Evidence-based medicine

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Evidence-based medicine ( EBM ) is a medical practice approach intended to optimize decision making by emphasizing the use of evidence from well-designed and well-done research. Although all science-based drugs have several levels of empirical support, EBM goes a step further, grouping evidence based on its epistemological strength and requiring only the strongest type (derived from meta-analysis, systematic review, and randomized controlled trials) can yield strong recommendations ; weaker types (such as from case-control studies) can only produce weak recommendations. The term was originally used to describe approaches to teaching medical practice and improving decisions by individual physicians about each patient. The use of the term is quickly extended to include a previously described approach that emphasizes the use of evidence in the design of guidelines and policies that apply to patient groups and populations ("evidence-based practice policies"). This then spread to illustrate the approach to decision making used in almost every level of health care as well as other areas (evidence-based practice).

Whether applied to medical education, individual decisions, guidelines and policies applied to the population, or the administration of general health services, evidence-based supporters to the maximum extent possible, decisions and policies should be based on evidence, not just practitioners, experts, or administrators. Thus try to ensure that the opinion of the physician, which may be limited by a knowledge gap or bias, is supplemented by all the knowledge available from the scientific literature so that best practices can be determined and applied. It promotes the use of formal and explicit methods to analyze evidence and make it available to decision makers. It promotes programs to teach methods to medical students, practitioners, and policy makers.

Video Evidence-based medicine

Background, history, and definitions

In its broadest form, evidence-based medicine is the application of scientific methods in health care decision making. Medicine has a long tradition in both basic and clinical research that at least comes from Avicenna and has recently become an exegesis of Protestant reform in the 17th and 18th centuries. The earliest criticism of statistical methods in medicine was published in 1835.

However, to date, the process by which research results are included in medical decisions is highly subjective. Called "clinical judgment" and "medical art", the traditional approach to making decisions about individual patients depends on having each individual physician determine what research evidence, if any, to consider, and how to combine that evidence with personal and other beliefs. factors. In the case of decisions applied to patient groups or populations, guidelines and policies will usually be developed by expert committees, but there is no formal process to determine the extent to which the research evidence should be considered or how it should be combined with the confidence of committee members. There is an implicit assumption that policy makers and decision makers will incorporate the evidence in their thinking appropriately, based on their education, experience, and ongoing studies of the applicable literature.

Clinical decision making

Beginning in the late 1960s, some shortcomings became apparent in the traditional approach to medical decision-making. Alvan Feinstein's publication of Clinical Judgment in 1967 focuses on the role of clinical reasoning and the identified bias that can influence it. In 1972, Archie Cochrane published Effectiveness and Efficiency, which illustrated the lack of controlled trials supporting many of the previously considered effective practices. In 1973, John Wennberg began documenting the various ways doctors practiced. Throughout the 1980s, David M. Eddy described the error in clinical reasoning and the gap in evidence. In the mid-1980s, Alvin Feinstein, David Sackett and others published textbooks on clinical epidemiology, which translated epidemiological methods into decision-making physicians. Towards the end of the 1980s, a group at RAND showed that the proportion of large procedures performed by doctors was deemed inappropriate even by the standards of their own experts. This field of research raises awareness of the weaknesses in medical decision making at both patient and individual population levels, and paves the way for the introduction of evidence-based methods.

Evidence-based

The term "evidence-based medicine", as used today, has two major tributaries. Chronologically, the first is the insistence on explicit evaluation of evidence of effectiveness when issuing clinical practice guidelines and other population-level policies. The second is the introduction of epidemiological methods into medical education and patient-level decision-making.

Evidence-based guidelines and policies

The term "evidence-based" was first used by David M. Eddy in his work on population-level policies such as clinical practice guidelines and new technology insurance coverage. He first began using the term "evidence-based" in 1987 in workshops and a manual commissioned by the Special Medical Society Council to teach formal methods for designing clinical practice guidelines. This manual was widely available in an unpublished form in the late 1980s and was eventually published by the American College of Medicine. Eddy first published the term "evidence-based" in March 1990 in an article in the Journal of the American Medical Association that outlines the principles of evidence-based guidelines and population-level policies, which Eddy described as "explicitly explaining evidence available in relation to policy and binding policy to evidence Consciously retaining policy, not current practice or expert beliefs, but for experimental evidence The policy should be consistent with and supported by evidence. "The relevant evidence should be identified, explained and analyzed. Policymakers must determine whether the policy is justified by evidence. Rationale must be written. "He discussed the" evidence-based "policy in several other papers published in JAMA in the spring of 1990. The papers are part of the 28 series published in JAMA between 1990 and 1997 on formal methods for designing population-level guidelines and policies.

Medical education

The term "evidence-based medicine" was introduced a little later, in the context of medical education. This evidence-based drug branch is rooted in clinical epidemiology. In the fall of 1990, Gordon Guyatt used it in an unpublished description of the program at McMaster University for prospective new medical students. Guyatt and others first published the term two years later (1992) to describe a new approach to teaching medical practice.

In 1996, David Sackett and colleagues clarified the definition of this evidence-based basin stream as "the best current use of evidence, explicitly, and wisely in making decisions about individual patient care... [It] means integrating individual, clinical skills with the best external clinical evidence available from systematic research. "This evidence-based medicine branch aims to make individual decisions more structured and objective by better reflecting the evidence of the study. Population-based data are applied to individual patient care, while respecting the fact that practitioners have clinical expertise that is reflected in effective and efficient diagnosis and wise identification and compassionate use of individual patient circumstances, rights, and preferences.

Children from these evidence-based drugs have a foundation in clinical epidemiology, a discipline that teaches health care workers how to apply clinical and epidemiological research studies to their practices. Between 1993 and 2000, the Evidence-based Medical Working Group at McMaster University published the method to a broad doctor's audience in a series of 25 "User Guides for Medical Literature" at JAMA . In 1995, Rosenberg and Donald defined individual evidence-based drugs as "the process of discovery, assessment, and use of contemporary research findings as a basis for medical decisions." In 2010, Greenhalgh used a definition that emphasized quantitative methods: "the use of mathematical estimates of benefits and hazards risks, derived from high-quality research on population samples, to inform clinical decision-making in diagnosis, investigation or individual patient management." Many other definitions have offered for individual-level evidence-based drugs, but which Sackett and colleagues cited most frequently.

Both original definitions highlight important differences in how evidence-based medicine is applied to populations rather than individuals. When designing guidelines that are applied to large groups of people in settings where there is relatively little opportunity for modification by individual physicians, evidence-based policies emphasize that there should be good evidence to document the effectiveness of tests or treatments. In individual decision-making settings, practitioners can be given greater freedom in the way they interpret the research and combine it with their clinical judgment. in 2005 Eddy offers a general definition for two EBM branches: "Evidence-based medicine is a set of principles and methods intended to ensure that as much as possible, medical decisions, guidelines, and other policy types are based on and consistent with evidence of good effectiveness and benefits. "

Progress

Both branches of evidence-based medicine spread rapidly. On evidence-based and policy-side guidelines, an explicit urge on evidence of effectiveness was introduced by the American Cancer Society in 1980. The US Preventive Services Task Force (USPSTF) began issuing guidelines for preventive intervention based on evidence-based principles in 1984. In 1985, Blue Cross Blue Shield Association applies stringent evidence-based criteria to cover new technologies. Starting in 1987, specialized societies such as the American College of Physicians, and voluntary health organizations such as the American Heart Association, wrote many evidence-based guidelines. In 1991, Kaiser Permanente, a managed care organization in the US, started an evidence-based guidance program. In 1991, Richard Smith wrote an editorial in the British Medical Journal and introduced evidence-based policy ideas in the UK. In 1993, Cochrane Collaboration created a network of 13 countries to produce systematic review and guidance. In 1997, the US Agency for Healthcare Research and Quality (later known as the Healthcare Policy and Research Agency, or AHCPR) established the Center for Evidence-based Practice (EPC) to produce evidence and technology assessment reports to support guidance development. That same year, the National Guideline Clearinghouse that followed the principles of evidence-based policies was created by AHRQ, AMA, and the American Association of Health Plans (now the American Health Insurance Plan). In 1999, the National Institute for Clinique Excellence (NICE) was established in England. The central idea of ​​this branch of evidence-based medicine is that the evidence should be classified according to the accuracy of its experimental design, and the strength of the recommendation must depend on the strength of the evidence.

On the medical education side, programs for teaching evidence-based medicine have been made in medical schools in Canada, the US, the UK, Australia and other countries. A 2009 study of the British program found more than half of UK medical schools offer some training in evidence-based medicine, although there are many variations in methods and content, and EBM teaching is limited by lack of curriculum time, trained tutors and teaching materials. Many programs have been developed to help individual physicians gain better access to evidence. For example, UpToDate was created in the early 1990s. The Cochrane Collaboration began publishing evidence reviews in 1993. The BMJ Publishing Group launched a periodical 6-monthly in 1995 called Clinical Evidence that provides a brief summary of the current state of evidence on clinical questions that are important to physicians. Since then many other programs have been developed to make the evidence more accessible to practitioners.

Current practice

The term evidence-based medicine is now applied to both programs that designed evidence-based guidelines and programs that teach evidence-based medicine to practitioners. In 2000, "evidence-based medicine" has become a generic term for emphasis on evidence in population and individual level decisions. In subsequent years, the use of the term "evidence-based" has been extended to other levels of health care systems. An example is "evidence-based health services", which seek to improve the competence of health-care providers and evidence-based medical practice at the organizational or institutional level. This concept also spread beyond health care; for example, in his 1996 inaugural address as President of the Royal Statistical Society, Adrian Smith proposed that "evidence-based policies" should be established for education, prisons and policy policies and all areas of government work.

Many children from evidence-based medicine share an emphasis on the importance of incorporating evidence from formal research into medical policies and decisions. But they differ in how far they need proof of effective effectiveness before setting guidelines or payment policies, and they differ in how much it deserves to include individual level information in decisions. Thus, evidence-based guidelines and policies may be unprepared for 'hybridisation' with experience-based practice oriented towards ethical judgment, and may lead to undesirable contradictions, contests, and crises. The most effective 'knowledge leaders' (managers and clinical leaders) use a variety of management knowledge in their decision-making, not just formal evidence. Evidence-based guidelines can provide the government with a basis in health care and consequently play a central role in governance away from contemporary health care systems.

Maps Evidence-based medicine

Method

Steps

Measures for designing explicit, evidence-based guidelines were described in the late 1980s: Formulating questions (population, intervention, comparative intervention, yield, time horizon, arrangement); search the literature to identify research that tells questions; interpreting each study to determine exactly what it says about the question; if some research answers questions, synthesizes their results (meta-analysis); summarizing the evidence in the "evidence table"; compare benefits, dangers, and costs in the "balance sheet"; draw conclusions about preferred practices; write down the guide; write the reason for this guide; asking others to review each step beforehand; implements the guidelines.

For the purpose of individual medical education and decision-making, the five EBM steps in practice were described in 1992 and the experiences of delegates attending the Evidence-based Health Teacher Development and Developers Conference 2003 are summarized into five steps and published in 2005 This five-step process can widely categorized as:

1. The uncertainty translation becomes a questionable question that includes critical questions, study designs, and evidence levels
2. Systematic retrieval of the best available evidence
3. A critical review of evidence for internal validity that can be broken down into several aspects:
   • Systematic error as a result of selection bias, information bias and assimilation
   • Quantitative aspects of diagnosis and treatment
   • The size and aspect of the effect related to its accuracy
   • The importance of clinical results
   • External validity or generalization
4. Application results in practice
5. Performance evaluation

Evidence

A systematic review of published research studies is a major part of a special care evaluation. Cochrane Collaboration is one of the most famous programs that perform systematic reviews. Like other systematic review collections, it requires writers to provide detailed and repeatable plans of their literature searches and evaluation of evidence. After all the best evidence is assessed, treatment categorized as (1) tends to be beneficial, (2) tends to be harmful, or (3) evidence does not support benefits or hazards.

A 2007 analysis of 1,016 systematic reviews of all 50 Cochrane Collaboration Review Groups found that 44% of reviewers concluded that the intervention might be useful, 7% concluded that the intervention might be dangerous, and 49% concluded that evidence did not support the benefit or danger. 96% recommend further research. A review of 2001 of 160 systematic review of Cochrane (excluding complementary treatments) in the 1998 database revealed that, according to two readers, 41% concluded positive or perhaps positive effects, 20% concluded the evidence had no effect, 8% concluded the harmful effects, and 21% from the review concluded that the evidence is not enough. A review of 145 reviews of Cochrane alternative medicine using a 2004 database revealed that 38.4% concluded positive effects or positive effects (12.4%), 4.8% concluded no effect, 0.7% concluded harmful effects, and 56, 6% concluded not enough evidence. In 2017, a study assessed the systematic review role generated by Cochrane Collaboration to inform US private payer policy making; it shows that although the medical policy documents of the major US private are informed by Cochrane systematic review; there is still scope to encourage further usage.

Assess the quality of evidence

The quality of evidence can be assessed by source type (from meta-analysis and systematic review of triple-blind randomized clinical trials with concealment of allocations and no atrial endpoints, down to conventional wisdom at the bottom), and other factors including statistical validity, relevance clinical, currency, and peer-reviewed acceptance. Evidence-based drugs categorize different types of clinical evidence and their level or value according to their freedom of power from the various biases that affect medical research. For example, the strongest evidence for therapeutic intervention is provided by a systematic review of randomized, triple-blind, placebo-controlled trials with hiding and complete follow-up allocations involving homogeneous patient populations and medical conditions. In contrast, patient testimony, case reports, and even expert opinion (however, some critics argue that expert opinion "is not included in the ranking of the quality of empirical evidence as it does not represent a form of empirical evidence" and continues that "expert opinion seems to be a separate type of knowledge and complexes that would not fit the hierarchy, if not limited to empirical evidence alone "). has little value as evidence due to placebo effect, inherent bias in case observation and reporting, difficulty in ensuring who is skilled and more.

Some organizations have developed a scoring system to assess the quality of evidence. For example, in 1989, the US Prevention Services Task Force (USPSTF) proposed the following:

• Level I: Evidence obtained from at least one randomly controlled, designed trial.
• Level II-1: Evidence obtained from well-designed controlled trials without randomisation.
• Level II-2: Evidence obtained from a well-designed cohort study or case-control study, preferably from more than one center or study group.
• Level II-3: Evidence obtained from various time series designs with or without intervention. Dramatic results in uncontrolled experiments can also be considered as this type of evidence.
• Level III: Respected authority opinion, based on clinical experience, descriptive study, or expert committee report.

Another example is the Oxford CEBM Proof Rate (UK). First released in September 2000, Oxford's CEBM Proof Rate provides 'evidence' levels for claims about prognosis, diagnosis, treatment benefits, treatment hazards, and screening, most of which are not ruled out by scoring schemes. The original CEBM level is Evidence Based on Calls to make the process of finding reasonable evidence and the results are explicit. In 2011, the international team redesigned the Oxford CEBM Level to make it easier to understand and to take account of the latest developments in the evidence rating scheme. The Oxford CEBM proof rate has been used by patients, physicians and also to develop clinical guidelines including recommendations for the optimal use of phototherapy and topical therapy in psoriasis and guidelines for the use of BCLC staging systems to diagnose and monitor hepatocellular carcinoma in Canada..

In 2000, a system was developed by the GRADE working group (short for Grading of Recommendations Assessment, Development and Evaluation) and considered more dimensions than just the quality of medical research. This requires that GRADE users evaluate the quality of evidence, usually as part of a systematic review, to consider the impact of different factors on their beliefs in the results. The author of the GRADE table assesses the quality of evidence to four levels, on the basis of their belief in the observed effect (numerical value) that approximates what the actual effect is. The value of confidence is based on the assessment set out in five different domains in a structured manner. The GRADE working group defines 'quality of evidence' and 'strength of recommendations' based on quality as two distinct concepts that are generally confusing one another.

Systematic reviews may include randomized controlled trials that have a low risk of bias, or, observational studies that have a high risk of bias. In the case of randomized controlled trials, the quality of evidence is high, but can be derived in five different domains.

• Risk of bias: Whether an assessment is made on the grounds that the bias in the included study has affected the forecast of the effect.
• Impedition: Is an assessment made on the basis of the possibility that the observed effect estimate may change completely.
• Disapproval: Whether an assessment is made on the basis of differences in the characteristics of how the research is conducted and how the outcome will actually apply.
• Inconsistency: Whether an assessment is made on the basis of yield variability throughout the study included.
• Publication bias: Whether an assessment is made on the question of whether all the research evidence has been considered.

In the case of observational studies per GRADE, the quality of evidence starts from the lower and can be increased in three domains in addition to being the subject of downgrades.

• Big effect: This is when a strongly methodological study shows that the observed effect is so large that the probability of change is very small.
• Reasonable confounding will change the effect: This is when despite the possible confounding factors expected to reduce the observed effect, the effect estimate still shows a significant effect.
• Dose gradient response: This is when the intervention used becomes more effective by increasing the dose. This suggests that further increases are likely to bring more effects.

The meaning of the level of evidence quality according to GRADE:

• High Quality Evidence: The authors strongly believe that the estimates presented are very close to their true values. One can interpret it as "there is a very low probability of further research that completely alters the conclusions presented."
• Medium Quality Proof: The authors are convinced that the estimate presented lies close to the true value, but it is also possible that it may be quite different. One can also interpret it as: further research can completely change the conclusions.
• Low Quality Proof: The authors are not sure in the estimates of the true effect and the value may be very different. One may interpret it as "further research is likely to change the conclusions presented completely."
• Very low quality evidence: The authors have no confidence in the estimate and the possibility that the true value is substantially different from that. One can interpret it as "new research will most likely change the conclusions presented completely."

Recommendation categories

In other guidelines and publications, recommendations for clinical services are classified by the balance of risks versus benefits and the level of evidence on which this information is based. The US Preventive Services Task Force uses:

• Level A: Good scientific evidence shows that the benefits of clinical services far outweigh the potential risks. Doctors should discuss services with qualified patients.
• Level B: At least fair scientific evidence shows that the benefits of clinical services outweigh the potential risks. Doctors should discuss services with qualified patients.
• Level C: At least fair scientific evidence indicates that there are benefits provided by clinical services, but the balance between benefits and risks is too close to make general recommendations. Doctors do not have to offer it unless there is individual consideration.
• Level D: At least fair scientific evidence suggests that the risk of clinical services exceeds potential benefits. Doctors should not routinely offer services to patients who are asymptomatic.
• Level I: Scientific evidence of poor, poor quality, or contradictory, so risk versus balance of benefits can not be assessed. Doctors should help patients understand the uncertainty surrounding clinical services.

GRADE guide panellists can make strong or weak recommendations based on further criteria. Some important criteria are the balance between desired and undesirable effects (not considering cost), quality of evidence, value and preferences and costs (resource utilization).

Despite the difference between systems, the goal is the same: to guide users of clinical research information in which studies tend to be most valid. However, individual studies still require careful critical assessment.

Statistical measurements

Drug-based evidence tries to express the clinical benefits of tests and treatments using mathematical methods. The tools used by practitioners of evidence-based medicine include:

• Possibility ratio Odds pre-test of a particular diagnosis, multiplied by the likelihood ratio, determines post-test opportunities. (Opportunities can be calculated from, and then converted into, probability [more familiar].) This reflects Bayes's theorem. The difference in likelihood ratio between clinical tests can be used to prioritize clinical trials according to their usefulness in certain clinical situations.
• The AUC-ROC area under the receiver operating characteristic curve (AUC-ROC) reflects the relationship between sensitivity and specificity for the given test. High-quality tests will have AUC-ROC approaching 1, and high-quality publications about clinical trials will provide information about AUC-ROC. The cutoff value for positive and negative tests may affect specificity and sensitivity, but does not affect AUC-ROC.
• Amount needed to treat (NNT)/Number needed to harm (NNH). The amounts needed to treat or the amount needed to harm are the means of expressing the effectiveness and safety, respectively, of interventions in a clinically meaningful way. NNT is the number of people who need to be treated to achieve the desired outcome (eg survival of cancer) in one patient. For example, if treatment increases the likelihood of survival by 5%, then 20 people need to be treated to have 1 additional patient who survives treatment. This concept can also be applied to diagnostic tests. For example, if 1,339 women age 50-59 should be invited to screen breast cancer for a period of ten years to prevent a woman dying of breast cancer, then NNT invited to screen breast cancer is 1339.

Quality of clinical trials

Drug-based evidence tries to objectively evaluate the quality of clinical research with critical assessment techniques reported by researchers in their publications.

• Trial design considerations. High quality studies have clearly defined the eligibility criteria and have minimal lost data.
• Generalizability considerations. The study can only apply to a narrowly defined patient population and can not be generalized to other clinical contexts.
• Follow-up. Sufficient time for specified results to occur can affect the results of prospective studies and the statistical power of a study to detect differences between treatment groups and control groups.
• Power. Mathematical calculations can determine whether the number of patients is sufficient to detect differences between treatment groups. A negative study may reflect a lack of benefits, or simply a lack of sufficient numbers of patients to detect differences.

src: evidencelive.org

Limitations and criticism

Although evidence-based medicine is considered a gold standard of clinical practice, there are a number of limitations and critiques of its use. Two widely quoted categorizing schemes for published criticisms of EBM include a threefold division of Straus and McAlister ("universal boundaries for medical practice, unique limitations for evidence-based medicine and evidence-based perception of drugs") and a five-point categorization of Cohen, Stavri and Hersh (EBM is a poor philosophical foundation for medicine, defining evidence is too narrow, not evidence-based, limited in usability when applied to individual patients, or reducing physician autonomy/patient relationships).

In no particular order, several published objections include:

• The theoretical idea of ​​EBM (that any narrow clinical question, which can reach hundreds of thousands, will be answered by systematic meta-analysis and systematic reviews of various RCTs) faces the limitations that research (especially RCT itself) is expensive; thus, in fact, for the foreseeable future, there will always be more demand for EBM than supply, and the best humanity can do is to triage the deployment of scarce resources.
• Research produced by EBM, such as from a randomized controlled trial (RCT), may not be relevant for all treatment situations. Research tends to focus on specific populations, but individuals may vary substantially from the norms of the population. Because certain population segments are historically under-researched (racial minorities and people with coexisting illnesses), evidence from RCTs can not be generalized to these populations. Thus EBM applies to a group of people, but this does not prevent clinicians from using their personal experience in deciding how to treat each patient. One authors suggest that "the knowledge gained from clinical research does not directly address major clinical questions of what is best for patients at hand" and suggests that evidence-based medicine should not ignore the value of clinical experience. Other authors state that "evidence-based medical practice means integrating individual clinical expertise with the best external clinical evidence available from systematic research."
• Research may be affected by biases such as publication bias and conflicts of interest. For example, studies with conflicts due to industry funding are more likely to favor their products.
• There is a pause when RCT is done and when the results are published.
• There was a pause when the results were published and currently implemented correctly.
• Hypocognition (the lack of a simple, consolidated mental framework that can be incorporated into new information) may inhibit EBM implementation.
• Values: while patient values ​​are considered in the original definition of EBM, the importance of value is not overly emphasized in EBM training, a potential problem in current research.

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Application evidence in clinical settings

One of the ongoing challenges with evidence-based medicine is that some healthcare providers do not follow evidence. This happens partly because the balance of evidence and current care is shifting constantly, and it is impossible to learn about any change. Even when the evidence is completely against medication, it usually takes ten years for another treatment to be adopted. In other cases, significant changes may require the generation of doctors to retire or die, and be replaced by doctors who are trained with more recent evidence.

Another major cause of doctors and other health care providers treating patients in a way that is not supported by evidence is that these health care providers are subject to the same cognitive biases as all other humans. They may deny evidence because they have a clear memory of a rare but surprising result (heuristic availability), such as patients dying after refusing treatment. They may overtreat to "do something" or to address the patient's emotional needs. They may worry about allegations of malpractice based on the difference between what is expected by the patient and what the evidence suggests. They may also overtreat or provide ineffective treatments because these treatments are biologically plausible.

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Education

The Berlin Questionnaire and Fresno Test are validated instruments for assessing the effectiveness of education in evidence-based medicine. This questionnaire has been used in various settings.

Campbell's systematic review of 24 trials tested the effectiveness of e-learning in improving evidence-based health care knowledge and practice. It was found that e-learning, compared to not learning, improves evidence-based care knowledge and skills but not attitudes and behaviors. There is no difference in results when comparing e-learning to face-to-face learning. Combining e-learning with blended learning has a positive impact on evidence-based knowledge, skills, attitudes and behavior. Associated with e-learning, medical school students have been involved with Wikipedia editing to improve their EBM skills.

src: www.regenexx.com

See also

src: www.ucl.ac.uk

References

src: bjgp.org

Bibliography

src: nutritionfacts.org

External links

• Evidence-Based Medicine - Oral History, JAMA and BMJ , 2014.
• Evidence-Based Medicine Center at Oxford University.

• Evidence Based Medicine in Curlie (based on DMOZ)

Source of the article : Wikipedia