COI Brainstorming Document Feedback from

COI: Brainstorming Document

Feedback from: • • • Jyotishman Pathak Dan Russler Matt Moores Alan Ruttenberg Parsa Mirhaji Lee Feigenbaum Ronan Fox Rachel Richesson Susie Stephens Eric Prud’hommeaux

Goal • To demonstrate the value of semantic web (SW) specifications in bridging the divide between clinical practice and clinical research – Collaborative development of a proof of concept (POC) that demonstrates key value propositions of using SW specifications • To get buy-in from a wide variety of stakeholders as a prelude to acceptance and adoption of semantic web specifications – Get buy-in for the use case – Get wide participation and involvement of the key stakeholders in various stages of analysis, design and development of the POC. – Re-use existing standards, terminologies, data and information models of existing communities to increase the probability of adoption.

Methodology The group has been functioning in a consensus driven manner where opinions are sought at each step from all the stakeholders and a decision is taken based on the consensus so created. It was realized that a critical success factor was to incorporate the views of various communities at each step.

Decision 1: Use Case • Use Case Development was lead by Rachel Richesson • Wide variety of use cases were investigated and discussed – Patient Recruitment – Adverse Event Detection – Tracking Patient Through a Clinical Trial • Decision: Focus on Patient Recruitment – Data was assumed to be in an EMR.

Re-use of existing Information Models • How can we re-use EMR data for Clinical Research? • HL 7/RIM/DCM descriptions may be viewed as a “format” for Clinical Research Data. – Typically clinical data in healthcare delivery systems and applications is represented or transformed into this “format” • CDISC/SDTM description may be viewed as a “format” for Clinical Research questions. – Typically clinical data in clinical trials systems and application is represented or transformed into this format? • Can we ask questions in one “format” when the data represented in another “format”? • How can we implement functionality to map across these “formats”? – The mapping module should be flexible to incorporate extensions in the “formats” – The mapping module should be flexible to “plug and play” with multiple “formats”

Decision 2: Information Models • Wide variety of Information Models were considered – – – – HL 7/RIM CDISC/SDTM Detailed Clinical Models from Intermountain Healthcare Galen POMR Ontology (Chimezie) Eligibility Criteria Ontology (Helen) Healthcare Delivery Encounter-based Meta Model (Parsa Mirhaji) • Conclusions – No one ontology/information model is likely to fit the bill – Align as closely as possible to existing information model and terminology standards as possible – Identify gaps and inadequacies in addressing the use case at hand. – Provide feedback to standards groups: CDISC, HL 7/RIM, BRIDG • Decision: Use CDISC/SDTM, Detailed Clinical Models, HL 7/RIM as “seed” ontologies to begin with – Iteratively refine them as gaps and inadequacies are discovered

Demonstrate Re-use • Re-use of data from the EMR for Clinical Research • Re-use of existing vocabularies, e. g. , NCI Thesaurus, Snomed, Med. DRA • Re-use of pre-existing information models e. g. , HL 7/RIM/DCM, SDTM • Identify and Re-use software components that can be used to enable a wide range of use cases – Patient Recruitment – Adverse Drug Event Detection – Tracking a Patient through a Clinical Trial • Develop the POC based on an implementation of these re-usable components – – Components that implement mapping Components that implement data retrieval Components that implement wrappers/trasnformations Components that implement checking for elgibility criteria, adverse events and other clinical events of significance.

Decision 3 • Decided to implement POC on a real world data set as opposed to a synthetically created data set. This raised the following issues: – What would be an appropriate “seed’ Information Model/Ontology to describe healthcare data based on current state. – What are appropriate terminologies (e. g. , Snomed, LOINC, Rx. Norm) that need to be considered to capture coded information in healthcare data based on current state • Parsa Mirhaji provided the data and his feedback was crucial in identifying the appropriate “seed” model/terminology

Decision 4 • Based on discussions with W 3 C folks such as Ralph Swick, Karen Myers, Steve Bratt, Eric P. – W 3 C is interested in working with external standards bodies such as HL 7 and CDISC and express their content using Semantic Web specification such as RDF and OWL – Steve Bratt at the Bio IT World Luncheon – Implication about W 3 C being a content neutral – Bron Kisler emphasized that since W 3 C is providing only the languages, a collaboration would be synergistic and would make sense – Is it possible to develop a collaborative interest group with involvement of HL 7, CDISC and others – conversation with Ralph Swick

One proposed Solution Architecture Protocol Specification Interface Mapping Module RDF Transformation Engine Eligibility Checking Module CTMS EMR System

Decision 5 • Current State Assumptions – Information Models and Vocabularies used in Clinical Trials Context are different from those used in the Healthcare Delivery context • Emphasis on the mapping aspect – Support Plug and Play of different Information Models and Vocabularies • Technology Choices: – SPARQL – N 3 rules

Mapping Module • Critical component of the key goal of this effort. – i. e. , To gain acceptance from a wide variety of stakeholders in the healthcare and clinical trials space. – HL 7/RIM/DCM – seek alignment with healthcare standards – CDISC/SDTM – seek alignment with clinical trials standards – Develop Mappings across these two models – Identify limitations and gaps across these models • Scope: – Focus only on those data items that are required for patient recruitment – Focus only on those data items that are related to diabetes and hypertension – To be driven in some part by “mock” diabetes and hypertension records

Use Case Step Through 1. 2. 3. 4. 5. 6. 7. Clinical Trial Administrator uses the Protocol Specification Interface to specify the eligibility criteria. The data items are specified using elements from the SDTM model. The mapping module translates the data items to the appropriate HL 7/RIM/DCM representation. Appropriate queries are made to the Mediator/Gateway module. The Mediator/Gateway module translates the query into the underlying database query language. The query is executed at the database and sent to the mapping module. The mapping module retranslates the data into terms from the SDTM model. The Eligibility Checking Module checks which patients satisfy the eligibility criteria. The selected patients are returned to the Clinical Trial Administrator Note: Some eligibility criteria may not be expressible using SPARQL queries and may required rules, etc.

Next Steps: Narrow Scope for Implementation • Choose a protocol for implementation #8 (second one) • Limit Scope to Medications, Lab Tests and Vital Signs • Develop Clinical Trials Ontology and Clinical Practice Ontology – Iterative development – Alignment with standards as closely as possible • Implement RDF data store based on data requirements and mock patients • Implement Mapping module using N 3 rules • Implement Eligibility checking module using SPARQL • Try to demonstrate another use case for Adverse Drug Event Detection.

Specification of Eligibility Criteria • Assume we will use an ontology or rule-based tool to specify eligibility criteria • Open to NLP/Ontology-based approaches that translate free text clinical protocol specifications that transform these into a structured form • Examples: – – Type 1 diabetes and/or history of ketoacidosis History of long-term therapy with insulin (>30 days) within the last year

Eligibility Criteria Specification • The functional requirements for this need to be identified and spec’ed out. For e. g. , – – Temporal Constraints Trends on clinical data and values … Out of Scope for POC. • May want to see if the CT or HC communities have done some work on standards for specifying eligibility criteria. – Out of Scope for POC

Design Choice: Eligibility Criteria as a “layer” around Data Items • Data Items – Problem: Type 2 Diabetes – History of Problem: Ketoacidosis – History of Therapy • Name: Insulin • Length: X days • Time Period: [Date 1, Date 2] • Eligibility Criteria: – – Rule conditions Patient has Type 2 Diabetes Patient has History of Ketoacidosis Patient has History of Therapy: • Name = Insulin • X > 30 days • Time Period < 1 year

Mappings: Goal/Methodology 1. Characterize the various data items required for patient recruitment (modulo scope) List of requirements on the data content Tab 1. • For each data item do the following: 1. 2. 3. 4. 1. http: //spreadsheets. google. com/ccc? key=p. INNry. Lt_vy. Di. Py. Hj 11 Wi. Dg&hl=en_ US&pli=1 Identify the RIM/DCM construct(s) that models that data item DCM column under Models Identify the SDTM construct(s) that models that data item SDTM column under Models Identify the terminologies that model some of the values required Terminology Columns including Snomed, Med. DRA and NCI Thesaurus Identify the data types and values that characterize the values of some of the data items Data Types and Units columns including those for RIM and SDTM We will be considering various constructs of HL 7/RIM, Detailed Clinical Models and other models in conjunction

Consider a Data Item Example: • History of Therapy – Name: Insulin – Length of Therapy: 100 days – Start. Date: Date – End. Date: Date

Mapping Methodology 1. Identify Information Model Elements 1. Therapy => 1. Substance. Administration (HL 7/RIM) 1. 2. 3. 2. Medication. Name (HL 7/RIM) Identify Controlled Vocabularies 1. • Medication. Name => Controlled Vocabulary Rx. Norm (also known as Terminology Binding) Identify Data Types • • Medication (sub. Class of Manufactured. Material, HL 7/RIM) Specific type of Participation called Consumable (HL 7/RIM) Insulin => 1. 2. effective. Time status. Code Dates and Times => TS data type in HL 7 Identify Units 1. Included in the definition of data types … taken from the UCUM standard

Mapping Methodology (Continued) 1. Mappings between Information Model elements; 1. Systolic. BP VSTEST, VSTESTCD = SYSBP • Mappings between controlled vocabularies: • • Systolic. BP “Some Snomed Concept” SYSBP “Some NCI Thesaurus Concept” “Some Snomed Concept” “Some NCI Thesaurus Concept” Between Data Types and Units • HL 7: PQ VSRESU

Design Choice: Leverage Existing Implementations and Systems • SHER System – Re-use the Reasoner to compute eligibility criteria • Semantic DB System – Re-use the NLP parser (if available) to parse the textual representation of the clinical trials criteria into structured queries, rules, whatever

Technical: Eligibility Criteria in OWL Patient that (has. Problem some Diabetes. Type 1 or has. History some Ketacidosis) and has. Therapy (some Therapy that has. Length all int[>30] and has. Time. Period all int[< 365]) Just for illustration purposes … need thorough and detailed analysis to get it right.

Technical Design: Eligibility Criteria using Rules IF (the_patient. has. Problem = Diabetes. Type 1 OR the_patient. has. History = Ketacidosis) AND the_patient. has. History. name = Insulin AND the_patient. has. History. length > 30 AND the_patient. has. History. time. Period < 365 THEN the_patient is eligible for the clinical trial

Mapping Design Issues • Are mappings always 1 -1? • Is it always possible to get synonym mappings? • What happens to these mappings when there are changes in the information models? • Are these mappings enough to enable a bidirectional flow through between EMR and Clinical trials data?