25 Years of PROMISES Lessons Learned from Modeling

25 Years of PROMISES: Lessons Learned from Modeling Professional Practices Extending Medical Enterprise Ontologies: Levels; Limits; and Tensions Draft 3 1

7 th International Protégé Conference July 6, 2004 • Bob Smith, Ph. D. Tall Tree Labs – robsmith 5@1 talltrees. com • Bill Elliott, Internal Medical Labs – Bill. Appl@AOL. com • Christian Fillies, Sem. Talk – clillies@semtalk. com www. sentalk. com • Gay Woods-Albrecht – www. bpmsolutionsgroup. com Draft 3 2

Outline: 25 Years of PROMISES Draft 3 3

Problem Oriented Medical Records and Guidance: Draft 3 4

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What happened to our Guidance expectations of 1980? Draft 3 6

Effective Supply and Demand Draft 3 7

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” Draft 3 8

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OMB’s US Statistical Abstract-XML Altova Project and Practices Draft 3 10

JIT Process Knowledge Integration Draft 3 11

BPMN. Org Perspectives on Liaison Options – June, 2004 Draft 3 13

Swim lanes Level 7 to Level 1(? ) 1. De Facto Standards (Current Practice Tensions between competing evolving-emergent standards: Knowledge Management, Process Management, Standards Management; Business Strategist’s Strategy (HBR)) 2. Standard Abstractions (MS, IBM, SUN: WS-I) 3. Regulatory Guidance Clusters (NIST, NIH, W 3 C, etc. ) 4. CEO-Supply Chain Integration (Health Care Infrastructure and Payment Systems) 5. Medical Practitioners (Internal Medicine Associates, Inc. ) 6. Technical Staff (IT-Lab Techs) 7. Patients with medical problem(s) and paper Med Records (Brave Dave with High PSA Radical Surgery) Draft 3 16

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 Draft 3 17

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…) Draft 3 18

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? Draft 3 19

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? Draft 3 21

Application Development Options (Architect Needed) • • • Protégé? Sem. Talk 2 ? MS_Dot. NET? Hybrid? See link: . . Sacramento_Wk101 MSDCFLab. Pics. June 04 a. htm Draft 3 22

Protege – Sage Project Architecture • Sharable Active Guidance Environment Draft 3 23

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Process Model: AS IS • Describe current workflow Draft 3 26

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References Draft 3 31

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’. “ Draft 3 41

25 Years of PROMISES Draft 3 47

Reference Domains 1. 2. 3. 4. 5. 6. 7. Protégé/Sage Project/Co. P 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 Draft 3 48