MarkLogic Semantics in Healthcare and Life Sciences for LIDER COPYRIGHT 2015 MARKLOGIC CORPORATION. ALL RIGHTS RESERVED.

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1 MarkLogic Semantics in Healthcare and Life Sciences for LIDER

2 The Only Enterprise NoSQL Database Search & Query ACID Transactions High Availability / Disaster Recovery Replication Government-grade Security Scalability & Elasticity DATABASE SEARCH On-premise or Cloud Deployment Hadoop for Storage & Compute Semantics Triple Store and Inferencing APPLICATION SERVICES Faster Time-to-Results SLIDE: 2

3 Enterprise Data Layer = Data as a Service Portals Web Applications BI Applications Mobile Search Retrieve Alerting Annotation RESTful Web Services User Data Management Search Information Domains Transformation & Aggregation Powerful APIs as-is content ingestion Semantic Driven Facets User Defined Functions Semantic Model Metadata/Repository Enterprise Data Layer Security & Auditing Transactional & ACID Geospatial Additional Content Alerting 4 Workflow Ingestion: RDF, Structured, Semi-structured, Unstructured RDBMS Documents Linked Data Binaries, Images, etc Others Twitter, FB, etc.... SLIDE: 3

4 MarkLogic is Schema-Agnostic { } "title": FDA Drug Safety " Communication", "date": " Z", "type": press release", event" : { "type": drug safety", "category": adverse event", }, "location": [ , ], "description": "A recent report " by the FDA links Byetta to prostate cancer ", "meta": [ {"triple": { "subject": "IRIID", "predicate": "isa", "object": drug" }}, {"triple": { "subject": "IRIID", "predicate": "value", "object": Byetta" }} ] SLIDE: 4

5 MarkLogic is Schema-Agnostic "title": FDA Drug Safety " "date": " Z" "type": press release" "type": Drug Safety " "category": adverse event" event" "meta" "description": "A recent report " "triple" "triple" "subject": "IRIID" "subject": "IRIID" "object": Byetta" "predicate": "value" "predicate": "isa" "object": drug" "location": [ , ] SLIDE: 5

6 Flexible Profiles Provider Beneficiary NPI Associations Metadata Providers Addresses Assoc - Hosp Assoc -Prov Statistics Visit Bill-Amt ID Address Address Address Inclusionreason Rx- Count Benedistance Annual - Billings Earlyrefill Distance Top- schedii- Rx SLIDE: 6

7 Flexible Profiles: Easy Adaption to Fraud Schemes Provider Beneficiary Board- Member Assoc - Hosp Rx- Count NPI Associations Assoc -Prov Statistics Annual - Billings Metadata Exclusion DEA Providers Visit Bill-Amt ID Personal- Property Addresses Address Address Address Inclusionreason Earlyrefill Benedistance Distance Top- schedii- Rx MRIcount Indictment Auto House Ambulancecount SLIDE: 7 $-value $-value

8 Semantics: A New Way to Organize Data Data is stored in Triples, expressed as: : Subject : Predicate : Object : Citalopram : iscontraindicatedby : MAOI(s) : MAOI : isdrugclassof : Isoniazid Rules tell us something about the triples: If (A iscontraindicatedby X) AND (X isdrugclassof) then (A iscontraindicatedby Y) Citalopram" iscontraindicatedby MAOI" isdrugclassof Isoniazid" iscontraindicatedby SLIDE: 8

9 Semantics Enterprise triple store, document store, database combined Store and query billions of facts and relationships; infer new facts Facts and relationships provide context for better search Flexible data modeling integrate and link data from different sources Standards-based for ease of use and integration RDF, SPARQL, and standard REST interfaces New in MarkLogic 8: SPARQL 1.1, graph traversal, automatic inference using rule sets, and SPARQL from server-side JavaScript and Node.js SLIDE: 9

10 Flexible Data Model Store and manage JSON, XML, RDF, and Geospatial data with a document-centric, schema-agnostic database JSON, XML, RDF, Geospatial data, and also large binaries all stored and managed on a single unified platform Document-centric and schema-agnostic for agility, reducing lost fidelity and functionality from data conversion and brittle ETL Use the data format that makes the most sense, keeping the data in its most readable form SLIDE: 10

11 When all you have is a hammer... Instead, put data where it makes sense Data provenance, access control, timeseries values, temporal qualities: XML or JSON Relationships: RDF unless there is need to apply metadata to relationships Ontologies: RDF Taxonomies: RDF sameas : thesaurus or RDF Analytics: XML/JSON/Columnar store Take advantage of envelope pattern (metadata section, embedded triples in documents) SLIDE: 11

12 MarkLogic Semantics Architecture Interface Layer JSON, XML, RDF, Geo, Binaries mlcp HTTP Protocol REST API Query Layer JavaScript XQuery SQL SPARQL JS XQuery SPARQL Indexes Universal Index Geospatial Index Triple Index Triple Cache Storage Layer Scalability and Elasticity ACID Transactions Bitemporal Triple Store SLIDE: 12

13 Better Together Inference Traversal Document Store + Data Store + Triple Store SLIDE: 13

14 healthcare MarkLogic Life Sciences & Healthcare Solutions

15 COMPETITIVE 360

16 Industry News and Events Competitive Timelines Alerting Customer/Patient Data Literature, Publications, SPL Competitive 360 Sales Data Knowledge Discovery Search & Query SLIDE: 16 <triple> <subject> IRIID </subject> <predicate> value </predicate> <object> ABC 123 </object> </triple> </description> Content Enrichment Geospatial & Map Integration

17 PATIENT

18 Patient 360 Medical Charts (Patient history, demographics, vitals, physician notes ) Education Services (Content, documents, classes, videos, blogs) Scheduling System (Exam rooms, doctor/nurse schedules, patient appointments, equipment, location/geospatial) Medical Imagery (X-rays, labs, photos, patient info, dates, prescribing doctor) Call Center System (History, case notes, call recordings) SLIDE: 18 Patient Billing (Treatments, billing codes, insurance information, credit card info, payment notes) Prescription System (Prescriptions, refills, doctor notes, allergies, drug interactions)

19 Patient 360 Medical Charts (Patient history, demographics, vitals, physician notes ) Education Services (Content, documents, classes, videos, blogs) Scheduling System (Exam rooms, doctor/nurse schedules, patient appointments, equipment, location/geospatial) Medical Imagery (X-rays, labs, photos, patient info, dates, prescribing doctor) Call Center System (History, case notes, call recordings) SLIDE: 19 Patient Billing (Treatments, billing codes, insurance information, credit card info, payment notes) Prescription System (Prescriptions, refills, doctor notes, allergies, drug interactions)

20 Enabling Multi-Dimensional Views of Patients Physician/Payer Data: Holistic view of an individual Activities: View medical history, tests and allergies Epidemiologist Data: Biographic, diagnosis & location Activities: Analyze aggregates and temporal and geospatial distributions Adverse Event Researcher Data: Prescriptions history, hospitalizations and doctors visits Activities: Detect anomalies, predictive analytics and trend analysis Patient s Record? Anonymized Information? Anonymized Information SLIDE: 20

21 REAL WORLD EVIDENCE

22 Real World Evidence Features Data Sources Anonymized Profiles Adverse Effect Aggregate Patient Profile Search & Discovery Semantic Search Predictive Analytics Geospatial Analytics Semantic Visualization Alerting Data Exchange HL7 v3 (CCD, CCDA, CCR) HL7 v2 (ADT, MDM, ORU) FHIR (Fast Healthcare Interoperability Resources) Common Technical Document (CTD) MEDLINE FDA Adverse Event Reporting System (FAERS) data files E2B R2 and R3 (FDA) FMQL Billing formats X12 Data Repository Exchanges Medical Records Claims Eligibility Criteria Healthcare Databases Social Media SLIDE: 22

23 OPERATIONAL REPOSITORY & DATA SERVICES HUBS

24 HEALTHCARE.GOV

25 Health insurance for millions of Americans Before MarkLogic Unable to handle complexity Impossible data model Development too slow Limited scalability Inflexible to change After MarkLogic Built for Today s Data Schema-agnostic data model that could handle various data sources and adapt to later changes with policies and regulations Agile Development 18-month timeframe from procurement to launch for what has been called the most complex government-it project of all-time Secure and Trusted Did not have to sacrifice any of the enterprise features required, and could rely on a system with government-grade security, ACID transactions, and HA/DR Successful Deployment Over 8 Million people signed up for health insurance in less than 5 months SLIDE: 25

26 Health Insurance Payers Income and Eligibility Confirmation HIM Millions of Subscribers State Exchanges DSH Data Services Hub SLIDE: 26

27 Health Insurance Payers Income and Eligibility Confirmation HIM Millions of Subscribers State Exchanges DSH Data Services Hub SLIDE: 27

28 8,000,000+ new beneficiaries 150,000+ concurrent users 0 zero data loss SLIDE: 28

29 ENTERPRISE DATA LAYER: REGULATORY

30 MarkLogic Adverse Events/Drug Reaction Profiles 360 View SAS, Excel Signal Profile Review Application Alerting Web Application After MarkLogic Simple, Fast Data Integration Bring together documents, data, and triples from over 90 different sources within and outside of Monsanto Powerful, Intelligent Data Layer Data analytics that leverage Monsanto s ontologies and uses semantics as the glue to answer new questions (e.g., What are all of the genomic elements (in)directly contributing, regulating or modulating a specified developmental or physiological Load data process as in is a plant or crop species? ) More Relevant Content Delivery Contextually tailor access to information by leveraging the user s role, context, activity, and location to dynamically deliver the right information FAERS Europe VIGIBASE Enterprise Scale IND, NDA, MedLINE EMBASE STRIDE All of Periodic the required Safety enterprise features HA/DR, security, etc. plus other features such as Updates Flexible Replication and Tiered Storage SLIDE: 30

31 Medical Device Submissions PDF Combine and enrich information from multiple sources Medical device manufacturer packets Adverse events reporting Data can now be searched & analyzed from one location Compare to past data on same classes of devices Compare to past packet contents (what s new, have issues been addressed, etc.) SLIDE: 31

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