Chap 13. GUIDELINES AND WORKFLOW MODELS

1. Chap 13. GUIDELINES AND WORKFLOW MODELS

2. Contents • 13.1 Introduction : Clinical Guidelines and Algorithms • 13.2 The Knowledge Contained in Clinical Guidelines • 13.2.1 The Quality of Narrative Guidelines • 13.2.2 The Types of Knowledge Contained in Narrative Guidelines • 13.3 Formal Methods for Specifying CIGS • 13.3.1 Task-Network Models • 13.3.2 Other CIG Modeling Methods • 13.4 From Narrative to Formal Representations of Guidelines

3. Contents • 13.5 Integration of Guidelines with Workflow • 13.6 Methods for sharing of CIG Content • 13.6.1 Interchanging among CIG formalisms • 13.6.2 Adapting a Single formalism as a Standard • 13.6.3 Standardizing CIG Components and fitting Them Together • 13.6.4 Sharing Guidelines at the Execution Level • 13.6.5 Assembling CIGs from Executable Components • 13.6.6 Libraries of GIGs

4. 13.1 Introduction : Clinical Guidelines and Algorithms • Clinical guidelines • statements to assist practitioner and patient decision-marking about appropriate health care for specific clinical circumstances • Aim of clinical guidelines • Eliminate errors • Reduce unjustified practice variation and wasteful commitment of resources • Encourage best practice and accountability in medicine

5. 13.1 Introduction : Clinical Guidelines and Algorithms • Clinical guidelines • Are created by medical experts or panel convened by specialty organizations • Are written as narrative text and table • Screening, diagnosis, management, treatment, or referral of patients with specific clinical conditions

6. 13.1 Introduction : Clinical Guidelines and Algorithms • Algorithms • Clinical guidelines are sometimes portrayed as algorithms (flowcharts) to more directly specify for providers the recommended steps of data gathering, decision-marking, and action. • Are based on the guidelines, but where evidence is not available, the gaps are filled in based on expert opinion.

7. 13.1 Introduction : Clinical Guidelines and Algorithms • In a cognitive study (Patel, Allen et al. 1998) • Physicians • Add organization and detail that were based on their knowledge, and which was not explicitly contained in the narrative guideline • Computer scientists • Produce more consistent algorithms, but which reflected more literal interpretations of the narrative text • Clinicians and computer scientist • Create algorithms of highest quality

8. 13.2 The Knowledge Contained in Clinical Guidelines • Narrative guideline • Contain a recommendation set that suggests options for optimal care • The nature of clinical guidelines is to suggest • Are written in a relaxed language that emphasized the fact that he judgment if the clinician should determine the care process • Is often unclear, vague, incomplete, ambiguous, and even contradictory, which creates a problem in interpreting the guideline in order to computerize it

9. 13.2.1 The Quality of Narrative Guidelines • The attributes for assessing guideline quality (Field and Lohr 1992) • Guideline contents • Validity • Reliability • Reproducibility • Clinical applicability • Process of guideline development or representation • Clarity • Multidisciplinary process • Scheduled review • Documentation

10. 13.2.1 The Quality of Narrative Guidelines • Guideline assessment tools • Appraisal of Guidelines Research and Evaluation (AGREE) instrument • Shaneyfelt’s appraisal tool • Line Implementability Appraisal (GLIA) • Australian Health Information Council (AHIC) • Criteria that should be confirmed to ensure that a narrative guideline is reliable and valid

11. 13.2.1 The Quality of Narrative Guidelines • Models for algorithm development • The Agency for Healthcare Research and Quality (AHRQ) - http://www.guideline.gov/ • The Society for Medical Decision Making (SMDM)

12. 13.2.2 The Types of Knowledge Contained in Narrative Guidelines • Guidelines Elements Model (GEM) • An XML-based knowledge model for guideline documents • Clinical Practice Guideline-Reference Architecture (CPG-RA) • An XML-Schema based knowledge model for structuring guidelines

13. 13.3 Formal Methods for Specifying CIGs • CIG • Computer-interpretable Guideline • CPG • Clinical Practice Guideline

14. 13.3.1 Task-Network Models • Task-Network Models (TNM) • A process-flow-like model • A hierarchical decomposition of guidelines into networks of component tasks that unfold over time

15. 13.3.1 Task-Network Models • 13.3.1.1 Asbru • 13.3.1.2 EON, PRODIGY, and GLIF • 13.3.1.3 GUIDE/NewGuide • 13.3.1.4 SAGE • 13.3.1.5 Proforma • 13.3.1.6 GLARE

16. 13.3.2 Other CIG Modeling Methods • 13.3.2.1 Arden Syntax • 13.3.2.2 GASTON • 13.3.2.3 OncoDoc

17. 13.4 From Narrative to Formal Representations of Guidelines • CPG-RA • Has not yet demonstrated transition from the markup into a formal representation • Georg and coauthors • Developed an approach for automatically generating decision rules from GEM-encoded guidelines • Digital electronic Guideline Library framework (DeGel) approach • A CIG is developed via a process in which conventional narrative guidelines gradually are transformed from traditional narrative forms to fully formal representations. • Document Exploration and Linking Tool (Delt/A)

18. 13.5 Integration of Guidelines with Workflow • First level • The basic CIG languages support modeling of guideline knowledge • Do not support data modeling intended to facilitate interfacing the guideline model with an HER • Ardne syntax, Asbru, Proforma, and the model developed by Seroussi • Very useful for implementing guidelines that require manual data entry or conducting a dialog of questions and answers

19. 13.5 Integration of Guidelines with Workflow • Second level • Include a patient information model • EON, SAGE and PRODIGY => vMR • GLIF3 => HL-7 RIM • Third level • Considers the workflow ofactivities • NewGuide, SAGE

20. 13.6 Method for Sharing of CIG Content • 13.6.1 Interchanging among CIG formalisms • 13.6.2 Adapting a Single formalism as a Standard • 13.6.3 Standardizing CIG Components and fitting Them Together • An object-oriented guideline expression language • A patient data model based on a VMR • Guideline control flow • 13.6.4 Sharing Guidelines at the Execution Level • 13.6.5 Assembling CIGs from Executable Components • 13.6.6 Libraries of GIGs