Patricia Flatley Brennan, RN, PhD, FAAN University of Wisconsin- Madison

1. Linking lay people to the professional literatureAn application of natural language processing to free-text e-mail Patricia Flatley Brennan, RN, PhD, FAAN University of Wisconsin- Madison Supported by Grants from the National Library of Medicine (LM 6249); Intel Corporation (Advanced Technologies for Health@Home), and Wisconsin Alumni Research Foundation (The Kellet Professorship )

2. Plan for the talk • Provide an update of the final results of the HeartCare randomized field experiment • Apply NLP tools to decode patient messages • Describe current work in two areas: • Community capacity building • Infrastructure-building

3. Investigators University of Wisconsin-Madison Patti Brennan Barrett Caldwell (Now Purdue) Mary Ellen Murray Dave Gustafson Case Western Reserve University Shirley Moore Sree Sreenath Cleveland Clinic Foundation Ralph O’Brien Undergraduate, Graduate, and Post-Doctoral trainees The HeartCare Team

4. Meeting the Challenges of CABG Recovery • Monitor, Manage, Mend, Motivate • Demands in the discharge encounter • Patient-centered, tailored information

5. HeartCareRecovery requires communication andtailored health information • Peer and professional communication • Information sequenced over time and tailored to the patient’s needs • Weeks 1-2: Symptom Management • Week 3-6: Resume physical activity • Weeks 6-12: Return to prior function • Weeks 12-26: Adopt healthy behaviors

6. HeartCare Evaluation • Randomized Field Evaluation • 6 Months experimental period • 140 adults recovering from CABG surgery • Mean age: 63; 35% Female; 19% Non-majority • Outcome Measures • Symptom Inventory, Sickness Impact Profile, POMS (Depression), Family function, Health Behavior change • Three Groups • Usual Care • CHIP, An Audiotape Intervention • HeartCare: WWW-based recovery support

9. Does access to HeartCare improve recovery from CABG? Yes!

10. 35 30 25 20 15 10 5 0 0 1 2 3 4 5 6 Sickness Impact Profile (SIP) SIP CHIP HeartCare Months Since Surgery

11. 14 12 10 8 6 4 2 0 0 1 2 3 4 5 6 POMS Depressive Symptom Scale CHIP Depression HeartCare Months Since Surgery

12. Summary • Participants in the HeartCare group recovered faster, with fewer symptoms, than those using the CHIP intervention. • Participants use HeartCare intensively during the early recovery phase. • E-mail used more often than public forum • Information reviewed on most encounter

13. What’s needed to make HeartCare-like interventions Scalable? • Strategies to understand information needs • Characterization of the ways lay people organize health information • Sustainable knowledge management approaches • Alignment of the CHI investments with the community’s health information assets • Robust health information infrastructure

14. What’s needed to make HeartCare-like interventions Scalable? • Strategies to understand information needs • Characterization of the ways lay people organize health information • Sustainable knowledge management approaches • Alignment of the CHI investments with the community’s health information assets • Robust health information infrastructure

15. Message to the Nurse blood clot Dear Connie, I've been out of the loop for a few weeks. I had a setback with the appearance of a blood clot 2 weeks ago and was back in the hospital for a week. I was released a week ago Friday and now am on several new medications. With all these new meds, I feel nauseous almost all the time and frequently dizzy. I have a visiting nurse coming to see me 3x a week, and she monitors my blood pressure, temperature and checks my legs for possible clots. But nothing seems to help the nauseous feeling and I have little appetite. The medication I am now taking are … I suspect the Lasix may be the culprit, since I had been on it a LONG time ago and it made me nauseous, but I don't know. Do I really need to be on all of these now? I take alot of them at the same time (meal time), but should I change this and stagger them? What order should I take them, or are there alternatives to this medication for now? Any advise you could give me before I go back to see my internist on Tuesday would be helpful, then I could discuss it with him again. I see the cardiologist on Thursday and hope to be cleared to start cardiac rehab after that. Right now, however, it is slow going and discouraging. Thanks, Bill little appetite blood pressure Lasix alternatives cardiac rehab

16. Can existing UMLS lexical tools decode patient information needs?

17. Mapped termsthat can launchsearches of electronic resources Dear Connie, I've been out of the loop for a few weeks. I had a setback with the appearance of a blood clot 2 weeks ago and was back in the hospital for a week. I was released a week ago Friday and now am on several new medications. SPECIALIST lexicon

18. Background • Federal initiatives to meet lay people’s information needs • Most common stimulus: query phrases • But… • Consumers’ don’t speak UMLS • Information need arise in colloquial conversations • However, • The UMLS and its lexical tools exist-- exploitable? • Electronic resources applying machine-readable indexing approaches

19. USUAL APPROACHES • Human intermediary • Natural language interpretation • Awakening from the dream stage • Terminological strategies • Query terms • Indexing Initiative

20. Can NLP tools built to manage professional vocabularies help patients? • Source document • 241 messages sent from patients to nurses in the HeartCare project • Pass thru Metamap • Parses text of electronic bibliographic databases • Strips capitalization, ignores word order • Assigns candidates from UMLS • Scores the adequacy of the concept match

21. Approach • Stimulus text acquired • Sanitizing process • Human Subjects’ issues • Preliminary Structuring • Demarked units • Title of message ---> Citation Title • Body of Message ---> Abstract

22. Preliminary Results • 241 Messages (1976 Utterances) • 15566 Phrases • 11,373 Candidate UMLS Concepts found • (mean 32.91 ; sd 42.7741) • 9903 phrases had no candidates • 7143 Mappings found (1.13; s.d.1.79)

23. Observations • Diagnosis, symptom and findings recognized • Health service elements not recognized (appointments, medication renewals) • No tolerance for mis-spellings • Idioms choke the system • Full UMLS may be too rich • Metamorph • Post-processing to remove selected components

24. Preliminary Thoughts • Promising but sparse; PARSING is key • Efficiency/interpretation tradeoff • Early work in a highly professional, highly controlled stimulus had a 70% mapping • Most messages deal with managing a health problem in the home ---> Nursing!

25. Vocabularies used in the Test • The Six Nursing Vocabularies • Nursing Plus: • International Classification of Primary Care (ICPC2E) • International Classification of Primary Care- Am English (ICPC2AE) • Micromedex DRUGDEX (MMX01) • National Drug Data File (NDFF01) • Thesaurus of Psychological Terms (PSY2001) • WHO Adverse Drug Reaction Terminology (WHO97) • NursingPLUS + Medical Subject Heading 2003 (MSH_2003) • NursingPLUS + SNOMED International Version 3.5 (SMNI98)

26. Vocabulary performance on a single message

27. How did the vocabularies perform?

28. Mapping Adequacy • Findings • True Positives • False Positives • Missing • Trade-off of recall and precision • Zeng’s Model of Mapping: • Lexical • Semantic • Mental Model

29. The Contexts of Care • Living Environments • Homes • Communities • Social Environments • Families • Cultural Groups • Psychological Environments • Illness representations • Human Information Processing • Technological Environments • Telecommunication • Consumer Electronics • Health Service Environment • Clinical Care Practices • Financing & Delivery Institutions

30. What’s needed to make HeartCare-like interventions Scalable? • Strategies to understand information needs • Characterization of the ways lay people organize health information • Sustainable knowledge management approaches • Alignment of the CHI investments with the community’s health information assets • Robust health information infrastructure

31. The Dodge-Jefferson Healthier Communities Partnership

32. Develop a model to generate design criteria for health-related IT solutions from an understanding of citizen health information managment behaviors and community resources

33. 49 Households in Central Wisconsin • Housing type • 39 Single Family, 9 Apartments, 1 Mobile Home • Most of those interviewed live alone • Over half of the 1 & 2 person families had one person over age 65 • Electronics • Phones: 49 • Cable: 42 • Internet: 26

34. Health of the Household • Respondents: • 7 Excellent; 12 Very Good; 10 Good; 3 Fair • No one indicated Poor • Respondent’s assessment of household generally matched • Health Concerns • Cancer, Cardiovascular disease, Hypertension, Arthritis • Also: depression, memory problems, nutrition, wellness • Income adequate • ? Health Insurance coverage • ? Health Care Provider

35. Where Do People get Health Information? Family Physician

36. Information Managed in the Home: Appointments Contact Info Insurance Treatments Provider Info Literature

37. Information types named by at least 20 respondents Appointment & Contact Information Medication Treatment Birth/Death records Household experiences Average 10.2 (sd. 3.3) information types Number and variety unrelated to age of respondents or presence of children Information Management in the home Where do they put all of this information?

41. What’s needed to make HeartCare-like interventions Scalable? Community-Partnership Digitial Library Project Dodge-Jefferson Healthier Communities Partnership Personal Consumer Health Information Exchange (P-CHIE) PKI Approaches to Secure E-Mail among Health Professionals • Strategies to understand information needs • Characterization of the ways lay people organize health information • Sustainable knowledge management approaches • Alignment of the CHI investments with the community’s health information assets • Robust health information infrastructure Assessment of Community Health Information Resources (ARCHIR)

42. State Health Dept Clinic Public Library Pharmacy Dentist Furtive Records Hospital trainee clinician patient Community-Centered Information System Consumer Health Information Network

43. Web Site:healthsystems.engr.wisc.edu pbrennan@engr.wisc.edu