Use Case Tutorial - Health Care (1/7)

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Part 3 of 7 of the Use Case Tutorial presented at DEBS'2009 in Nashville, TN

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Use Case Tutorial - Health Care (1/7)

  1. 1. epts event processing technical society epts event processing technical society Health Care Use Case Pedro Bizarro (University of Coimbra) Dieter Gawlick (Oracle)
  2. 2. epts event processing technical society “I am sitting on a mountain of data hidden behind procedural code” Dr. Kimball University of Utah Medical Center 2
  3. 3. epts event processing technical society Medical Objectives Decrease morbidity and mortality Identify situations of concern as they happen Identify situations of concern before they happen Alert medical personal of time critical situations 3
  4. 4. epts event processing technical society IT Objectives Extract information from raw data in real-time - pull and push - Disseminate time critical information Highly extensible - meta-data driven Avoid alert fatigue - customizability 4
  5. 5. epts event processing technical society Overview Deduced Patient Information and State Domain Knowledge Push Pull Alerts Vocabularies Classifications Rules Patient Data Medical Medical Administrative Personnel History - Incidents History (Predictive) Models Suggestions/What if Visualizations * Observations Notes Vitals Ins/ Blood Radio- Other Questionnaires Outs Tests logy Input* Diagnosis structured, structured, structured, unstructured, Treatment automatic, manual, manual, manual & real-time small delay big delay automatic, …. big delay Import/Export 5
  6. 6. epts event processing technical society Access to Patient Information Pull the history of the patient Push the state of the patient Information needs for both cases Aggregations & Data mining models What if/guided resolution … 6
  7. 7. epts event processing technical society Domain Knowledge • Vocabularies – The data structures of health records • Classifications – The expression used by medical personnel to qualify data; e.g., critical, rapidly deteriorating blood pressure • Rules – A condition (state of the patient) that the medical personnel has to be made aware of • Models – Objects that captures a (complex) state of concern. Models will be derived through data mining and will be supervised and improved – Models will be scored when conditions require to do so • All elements of the domain knowledge can be shared between institutions and can be customized 7
  8. 8. epts event processing technical society System Overview – Conceptual View Medical Human Monitors Services Interaction (external) C/S based Message based Applications Registries - Minimal or no procedural code - Medical Data Personnel/ Event Service External - preferable as extension of data base technology - Services History … Meta-Data (Medical Knowhow) Tools Incidents Taxonomies/Classifications User Registered Queries (Rules) Dev. (Predictive) Models Admin Infra-structure (SW/HW) 8
  9. 9. epts event processing technical society Steps of Processing • Capture – Capture and keep all raw data • Analyze – Applies all rules and data mining models on incoming data • Identify situation of interest – Capture any match, alert doctor if situation is time critical – Explain / provide background • Investigate/Suggest – Provide access to any patient information – Support investigation with guided resolution • React – Determine/adjust treatment – Records who/when/if alerts are dealt with 9
  10. 10. epts event processing technical society Events, Alerts, Notifications • There are two event types – New raw data are entered - this will trigger the evaluation of all rules and may trigger the scoring of models – An interval/timeout has expired – this will handle the non-events • Incidents – If an instance of a pattern/a high enough scoring has been found, capture information in an incident object – Actionable incidents have to be reacted to in time; otherwise a reminder will be send • Alerts – Only if an incident requires immediate attention a notification should be sent to a pager – High selectivity/customization of alerts should be used to prevent alert fatigue 10
  11. 11. epts event processing technical society Functional Requirements • Information storage with easy access – Current state/history of raw data, aggregations, classifications, … – Pull and push with highly selective notification • Rich type system – SQL, XML(HL7), RDF, ..., DICOM, extensibility • Support of data mining – (Predictive) pattern detection (e.g. cardiac arrest prediction) – Scoring • Application development based on declarative constructs only – Rapid deployment, low maintenance cost and extensibility – High flexibility • Customization - hospital, doctor, patient 11
  12. 12. epts event processing technical society Operational Characteristics • Always available – Recovery/restart/fault tolerance • Scalability – Large amount of (historical) data – Large amount of rules/models • Security – to control access to medical records – Fine grain – Contextual There is at least one lawyer • Auditing behind each doctor when things go wrong – History of all data and rules – Record of all accesses to data 12
  13. 13. epts event processing technical society Acknowledgement/More Information • Diogo Guerra - prototype to be published as Master Thesis (University of Coimbra) – Reference to be added when available (est. end of July 2009) – DEBS 2009 demo Please attendthe integration of presentation – Diogo worked through Diogo’s OLTP, temporal, OLAP, data mining and (complex) event processing technology Please look at Diogo’s demo • Ute Gawlick - SICU research project (University of Utah Medical Center) – Reference to be added when available (est. end of July 2009) – Ute provided the medical knowledge, formalized aspects of medical terminology and focused the project on leveraging data mining in conjunction with event processing 13
  14. 14. epts event processing technical society A Demo Preview

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