Bringing the Pieces Together Dr. Dirk Colaert MD Advanced Clinical Application Research Manager

1. Bringing the Pieces TogetherDr. Dirk Colaert MDAdvanced Clinical Application Research Manager

2. …Towards a decentralized healthcare model The Healthcare paradigm shift... Yesterday Today and Tomorrow Workflow Integrated & automated Fragmented The bottom line: Increase quality Lower cost Diagnosis& Treatment Less invasive, image-based, lifetime care Invasive, often acute Focus Provider-centric Patient-centric Decentralized, community based Follow-up Hospital based

3. Assessment Information Activities The Building Blocks of the Clinical Process collect, distribute, share Information plan, schedule, act, collaborate  Workflow think, decide  Reasoning

4. Bringing the pieces together Clinical Process plan, schedule, act, collaborate  Workflow think, decide  Reasoning collect, distribute, share Information

5. Scaling out into e-Health Information Information Decision Support Decision Support Rules and reasoning Rules and reasoning Clinical Workflow Clinical Workflow Workflow + collaboration Workflow + collaboration Phase 1 Phase 2 Phase 3 e-Health Shared Pathways Shared Pathways Disease Mgmt. Programs Drug interactions, monitoring, compliance, alerts, … Health Monitoring Drug interactions, monitoring, compliance, alerts, … individual Inter-professional collaboration e-Prescribe, CPOE e-Prescribe, CPOE Personal Health Plan Personal Health Record (PHR) Personal Health Record (PHR) Shared Information (EHR) + collaboration Shared Information (EHR) + collaboration Electronic Health Record Population monitoring, epidemiology, Bio surveillance Population monitoring, epidemiology, Bio surveillance Health Mgmt. Programs population Research from bench to bed, from bed to bench Research from bench to bed, from bed to bench Translational Medicine

6. I W R InformationDecision SupportWorkflow

7. Information  Connected Knowledge • Knowledge • Data  information  knowledge •  Intelligence  Latin: inter+legere  “connect between” • Semantics • “sema”  sign  significance  meaning • The Semantic Web • A set of web technologies and standards to use, communicate knowledge and pertain meaning • Ontologies • Formal description of a domain using concepts, facts and relations • standards: OWL, RDF • Connected knowledge: merging of ontologies - the value of the sum is bigger than the sum of the values ! • Rules • A set of rules makes a ‘theory’ (no standard yet, RIF?) • Unifying Logic • Rules about rules, policies • Proof and trust • The ultimate data exchange

8. Simple ontology hobbies Religion Other Brands Audi Opel Salary Me Model of Instance of A3 A4 owns ABC 1234_567 Audi A6 has color Green

9. Knowledge: traditionally ‘assumed’ visit Aspirin ? Lab Test Tenormin hypertension

10. Connected Knowledge: explicit visit Conclusion of Aspirin Lab Test Tenormin Indication for hypertension threated by

14. Information: anytime, anyplace

15. Information @ Home  Home Devices Internet

16. I W R InformationDecision SupportWorkflow

17. Assessment MedicalKnowledge Medical Knowledge Shows why and how it works Rule deduction Bayesian networks Shows that it works Rule translation Clin. Pathways Medical Evidence Rule deduction Guidelines on paper genome Clinical Data Med. Science: Pharmacology, Physiology, Genomics, Anatomy, Biochemistry, … Clinical Trials proteome Patient Rule induction Images … New hypotheses

18. Reasoning • Clinical knowledge + medical knowledge + reasoning  decision support  generation of clinical pathways • Reasoning engine: Agfa’s Euler engine (open source) • Seamless integration of deductive and Bayesian inference

19. Decision support Human Interaction Guidelines Policies Clinical Data Events Requests (Local, Operational, Community, ...) Request for Support Recommendation Action Desicion Desicion support

20. Decision support is fractal (scalable from pixel to community) Country  World  Healthcare Management Region  Disease Management Institution  Clinical Pathway Department  Order Workstation/User  Task Application  Event

21. hobbies Religion Other Brands Audi Opel Salary Me Model of Instance of A3 A4 owns ABC 1234_567 Audi A6 has color Green Issues when merging ontologies • Inconsistencies • Ontologies are build without other ontologies in mind. When merged they can contain contradictions. • This can be detected and brought to the attention of the user. • Semantic differences • See the example above about “Audi” as a car and “Audi” as a brand. • Can be solved by using standard ontologies as much as possible (e.g. SNOMED in the medical domain) • Side effects • Duplicate examinations • Bad sequence • Wrong conclusions • Trust • When an external ontology is about to be merged the source must be trustworthy

22. Duplicate examinations • CP 1+2 • Day 1 • CP1_Action1 • CP2_Action1 • Day 2 • Lab test: RBC • CP2_Action2 • Day 3 • CP1_Action3 • Lab test: RBC • Day 4 • CP1_Action4 • CP2_Action4 • CP 1 • Day 1 CP1_Action1 • Day 2 Lab test: RBC • Day 3 CP1_Action3 • Day 4 CP1_Action4 • CP 2 • Day 1 CP2_Action1 • Day 2 CP2_Action2 • Day 3 Lab test: RBC • Day 4 CP2_Action4

23. Bad sequences • CP 1+2 • Day 1 • CP1_Action1 • CP2_Action1 • Day 2 • RX+contrast • CP2_Action2 • Day 3 • CP1_Action3 • RX • Day 4 • CP1_Action4 • CP2_Action4 • CP 1 • Day 1 CP1_Action1 • Day 2 RX+contrast • Day 3 CP1_Action3 • Day 4 CP1_Action4 • CP 2 • Day 1 CP2_Action1 • Day 2 CP2_Action2 • Day 3 RX • Day 4 CP2_Action4

24. Wrong conclusion • CP Ischemia+GU • Rule x • Rule: … Aspirine … • Rule y • Rule a • Rule b • Rule … • CP Ischemia • Rule x • Rule: … Aspirine … • Rule y • CP Gastric Ulcus • Rule a • Rule b • Rule …

25. Bayesian Believe Networks

26. Bayesian Believe networks

31. I W R InformationDecision SupportWorkflow

32. Clinical workflow is iterative and adaptable

33. Clinical workflow is iterative and adaptable

34. Assessment Information Activities Clinical workflow is holistic Adaptable Clinical Workflow Theoretical World Semantic World Real World Executional World Quality Control Efficiency Practice Knowledge Clinical Knowledge Operational Knowledge Medical Knowledge Rule deduction Bayesian networks Rule translation Clin. Pathways Orders Appointments Medical Evidence Rule deduction Guidelines on paper Clinical Data Machines Hospital Care Providers Med. Science: Pharmacology, Physiology, Genomics, Anatomy, Biochemistry, … Clinical Trials Patient Images Pharmacist Rule induction New hypotheses Rooms/Beds . . . GP

35. Semantic world: Rules and Ontologies • Example of generic rule: • If a diagnosis is an exclusion for process X and this diagnosis is assessed by process Y and if the diagnosis is not known, then process Y is a necessary step. • Clinical example: • Pregnancy is assessed by HCG-labtest • Pregnancy is an exclusion for CT •  if a CT is needed HCG-labtest will be inserted in the workflow

36. Hip fracture case • Specific medical knowledge • Orthopedist: • If the patients complains about the hip you can do a CT or a Physical Examination • If the outcome of the reading gives “fracture” (<=0.5) then rehabilitation must follow else surgical treatment • If the assessment gives good result (>=0.5) then rehabilitation else redo the surgical treatment • General • Surgical treatment consists of: pre-surgical exam, anesthesia, surgical intervention and assessment • Anesthesia must be completed before starting the intervention • Pre-surgical exam must be completed before starting the intervention • Intervention must be completed before starting the assessment • + Radiation Protection Guidelines + gynecology + lab • Patient state • Medical context with Jane as patient and a CT reading giving the diagnosis of hip fracture (0.7-0.9) • outcome

37. Generated output for hip case (partial)

38. Drawbacks of Classical Clinical Pathways • Static or limited dynamicity • Standard procedures  not patient specific • Doesn’t take into account the most current operational and medical knowledge • Procedural (as opposed to declarative) • Merging issues, maintainability • These are also pretty much the disadvantages of many generic workflow systems

39. The executional world • Goal • Holistic behavior • without central control • without the need for one database • Scalable • Extendable • Smart • Non disruptive (embed existing stuff) • Method: • Peer to peer processes with following capabilities: • Reasoning (driven by KPI’s)  think • (abstracted) Communication of knowledge  talk • Rooted in the executional world  do

40. Semantic world: ontologies, rules, logic framework, proof Federated workflow: choreography of processes communication and knowledge bus (Web Services, HTTP Get/Post, XDS, email, …) workflow monitoring process form generator work list process scheduling process task process clinical decision process health monitoring process Executional data: instance data, real world

41. communication and event bus: share knowledge and evidence health monitoring process health monitoring process Country  World  Healthcare Management clinical decision process clinical decision process scheduling process scheduling process workflow monitoring process workflow monitoring process task process task process work list process work list process form generator form generator Region  Disease Management Institution  Clinical Pathway Department  Order Workstation/User  Task Application  Event

42. I W R Bringing the pieces together

43. Bringing the pieces together plan, schedule, act, collaborate  Workflow think, decide  Reasoning collect, distribute, share Information

44. The e-Health Infrastructure Hospital Polyclinic Government Pharmacy Patients eHealth infrastructure GP A central infrastructure enabling e-Health This can but doesn’t need to imply that data is physically persisted in a central storage The bottom line: a user should have a consolidated and complete view on the clinical data of the patient Laboratory

45. The goal: e-Health Phase 3 E-Health Phase 2 Phase 1 The Roadmap to e-Health usability, added value E-Health Aggregation services: patient summary, drug prescription Monitoring services: interaction checking, harm detection, alerts Decision Support services: recommendations, guidelines Workflow services: care plans, chronic disease management, personal health plans Statistical and epidemiological services Financial services, Administrative services, … Knowledge Sharing 3: Services Workflow Monitoring 2: Messaging Subscription or transaction based Information sharing 1: Infrastructure Installation and maintenance of servers, network, databases Infra- structure

46. The goal: e-Health Phase 3 E-Health Phase 2 Phase 1 The Roadmap to e-Health Knowledge Sharing Health Management Programs Personal Health Plans Messaging based on ontologies Disease Management Programs Other ontologies: genomics, images, physiology virtual human body Workflow Add clinical content (rules) Adaptable pathways Monitoring Monitoring and alerts Home devices Patient Portal Information sharing Virtual EHR Workflow ontologies Clinical ontologies Share simple documents Terminology Standards (UMLS, SNOMED?) Procedural pathways Privacy Infra- structure Information Workflow Decision Support

47. plan, schedule, act, collaborate  Workflow I think, decide  Reasoning collect, distribute, share Information W R Bringing the pieces together Let’s just do it . . .