1. Representing Nursing Knowledge Applications for Database Design Josette Jones, RNc Patricia Brennan, RN, PhD

2. University of Wisconsin–Eau Claire Presentation Overview Introduction Background and Significance Role of Knowledge Representation Systems Indexing WebPages Using MESH Information Retrieval Evaluation

3. University of Wisconsin–Eau Claire Introduction Effective Use of Information in HealthCare – Accessible context – Matching to individual needs Indexing and Organizing On-line Resources – Content – Anticipated Usage

4. University of Wisconsin–Eau Claire Background and Significance Changes in healthcare impact discharge teaching Patient-specific health information is available on the WWW

5. University of Wisconsin–Eau Claire Role of Knowledge Representations Systems in Indexing Indexing techniques Description attributesKeyword attributes Knowledge Representation Systems (KRS)

6. University of Wisconsin–Eau Claire Knowledge Representation Systems A Knowledge Representation System has: – An underlying knowledge representation language (meta-language) with its vocabulary and explicit structure – A semantic (meaning of the expressions of the language) – A restricted syntax (set of reasoning rules) Examples of Health Care / Nursing Knowledge Representation Systems

7. University of Wisconsin–Eau Claire Indexing WebPages Using a Medical Thesaurus

8. University of Wisconsin–Eau Claire The HeartCare Project Providing health information – Graduated to patient’s stage of recovery – Tailored to his/her medical profile and individual needs Filtered set of cardiac recovery resources available on the web stored in an Access © database – Self constructed web pages – Web pages are described with index terms – Index terms describe the medical profile Matching algorithm web page – patient

9. University of Wisconsin–Eau Claire Indexing Web Pages in HeartCare Nurse-clinicians tagged web documents with: Selected concepts from Medical Language Subject Heading (MeSH) Supplemented with terms reflecting local clinical practice

10. University of Wisconsin–Eau Claire Example of Indexing http://www.women.americanheart.org/physicians/sub_content/ten.html http://www.women.americanheart.org/physicians/sub_content/ten.html tagged with the terms “ diet ” and “ weight ” is pulled 4 different times for the menu heading “ Ten questions a woman should ask her healthcare provider ” http://www.amhrt.org/Heart_and_Stroke_A_Z_Guide/calccb.html http://www.amhrt.org/Heart_and_Stroke_A_Z_Guide/calccb.html tagged with terms “ Beta Blockers/Calcium Channel Blockers ” and “ Medications ” are pulled for all conditions that have the subject heading assigned, even when not applicable

11. University of Wisconsin–Eau Claire Implications for Retrieval Too many pages pulled per patientToo many duplicate pages Some pages were pulled that did not exactly match the patient profile

12. Examples of Total Web Pages in Combination with Menu Title Retrieved for Patients Patient Combinations Retrieved Unique Combinations Patient 1266138 Patient 2891647 Patient 3324281 Patient 4584203

13. Menu TitleCondition Taking charge of your health - Week 3-6Diabetes Taking charge of your health - Week 3-6Hypertension Taking charge of your health - Week 3-6Smoking Beginning lifestyle changes - Week 7-12Hypertension Beginning lifestyle changes - Week 7-12Smoking Beginning lifestyle changes - Week 7-12Diabetes Changing your lifestyle - Week 13-26Diabetes Changing your lifestyle - Week 13-26Hypertension Changing your lifestyle - Week 13-26Smoking http://rex.nci.nih.gov/NCI_Pub_Interface/Clearing_the_Air/clearing.html Example of Duplicate Page Retrieval Using Keywords “smoking and behavior changes”

14. University of Wisconsin–Eau Claire Pages Retrieved that Does not Match the Patient’s Profile Sample Male Patient with Risk Factors Hypertension and Stress # of Web pagesNon-matching topic 11Risk of smoking and smoking cessation 8Risk factors for women 5Being overweight and weight loss 2Diabetes management

15. University of Wisconsin–Eau Claire Evaluation Flawed indexing systemLacking structure of index termsConceptualization problem

16. University of Wisconsin–Eau Claire Discussion Keywords must be part of semantic representation understood by users and indexers Relation content and usage must be explicatedKeywords must converge

17. University of Wisconsin–Eau Claire This study is supported by NLM/NINR Grant LM06249, Principal Investigator Dr. P.F. Brennan The authors want to thank the members from the HeartCare team for their advice and support.

18. Patricia F. Brennan pbrennan@ie.engr.wisc.edu University of Wisconsin Madison Josette Jones wouterjf@uwec.edu University of Wisconsin Eau Claire