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Knowledge Representation In Nursing and Its Impact on Database Design Josette Jones, RNc, Licentiate Nursing, Licentiate MIS Patricia F. Brennan, RN, Ph.D.,

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Presentation on theme: "Knowledge Representation In Nursing and Its Impact on Database Design Josette Jones, RNc, Licentiate Nursing, Licentiate MIS Patricia F. Brennan, RN, Ph.D.,"— Presentation transcript:

1 Knowledge Representation In Nursing and Its Impact on Database Design Josette Jones, RNc, Licentiate Nursing, Licentiate MIS Patricia F. Brennan, RN, Ph.D., FAAN, FACMI University of Wisconsin Madison

2 Purpose of the Study Report on Indexing web pages using a subset of MeSH to create a set of patient specific information

3 Background and Significance Changes in health care Consumerism Cost management Need for indexing health information

4 Methods of Indexing Keyword indexing Keywords are terms occurring in the documents itself (title, abstract, body, URL) Concept indexing Concept terms describe the subject matter of the document. Concept terms may or may not occur in the document Concept terms are taken from Knowledge Representation Models

5 Knowledge Representation Model A Knowledge Representation Model 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 Models

6 The HeartCare Project

7 Features P roviding health information graduated to patient’s stage of recovery and tailored to his/her medical profile and individual needs Design Filtered set of cardiac recovery resources available on the web and self constructed web pages are stored in an Access© database. Web pages are described with index terms Index terms are also used to describe the medical profile Matching algorithm web page – patient

8 Indexing Web Pages in HeartCare Nurse-clinicians tagged web documents with: Selected concepts from Medical Language Subject Heading (MeSH), a class of Knowledge Representation Models for bio-medicine Supplemented with terms reflecting local clinical practice

9 Implications for Retrieval Issues: (1) too many pages pulled per patient (2) too many duplicate pages (3) some pages were pulled that did not exactly match the patient profile.

10 Examples of Total Web Pages in Combination with Menu title Retrieved for a Patient PatientCombinations Retrieved Unique Combinations patient patient patient patient

11 Example of duplicate page retrieval for a Patient: Web Page keywords smoking and behavior changes Menu Title Taking charge of your health - Week 3-6 Beginning lifestyle changes - Week 7-12 Changing your lifestyle - Week Condition Diabetes Hypertension Smoking Hypertension Smoking Diabetes Hypertension Smoking

12 Pages Retrieved that Does not Match the Patient’s Profile Sample Male Patient with Risk Factors Hypertension and Stress 11 different pages relating to the risk of smoking and smoking cessation 8 web pages relating to risk factors for women 5 pages specific about being overweight and weight loss 2 pages on the topic of diabetes management

13 Example of Indexing 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 ” withterms “Beta Blockers/Calcium Channel Blockers” and “Medications” are pulled for all conditions that have the subject heading assigned, even when not applicable

14 Discussion Flawed indexing system Conceptualization problem Lacking structure of index terms

15 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.

16 Josette Jones Patricia F. Brennan University of Wisconsin Madison

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