1. Semantic Data Capture Initiative Hemant Shah M.D., M.Surg. Sr. Research Informatician Henry Ford Health System hshah2@hfhs.org

2. What is Semantic Data Capture Initiative (SDCI)? A project to develop decision support capabilities combined with structured data capture for CarePlus NG, and its evaluation Under a TATRC (DoD) supervised grant All code is available as Open Source (EPL) Period of Performance  From April 2008 to October 2010

3. The Team Investigators:  R. David Allard, MD (PI)  Hemant Shah, MD (Co-PI)  Patricia Williams  Ganesh Krishnan (Lead Developer) Support from:  CPNG development team  Rich Vollmerhausen  CSRI Research Staff  CSRI Training Staff  Gloria Whitten  Marie McLenaghan External Adviser:  Prakash Nadkarni MD

4. Goal Provide Data Capture in CarePlus EMR, which is:  Structured  Standard Based  Semantic Encourage point-of-care data entry with Decision Support as an incentive for the clinician

5. Hypothesis Clinical documentation templates that leverage metadata with controlled medical vocabularies and interoperate with clinical decision support can be integrated into key ambulatory care processes in a manner acceptable to clinicians and support staff

6. Components of SDCI Research Component Software Development Component Knowledge Development Component Evaluation Component

7. Research Component Structured Data Entry Literature Review Guideline Systems Comparative Analysis Controlled Medical Vocabulary Analysis  Criteria specification for Vocabularies for CPNG and Decision Support  Examine Vocabularies SNOMED-CT LOINC ICD-9 CPT NCI Thesaurus UMLS Metathesaurus MEDCIN 3.0  Application of Criteria Industry Scan of existing systems

8. Development Component Protean – Guideline Authoring Tool Proteus Guideline Engine Greed – Rule Authoring tool Semantic Annotation Tool Interactions with CPNG  Metadata Repository  Patient Record

9. Editable Clinical Process

11. Internet Knowledge Component Repository Editable Clinical Process Cardiovascular System Expert Diabetes Expert

12. Proteus may be used for… Creating executable clinical guidelines to provide decision-support to clinicians about individual patients Creating process-oriented EMRs with integrated clinical decision-support

13. Proteus Model Contains… A specification of an architecture for:  KCs  Executable Workflows (Guidelines) built with KCs  Tools and Systems to handle them A graphical language for Workflows  Human & machine readable  Proteus Graphical Language - PGL

14. Knowledge Component (KC) A modular building block for Clinical Processes Each KC Represents either a Clinical Action, a Clinical Event or a Clinical Process Contains knowledge about a clinical activity:  Actions to be performed  Events to look for  Data to be collected from the actions and events  Interpretation and implications of that data  Supplementary information about the activities (e.g. links to websites)

15. Knowledge Component Basics KC Represents:  Clinical Process (e.g. diagnosis of acute abdomen pain)  Clinical Atomic Activity, which may be: Clinical Action (e.g. palpation of liver) Clinical Event (e.g. vomiting) Knowledge Component (KC) KC may contain data-fields describing the underlying clinical entity Lump Tenderness Vomiting Temperature Abstraction Value of KC

16. Knowledge Component Features KCs can be Nested To represent composite processes To reduce complexity KCs can be linked by Activity-links To represent processes To define guidelines Lump Tenderness Vomiting Temperature Instantiated (executed) KCs become medical record Abstraction yes severe yes 102  F

17. Inference Tools as Components (SOA) Only a reference to an Interface  Swappable  Location neutral  Inferencing technology neutral Technologies Hard Coded Algorithm Production Rules Decision Tables Decision Theory Neural Networks Fuzzy Logic Any other … Patient assisted decisions Human expert (even user) Combination of these Technologies Hard Coded Algorithm Production Rules Decision Tables Decision Theory Neural Networks Fuzzy Logic Any other … Patient assisted decisions Human expert (even user) Combination of these

18. Inference Tools as Components Two Types of Inference Tools  Inference tool for Abstraction  Inference tool for Action

19. Inference Tool for Abstraction Decides  Abstraction – The value of the KC Lump Tenderness Vomiting Temperature Abstraction

20. Inference Tool for Action Decides  If an Action has to be triggered based upon some intelligence Test A Test B Test C Action A Action B

21. Protean – An Integrated Process Authoring Tool A Demo

22. Data Elements Patient Data Vocab Server Patient Data Semantic Data Elements Template Protean CarePlus NG View EHR Adaptor (vMR) Proteus Guideline Engine GreEd Rule Engine Guideline Repository Semantic Annotation Tool Semantic Data Capture Initiative – Architecture SNOMED-CT

23. What is Greed? A tool to author and edit rules  Easy to use graphical representation of rules  Drag and drop is all you need  Internal rule syntax inspired by LISP  Ability to create rules in multiple languages, e.g., Arden Syntax, Java, RuleML, Jess, JBoss Rules etc.  Semantic and Completeness checks on rules  Allows testing of rules from within the environment  Currently in use by Protean  Future Plans: Use ISO/IEC 11179 data elements for conditions and inferences Extensibility – New logical or math operations can be added Rule repository related features To be made available as an independent Rules system

24. Semantic Annotation Tool Allows authoring of standard based data elements that are linked (annotated) with concepts in SNOMED-CT Connects to CPNG metadata repository and gets ‘raw’ data elements Allows authors to select appropriate concepts for them and annotate the data elements with them Stores the annotated data elements which are now called Semantic Data Elements Semantic Data Elements provide interoperability Semantic Data Elements are used by:  Processes authored in the Process authoring environment (Protean)  Proteus engine  Greed and the Rules Engine to understand what Proteus Engine is expecting inferences for

25. Knowledge Development Processes Selected  Follow-up visit for Hypertension Patients  Uncomplicated Upper Respiratory Tract Infection Selected Processes were analyzed Improvement Opportunities were identified in the Processes Proteus Process (knowledge) Authoring Ongoing Iterative refinement Deployment of Proteus processes

26. Interaction between CPNG and SDCI Apps – Metadata API To be used only at author time Method suggestions searchDataElements() getDataElementByValue() getDataElementsByFormName() getDataElementsByFormID() getDataElementsByModuleName() getDataElementsByModuleID()

27. CPNG Web App SDCI Web App CPNG Web Service hyperlink with the encrypted authentication info Method call using encrypted authentication info Interaction between CPNG and SDCI Apps – Patient Data API For accessing existing patient data, during run-time Aim is to prevent re-entry of data already available in CPNG Save data collected during execution of a Proteus process to be available for future use by CPNG or SDCI App users

28. Interaction between CPNG and SDCI Apps – Patient Data API Suggested Methods getAllPatientByDataValues() for the time range getPatientDataValue() for the data element specified by data element id savePatientData() for the specified patient and given data element id

29. thank you

30. Greed (Rules Authoring Tool) Protean (Process Authoring Tool) CPNG (EMR) Metadata Patient Data vMR Patient Data Template Henry Ford KR KC Proteus Engine (Process/Guideline Engine) A B D C E C1C1 Rules Engine Morningside KR KC The Big Picture

31. Deployable Guideline A B D C E C1C1 Value of Editability – Effort and Time Reduction Developers Domain Experts Consensus BuildingInitial Development Testing Modification Without Authoring Tool

32. Deployable Guideline A B D C E C1C1 Value of Editability – Effort and Time Reduction Domain Experts Consensus Building Testing Authoring With Authoring Tool

33. Value of Editability – Applicability to different Locales Disease Population Medical Setup/Skills Level A Level A Guideline Top Level Top - Level Guideline Level B Level B Guideline

34. Evaluation