The Culture of Healthcare

Evidence-Based Practice

Lecture d

This material (Comp2_Unit5d) was developed by Oregon Health and Science University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information

Technology under Award Number IU24OC000015.

Evidence-Based PracticeObjectives

• Define the key tenets of evidence-based medicine (EBM) and its role in the culture of health care (Lectures a, b)

• Construct answerable clinical questions and critically appraise evidence answering them (Lecture b)

• Apply EBM for intervention studies, including the phrasing of answerable questions, finding evidence to answer them, and applying them to given clinical situations (Lecture c)

• Understand EBM applied to the other key clinical questions of diagnosis, harm, and prognosis (Lectures d, e)

• Discuss the benefits and limitations to summarizing evidence (Lecture f)

• Describe how to implement EBM in clinical settings through clinical practice guidelines and decision analysis (Lecture g)

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Lecture d

Using EBM To Assess Questions About Diagnosis

• Diagnostic process involves logical reasoning and pattern recognition

• Consists of two essential steps– Enumerate diagnostic possibilities and estimate their

relative likelihood, generating differential diagnosis– Incorporate new information from diagnostic tests to

change probabilities, rule out some possibilities, and choose most likely diagnosis

• Two variations on diagnosis also to be discussed– Screening– Clinical prediction rules

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Lecture d

Diagnostic (Un)Certainty Can Be Expressed As Probabilities

• Probability is expressed from 0.0 to 1.0– Probability of heads on a coin flip = 0.5

• Alternative expression is odds– Odds = Probability of event occurring /

Probability of event not occurring– Odds of heads on a coin flip = 0.5/0.5 = 1

• Rolling a die– Probability of any number = 1/6– Odds of any number = 1/5

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Lecture d

Some Other Probability Principles

• Sum of all probabilities should equal 1– e.g., p[heads] + p[tails] = 0.5+0.5 = 1

• Bayes’ Theorem in diagnosis– Post-test (posterior) probability a function of

pre-test (prior) probability and results of test– Post-test probability variably increases with

positive test and decreases with negative test

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Lecture d

Diagnostic And Therapeutic Thresholds (Guyatt, 2008)

5.4 Figure: (adapted from Guyatt, Rennie, Meade, & Cook, 2008)

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Lecture d

Screening Tests For Disease• “Identification of unrecognized disease”• Aim to keep disease (or complications)

from occurring (1° prevention) or stop progression (2° prevention)

• Requirements for a screening test– Low cost– Intervention effective – ideally shown in randomized

controlled trial– High sensitivity – do not want to miss any cases;

usually follow up with test of high specificityHealth IT Workforce Curriculum Version 3.0/Spring 2012

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Lecture d7

Americans Love Screening Tests Despite Lack Of Evidence

• Despite their limitations, screening tests for cancer are very popular with Americans (Schwartz, Woloshin, Fowler, & Welch, 2004)

• But cost of FP tests is substantial; in one study of screening for prostate, lung, colorectal, and ovarian cancer (Lafata, et al., 2004)– 43% of sample had at least one FP test– Increased medical spending in following year by

over $1000• Controversies in recent years over screening for

• Breast cancer (Nelson, et al., 2009; Kolata, 2009)• Prostate cancer (Chou, et al., 2011; Harris, 2011)

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Lecture d8

Clinical Prediction Rules• Use of results of multiple “tests” to predict

diagnosis• Best evidence establishes rule in one

population and validates in another independent one

• Examples of clinical prediction rules– Predicting deep venous thrombosis (DVT) (Wells, et al.,

2000; Wells, Owen, Doucette, Fergusson, & Tran, 2006)• High sensitivity, moderate specificity• Better for ruling out than ruling disease

– Coronary risk prediction – newer risk markers do not add more to known basic risk factors (Folsom, et al., 2006)

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Lecture d9

Evidence-Based Practice Summary – Lecture d

10

• Another common type of question for which we seek evidence is diagnosis

• Process of diagnosis involves logical reasoning and pattern recognition

• Diagnosis consists of two essential steps• Generating a differential diagnosis• Incorporating new information from

diagnostic tests to choose the most likely diagnosis

Health IT Workforce Curriculum Version 3.0/Spring 2012

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Lecture d

Evidence-Based PracticeReferences – Lecture d

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ReferencesChou, R., Croswell, J., Dana, T., Bougatsos, C., Blazina, I., Fu, R., . . . Lin, K. (2011). Screening for prostate cancer: a

review of the evidence for the U.S. Preventive Services Task Force. Annals of Internal Medicine, Epub ahead of print.

Folsom, A., Chambless, L., Ballantyne, C., Coresh, J., Heiss, G., Wu, K., . . . Sharrett, A. (2006). An assessment of incremental coronary risk prediction using C-reactive protein and other novel risk markers: the atherosclerosis risk in communities study. Archives of Internal Medicine, 166, 1368-1373.

Guyatt, G., Rennie, D., Meade, M., & Cook, D. (2008). Users' Guides to the Medical Literature: Essentials of Evidence-Based Clinical Practice. New York, NY: McGraw-Hill.

Harris, G. (2011, October 6, 2011). U.S. Panel Says No to Prostate Screening for Healthy Men, New York Times. Retrieved from http://www.nytimes.com/2011/10/07/health/07prostate.html

Kolata, G. (2009, November 22, 2009). Behind Cancer Guidelines, Quest for Data, New York Times. Retrieved from http://www.nytimes.com/2009/11/23/health/23cancer.html

Lafata, J., Simpkins, J., Lamerato, L., Poisson, L., Divine, G., & Johnson, C. (2004). The economic impact of false-positive cancer screens. Cancer, Epidemiology, Biomarkers, & Prevention, 13, 2126-2132.

Nelson, H., Tyne, K., Naik, A., Bougatsos, C., Chan, B., & Humphrey, L. (2009). Screening for breast cancer: an update for the U.S. Preventive Services Task Force. Annals of Internal Medicine, 151, 727-737.

Schwartz, L., Woloshin, S., Fowler, F., & Welch, H. (2004). Enthusiasm for cancer screening in the United States. Journal of the American Medical Association, 291, 71-78.

Evidence-Based PracticeReferences – Lecture d (continued)

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Lecture d

Charts, Tables, Figures5.4 Figure: adapted from Guyatt, G., Rennie, D., Meade, M., & Cook, D. (2008). Users' Guides to the Medical

Literature: Essentials of Evidence-Based Clinical Practice. New York, NY: McGraw-Hill.

References (continued)Wells, P., Anderson, D., Rodger, M., Ginsberg, J., Kearon, C., Gent, M., . . . Hirsh, J. (2000). Derivation of a simple

clinical model to categorize patients probability of pulmonary embolism: increasing the models utility with the SimpliRED D-dimer. Thrombosis and Haemostasis, 83, 416-420.

Wells, P., Owen, C., Doucette, S., Fergusson, D., & Tran, H. (2006). Does this patient have deep vein thrombosis? Journal of the American Medical Association, 295, 199-207.