequential, Parallel, Any-order, Unordered.


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Asbru

<plan-library>

<library-info title="Skeleton of a Plan Library“/>

<library-defs>

<domain-defs>…</domain-defs>

<variable-def name="List-1" scalar-or-not="list" type="string">

<comment text="List-1 is a list of strings"/>

</variable-def>

<constant-def name="PI">

<numerical-constant unit="amount" value="3.1415"/>

</constant-def>

<function-def class-name="asgaard.checkit“ method-name="add_em_up“ name="add“ return-type="length"/>

</library-defs>

<plans>

<plan-group>

<plan name="Plan-A">…</plan><plan name="Plan-B">…</plan>

</plan-group>

</plans>

</plan-library>


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Asbru

  • Records can also be defined in domain definitions and used as an interface to plans


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DeGel

  • Digital Electronic Guideline Library

  • Developed tools to support the development and implementation of guideline applications.

  • “Expert physicians cannot program in guideline specific language, while engineers do not understand the clinical semantics”

  • Problem: “How will the large mass of free text guidelines be converted to a formal machine-readable language?”


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DeGel

  • Based on a hybrid (multiple-format) electronic representation of guidelines

  • A guideline is first converted from free text into semantically semi-structured text

  • Then from semi-formal language by a medical expert using a markup editor, to a fully formal representation by a knowledge engineer

  • The current default target language is Asbru



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DeGel

  • The framework provides the following tools:

    • Uruz - Gradual conversion of free-text clinical guidelines into a machine-comprehensible representation in a given target guideline ontology

    • IndexiGuide - Manual or automated classification of clinical guidelines along multiple semantic axes

    • Vaidurya - Search and retrieval of clinical guidelines represented in free text, or in a semi-structured format that uses the labels of a given target ontology

    • VisiGuide - Visualization and browsing of a set of guidelines in a target ontology


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Uruz

  • Guideline markup tool

  • Similar to GEM Cutter Editor

  • Source guideline (free-text) is loaded and marked up with semantic labels of the target ontology.

  • The target ontology can only be Asbru or GEM

  • The result is an XML document


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DeGel

URUZ


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Uruz

  • Plan Body Builder:

    • Specific to Asbru

    • Used for defining guidelines control structure

    • Decompose actions into atomic actions and other sub-guidelines


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IndexiGuide

  • Allows medical experts to index the guidelines with semantic axes

  • Semantic axes can be signs, symptoms, diagnostic findings, disorders, treatments and so on.

  • Semantic axes are headers of standardized vocabularies such as MeSH, ICD-9, CPT


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Vaidurya

  • Guideline search and retrieval tool

  • The user can search based on semantic axes

  • The marked-up guidelines can also be queried for the existence of the terms within internal context


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Visiguide

  • Visualization of multiple and single guidelines

  • Free text, semi-structured text and formal language (Asbru).

  • Organizes the guidelines along semantic axes


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GUIDE- NewGuide

  • GUIDE 1998

  • Reengineered to NewGuide in 2002

  • Laboratory for Medical Informatics, Department of Computer and System Science, University of Pavia, Italy

  • The Guide environment integrates three main independent modules:

    • Guideline Management System (GlMS) (providing clinical decision support)

    • Electronic Patient Record (EPR)

    • Workflow Management System (WfMS or CfMS) (providing organisational support)



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GUIDE- NewGuide

  • “Different views of the formalized knowledge to allow different people with different roles (e.g. clinicians, patients, administrators...) to have their own context-specific interactions with the system” www.openclinical.org

  • For example, if a guideline suggests taking a blood sample (Lab Test), the physician view would incorporate the interpretation of the examination results (CPG), while the patient view would provide a reminder and a facility to book the blood examination (Healthcare Process)


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GUIDE- NewGuide

  • Guideline management system for handling whole lifecycle of a CCPG

  • Two main levels: Central, Local

  • The Components:

    • An Editor to formalize guidelines

    • Repository to store

    • Inference Engine to implement

    • Reporting System to logging

  • Implemented in Java and uses SOAP for the integration with HIS


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Manage GLs by some health authority or scientific organization

GUIDE- NewGuide

Healthcare Organization adopting one or more GLs


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GUIDE- NewGuide organization

  • GL Lifecycle:

  • Constructing GL with NewGuide Editor

  • Storing GL by Repository Manager at local and central level. Two DB, one for metadata, one for GL Template

  • Final user retrieves GL Template by Inference Engine and creates an instance with VMR of the patient

  • Inference engine produces recommendations such as drug pres., lab. test by updating log


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GUIDE- NewGuide organization

  • NewGuide Editor

  • Produces four XML data structure:

    • General properties in GEM

    • The set of medical terms based on ICD and LOINC

    • Abstractions

    • GL Flow


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GUIDE- NewGuide organization

  • NewGuide Repository

  • Manages the Guidelines

  • GL general properties are used for querying


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GUIDE- NewGuide organization

  • Inference Engine

  • An instance of a GL is created by using the VMRs

  • Includes Instance Manager for the management of instances.

  • Instance Manager can start, finish, drop, suspend, activate GL execution

  • CfMS manages the flow and timing of the GL


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GUIDE- NewGuide organization

  • For a recommendation such as “Wait for 2 days”, CfMS decides to put the instance to “stand by”.

  • GL represents medical knowledge, while CfMS is responsible for execution.

  • When an info acquisition task is scheduled, inference engine can request through the SOAP from the HIS. GL is put on stand-by


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MyHeart organization

  • Project Acronym:MYHEARTProject Reference: 507816 Start Date: 2003-12-31 Duration: 45 months Project Cost: 34.92 million euro Contract Type: Integrated Project End Date: 2007-09-29 Project Status: Execution Project Funding: 16.00 million euro

  • PHILIPS GMBH AACHEN GERMANY

  • http://www.extra.research.philips.com/euprojects/myheart/


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MyHeart organization

  • aims to fight cardio-vascular diseases by prevention and early diagnosis. 

  • Intelligent Biomedical Clothes: The combination of functional clothes (including sensors) and integrated electronics.

  • Intelligent Biomedical Clothes for monitoring, diagnosing and treatment.


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MyHeart organization

  • The main Technical Challenges are: - Continuous Monitoring- Continuous Personalised Diagnosis- Continuous Therapy- Feedback to user- Remote Access and Professional Interaction


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MyHeart organization

  • Works to be done during the project.

    • Applications and personalized algorithms.

    • Functional Clothes including sensors with long-term monitoring capability.

    • Developing on-body electronics integrated to the clothes.

    • Developing a system architecture for user and professional interaction.

  • No public results yet


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EON organization

  • A component-based suite of models and software components for the creation of guideline-based applications

  • Stanford Medical Informatics

  • Support: the National Library of Medicine

  • Uses Protégé


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EON organization

  • Provides an extensible set of ontologies covering different aspects of concepts and relations needed for encoding CPG

  • Ontologies are:

  • Patient Data Model( the classes and attributes of patient data (EMR))

  • Concept Model (like archetypes)

  • Guideline Model

  • Expression/Criterion Model

  • Temporal Model


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EON organization

  • Patient Data Model

    • Patient class: hold demographic information

    • Note_Entry: class that describes qualitative observations about patients

    • Numeric_Entry: class that represent results of quantitative measurements

    • Medication and Procedure: model drugs and medical procedures

  • Not try to create a data model that replicates everything that an EMR holds, but only those relevant for modeling guidelines.


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EON organization

  • Concept Model

  • The concepts we want to model in the concept model are abstract entities that can be organized into taxonomic hierarchies.

  • Concrete subclasses are created and used by the Guideline Model


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EON organization

  • Guideline Model

  • Uses the patient data and concept models to create GL

  • Classes to model Guideline:

    • Goal and Step

    • Clinical algorithm

    • Activity and Action Specifications


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Athena organization

  • Assessment and Treatment of Hypertension: Evidence-based Automation

  • Decision support system for the management of hypertension in primary care

  • Mostly depend on Sixth report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure(JNC6)

  • Currently JNC7 is available

  • Stanford Medical Informatics and VA Palo Alto Health Care System


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Athena organization

  • Encourages blood pressure control

  • Recommends guideline-concordant choice of drug therapy

  • Easily modifiable knowledge base that specifies eligibility criteria, risk stratification, blood pressure targets, relevant diseases, guideline-recommended drug classes for patients preferred drugs within each drug class, and clinical messages.

  • Designed to allow clinical experts to customize the knowledge base to incorporate new evidence or to reflect local interpretations of guideline ambiguities.

  • Database mediator, Athenaeum

  • Physical and logical data independence from the legacy Computerized Patient Record System (CPRS) supplying the patient data


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Athena organization

  • Two major components:

    • A knowledge base that models hypertension independently of its use

    • Guideline interpreter creating patient specific treatment recommendations


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Athena organization

  • Knowledge base:

    • Based on EON

    • A computerized version of JNC6 (Prevention, Detection, Evaluation, and Treatment of High Blood Pressure) GL

    • Clinicians can customize through Protégé


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Athena organization

  • The EON based system also determines:

    • Whether or not the GL is applicable to a patient

    • Which portion is applicable

    • Whether the goal has been reached

    • Applies criteria for selecting an action

  • For the EON based system to work, patient clinical data is needed


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Athena organization

  • Athenaeum: Database Mediator

  • Maps legacy database onto data model of Athena DSS

  • In addition to data model, terminology is also mapped:

    • From ICD-9 to EON internal codes


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Athena organization


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Athena organization

  • Advisory for clinicians:

    • Clinical Assumptions used for reasoning

    • Recommendations


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Athena organization

  • Clinical Assumptions:

    • Patient Risk Class

    • Patient Data considered in calculation

    • Target Blood Pressure and whether it is achieved or not

    • Additional Blood Pressure Readings entered by clinicians


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Athena organization

  • Recommendations:

    • Increasing/decreasing the dose of a specific drug

    • Using a new drug

    • Warnings to patient or clinician


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Athena organization

  • Tested in 100 cases:

    • 224 drug recommendations

    • 87 disagreements between clinicians and Athena

    • 12 ATHENA errors!!!


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GLIF organization

  • The Guideline Interchange Format

  • InterMed Collaboratory (Stanford Medical Informatics, Harvard University, McGill University and Columbia University)

  • GLIF

  • Defines an ontology for representing guidelines, and a medical ontology for representing medical data and concepts.

  • Tools are under development to support guideline authoring and execution.


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GLIF organization

  • Guidelines are represented as a flowchart of guideline steps

  • Guideline steps:

    • Decision Step

    • Action Step

      • Medically oriented actions

      • Programming-oriented actions

    • Branch, Synchronization Step

    • Patient State Step


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GLIF organization

  • Layers of abstraction:

    • Conceptual Level of Representation (Level A)

    • Computable Level of abstraction (Level B)

    • Implemental Level (Level C) (Not completed yet)


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GLIF organization

  • Level A: When a guideline is first authored, a conceptual level of representation is created

    • the guideline author to concentrate on conceptualizing a guideline as a flowchart

    • the author is not concerned with formally specifying details, such as decision criteria, relevant patient data, and iteration information that must be provided to make the specification computable


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GLIF organization

  • Guideline Model

    • Guideline class: Actual GL subGL class

    • Algorithm: a flowchart of GL steps

    • Maintenance Info: metadata about GL

  • A GL uses the instances of the Medical Ontology through its data_items and parameters_passed attributes


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GLIF organization

  • Expressions

  • Guideline_Expression class

  • Guideline Expression Language (GEL) is developed based on the Arden Syntax grammar

  • A new language, GELLO, is developed based on the decision support execution model proposed in HL7 Clinical Decision Support TC

  • This standard with GELLO will be adopted when it is published by HL7


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GLIF organization

  • Medical Ontology

  • GLs and GL Components (Logical expressions and action specifications) use the Patient Data and Medical Concepts

  • The concepts are defined by referencing controlled vocabularies (UMLS) and standard medical data models (HL7 RIM)


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GLIF organization

  • Layers of Medical Ontology:

    • Core GLIF

    • Reference Information Model (RIM)

    • Medical Knowledge Layer (Under Development)


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GLIF organization

  • Core GLIF

  • Defines medical data model

  • Data types are classified into:

    • Primitive Data Item

    • Data Item

    • Knowledge Item


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GLIF organization

  • Reference Information Model (RIM)

  • Adopted from HL-7 RIM Version 0.94

  • The Act class is renamed to Patient_Data

  • Extension?

    • Patient Data in HIS message ontology can be mapped to current RIM if it is adequate

    • The sensor data may require additional classes

    • A new RIM can be adopted by defining a new ontology


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GLIF organization


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GLIF organization

  • A Draft Standard

  • The tools are not available:

    • For creating GLs in three layers of abstraction

    • For validation and testing

  • Protégé is not so user-friendly for GL definition

  • Medical Knowledge Layer is not implemented yet

  • RIM may require extensions

  • Guideline Expression Languages

    • GEL

    • GELLO (Not adopted)


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GLIF organization

  • Consensus based multi-institutional process

  • Open process

  • Planning to support the use of multiple vocabularies and data models

  • Incorporates complementary specifications such as Arden Syntax, HL7



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Towards Standardization organization

  • International Workshop, “Toward Sharable Guideline Representation” by InterMed Collaboratory

  • Near-term goals:

    • To move toward a common standard

    • To create prototype authoring tools

    • Provide mechanisms to link GL to the EHRs


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What is next? organization

  • Intelligent Clinical Decision Support Systems

    • Decision support systems survey

      • ATHENA, CEMS, DXplain, ERA , PRODIGY, RetroGram…

    • Agent based clinical decision support systems

    • Design an engine

      • Glif3 Guideline Execution Engine (GLEE)

      • GELLO, An Object-Oriented Query and Expression Language for Clinical Decision Support

    • HL7 CDSTC HL7 Clinical Decision Support Technical Committee


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References organization

  • Field MJ, Lohr KN (Eds). Guidelines for clinical practice: from development to use. Institute of Medicine, Washington, D.C: National Academy Press, 1992.

  • Shekelle, P.; Woolf, S.; Eccles, M.; Grimshaw, J. Clinical guidelines: developing guidelines / British Medical Journal (BMJ) , 1999

  • GLIF 3.5 Technical Specification, InterMed Collaboratory


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References organization

  • Sutton D, Fox J. The syntax and semantics of the PROforma guideline modelling language.

  • Shakar et. al. DeGel: A hybrid, multiple ontology framework for specification and retrieval of Clinical Guidelines

  • Elkin et. al. Toward Standardization of Electronic Guideline Representation


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References organization

  • Coiera E., Clinical Decision Support Systems, Book Chapter, Guide to Health Informatics 2nd Edition

  • Samson et. al., Modelling Data and Knowledge in the EON Guideline Architecture

  • Goldstein et al., Implementing Clinical Practice Guidelines while taking account of changing evidence


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References organization

  • Peleg et al., Comparing Computer-Interpretable Guideline Models: A Case-study approach

  • Ciccarese et al., A guideline management system

  • Sackett et al, Evidence-based medicine: what it is and what it isn’t.

  • www.openclinical.org

  • Peter et al, A virtual Medical Record for Guideline-Based Decision Support


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References organization

  • Specifications of:

    • PROforma

    • Asbru

    • EON

    • Arden

    • GEM



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