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2. The Culture of Health Care
Evidence-Based Practice
Welcome to The Culture of Health Care: Evidence-Based Practice. This is Lecture d.
The component, The Culture of Health Care, addresses job expectations in healthcare settings. It discusses how care is organized within a practice setting, privacy laws, and professional and ethical issues encountered in the workplace.
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 IU24OC 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

3. Evidence-Based Practice Learning 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).
The objectives for Evidence-Based Practice are to:
Define the key tenets of evidence-based medicine (EBM) and its role in the culture of health care.
Construct answerable clinical questions and critically appraise evidence answering them.
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.
Describe how EBM can be applied to key clinical questions of diagnosis, harm, and prognosis.
Discuss the benefits and limitations to summarizing evidence.
Describe how EBM is used in clinical settings through clinical practice guidelines and decision analysis.

4. 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
This lecture discusses diagnoses, particularly the effectiveness of diagnostic tests.
How can we use evidence-based medicine to assess questions about diagnosis? If we look at the diagnostic process—the process of evaluating a patient and forming a diagnosis—we see that it involves both logical reasoning and pattern recognition. Logical reasoning is the ability to take into account different symptoms to rule in or rule out possible diagnoses based on the frequency, duration, severity, and other characteristics of the symptoms. Pattern recognition, as it applies to diagnosis, is the ability to look at the patterns of symptoms that we commonly see in various diseases.
The diagnostic process actually has two essential steps. Before we can begin talking about diagnostic tests, we have to enumerate all the diagnostic possibilities and estimate their likelihood. Diagnostic decision support systems generate a differential diagnosis not only of the possibilities but also of the likelihood of each possibility based on the data collected about the patient and his or her condition. The second step is to incorporate new information from diagnostic tests that affect the probabilities for different items of the differential diagnosis. We can then rule out some possibilities and choose the most likely diagnosis.
In this lecture, we also discuss two variants on diagnosis. One is screening, which is the use of diagnostic tests to screen people who are healthy in an attempt to intervene early to alter the disease process. The second is clinical prediction rules, where many pieces of information, including diagnostic tests, are used to try to predict the presence or absence of a disease.

5. 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
When we talk about diagnosis, we usually talk about the certainty or perhaps the uncertainty of the diagnosis. We typically express certainty or uncertainty as a mathematical probability, which can sometimes seem daunting—particularly to those who have not been exposed to probability or diagnostic decision making. When we talk about probabilities, we talk about them on a scale from zero to one, which corresponds to the scale of zero percent to one-hundred percent. For example, when we flip a coin, the probability of getting heads is point five, or fifty percent. The same is true for the probability of getting tails if it’s a fair coin. An alternative expression of probabilities is to talk about the odds. The odds are the probability of an event occurring versus the probability of an event not occurring, or the ratio. The odds of getting heads on a coin flip is 1:1 [one to one]—one is another way to say it. When we roll a single die with six possibilities on the sides of the die, the probability of getting any number is one-sixth; the odds of getting any number are one to five.

6. Some Other Probability Principles
Sum of all probabilities should equal 1
e.g., p[heads] + p[tails] = = 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
Another principle to consider when talking about probability is that the sum of all probabilities should equal one. For example, with a coin flip, the probability of head or tails is each point-five, which adds up to one.
When we calculate the probability of a disease with information from a diagnostic test, we use Bayes’ [bayz] theorem, which is a statistical formula that gives us the post-test probability, sometimes called the posterior probability. In this case, it gives us the post-test probability of a disease being present. Bayes’ theorem has many uses in addition to medical diagnosis. The post-test probability is a function of both the pre-test probability and the results of the test. Bayes’ theorem tells us that it’s important to know what the prior or pre-test probability is because that information is used to calculate a new probability when test results are added.
When we calculate the probability of a disease with information from a diagnostic test, we use Bayes’ [bayz] Theorem, which is a statistical formula that gives us the post-test probability, sometimes called the posterior probability. It gives us the post-test probability of, in this case, a disease being present. Bayes’ [bayz] Theorem is also used for things other than medical diagnosis. The post-test probability is a function of both the pre-test probability and the results of the test. Bayes’ [bayz] Theorem tells us that it is important to know what the prior or pretest probability is as that information is used to calculate a new probability when test results are added.

7. Diagnostic and Therapeutic Thresholds (Guyatt, 2008)
Thresholds are also useful in diagnostics. This figure, from Guyatt’s [guy-ihtz] evidence-based medicine textbook, shows that there’s anywhere from a zero-to-one-hundred percent chance that a patient has a disease. Although we typically don’t quantify this in routine medical practice, there’s actually a threshold at which we decide to test the patient for a disease and a threshold at which we decide to treat the patient. Below the test threshold, we think the disease is so unlikely or perhaps so unimportant that no testing is warranted. At some point, we reach the threshold where we say, “We should really get a test to see if the patient has this disease.” So, our probability estimate tells us that further testing is required when we exceed the test threshold. As testing proceeds, we may reach a point—and it may not be 100 percent—where we are highly certain that the disease is present. When we cross over from the test to the treatment threshold, the probability that the patient has the disease is so high that we can make a confident, informed diagnosis and begin treatment. This process is different for different diseases, and the treatment threshold depends on both the benefit and the risk of the treatment. If the treatment for a serious disease has high benefit and relatively low risk, the treatment threshold may actually be lower than if it’s a treatment that potentially has a lot of adverse effects.
5.4 Figure: Adapted from Guyatt, Rennie, Meade, & Cook, 2008

8. 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
Screening is related to diagnosis but is not quite the same. Screening is the identification of unrecognized disease. What we hope to do with screening is recognize disease so we can intervene at an early stage. We may aim to keep the disease or its complications from occurring, sometimes called primary prevention. Or we may want to prevent complications from developing when the disease is already present, sometimes called secondary prevention. What are the attributes of a good screening test? It should have a low cost because we typically apply screening to large numbers of people. A good screening test has to lead to an effective intervention, ideally documented by a randomized controlled trial of the screening intervention. Finally, the test should be of high sensitivity because we don’t want to miss any cases, for example, or have any false-negative cases. A positive screening is usually followed up with a test of high specificity to make sure the screening result isn’t a false-positive.

9. 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)
Americans love screening tests even though there’s a lack of evidence for many of them. People are willing to have a test done even if a medical professional warns that the test may not be completely accurate or that there’s no good treatment for a particular disease even if it’s detected early. A key problem with screening tests is that the cost of false-positive tests is substantial. A 2004 study looked at the costs associated with false-positive results of prostate, lung, colorectal, and ovarian cancer screening. This study found that forty-three percent of people screened had at least one false-positive test result. That false-positive test led to increased medical spending in the following year by over one-thousand dollars per person screened.
In recent years, there have been major public controversies over screening tests. In 2009, a review of the evidence called into question the value of mammography screening for breast cancer in women under 50, raising concern that for this population, the screening test caused more harm than benefit. In 2011, a similar situation occurred for prostate cancer screening with prostate-specific antigen, or PSA [P-S-A], raising questions about whether the excess surgery and its complications in those with a disease never destined to spread beyond the prostate outweighed the benefits of those with a disease likely to be fatal. The references on this slide point to both scientific analyses as well as well-written articles about screening controversies.

10. 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)
We finish this lecture with some discussion about clinical prediction rules. We don’t go into great detail, but many of you who are regular readers of the medical literature have probably seen papers where they are used. The idea behind clinical prediction rules is that we use results from multiple “tests,” in quotes here because the information used in clinical prediction rules includes not only things like blood tests and x-rays but also the presence of certain clinical findings, signs, and symptoms. All of these different pieces of data are used to predict the diagnosis. There are rules for critically appraising clinical prediction rule studies, and in essence, the best evidence for clinical prediction rules will establish the rule in one population and then validate it in another independent one.
As an example, the prediction of deep vein thrombosis, or DVT [D-V-T] is very important clinically because there are no medical tests that can definitively diagnose this condition. DT is a blood clot in the deep veins of the lower extremities, which as all clinicians know, puts the patient at risk for the clot breaking off and causing a pulmonary embolism—which can be serious and even fatal. Unfortunately, there are no tests that are both highly sensitive and specific for DVT, and so it’s helpful to try to develop clinical prediction rules that give us confidence in the diagnosis or help us rule out the diagnosis when we are seeing a patient who might have this condition. The prediction rule for DVT that Wells and colleagues developed has high sensitivity but moderate specificity. This is probably helpful because having high sensitivity, it’s good at ruling out disease, more so than at ruling it in. And with something as serious as DVT that can predispose to pulmonary embolism, it’s probably more important to be confident that we ruled out the disease rather than ruled it in.
There are many other areas where clinical prediction rules have been applied. One study looked at predicting coronary artery disease by looking at all the different so-called markers that have been proposed for coronary artery disease in recent years. Interestingly, this study found that none of these newer risk markers add more to known basic risk factors, such as cholesterol level, family history, hypertension, and diabetes. Another study showed limited evidence and inconsistent results about the relative prognostic ability of the most popular risk prediction models for cardiovascular disease.
The techniques of clinical prediction rules can be used to evaluate new markers for disease as they are developed as well as to compare results between studies.

11. 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
This concludes Lecture d of Evidence-Based Practice. In summary, another common type of question for which we seek evidence is diagnosis. The process of diagnosis involves logical reasoning and pattern recognition. It consists of two essential steps: generating a differential diagnosis and then incorporating new information from diagnostic tests to choose the most likely diagnosis.

12. Evidence-Based Practice References – Lecture d
Adams, S. & Leveson, S. (2012) Clinical prediction rules. BMJ 2012;344:d Retrieved from (doi:
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
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
Kolata, G. (2009, Nov. 22). Behind cancer guidelines, quest for data. New York Times. Retrieved from
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.
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13. Evidence-Based Practice References – 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),
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:e Retrieved from
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.
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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.

14. The Culture of Health Care Evidence-Based Practice Lecture 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 IU24OC This material was updated in 2016 by Bellevue College under Award Number 90WT0002.
No audio.
Health IT Workforce Curriculum Version 4.0