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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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outl ine
  • Evidence-Based Medicine
  • Clinical Guidelines
  • Developing Guidelines
  • Computerized Clinical Guidelines
  • Arden Syntax
  • GEM
  • PROforma & Arezzo
outl ine cont d
Outline cont’d
  • Asbru & DeGel
  • GUIDE & NewGuide
  • MyHeart
  • EON & Athena
  • GLIF
  • Towards Standardization
  • What is next?
  • References
evidence based medicine
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.
clinical guidelines5
Clinical Guidelines
  • “systematically developed statements to assist practitioners and patients on decisions about appropriate health care for specific circumstances" [Field and Lohr [1990] ]
developing guidelines
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.
developing guidelines8
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
developing guidelines9
Developing Guidelines
  • Setting
    • Inpatient
    • Outpatient
  • Time Frame
    • Emergency
    • Acute
    • Chronic
developing guidelines12
Developing Guidelines
  • Identifying and Assessing the evidence
    • Best done by systematic review.
  • The Cochrane Library contains references to over 218000 clinical trials
  • Once gathered, the evidence is interpreted and translated into CPG.
computerized clinical guidelines
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
computerized clinical guidelines16
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
arden syntax
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
arden syntax18
Arden Syntax


title: Hepatitis B Surface Antigen in Women;;

mlmname: hepatitis_B_mlm;;

arden: version 2.1;;



keywords: hepatitis B;


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



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;


if last_creat is not present then

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

conclude true;

end if;;


arden syntax19
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.
arden syntax20
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.
  • Guideline Elements Model
  • XML-based guideline markup model
  • International ASTM (American Society for Testing and Materials) standard.
  • The free-text is markup in XML.
pro forma
  • 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
  • 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
  • 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.


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



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

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


<constant-def name="PI">

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


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




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




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


  • Plan Body Builder:
    • Specific to Asbru
    • Used for defining guidelines control structure
    • Decompose actions into atomic actions and other sub-guidelines
  • 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
  • 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
  • Visualization of multiple and single guidelines
  • Free text, semi-structured text and formal language (Asbru).
  • Organizes the guidelines along semantic axes
guide newguide
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)
guide newguide52
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”
  • 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)
guide newguide53
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
guide newguide54

Manage GLs by some health authority or scientific organization

GUIDE- NewGuide

Healthcare Organization adopting one or more GLs

guide newguide55
GUIDE- NewGuide
  • 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
guide newguide56
GUIDE- NewGuide
  • NewGuide Editor
  • Produces four XML data structure:
    • General properties in GEM
    • The set of medical terms based on ICD and LOINC
    • Abstractions
    • GL Flow
guide newguide57
GUIDE- NewGuide
  • NewGuide Repository
  • Manages the Guidelines
  • GL general properties are used for querying
guide newguide58
GUIDE- NewGuide
  • 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
guide newguide60
GUIDE- NewGuide
  • 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
  • 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
  • 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.
  • The main Technical Challenges are: - Continuous Monitoring- Continuous Personalised Diagnosis- Continuous Therapy- Feedback to user- Remote Access and Professional Interaction
  • 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
  • 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
  • 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
  • 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.
  • 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
  • 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
  • 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
  • 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
  • Two major components:
    • A knowledge base that models hypertension independently of its use
    • Guideline interpreter creating patient specific treatment recommendations
  • 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
  • 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
  • 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
  • Advisory for clinicians:
    • Clinical Assumptions used for reasoning
    • Recommendations
  • 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
  • Recommendations:
    • Increasing/decreasing the dose of a specific drug
    • Using a new drug
    • Warnings to patient or clinician
  • Tested in 100 cases:
    • 224 drug recommendations
    • 87 disagreements between clinicians and Athena
    • 12 ATHENA errors!!!
  • 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.
  • 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
  • Layers of abstraction:
    • Conceptual Level of Representation (Level A)
    • Computable Level of abstraction (Level B)
    • Implemental Level (Level C) (Not completed yet)
  • 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
  • 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
  • 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
  • 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)
  • Layers of Medical Ontology:
    • Core GLIF
    • Reference Information Model (RIM)
    • Medical Knowledge Layer (Under Development)
  • Core GLIF
  • Defines medical data model
  • Data types are classified into:
    • Primitive Data Item
    • Data Item
    • Knowledge Item
  • 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
  • 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)
  • 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
towards standardization108
Towards Standardization
  • 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
what is next
What is next?
  • 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
  • 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
  • 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
  • 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
  • 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.
  • Peter et al, A virtual Medical Record for Guideline-Based Decision Support
  • Specifications of:
    • PROforma
    • Asbru
    • EON
    • Arden
    • GEM