Automated Clinical Guideline Systems: A Comparison Gillian Hubble MDI 207 6/7/00.

Slides:



Advertisements
Similar presentations
HL7 Decision Support Activities Rev 1 Draft Feb 23.
Advertisements

1 Using Ontologies in Clinical Decision Support Applications Samson W. Tu Stanford Medical Informatics Stanford University.
E-Preference: A Tool for Incorporating Patient Preferences into Health Decision Aids Amar K. Das, MD, PhD Assistant Professor Departments of Medicine (Medical.
Mapping Studies – Why and How Andy Burn. Resources The idea of employing evidence-based practices in software engineering was proposed in (Kitchenham.
MOLEDINA-1 CSE 5810 CSE5810: Intro to Biomedical Informatics The Role of AI in Clinical Decision Support Saahil Moledina University of Connecticut
Fusion and Automation Fusion and Automation Human Cognitive and Visualization Issues Jean-Remi Duquet Marc Gregoire Maura Lohrenz Kesh Kesavados Elisa.
Software Reuse Building software from reusable components Objectives
Application architectures
Adaptation of Practice Guidelines for Clinical Decision Support: A Case Study of Diabetic Foot Care Mor Peleg 1, Dongwen Wang 2, Adriana Fodor 3, Sagi.
Guideline interaction scenarios  At the point of care Physicians apply marked-up guidelines, thus they Need to find an appropriate guideline in “ real.
Knowledge-Based Interpretation, Visualization, and Exploration of Time-Oriented Medical Data Yuval Shahar, M.D., Ph.D. Medical Informatics Center Information.
Developing an Ontology-based Metadata Management System for Heterogeneous Clinical Databases By Quddus Chong Winter 2002.
The InterMed TM Collaboratory –the early years ( )  Biomedical informatics researchers & systems developers at 5 sites: Harvard/Brigham and Women’s.
McGraw-Hill/Irwin ©2005 The McGraw-Hill Companies, All rights reserved ©2005 The McGraw-Hill Companies, All rights reserved McGraw-Hill/Irwin.
The IRB's Position on Quality Projects vs. Research R. Peter Iafrate, Pharm.D. Chairman, Health Center IRB University of Florida.
Critical Appraisal of Clinical Practice Guidelines
©Ian Sommerville 2004Software Engineering, 7th edition. Chapter 13 Slide 1 Application architectures.
Decreasing Variability in Health Care HST950 Decision Systems Group, Brigham & Women’s Hospital Harvard Medical School Harvard-MIT Division of Health Sciences.
Methods for Computer-Aided Design and Execution of Clinical Protocols Mark A. Musen, M.D., Ph.D. Stanford Medical Informatics Stanford University.
“Enhancing Reuse with Information Hiding” ITT Proceedings of the Workshop on Reusability in Programming, 1983 Reprinted in Software Reusability, Volume.
Design Science Method By Temtim Assefa.
Building Blocks for Decision Support in HL7 Samson W. Tu Stanford Medical Informatics Stanford University School of Medicine Stanford, CA.
OracleAS Reports Services. Problem Statement To simplify the process of managing, creating and execution of Oracle Reports.
Query Health Operations Workgroup HQMF & QRDA Query Format - Results Format February 9, :00am – 12:00am ET.
A Multiple Ontology, Concept-Based, Context-Sensitive Search and Retrieval Robert Moskovitch and Prof. Yuval Shahar Medical Informatics Research Center.
Second Generation ES1 Second Generation Expert Systems Ahme Rafea CS Dept., AUC.
Temporal Reasoning and Planning in Medicine Automated Support to Guideline-Based Care Yuval Shahar, M.D., Ph.D.
1 Software Design Reference: Software Engineering, by Ian Sommerville, Ch. 12 & 13, 5 th edition and Ch. 10, 6 th edition.
Košice, 10 February Experience Management based on Text Notes The EMBET System Michal Laclavik.
Development Process and Testing Tools for Content Standards OASIS Symposium: The Meaning of Interoperability May 9, 2006 Simon Frechette, NIST.
Design engineering Vilnius The goal of design engineering is to produce a model that exhibits: firmness – a program should not have bugs that inhibit.
HYGIA: Design and Application of New Techniques of Artificial Intelligence for the Acquisition and Use of Represented Medical Knowledge as Care Pathways.
1 Software Design Overview Reference: Software Engineering, by Ian Sommerville, Ch. 12 & 13.
1 Notes 8 Guideline Execution Models and Systems.
Intro: FIT1001 Computer Systems S Important Notice for Lecturers This file is in skeleton form only Lecturers are expected to modify / enhance.
1 5 Nov 2002 Risto Pohjonen, Juha-Pekka Tolvanen MetaCase Consulting AUTOMATED PRODUCTION OF FAMILY MEMBERS: LESSONS LEARNED.
Service Modeling Based on SOA: Concepts, Technology, Design by Thomas Erl MIS 181.9: Service Oriented Architecture 2 nd Semester,
Temporal Mediators: Integration of Temporal Reasoning and Temporal-Data Maintenance Yuval Shahar MD, PhD Temporal Reasoning and Planning in Medicine.
1 Incorporating Data Mining Applications into Clinical Guidelines Reza Sherafat Dr. Kamran Sartipi Department of Computing and Software McMaster University,
Proposed NWI KIF/CG --> Common Logic Standard A working group was recently formed from the KIF working group. John Sowa is the only CG representative so.
Christoph F. Eick University of Houston Organization 1. What are Ontologies? 2. What are they good for? 3. Ontologies and.
Common Terminology Services 2 CTS 2 Submission Team Status Update HL7 Vocabulary Working Group May 17, 2011.
This material was developed by Duke University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information.
SQL Based Knowledge Representation And Knowledge Editor UMAIR ABDULLAH AFTAB AHMED MOHAMMAD JAMIL SAWAR (Presented by Lei Jiang)
Issues in Ontology-based Information integration By Zhan Cui, Dean Jones and Paul O’Brien.
Focusing the question Janet Harris
Formal Specification: a Roadmap Axel van Lamsweerde published on ICSE (International Conference on Software Engineering) Jing Ai 10/28/2003.
DANIELA KOLAROVA INSTITUTE OF INFORMATION TECHNOLOGIES, BAS Multimedia Semantics and the Semantic Web.
Approach to building ontologies A high-level view Chris Wroe.
Problem-Solving Methods in Protégé-2000 Monica Crubézy Stanford Medical Informatics MIS November 1999.
Henrik Eriksson Department of Computer and Information Science Linkoping University SE Linkoping, Sweden Raymond W. Fergerson Yuval Shahar Stanford.
SEESCOASEESCOA SEESCOA Meeting Activities of LUC 9 May 2003.
Explorations in Internet-Based Collaborative Informatics Research: A Cognitive Evaluation Vimla L. Patel, PhD, DSc Departments of Medical Informatics and.
Knowledge Support for Modeling and Simulation Michal Ševčenko Czech Technical University in Prague.
Concept mining for programming automation. Problem ➲ A lot of trivial tasks that could be automated – Add field Patronim on Customer page. – Remove field.
CONSORT 2010 Balakrishnan S, Pondicherry Institute of Medical Sciences.
Ontologies Reasoning Components Agents Simulations An Overview of Model-Driven Engineering and Architecture Jacques Robin.
SAGE Nick Beard Vice President, IDX Systems Corp..
Rule Engine for executing and deploying the SAGE-based Guidelines Jeong Ah Kim', Sun Tae Kim 2 ' Computer Education Department, Kwandong University, KOREA.
Access To Distributed Clinical Digital Libraries Jonghoon Chun Division of Computer Science & Engineering Myongji University
Software Design Process. What is software? mid-1970s executable binary code ‘source code’ and the resulting binary code 1990s development of the Internet.
Strategic and operational plan
The Semantic Web By: Maulik Parikh.
Intelligent Systems Development
1st International Online BioMedical Conference (IOBMC 2015)
Semantic Web - Ontologies
Diabetes Self-Management Education and Support: Component of Standard Diabetes Care 1, 2 “… Ongoing patient self-management education and support are.
Developing an Ontology for Randomised Controlled Trials
Temporal Reasoning and Planning in Medicine The Asgaard Project: A Task-Specific Framework for the Application and Critiquing of Time-Oriented Clinical.
Reportnet 3.0 Database Feasibility Study – Approach
Presentation transcript:

Automated Clinical Guideline Systems: A Comparison Gillian Hubble MDI 207 6/7/00

Overview Why clinical guideline systems? Logic implementation GLIF vs. EON Future directions

Why Clinical Guideline Systems? A touchy topic! Goal = guideline reuse (write once, use many) New uses: “JIT” education prediction of performance Protocol Organization modeling for simulation

QA in Healthcare 80’s: Measure product quality Quality out of control by the time problems are detected Example: hospital accreditation 90’s: Control variance in processes Manage quality problems as they arise Example: protocols and guidelines 2000  : Design work processes and organizations Anticipate and manage quality problems before they arise Example: reconfigure work processes and/or organization based on what-if scenarios Adapted from

Example Academic Systems PROforma (UK) MBTA (MGH) GEODE-CM (Harvard) OzCare (Columbia) GLIF (InterMed Collaboratory) EON (Stanford)

From Many Knowledge Representations

Logic Implementation: Two Camps Rule-based systems GLIF is (arguably) the best example The “lowest common denominator” ESPR (Episodic Skeletal Plan Refinement) Only one system: EON Complex logic implementation scheme Reusable systems using qualitative, not quantitative DS methods

GLIF A rule-based system Originally only a knowledge representation format (meant for guideline interchange…hence the name!) GLIF model GLIF syntax

GLIF Model

GLIF Syntax Guideline Example { name = ‘Guideline for vaccine X’; authors = SEQUENCE 1 (‘Mary Doe, MD’;); eligibility_criteria = NULL; intention = ‘Decide whether to recommend the Generic vaccine and at what dosage’; Steps = SEQUENCE 8 { (Branch_Step 1);(Action_Step 1);(Action_Step 2); … }; first_step = (Branch_Step 1); Didactics = SEQUENCE 1 { (Supplemental_Material 1 { material = ‘Published guideline does not contain explicit eligibility criteria.’); }; ETC… From Ohno-Machado et al, 1999

An Executable System Guideline authoring tool Guideline viewing tool Guideline server (imports, exports guidelines in XML markup) Free Guideline Engine (any rule-based engine)

Authoring Tool

Viewing Tool

EON Not as straightforward as GLIF! Developed for protocol-based care Logic implementation: ESPR Linear or non-linear? How does it work?? Used in: Breast cancer clinical trial protocols AIDS clinic

EON Guideline Model

System Architecture Problem-solving systems Domain-specific knowledge bases Temporal abstraction system Temporal query system

System Architecture

Problem-Solving Components Problem solving methods Eligibility determination ESPR: propose plan, identify problem, revise plan Many more, and can develop new ones as needed Protégé knowledge acquisition tool Develop domain ontologies Use the ontologies to construct domain- specific knowledge bases

ESPR

Ontology Editor: Protégé

Temporal Abstraction System Resume: abstracts clinical concepts from data Abstraction of platelet and granulocyte values into myelotoxicity

Temporal Query System Chronus temporal query language TimeLine SQL (TLSQL) Allows temporal comparisons of time stamps Addresses SQL’s lack of expression for intervals (Start Time  Stop Time) Tzolkin DBMS Handles the TLSQL “When” clause Allows queries of both primary data and abstractions of the data

TLSQL Example

Knowledge Representation Originally Asbru (now??) an intention-based language “The problem with the unsolicited model of CDSS is that clinician intentions are often misunderstood” --Van Bemmel, Handbook of Medical Informatics Guideline decomposed into a set of plans with names, preferences, intentions, conditions, effects Example: severe anemia for 2 nd consecutive week on chemotherapy protocol Protocol: decrease drug dose Clinician action: blood transfusion No alert generated, as both actions increase the desired parameter by using different mechanisms

More Components Recently Added… Dharma guideline model Padda guideline execution interface Yenta eligibility-determination interface WOZ explanation system …And it keeps on growing!

Assessment EON Strengths High degree of functionality Expressive (if the author was) Responsive? Weaknesses Monolithic! Overly prescriptive for general medical care Reusability questionable; difficult to implement (The joy of ontology building…)

Assessment GLIF Strengths Few components, very practical Flexible implementation Weaknesses No domain ontology component No plan revision functionality Over-simplification of rule-based logic

Functionality vs. Practicality Which system is better suited to rapid guideline development and reuse? GLIF Standard development began 1994 Used in 4 projects so far EON Began development in 1988 Used in 2 projects (not well described) Not “author friendly” by a long stretch May be better for modeling/simulation Why haven’t any “biopsychosocial”aspects of these systems been published?

Current and Future Projects Working with GLIF model Proposal submitted Develop an automated A&R system for preventive care Ongoing project Develop a potentially machine-tractable referral guideline DTD mapped to the GLIF DTD Web-based system with on-the-fly algorithm generation and XML-based documents for providers Future: explore potential inclusion of AI method for conditions of uncertainty

The End