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The Pain Points in Health Care and the Semantic Web. Advanced Clinical Application Research Group Dr. Dirk Colaert MD. Healthcare is changing…. Today. Tomorrow. Scope. Cure Patients. Care for Citizens. Focus . On the process and provider. On the patient. Time. Symptomatic, curative.

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the pain points in health care and the semantic web

The Pain Points in Health Care and the Semantic Web

Advanced Clinical Application Research Group

Dr. Dirk Colaert MD

healthcare is changing
Healthcare is changing…

Today

Tomorrow

Scope

Cure Patients

Care for Citizens

Focus

On the process and provider

On the patient

Time

Symptomatic, curative

Preventive, lifetime

Location

Hospital

Decentralized, at home

Methods

Invasive

Less invasive

slide4

De processes are changing …

Today

Tomorrow

Clinical Decisions

Personal preferences

Guide lines / evidence based

The Process

disease mgt.

Fragmented, isolated

Experience

Best Practices

Individual

Order Process

Manual

Automated

Fragmented, isolated

Consolidated / complete

Information

slide5

IT is changing …

Today

Tomorrow

Technology

Isolated systems

Integrated systems

Data access

Limited, Difficult

Any time, any place

Data integrity

Systematic mgt. and control

Manual/error prone

Consolidated

Fragmented

Data completeness

Slow

Real time

Data availability

the health care is under pressure
The health care is under pressure ...
  • Costs must decrease
  • Quality must increase
    • E.g. Medication errors: in the US 80.000 people died in 2004. (=8th cause of death)
the hospital

Assessment

Information

Activities

The Hospital

High Quality

Cost Effective

Medical Knowledge

needs

needs

produces

healthcare as a process

Assesment

Society

subjective

objective

Medical

Community

Diagnostic Action

Therapeutic Action

Planning

operational

Care Action

Healthcare as a Process
healthcare as a process pain points

Assesment

Society

subjective

objective

Medical

Community

Planning

operational

Healthcare as a Process: pain points
  • Complex desicions
  • Lack of training
  • Changing knowledge
  • Medical errors
  • Inefficient workflow
  • Understaffing
  • No operational information
  • No infrastructure information
  • No common language
  • 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

Input - Output

Process

Workflow

Clinical Desicions

Information

Action

cure for the pain points wave 1

Assesment

Society

subjective

objective

Medical

Community

Planning

operational

Cure for the pain points – wave 1
  • PAS: Patient Adminstration System
  • HIS: Hospital Information System
  • Result Distribution

Input - Output

Process

Workflow

Clinical Desicions

Information

Collect

Action

cure for the pain points wave 2

Assesment

Society

subjective

objective

Medical

Community

Planning

operational

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

Process

Workflow

Clinical Desicions

Information

Optimization

Collect

Desicion support

Action

cure for the pain points wave 3

Assesment

Society

subjective

objective

Medical

Community

Planning

operational

Cure for the pain points – wave 3
  • feature extraction from unstructured or massive information (images, free text)
  • Advanced connectivity
  • Content
  • Workflow optimization
  • Intelligent patient portals
  • Remote data capture
  • Community HealthCare
  • Information filtering
  • Decision support
  • Semantic driven UI
  • Clinical Pathways
  • Evidence based medicine
  • Clinical Trials (in- and exclusion criteria, data mining)
  • Terminology

Common to all this is …

Input - Output

Process

Workflow

Clinical Desicions

Information

Optimization

Knowledge

Desicion support

Action

connected knowledge
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!
simple ontology
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

knowledge traditionally assumed
Knowledge: traditionally ‘assumed’

visit

Aspirin

?

Lab Test

Tenormin

hypertension

connected knowledge explicit
Connected Knowledge: explicit

visit

Conclusion of

Aspirin

Lab Test

Tenormin

Indication for

hypertension

threated by

connected knowledge1
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
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

Appendicitis

Concept

Appendix

inflamation of

Code

XYZ

Term for

Term for

ICD9 code for

Term

Appendix

Term

Blindedarm

decision support and clinical pathways
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
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

Assesment

Society

subjective

objective

Medical

Community

Diagnostic Action

Therapeutic Action

Planning

operational

Care Action

Adaptable Clinical Workflow Framework
adaptable clinical workflow compare to gps1
Adaptable Clinical Workflow (compare to GPS)

After deviation from the calculated course the system adapts the itinerary

from pixel to community
From pixel to community

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

Human Interaction

Guidelines

Policies

Clinical Data

Events

Requests

(Local, Operational, Community, ...)

Recommendation

Desicion

Action

Desicion support

slide29

Country  World  Healthcare Management

Region  Disease Management

Institution  Clinical Pathway

Department  Order

Workstation/User  Task

Application  Event

slide30

communication and event bus: share knowledge and evidence

Country  World  Healthcare Management

health monitoring

process

clinical decision

process

scheduling

process

workflow

monitoring process

task process

work list process

form generator

Region  Disease Management

Institution  Clinical Pathway

Department  Order

Workstation/User  Task

Application  Event

issues when merging ontologies
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
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
solution
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
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
solution1
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
Wrong conclusion
  • CP Rheuma+GU
    • Rule x
    • Rule: If pain  Aspirine
    • Rule y
    • Rule a
    • Rule b
    • Rule …
  • CP Rheuma
    • Rule x
    • Rule: If pain  Aspirine
    • Rule y
  • CP Gastric Ulcus
    • Rule a
    • Rule b
    • Rule …
wrong conclusions
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
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
slide39

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.