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Evidence-Based Practice in the Culture of Health Care

This lecture explores the key tenets of evidence-based medicine (EBM) and its role in the culture of health care. It covers constructing clinical questions, critically appraising evidence, applying EBM to intervention studies, and using EBM in clinical settings. The lecture also discusses diagnosing uncertainties and utilizing screening tests and clinical prediction rules in diagnosis.

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Evidence-Based Practice in the Culture of Health Care

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  1. The Culture of Health Care Evidence-Based Practice Lecture d This material (Comp 2 Unit 5) was developed by Oregon Health & Science University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number IU24OC000015. This material was updated in 2016 by Bellevue College under Award Number 90WT0002. This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/.

  2. Evidence-Based PracticeLearning Objectives • 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). • Explain how EBM can be applied to intervention studies, including the phrasing of answerable questions, finding evidence to answer them, and applying them to given clinical situations (Lecture c). • Describe how EBM can be applied to key clinical questions of diagnosis, harm, and prognosis (Lectures d, e). • Discuss the benefits and limitations to summarizing evidence (Lecture f). • Describe how EBM is used in clinical settings through clinical practice guidelines and decision analysis (Lecture g).

  3. 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 to be discussed: • Screening • Clinical prediction rules

  4. 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

  5. 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 variable increases with positive test and decreases with negative test

  6. Diagnostic and Therapeutic Thresholds (Guyatt, 2008) 5.4 Figure: Adapted from Guyatt, Rennie, Meade, & Cook, 2008

  7. Screening Tests for Disease • “Identification of unrecognized disease” • Aim to keep disease (or complications) from occurring (primary prevention) or stop progression (secondary 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 specificity

  8. 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 false-positive tests is substantial; in a study of screening for prostate, lung, colorectal, and ovarian cancer (Lafata et al., 2004): • 43% of sample had at least one false-positive test • Increased medical spending in following year by over $1000 per person screened • Controversies in recent years over screening for • Breast cancer (Nelson et al., 2009; Kolata, 2009) • Prostate cancer (Chou et al., 2011; Harris, 2011)

  9. Clinical Prediction Rules • Use of results of multiple “tests” to predict diagnosis (Adams, 2012) • 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; Righini, 2013) • High sensitivity, moderate specificity • Better for ruling out than ruling in disease • Coronary risk prediction—newer risk markers do not add more to known basic risk factors (Folsom et al., 2006) • Inconsistent results for prognostic ability of popular risk prediction models (Siontis, 2012)

  10. Evidence-Based Practice Summary – Lecture d • 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

  11. Evidence-Based PracticeReferences – Lecture d References Adams, S. & Leveson, S. (2012) Clinical prediction rules. BMJ 2012;344:d8312. Retrieved from http://www.bmj.com/content/344/bmj.d8312 (doi: http://dx.doi.org/10.1136/bmj.d8312) Chou, R., et. al. (2011). Screening for prostate cancer: A review of the evidence for the U.S. Preventive Services Task Force. Annals of Internal Medicine, 155(11), 762–771. Ebright, P. (2014). Culture of safety, part one: Moving beyond blame. University of California. MERLOT. Retrieved from http://tlcprojects.org/NEAT/CultureSafety_P1.swf Folsom, A., et al. (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. (2014). Users’ guides to the medical literature: Essentials of evidence-based clinical practice, 3rd edition. New York: McGraw-Hill. Harris, G. (2011, Oct. 6). 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, Nov. 22). 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.

  12. Evidence-Based PracticeReferences – Lecture d Continued 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. Righini, M., et als. (2013). Predicting deep venous thrombosis in pregnancy: External validation of the LEFT clinical prediction rule. Haematologica, 98(4), 545-548 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. Siontis, G. (2012). Comparisons of established risk prediction models for cardiovascular disease: systematic review BMJ ;344:e3318. Retrieved from http://www.bmj.com/content/344/bmj.e3318.full.pdf+html Wells, P., et al. (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. Charts, Tables, Figures 5.4 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: McGraw-Hill.

  13. The Culture of Health CareEvidence-Based PracticeLecture d This material was developed by Oregon Health & Science University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information Technology under Award Number IU24OC000015. This material was updated in 2016 by Bellevue College under Award Number 90WT0002.

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