Knowledge Engineering in Health Informatics

1. Front Matter

   Pages i-xvi

   PDF

2. Background and Legacy

   • Homer R. Warner, Dean K. Sorenson, Omar Bouhaddou

   Pages 1-9

3. The Expert System Model

   • Homer R. Warner, Dean K. Sorenson, Omar Bouhaddou

   Pages 10-16

4. Iliad: The Model Used for This Text

   • Homer R. Warner, Dean K. Sorenson, Omar Bouhaddou

   Pages 17-34

5. The Data Dictionary: Limiting the Domain of the Model

   • Homer R. Warner, Dean K. Sorenson, Omar Bouhaddou

   Pages 35-44

6. The Knowledge Engineering Process

   • Homer R. Warner, Dean K. Sorenson, Omar Bouhaddou

   Pages 45-65

7. Evaluation of the Model

   • Homer R. Warner, Dean K. Sorenson, Omar Bouhaddou

   Pages 66-68

8. Applications of the Model

   • Homer R. Warner, Dean K. Sorenson, Omar Bouhaddou

   Pages 69-78

9. Lessons Learned

   • Homer R. Warner, Dean K. Sorenson, Omar Bouhaddou

   Pages 79-83

10. Knowledge Engineering Tools

    • Homer R. Warner, Dean K. Sorenson, Omar Bouhaddou

    Pages 84-88

11. Example Knowledge Bases

    • Homer R. Warner, Dean K. Sorenson, Omar Bouhaddou

    Pages 89-93

12. Future Challenges

    • Homer R. Warner, Dean K. Sorenson, Omar Bouhaddou

    Pages 94-98

13. Back Matter

    Pages 99-265

    PDF

This monograph series is intended to provide medical information scien­ tists, health care administrators, physicians, nurses, other health care pro­ viders, and computer science professionals with successful examples and experiences of computer applications in health care settings. Through these computer applications, we attempt to show what is effective and efficient, and hope to provide guidance on the acquisition or design of medical information systems so that costly mistakes can be avoided. Health care provider organizations such as hospitals and clinics are experiencing large demands for clinical information because of a transition from a "fee-for-service" to a "capitation-based" health care economy. This transition changes the way health care services are being paid for. Previ­ ously, nearly all health care services were paid for by insurance companies after the services were performed. Today, many procedures need to be pre approved and many charges for clinical services must be justified to the insurance plans. Ultimately, in a totally capitated system, the more patient care services are provided per patient, the less profitable the health care provider organization will be. Clearly, the financial risks have shifted from the insurance carriers to the health care provider organizations. For hospitals and clinics to assess these financial risks, management needs to know what services are to be provided and how to reduce them without impacting the quality of care. The balancing act of reducing costs but maintaining health care quality and patient satisfaction requires accurate information about the clinical services.

• Computer
• Computerassistierte Detektion
• Internal medicine
• Knowledge Engineering
• Master Patient Index
• Radiologieinformationssystem
• calculus
• diagnosis
• knowledge representation
• logic
• modeling
• screening
• system development

• Department of Medical Informatics, University of Utah School of Medicine, Salt Lake City, USA

  Homer R. Warner, Omar Bouhaddou

• Mosby Consumer Health, Salt Lake City, USA

  Dean K. Sorenson

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