CDA vs Archetypes: Use Cases March Tomaž Gornik, Co-founder and CEO

Transcription

1 CDA vs Archetypes: Use Cases March 2014 Tomaž Gornik, Co-founder and CEO

2 $25M revenue Company facts 120 employed professionals 80 experienced software developers Products, References and domain knowledge in healthcare and telecommunications 24 years in IT ISO 9001 & certified 2

3 Marand Healthcare Solutions National OnLine Health Insurance Card (IBM) Cancer Registry of Slovenia, Cancer Screening Think!Med Clinical TM» Institute of Oncology systems» UMC Ljubljana Children s Hospital Cardio Surgery, Infections Clinic, Nuclear Medicine, Radiology Think!EHR TM Platform Slovenia s national ehealth Infrastructure City of Moscow ehealth Project

4 Agenda Use Cases: 1. EHR system for Ljubljana Children s Hospital 2. Slovenia s National ehealth Infrastructure 3. Moscow City EHR Project Summary 4

5 Case 1: Hospital EHR EHR system for UMCL Children s Hospital Challenges» Storing and managing all clinical data» Analytics to support clinical processes» Enable Clinical Decision Support» Short time-to-delivery 5

6 Ljubljana Children s Hospital Part of University Medical Center Ljubljana 10 specialities, including ICU and surgery New, state-of-the-art facility» 200+ beds, 14 ICU, 4 OR, 5 Recovery» PCs, Touchscreens, ipads» New medical devices Integrated barcode, medical devices All clinical content in archetypes 6

7 Next Generation Applications 7

8 Meaning preservation Key Requirements» Vertical semantic framework from DB to GUI Sharing moving the data» Ability to merge data from multiple sources - Solid data standards & protocols Aggregation putting the data together» Semantic merging using shared content models» Portable querying Evolution of systems» data models need to evolve through time 8

9 openehr An open, domain-driven platform for developing flexible e-health systems Separation of content and technology Computable data Two-level modeling» Archetypes maximal data set» Templates - data set for use case Querying Multi-lingual 9

10 Content Technology HC Professional Domain knowledge Software vendor Technical knowledge

11 The Clinical Process

12 Archetypes

13 Templates

14 Meaning Preservation Vertical semantic framework from GUI to DB 14

15 Queries 15

16 Visual Form Builder

17 Clinical Decision Support 17

18 Clinical Decision Support 18

19 SMART API integration 19

20 EHR Search 20

21 Case 2: ehealth Programme Slovenia s National ehealth Infrastructure Challenges» Governance of knowledge artifacts» Integration with provider IT solutions» Exchange of main clinical documents» Population health analytics 21

22 Slovenia s ehealth Infrastructure Interoperability backbone to exchange clinical data among all 120 healthcare stakeholders Infrastructure for all ehealth including ereferral, eprescription and health registries Based on industry standards - IHE and openehr Cross-border integrations through epsos Consortium of eight companies led by Marand Delivered in under six months! 22

23 Tools to Manage Knowledge 23

24 ehealth - Seamless integration 24

25 Simple Population Questions? How many patients have been diagnosed with Sickle Cell disease last year? How many diabetes patients are controlling their sugar? What is the percentage of patients with high BMI? 25

26 ehealth Infrastructure e-health Applications ereferral eprescribing Patient Summary MPI Terminology Provider registries Health Information Access Layer Service Bus Connectivity Transformation & Routing Orchestration Management & Monitoring Security & Privacy HCP app 1 HCP app 2 HCP Point-of-Service Applications CDR CDR CDR Documents Structured data Images DWH

27 openehr & IHE can coexist Archetypes maximal data set key for agreement on data structures Use Templates to generate XML message structures to map to CDA L2/L3 Best of both worlds» Standard integration infrastructure including document exchange» Structured data aggregation and querying 27

28 Population Health Analytics Export (EHR, Query result) Real-time synch with relational tables ODATA interface (Excel, Tableau) Streaming API Triggers/Events API 28

29 Case 3: City-wide EHR Moscow City EHR Project Challenges» Scale: 12 million patients, 1B documents» Many applications, vendors, one CDR» ehealth platform for the future» Short time-to-delivery 29

30 City of Moscow ehealth Moscow city medical facilities, including: 149 hospitals, 76 health centers, 428 policlinic institutions Volume: Patients- 12 million, Beds in hospitals 83,000 Physicians 45,000, all users 130,000 Patient visits/year million Documents/year - 1 Billion, 25TB Based on IHE and Think!EHR TM Platform Pilot live at 6 clinics as of Aug 2013! 30

31 Lifetime Health Records How will we read EHR data 50 years from now? Separate the semantics from the software 31

32 Vendor-Neutral Data Images - PACS Documents CDA/IHE Structured Data -? Options» HL7v3 RIM» openehr/en

33 ehealth - City of Moscow 33

34 Proven performance Tested and certified on Benchmark:» 20M Patients» 1B documents, 25TB data» Average Query Response time: < 1s Record performance: total average system performance of 39,942 AQL TPS Supports IBM Websphere, IBM DB2, IBM Puresystems Ready Oracle DB, Oracle Weblogic Server, Oracle Exadata Database Machine 34

35 Summary 35

36 Why a CDR Platform? Clinical data aggregation Reporting and analytics Building new applications» Clinical Decision Support» Single Patient View» Patient Portals» Mobile apps 36

37 HIE Solution Examples» National ehealth Backbone (Slovenia) Clinical Registries» National Cancer Registry (Slovenia)» National Breast Cancer Screening Programme Clinical Decision Support» Cambio COSMIC (Sweden) Full-Blown EHR» Think!Med Clinical - University Medical Center» SIMI Moscow City EHR 37

38 openehr provides Semantic coherence in the application stack (all layers of software know what the data mean) A high level of re-use of artefacts define once, reuse and generate many times A single, stable reference model for sharing clinical and related information A standardised query language for writing portable queries A standardised, re-usable way of connecting to terminology 38

39 Benefits vs HL7 RIM Faster time-to-market, lower complexity» Model to Application using tools Flexible Clinical Models» Clinician involvement» Maximal vs minimal data sets, full context» Easily extended, versioned Portable query language Form/View Builder and server Proven Performance 39

40 Clinical Information Modeling Initiative Chair: Stan Huff, Intermountain Health Cambio Healthcare Canada Health Infoway CDISC Electronic Record Services EN Association GE Healthcare HL7 IHTSDO Intermountain Healthcare Kaiser Permanente Mayo Clinic MOH Holdings Singapore National Institutes of Health (USA) NHS Connecting for Health Ocean Informatics openehr Foundation Results4Care SMART South Korea Yonsei University Veterans Health Administration

41 Thank you! Tomaž Gornik,

42 Bridging the Gap What is needed for Semantic Interoperability? 1. Agreement on vendor-neutral data structures and formats 2. Available clinically modeled data structures to support longterm health records for care coordination and research 3. A reference model ensuring computability of health information 4. A new breed of solutions that take advantage of semantically interoperable data 42

43 Think!EHR TM Platform Think!EHR TM Platform is a vendor-independent, big-data, high-performance Clinical Data Repository designed to store, manage, query, retrieve and exchange structured electronic health record data based on open standards Think!EHR TM Platform includes: Think!EHR Server, Think!EHR Explorer, Think!EHR Integration, Think!EHR EventTracker, Think!EHR Development Toolkit 43

44 Full Lifecycle Support Knowledge Management Operation Application Development Deployment Integration 44

45 openehr & IHE can coexist Archetypes maximal data set key for agreement on data structures Use Templates to generate XML message structures to map to CDA L2/L3 Best of both worlds» Standard integration infrastructure including document exchange» Structured data aggregation and querying 45