25 years of promises lessons learned from modeling professional practices
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25 Years of PROMISES: Lessons Learned from Modeling Professional Practices. Extending Medical Enterprise Ontologies: Levels; Limits; and Tensions. 7 th International Protégé Conference July 6, 2004. Bob Smith, Ph.D. Tall Tree Labs robsmith5@1talltrees.com Bill Elliott, Internal Medical Labs

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25 Years of PROMISES: Lessons Learned from Modeling Professional Practices

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25 Years of PROMISES: Lessons Learned from Modeling Professional Practices

Extending Medical Enterprise Ontologies: Levels; Limits; and Tensions

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7th International Protégé ConferenceJuly 6, 2004

  • Bob Smith, Ph.D. Tall Tree Labs

    • robsmith5@1talltrees.com

  • Bill Elliott, Internal Medical Labs

    • BillAppl@AOL.com

  • Christian Fillies, SemTalk

    • clillies@semtalk.comwww.sentalk.com

  • Gay Woods-Albrecht

    • www.bpmsolutionsgroup.com

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Outline: 25 Years of PROMISES

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Problem Oriented Medical Records and Guidance:

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What happened to our Guidance expectations of 1980?

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Effective Supply and Demand

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Comprehensive Computer Supported Medical Decision Support Systems?

  • Comprehensive: Intelligent, Robust, Adaptive?

  • Computer Supported: Knowledge, Model Driven, and Data (Factual) Informed?

  • Medical: Ecology: Public and Private Health Care and “caring systems”

  • Decision Support: NOT Professional Automation but Professional Reasoning Enhancements

  • Systems: Social components, technical components, cultural components with explicit guidance “rules for rule making in informed communities”

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OMB’s US Statistical Abstract-XML Altova Project and Practices

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JIT Process Knowledge Integration

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BPMN.Org Perspectives on Liaison Options – June, 2004

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Swim lanes Level 7 to Level 1(?)

  • De Facto Standards (Current Practice Tensions between competing evolving-emergent standards: Knowledge Management, Process Management, Standards Management; Business Strategist’s Strategy (HBR))

  • Standard Abstractions (MS, IBM, SUN: WS-I)

  • Regulatory Guidance Clusters (NIST, NIH, W3C, etc.)

  • CEO-Supply Chain Integration (Health Care Infrastructure and Payment Systems)

  • Medical Practitioners (Internal Medicine Associates, Inc.)

  • Technical Staff (IT-Lab Techs)

  • Patients with medical problem(s) and paper Med Records (Brave Dave with High PSA Radical Surgery)

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This Protégé Conference demonstrates top down strategies

  • Vast changes in the supply of technical capability with ontologies, semantic web services standards, tools, vendors: with obvious economic and social ripple effects;

  • Vast changes in the demographics of demand for effective and efficient integrated and orchestrated medical practice

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Bottom Up Strategy

  • Size distribution of medical practice and associated IT and Process maturity

    • How and where do most patients receive medical care?

      • Garfield model: Distributed health delivery areas

    • Scenario: You are the technology “gatekeeper” for an 8 physician practice with a Stat Lab (Statistics go here…)

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Dialectics from HBR?

  • Harvard Business Review June 2004 article by Michael Porter challenging current assumptions of US Health Care Competitive Strategies

  • Can the Porter-Teisberg policy changes be modeled? With Ontology and Process Management-Knowledge Management simulators?

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Coherent Architectural Plans?

  • What kind of a roadmap would you sketch for yourself, today, in thinking about the real needs of these physicians in your organization?

  • How might you arrange to brainstorm the options using available process modeling and simulation tools to position Protégé and SAGE Projects in context?

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Application Development Options (Architect Needed)

  • Protégé?

  • SemTalk2 ?

  • MS_DotNET?

  • Hybrid?

  • See link: ..\Sacramento_Wk\101MSDCF\LabPicsJune04a.htm

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Protege – Sage Project Architecture

  • Sharable Active Guidance Environment

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Process Model: AS IS

  • Describe current workflow

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Alan Rector: Where are we going?

  • Citation: Rector, AL (2001) AIM: A personal view of where I have been and where we might be going. Artificial Intelligence in Medicine 23:111-127

  • “My own career in Medical Informatics and AI in Medicine has oscillated between

    • concerns with medical records and

    • concerns with knowledge representation with decision support as a pivotal integrating issue.

  • It has focused on using AI to organize information and reduce ‘muddle’ and

  • improve the user interfaces to produce ‘useful and usable systems’ to help doctors with a ‘humanly impossible task’. “

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25 Years of PROMISES

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Reference Domains

  • Protégé/Sage Project/CoP linkages

  • Ontology Management of OE

  • Health Care Technology Trends (Cladistics)

  • Strategy and Policy (Direction and Guidance)

  • Business Semantic Primes

  • Knowledge Flow Metrics

  • Process Knowledge Management

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