Semantic Technology for Provider-Payer-Pharma Data Collaboration


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Semantic Technology for Provider-Payer-Pharma Cross-Industry Data Collaboration
Building Intelligent Health Data Integration

The cost to cover the typical family of four under an employer health insurance plan is expected to top
$20,000 this year. The integration of health data (including electronic health records, health insurer records, pharma research and clinical data, and real-world evidence) will increase transparency and efficiency, improve individual and population health outcomes, and expand the ability to study and improve quality of care.

Traditional approaches to data integration and analytics depend on widely understood data and well-defined use cases for analyzing that data. The integration of pharma, provider, payer, and real-world data will identify new ways in which health data can be combined and analyzed to improve quality of care. Semantic technology can speed integration of health data, while supporting an evolutionary approach to developing and leveraging expertise.

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Semantic Technology for Provider-Payer-Pharma Data Collaboration

  1. 1. Semantic Technology for Provider-Payor-Pharma Data Collaboration Building Intelligent Health Data Integration ©2013, Cognizant
  2. 2. Healthcare Expenditure as a % of GDP th United States ranked 1st in st in Expenditure,in Life Expectancy United States ranked 1 Expenditure, 27 27th in Life Expectancy Health expenditure as a share of GDP, OECD countries, 2012 Strong need to drive down the cost of Healthcare while improving Outcomes Source: OECD Health data, June 2012 1 | ©2013, Cognizant
  3. 3. Shift to Personalized Medicine and Targeted Therapies The emerging patient-centric healthcare services will need to be outcomes-driven, service oriented, and adaptive to respond to human behaviors Patient Wellness & Quality of Life Personalized Healthcare and improved Disease Management 2 | ©2013, Cognizant 02 Compliance Connected Personal Health Engaging Customers Interactive & game-based activity to connect and engage better with patients to drive adherence and compliance 01 03 Patient Centric Improved Patient Outcomes Connected Health Using Technology to provide Healthcare remotely (Care Management) Cost Containment 04
  4. 4. Remote Health Monitoring is a Key Element of Connected Health Collect Transmit Evaluate Patients + Data Insight 3 | ©2013, Cognizant Engage Intervene
  5. 5. Patient-Centric Integrated Health Data 4 | ©2013, Cognizant
  6. 6. Big Data in Healthcare Four distinct big data pools exist in the U.S. health care domain today with little overlap in ownership and low levels of integration. Owner Owner Pharmaceutical companies; Academia Example datasets Various, including stakeholders outside of healthcare Pharmaceutical R&D Data Clinical Trials; Compound Libraries Patient Behavior Data Integration of Data Pools Required for Major Opportunities Example datasets Utilization of Care; Costs Estimates Claims and Costs Data Patient behaviors & preferences; Exercise data captured in running shoes and wearable health monitors Owner Owner Payers, Providers Example datasets Clinical Data Providers Example datasets Electronic Medical Records; Medical Images; Prescription Data Source: Big Data: The Next Frontier for Innovation, Competition and Productivity; McKinsey Global Institute, May 2011 5 | ©2013, Cognizant
  7. 7. Semantic Technology “Super Charging” Health Data Integration Intelligent Health Data Integration Technology Stack Health Data Exchange Technology Stack Semantic Technology CDISC Expert Knowledge PRM Entity Resolution CDASH Patient Behavior Data ODM SDTM ADaM SHARE SEND Patient Privacy Data Virtualization Data Federation Claims EDI Eligibility Linked Data Claim Submission Claim Status Services Review 6 | ©2013, Cognizant CDA CCD RIM CCOW HL7 QRDA GELLO ICSR SPL Provenance
  8. 8. Type 2 Diabetes Research using Semantic Technology Mayo Clinic used Semantic Web technologies to develop a framework for high throughput phenotyping using EHRs to analyze multifactorial phenotypes 1 4 Diseasome Mapped Clinical Database to Ontology Model DBPedia ChemBL Find Genes or Biomarkers associated with T2D, as Published in the Literature 2 5 RxNorm DailyMed Clinical DB Find All FDA-approved T2D Drugs; Find All Patients Administered these Drugs Diseasome RxNorm ChemBL DrugBank Clinical DB Selected Genes have Strong Correlation to T2D. Find All Patients Administered Drugs that Target those Genes. 3 6 RxNorm SIDER Clinical DB Find Which of these Patients are having a Side Effect of Prandin Diseasome RxNorm ChemBL | ©2013, Cognizant Clinical DB Find All Patients that are on Sulfonylureas, Metformin, Metglitinides, and Thiazolinediones, or combinations of them Reprinted with permission from Jyotishman Pathak, Ph.D., Mayo Clinic 7 DrugBank
  9. 9. Semantic Technology Components User interface and applications Trust Proof Unifying logic Querying: SPARQL Ontologies: Predicate QWL Rules: Object RIF/SWRL Taxonomies: RDFS Data interchange: RDF Syntax: XML Identifiers: URI 8 | ©2013, Cognizant Character set: UNICODE Cryptography Subject
  10. 10. Semantic Technology Components SNOMED Clinical Terms Ontology Integrating Expertise: Selecting for Hypothyroidism User interface and applications Case Medications Levothyroxine, synthroid, levoxyl unithroid, armour thyroid, desicated thyroid, cytomel, triostat, liothyronine, synthetic trilodothyronine, liotrix, thyrolar ICD-9 Codes for Hypothyroidism 244, 244.8, 244.9, 245, 245.2, 245.8, 245.9 Abnormal Lab Values TSH > 5 OR FT4 < 0.5 Trust ICD-9 Codes for Secondary Causes of Hypothyroidism 244.0, 244.1, 244.2, 244.3 } Cryptography ICD-9 Codes for Post Surgical or Post Radiation Case Definition Hypothyroidism All three conditions required: Proof 40930008 sno:40930008for hypothyroidism OR abnormal TSH/FT4 ID 193*, 242.0, 242.1, 242.2, 1. ICD-9 code sno:40930008 Preferred Name use Hypothyroidism 242.3, 242.9, 244.0, 244.1, 2. Thyroid replacement medication 244.2, 244.3, 258* 3. Require at least 2 instances of either medication or lab Pregnancy Exclusion SPARQL queryID between the first244 last (abbreviated) with at least 3 months and ICD-9 Codes icd9:244 CPT Codes for Post Unifying lab instance of medication andlogic Any pregnancy billing code icd9:244 Preferred Name Acquired hypothyroidism Radiation Hypothyroidism or lab test if all Case 77261, 77262, 77263, 77280, icd9:244.8 ID 244.8 Definition codes, labs, or 77285, 77290, 77295, 77299, Ontologies: Rules: acquired icd9:244.8 DISTINCT ?patientID, ?patientName medications fall within 6 {Case Exclusions Preferred Name Other specified SELECT 77300, 77301, 77305, 77310, QWL RIF/SWRL months before pregnancy Exclude at any time in etc. Querying: if the following information occurshypothyroidism to one year after the record: SPARQL WHERE pregnancy ind:4093008 causes of hypothyroidism ID 40930008 • Secondary Exclusion Keywords V22.1, V22.2, 631, 633, Taxonomies: RDFS { • Post surgical or post radiation hypothyroidism Multiple endocrine neoplasia, ind:4093008 Defined By sno:40930008 633.0, 633.00, 633.1, • Other thyroid diseases ICD “HYPOTHYROIDISM” MEN I, MENII, thyroid cancer, ?patient Inclusion ?indication icd9:244 ind:4093008 633.10, 633.20, 633.8, • Thyroid altering medication thyroid carcinoma icd9:244.8 633.80, 633.9, 633.90, } 645.1, 645.2, 646.8, etc. DataExclusion ICD RDF interchange: icd9:631 ind:4093008 icd9:633 Thyroid-Altering Medications Case Exclusions Exclusion Keywords Phenytoin, Dilantin, Infatabs, Time dependent case exclusions: Optiray, radiocontrast, Dilantin Kapseals, Dilantin-125, Syntax: XML • Recent pregnancy TSH/FT4 iodine, omnipaque, Phenytek, Amiocarone • Recent contrast exposure visipaque, hypaque, Pacerone, Cordarone, Lithium, Conway et al.; Denny et al. ioversol, diatrizoate, Eskalith, Lithobid, iodixanol, isovue, Methimazole, Tapazole, Identifiers: URI Character set: UNICODE iopamidol, conray, Northyx, Propylthiouracil, PTU iothalamate, renografin, sinografin, cystografin, Source: SNOMED-CT Ontology, IHTSDOpermission from Jyotishman Pathak, Ph.D., Mayo Clinic conray, iodipamide Reprinted with 9 | ©2013, Cognizant
  11. 11. Semantic Technology Components User interface and applications Linked Open Drug Data (LODD) Cloud Trust Proof Unifying logic Rules: RIF/SWRL Taxonomies: RDFS Data interchange: RDF Cryptography Querying: SPARQL Ontologies: QWL Source: SemanticSyntax: XML Web for Health Care and Life Sciences Interest Group Identifiers: URI 10 | ©2013, Cognizant Character set: UNICODE
  12. 12. Linked Data Case Study Highlights Detecting off label prescribing based on adverse events 11 | ©2013, Cognizant Monitoring emerging therapies for growing disorder populations
  13. 13. Adding Semantic Technology to Health Data Integration Gets Us Closer to Solving Connected Health Semantic Technology Connected Health Collaboration • Population Registry • Care Management • Dynamic Care Plan • Medical Management • Productivity Management • Workflow Automation • Alerts • Providers, Members • Community Organizations Analysis • Risk Stratification • Care Engine Rules • Utilization Trends • Population Management • Care Gaps (Trigger) • Episode Grouper • Predictive Analysis • Patient Adherence • EMPI (Master Person Record • Relationships across data • Claims • Lab • Pharmacy • Unstructured to structured usable data • Extended EMRs • Member Messaging Engine • External EHR • Self-Reported • Next Gen • Creation of Cleanest Record • Identify Opportunities for Action • Identify Clinical Concepts Integration Data Expert Knowledge Linked Data Data Virtualization HL7 12 | ©2013, Cognizant • Cerner • Internal EHR Provenance Entity Resolution CDISC Data Federation Claims EDI
  14. 14. Call for Action 1 2 3 4 Assess Identify Define Execute how the patientcentric model affects your programs the relevant patient behavior data that you can use use cases that drive from the disease state perspective projects that rapidly achieve capabilities, but don’t try to boil the ocean    13 | ©2013, Cognizant Pilots Proofs of Concept Agile, Incremental Development
  15. 15. Q&A ©2013, Cognizant
  16. 16. Speakers Nagaraja Srivatsan, Senior Vice President, Cognizant Srivatsan has more than two decades of experience in the Information Technology industry and deep knowledge of the Healthcare & Life Sciences domain. Srivatsan drives Cognizant’s strategy in Healthcare and Life Sciences. Srivatsan was recognized as one of the top 100 most inspiring people in the life sciences industry award by PharmaVOICE publication and has been regularly quoted in national and global magazines like CIO, PharmaVoice, and CNNFn. Thomas (Tom) Kelly – Practice Director, EIM Life Sciences Thomas is a Practice Leader in Cognizant’s Enterprise Information Management (EIM) Practice, with over 30 years of experience, focusing on leading Data Warehousing, Business Intelligence, and Big Data projects that deliver value to Life Sciences and related health industries clients. 15 | ©2013, Cognizant
  17. 17. Thank you ©2013, Cognizant