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Course Overview: Medical Information for Decision Making HuBio 590, Fall 2008

October 21, 2008. Course Overview: Medical Information for Decision Making HuBio 590, Fall 2008. Peter Tarczy-Hornoch MD Head and Professor, Division of Biomedical & Health Informatics Professor, Division of Neonatology Adjunct Professor, Computer Science and Engineering

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Course Overview: Medical Information for Decision Making HuBio 590, Fall 2008

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  1. October 21, 2008 Course Overview:Medical Information for Decision MakingHuBio 590, Fall 2008 Peter Tarczy-Hornoch MD Head and Professor, Division of Biomedical & Health Informatics Professor, Division of Neonatology Adjunct Professor, Computer Science and Engineering faculty.washington.edu/pth

  2. Course Objectives • Describe the value of high quality medical information for clinical care • Describe the range of factors that influence the clinical decision making process • Translate a clinical scenario into a searchable question • Describe advantages and limitations of various medical information resources and types of documents • Find documents from one or more medical information resource(s) that may address the clinical situation • Assess systematically the relevance and validity of a given document with respect to the clinical situation • Compare relevance and validity across two documents

  3. Course Overview • Session 1, Tuesday October 21 • “Medical Information and Medical Decision Making” & “Statistics 101” (1:30-2:30; Learning Objectives 1,2,3) • Small Group: Introductions (2:40-3:20) • Session 2, Wednesday October 22 • Small Group: Translating a clinical question into a searchable one (2:00-2:50; Learning Objective 3) • “Finding Medical Information in a Clinical Context” (3-3:50; Learning Objectives 4,5; L. St. Anna) • Session 3, Monday October 27 • Small Group: Hands on session searching on-line databases (2:00-2:50; Learning Objectives 4,5; Librarians join groups) • “Assessing a Document on Treatment” (3:00-3:50; Learning Objective 6 with focus on treatment) • Session 4, Wednesday October 29 • Small Group: Practice assessing document on treatment

  4. Course Overview • Session 4, Wednesday October 29 • Small Group: Review of sample problems and discussion of real world examples of interpreting articles on treatment (2:00-2:50; Learning Objective 6 with focus on treatment) • “Assessing a Document on Diagnosis”(3:00-3:50; Learning Objective 6 with focus on diagnosis) • Session 5, Monday November 3 • Small Group: Review of sample problems and discussion of real world examples of interpreting articles on diagnosis (2:00-2:50; Learning Objective 6 with focus on diagnosis) • “Assessing Multiple Studies” (3:00-3:50; Learning Objectives 6,7 with focus on systematic reviews) • Session 6, Wednesday November 5 • Small Group: Review of sample problems (2:00-2:50) • “Applying MIDM Concepts in the Real World” (3:00-3:50)

  5. Course Logistics • Lectures: T-435 • Lectures cover key content in the syllabus (see courses.washington.edu/midm “Syllabus” for content outline for each session). Assignments permit practice of material presented prior to small group session. • Small Groups: • T538, T540, T543, T546, T548, T547, T549 • Focus on application and discussion of lecture material • See web page “Small Group Assignments” (note user name/password information e-mailed earlier) • Office Hours • Peter Tarczy-Hornoch, I264A • By arrangement – e-mail pth@u.washington.edu

  6. Grading & Class Attendance • To pass the course the following is required: • Attendance at all six small group sessions • If you miss a small group session then a makeup assignment will be required. • Assignments will be given after Lectures 1-5 to prepare for small group sessions but these assignments will not be graded • Passing the final exam • 70% is a pass • Multiple choice and fill in the blanks • Take-home, open book, web administered • Available at 5P Wed Nov 5th; due 5P Wed Nov 12th

  7. courses.washington.edu/midm

  8. Questions? • The complete syllabus (PDF) contains the following sections: • Course Description • WWAMI Course Chairs • Seattle Course Chair and Small Group Leads • Learning Objectives • Course Organization • Grading and Class Attendance • Required Textbook/Readings • Schedule for 2008-9 • Content outline for each of the six sessions

  9. October 21, 2008 Session 1a: Medical Information and Medical Decision Making Peter Tarczy-Hornoch MD Head and Professor, Division of BHI Professor, Division of Neonatology Adjunct Professor, Computer Science and Engineering faculty.washington.edu/pth

  10. Medical Information and Medical Decision Making • Medical Decision Making • Nature of Medical Information • Reducing Errors & Improving Quality • Finding Knowledge and Evidence

  11. Medical Decision Making Requires Integrating Information Diagnostic Testing (What is it?) (Session 4) Patient Data & Information (ICM) Therapy/Treatment (What do I do for it?) (Session 3) Case specific decision making General Information & Knowledge (MIDM – finding/assessing)

  12. Patient Information Comes From Diverse Sources • Past Visit Info: • - Paper chart(s) • History • Physical • - Family history • - Problem lists • - etc. Radiology System (X-rays) Pharmacy System (drugs) Lab System (test results) Current Visit Info: - Symptoms - History - Findings Transcription System Billing System - Stay / Visit / Cost - Diagnoses / Treatments

  13. Medical Knowledge Acquired From Diverse Sources Books School Journals Networked Information Sources CD ROM books, CME, etc.

  14. Medical Decision Making Requires Integrating Information • Patient Specific Knowledge: • 6 year old boy • History of chicken pox exposure • Currently on steroids for asthma • Exam showing “dewdrop on a rose petal” • General Medical Knowledge: • Diagnosis and management of chicken pox • Management of asthma • Risk of steroids and chicken pox • Therapy Decision: Stop steroids, treat with Acyclovir

  15. Clinical Encounters Generate Questions • “…conservative to conclude that every interaction between a patient and a doctor is likely on average to generate at least one question” R. Smith, BMJ, 1996 • Types of information needed (data to knowledge) • Patient specific (laboratory, radiology, immunization) • Guidelines, policies, standards (national or local) • Drug information (dosage, interactions, side effects) • Medical literature (textbooks, reference books, journals) • Information needed in the context of a specific encounter (e.g. immunization) • Known/unknown & met/unmet information needs

  16. Medical Information and Medical Decision Making • Medical Decision Making • Nature of Medical Information • Reducing Errors & Improving Quality • Finding Knowledge and Evidence

  17. Managing Medical Information a Longstanding Challenge • “The Art is long, life is short, opportunity fleeting, experience delusive, judgment difficult” Hippocrates ~400 B.C. • “While the continuing gains in medical knowledge and the accompanying ability of doctors to treat the sick have been real, the passage of time has too often proven the espoused remedies of one era to be of limited value or frankly harmful in the next…How much of what we embrace as truth today will suffer this fate over the ensuing decades” LC Epstein 1997

  18. “the Art is long”: Information Overload • “if the most conscientious physician were to attempt to keep up with the literature by reading two articles per day in one year this individual would be 800 years behind” O. Barnett, 1990 • 2/3 of primary care practitioners surveyed: “the current volume of scientific literature is unmanageable” J.W.Williamson, 1989 • “Although well over 1 million clinical trials have been conducted, hundreds of thousands remain unpublished or are hard to find and may be in various languages. In the unlikely event that the physician finds all the relevant trials of a treatment, these are rarely accompanied by any comprehensive systematic review attempting to assess and make sense of the evidence” Bero & Rennie, JAMA 1995

  19. “experience delusive”: unproven treatments • Best evidence may not be as good as you’d wish • n=2500 treatments in BMJ Clinical Evidence See http://www.clinicalevidence.com/ceweb/about/knowledge.jsp

  20. “experience delusive”: data vs. opinion “Types” of medical knowledge • Compiled formal/scientific knowledge (best) • E.g. systematic reviews, evidence based medicine, some Up To Date entries • Uncompiled formal/scientific knowledge (good) • E.g. “raw” PubMed search, a single randomized trial • Compiled informal/experiential knowledge (ok) • E.g. consensus statements, “standard of care”, books, “Spiral” manuals, some Up To Date entries • Uncompiled informal/experiential knowledge (ok) • E.g. opinions/customs of experts and consultants

  21. “judgment difficult”Uncertainty Impacts Decision Making • Diagnostic uncertainty • Horses vs. Zebras (infection vs. genetic problem) • Availability bias (last patient I saw with this had X) • Therapeutic uncertainty • Attributes of patient vs. study population • Study drug vs. class of drugs • Population vs. individual (genes + drugs) • “Islands of certainty in seas of uncertainty” • Studies vs. experience/judgment

  22. “judgment difficult”Biases Impact Decision Making • Bias: 2a. A preference or an inclination, especially one that inhibits impartial judgment. 3. A statistical sampling or testing error caused by systematically favoring some outcomes over others. (American Heritage Dictionary) • Recall bias: inaccurate recollection of information • Availability bias: recent or memorable information/decisions easier to remember • Sampling bias: personal experience around information/decision making not representative • Publication bias: “negative” studies hard to publish

  23. Biomedical Informatics Studies the Use and Nature of Medical Information • Doctors use medical knowledge for clinical problem solving and decision making • Biomedical informatics focuses on the general issues of biomedical knowledge capture, retrieval, and application • “the scientific field that deals with biomedical information, data, and knowledge – their storage, retrieval, and optimal use for problem solving and decision making” Shortliffe, E.H., 2006 • Important discipline in the context of medical information for decision making • Division of Biomedical and Health Informatics at UW • www.bhi.washington.edu

  24. “Just in time information”: unmet need • “At the bedside or in the office, physicians should have instantaneous, up-to-date assistance from an affordable, universally available database of systematic reviews of the best evidence from clinical trials” Bero and Rennie, JAMA, 1995 • “New information tools are needed: they are likely to be electronic, portable, fast, easy to use, connected to both a large valid database of medical knowledge and the patient record” R. Smith, BMJ, 1996

  25. Medical Information and Medical Decision Making • Medical Decision Making • Nature of Medical Information • Reducing Errors & Improving Quality • Finding Knowledge and Evidence

  26. Do these challenges around medical information and decision making matter?

  27. Hippocratic Oath • Hippocratic Oath has evolved since 400 BC as societal values and standards have changed • Two aspects particularly relevant to HuBio 590 Medical Information for Decision Making: • “To practice and prescribe to the best of my ability for the good of my patients, and to try to avoid harming them.” => finding and applying the best information • “To keep the good of the patient as the highest priority” => integrating patient specific information • How successful are we at this today?

  28. 2000 • Press release • “…medical errors kill some 44,000 people in U.S. hospitals each year. Another study puts the number much higher, at 98,000 ” • “Even using the lower estimate, more people die from medical mistakes each year than from highway accidents, breast cancer, or AIDS.” • Some errors are unavoidable mistakes, some errors are due to missing or incorrect data or knowledge

  29. 2001 • Press release • “The nation's health care industry has foundered in its ability to provide safe, high-quality care consistently to all Americans” • Studies estimate 3-4% of hospitalizations result in adverse events • Recommendation 2 “…six major aims: specifically, health care should be safe, effective, patient-centered, timely, efficient, and equitable”

  30. 2007 • Press release • “Medication errors are among the most common medical errors, harming at least 1.5 million people every year, says a new report from the Institute of Medicine of the National Academies.  The extra medical costs of treating drug-related injuries occurring in hospitals alone conservatively amount to $3.5 billion a year” • Recommendation 3: “All health care organizations should immediately make complete patient-information and decision-support tools available to clinicians and patients. Health care systems should capture information on medication safety and use this information to improve the safety of their care delivery systems.”

  31. Economic & Legal Context • Rising healthcare costs • 2005: $2 Trillion, 16% GDP, 6.9% (2 x inflation) • Each diagnostic & therapeutic decision has a cost • If 47% of treatments are “of unknown effectiveness” then what is the cost implication of this? • Malpractice • Malpractice “the provider failed to conform to the relevant standard of care.” • Historically standard of care = “reasonable person” • Evolution of standard of care = “what is the evidence”

  32. Medical Information and Medical Decision Making • Medical Decision Making • Nature of Medical Information • Reducing Errors & Improving Quality • Finding Knowledge and Evidence

  33. First Randomized Controlled Trial - 1948

  34. Evidence Based Medicine – 1992-2008 • “Evidence-Based Medicine: A New Approach to Teaching the Practice of Medicine”, JAMA 1992 (classic article, see MIDM website) • “Evidence-based medicine (EBM) requires the integration of the best research evidence with our clinical expertise and our patient’s unique values and circumstances” in Evidence Based Medicine: How to Practice and Teach EBM, Straus et al 2005 (the definitive textbook) • “Progress in Evidence-Based Medicine”, JAMA 2008 (reviews 1992 article, see MIDM website)

  35. Evidence Based Medicine - Caveat • Parachute use to prevent death and major trauma related to gravitational challenge: systematic review of randomised controlled trials. Smith and Pell, BMJ, Dec 2003

  36. Where to get “best research evidence”? Books School Ideal: Synthesized Authoritative Current Searchable Journals Networked Information Sources CD ROM books, CME, etc.

  37. No single source for “best evidence” • Ideal: continually updated, synthesized, expert authored, peer reviewed, electronic knowledge base • Cochrane collaboration (www.cochrane.org) and similar databases are closest we have but… • Restricted to areas with sufficient literature • Updated episodically, not real time • Developing tools for finding evidence is an active area of biomedical informatics research nationally

  38. So What Do You Do? • Use Evidence Based Medicine Resources • Learn General Standard of Care • Manuals/textbooks • National policy statements, e.g. American Academy of Pediatrics • Learn Local Standards of Care • Policies/guidelines, e.g. UW • Prevailing practice – conferences/grand rounds • Keep Up to Date on New Clinical Studies • Journals • Journal abstracting/summarizing services • Conferences • Learn to search databases of medical knowledge…

  39. Steps to Finding & Assessing Information • Translate your clinical situation into a formal framework to get a searchable question (today) • Choose source(s) to search (Session 2) • Search your source(s) (Session 2) • Assess the resulting articles (documents) • Therapy documents (Session 3) • Diagnosis documents (Session 4) • Systematic reviews/comparing documents (Session 5) • Decide if you have enough information to make a decision, repeat 1-4 as needed (ICM, clinical rotations, internship, residency) (Session 6)

  40. Step 1: Frame the question (I) • Translate clinical question to searchable question (PPICONSSframework for assessing a document, PPICOS framework for formulating a search/finding information) • P P: Problem • PP: Patient/Population • II: Intervention • CC: Comparison • OO: Outcome • N: Number of Subjects • SS: Study Design/Type/Statistics • S: Sponsor

  41. Step 1: Frame the question (II) • Translate clinical question to searchable question (PPICOS) • P: Problem • What is the question of interest? • E.g. “How to treat athletes foot?” • P: Patient • Demographics (e.g. gender/age range), condition, disease • E.g. “Healthy female college athlete with skin/nails affected” • I: Intervention • Diagnosis/treatment, which one is of primary interest/preferred a priori • E.g. “Treatment with over the counter ointment” • C: Comparison • Alternative diagnosis(es)/treatment(s) (of secondary interest) • E.g. “Over the counter ointment vs. prescription ointment vs. pill” • O: Outcome • Diagnostic accuracy, complication, death, cost, etc. • E.g. “Cheapest and safest cure since no insurance” => Cost, how often does each alternative cure it, what are side effects of each treatment • S: Study Design/Type • Ideally what type of study/document are you looking for • E.g. “Systematic Review”

  42. Compare Results to Search (I)

  43. Compare Results to Search (II) Conclusion: probably not what we want, look for another document

  44. October 21, 2008 Session 1b: Statistics 101 Peter Tarczy-Hornoch MD Head and Professor, Division of BHI Professor, Division of Neonatology Adjunct Professor, Computer Science and Engineering faculty.washington.edu/pth

  45. Statistics 101: Mean, Standard Deviation • *Population: weights of all medical students in the class • *Sample: weights of 10 randomly chosen students: • 50, 53, 56, 60, 65, 67, 70, 73, 73, 75 kg • Sample Mean: • Mean=sum{x1..xn}/n • Mean=sum{50,53,…75}/10=64.2 kg • Sample Variance • s2=sum{(x1-mean)2,...(xn-mean)2}/(n-1) • s2=sum{(50-64.2)2,...(75-64.2)2}/(10-1) =80.6 • Sample Standard Deviation • s=sqrt(s2) • s=sqrt(80.6)=8.97 kg

  46. Statistics 101: Normal Distribution • 68% of values are +/- one  from the mean • 95.4% of values are +/- two  from the mean • 99.6% of values are +/- three  from the mean • Sample standard deviation and mean estimate population  and mean *Population *Sample 1=X *Sample 2=O X X X O O O http://en.wikipedia.org/wiki/Normal_distribution

  47. Statistics 101 – Sample Sizes • Mean: • Mean=sum{x1..xn}/n • Increasing n does not impact mean • Sample Variance • s2=sum{(x1-mean)2,...(xn-mean)2}/(n-1) • Increasing n decreases sample variance • Sample sizes • Larger sample sizes decrease variance and allow you to see smaller differences between groups • Rule of thumb for sample size for a strong study n=400

  48. Statistics 101: p values • “Treatment 1 was better than treatment 2 (P<0.05)” • P<0.05 roughly means less than 5% (0.05) chance treatment 1 and 2 are the same • “Treatment 1 was better than treatment 2, P=0.001” • P=0.001 roughly means 1/1000 (0.001) chance treatment 1 and 2 are the same • p=0.04 vs. p=0.05 vs. p=0.06 • All roughly the same, choice of “p<0.05” as “statistically significant” is arbitrary • Study 1: mortality cut by 50% with p=0.04 vs Study 2: mortality cut by 1% with p=0.01 • Cutting mortality by 50% clinically more significant than by 1% • P=0.01 is statistically more significant than P=0.04 See http://www.acponline.org/journals/ecp/julaug01/primer.pdf Also http://www.acponline.org/journals/ecp/primers.htm

  49. Statistical  Clinical Significance • Statistical Significance: are the treatments the same? • Clinical Significance: if they are different then do we care about the difference? • Examples: • Duration of pharyngitis: 8.1 days to 7.4 days • Weight: 279 lbs to 266 lbs after 3 months • Survival increased from 4.5 mos to 5.2 mos with 100% mortality at 12 months • Claudication: Increase in walking distance by 34 ft.

  50. Small Group Sessions 1 & 2 • S1: Small group leads to introduce themselves • Name, where they are from, where they went to medical school, their clinical practice, interesting fact about their background • S1: Students to introduce themselves • Name, where they are from, interesting fact about their background, what they hope to get from small group sessions • S1: Small group leads to give examples of translating clinical situations/scenarios into a searchable question (using PPICOS framework) • S2: Assignment for 10/22 (tomorrow) for students • Come up with one clinical “situation” you have wondered about or been asked about and translate it into PPICOS framework • Work through 4 scenarios “Formulating a searchable question” (http://courses.washington.edu/midm/schedule.htm) • Bring paper or electronic copy of your completed assignment • Small groups to identify two PPICOS scenarios for assignment for small group on 10/27 • * Reminder: students please sign in, group leads please turn in sign in sheets to Donna Rowe, Box 357240

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