The Pain Points in Health Care and the Semantic Web

Advanced Clinical Application Research GroupDr. Dirk Colaert MD

Today Tomorrow

Location Hospital Decentralized, at home

Time Symptomatic, curative Preventive, lifetime

Focus On the process and provider On the patient

Scope Cure Patients Care for Citizens

Methods Invasive Less invasive

Healthcare is changing…

Order Process Manual Automated

Experience Individual Best Practices

The Process Fragmented, isolated disease mgt.

Clinical Decisions Personal preferences Guide lines / evidence based

De processes are changing …

Information Fragmented, isolated Consolidated / complete

Today Tomorrow

Data completeness

Fragmented Consolidated

Data integrity Manual/error prone Systematic mgt. and control

Data access Limited, Difficult Any time, any place

Technology Isolated systems Integrated systems

IT is changing …

Data availability

Slow Real time

Today Tomorrow

• Costs must decrease• Quality must increase

– E.g. Medication errors: in the US 80.000 people died in 2004. (=8th cause of death)

The health care is under pressure ...

The Hospital

Medical Knowledge

High QualityCost Effective

needsActivities InformationAssessmentneeds

produces

Healthcare as a Process

Process

Output

Input

Society

subjectiveobjective

Medical

Community

Assesment

operational

Care Action

Therapeutic ActionDiagnostic Action

Planning

Healthcare as a Process: pain points Isolated information

Fragmented information

Not accessable information

Too much information

Bad information presentation

Only clinical data is kept (no knowledge)

Some information is not computer usable (free text, image features, (genome in the future))

No feed back to medical community and society

Complex desicions

Lack of training

Changing knowledge

Medical errors

Inefficient workflow

Understaffing

No operational information

No infrastructure information

No common language

Input - Output

Information

Process

Clinical Desicions Workflow

ActionMedical

Community

operational Society objective subjective Assesment Planning

Input - Output

Information

Process

Clinical Desicions Workflow

Cure for the pain points – wave 1

PAS: Patient Adminstration System

HIS: Hospital Information System

Result Distribution

ActionMedical

Community

operational Society objective subjective Assesment PlanningCollect

Cure for the pain points – wave 2 PACS: Picture Archiving And Communication Sytem

PAS: Patient Adminstration System

HIS: Hospital Information System

CIS: Clinical Information System

Care

Order Entry

Medication prescription

Result Distribution

Input - Output

Information

Process

Clinical Desicions Workflow

ActionMedical

Community

operational Society objective subjective Assesment PlanningCollect

Desicion support

Optimization

Cure for the pain points – wave 3

Information filtering

Decision support

Semantic driven UI

Clinical Pathways

Evidence based medicine

Clinical Trials (in- and exclusion criteria, data mining)

Terminology

feature extraction from unstructured or massive information (images, free text)

Advanced connectivity

Content

Workflow optimization

Intelligent patient portals

Remote data capture

Community HealthCare

Input - Output

Information

Process

Clinical Desicions Workflow

ActionMedical

Community

operational Society objective subjective Assesment PlanningKnowledge

Desicion support

Optimization

Common to all this is …

Connected Knowledge

• Knowledge is a higher form of Information

• Knowledge (meaning, understanding) begins when facts and concepts (information) are connected

• Latin ‘intellectus’ comes from intelligere, inter + ligere = connect between

• A formal description of a domain, using connected facts and concepts is called ‘an ontology’

• The W3C organization provides standards: RDF (Resource Definition Framework) , OWL (Ontology Web Language)

• The “semantic web”: use the W3C standards and the inherent communication and linking properties of the WWW.

• By linking ontologies they can be merged to “connected knowledge”: very powerfull but dangerous!

Salary

Religionhobbies

Simple ontology

Me

Audi

Green

owns

has color

Audi Opel Other Brands

A3 A4

A6

Model of

ABC 1234_567

Instance of

Knowledge: traditionally ‘assumed’

visit

hypertension

Tenormin

Aspirin

Lab Test

?

Connected Knowledge: explicit

visit

hypertension

Tenormin

Aspirin

Lab Test

Conclusion of

threated by

Indication for

Connected Knowledge: scalable

Connected Knowledge

Examples of ontologies and rules: medical vocabulary, patient clinical data, infrastructural data

Because ontologies are formaly described, computers can use them, take rules and reason about the concepts.

Technologies, able to connect facts into ontologies, connect ontologies to each other and reason about it with rules gives us the means to improve vastly the current painfull processes in healthcare.

Examples:

Use of a Terminology Server for Controled Medical Vocabulary

Decision support and clinical pathways

Terminology Server• Purpose:

– Easy entry of data into the medical record keeping ‘freedom of speech’ and still be able to document in a uniquely defined and coded way. (e.g. ICD9)

• Example– Data entry: “blindedarm onsteking” (Dutch)– Results in: ICD9 XYZ (“appendicitis”)– No single part of the search string is found in the result. This can only

be achieved by a system ‘knowing’ the domain.

Concept

Appendix

Term

Appendix

Term

Blindedarm

Concept

Appendicitis

Code

XYZ

inflamation of

ICD9 code for

Term for

Term for

Decision Support and Clinical Pathways

• Clinical Pathway: a way of treating a patient with a standardized procedure in order to enhance the efficiency, increase the quality and lower the costs.

• Usually represented in a script book and/or flow chart diagram

• Issues with conventional Clinical Pathways:– Not very dynamic: “one size fits all”

• Not adapted 100% to the individual patient– Not mergeable

• How can you enroll a patient into 2 pathways?– Difficult to maintain: mix op procedural and declarative

knowledge

Agfa’s Advanced Clinical Workflow research• Combining

– knowledge, declared in rules and concepts (the ontologies)

• Medical domain• Clinical data about the patient• Operational (local policies)• Infrastructural (machines, people)• Workflow theory and ontology (pi-calculus)• Fuzzy sets theory and ontology

• Calculating the procedure to follow: the next step(s)

• After each action a recalculation is done

Adaptable Clinical Workflow Framework

Society subjective objectiveMedical

Community

operational

Assesment

Care Action

Therapeutic Action

Diagnostic ActionPlanning

Adaptable Clinical Workflow (compare to GPS)

Adaptable Clinical Workflow (compare to GPS)

After deviation from the calculated course the system adapts the itinerary

From pixel to community

Guidelines

Policies

Clinical Data

Events

Requests

(Local, Operational, Community, ...)

Desicion support

Human Interaction

Recommendation Desicion Action

The box is a fractal unit that can be scaled from “pixel to community”

Institution Clinical Pathway

Department Order

Workstation/User Task

Application Event

Region Disease Management

Country World Healthcare Management

Institution Clinical Pathway

Department Order

Workstation/User Task

Application Event

Region Disease Management

Country World Healthcare Management

health monitoringprocess

form generator

clinical decisionprocess

workflowmonitoring process

task process

schedulingprocess

work list process

communication and event bus: share knowledge and evidence

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 avove 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

Duplicate examinations

• 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

• 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

Solution

• By adding extra rules this can be solved.• “If the outcome of an examination is valid

for x days than any duplicate examination within that period can be canceled”

• These are “rules about rules” or “policies”

Bad sequences

• 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

• 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

solution

• Extra rule– “Examination X cannot be performed within x days after

the administration of contrast medium Y”

• Policy– Rules can be abstracted further into policies:– “All examinations must be checked against exclusion

criteria”

Wrong conclusion

• CP Rheuma– Rule x– Rule: If pain

Aspirine– Rule y

• CP Gastric Ulcus– Rule a– Rule b– Rule …

• CP Rheuma+GU– Rule x– Rule: If pain

Aspirine– Rule y– Rule a– Rule b– Rule …

Wrong conclusions

• Because of the specific focus when making a clinical pathway, merging CP’s can potentially be dangerous.

• Solution:– Detect possible patterns and add policies to cope

with them.– For example: “For any medication prescription

(outside the scope of the original CP), check interaction with the medical history and problems of the patient”

Trust

• Inference engines can produce, as a side product, the proof that, what is concluded, is logically true.

• We need standards to communicate and represent these proofs

Conclusion

Ontologies, together with theories (rules) can help health care providers to treat patients with better quality and less costs.

The intrinsic possibility of connecting ontologies and theories allow systems and people to use each others experience.

Extra policies can possibly detect and neutralize problem patterns within merged ontologies. Further research is needed here.

Scaling ontologies and theories outside the boundaries of the hospitals can be used to orchestrate effective community healthcare and regional healthcare programs.

Thanks