Evidence-based medicine (EBM) applies the scientific method to medical practice. According to the Centre for Evidence-Based Medicine, "Evidence-based medicine is the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients."
Using techniques from science, engineering and statistics, such as meta-analysis of scientific literature, risk-benefit analysis, and randomized controlled trials, it aims for the ideal that healthcare professionals should make "conscientious, explicit, and judicious use of current best evidence" in their everyday practice.
Generally, there are three distinct, but interdependent, areas of EBM. The first is to treat individual patients with acute or chronic pathologies by treatments supported in the most scientifically valid medical literature. Thus medical practitioners would select treatment options for specific cases based on the best research for each patient they treat. The second area is the systemic analysis of medical literature to evaluate the best studies on specific topics. This processs can be very human-centered, as in a journal club, or highly technical using computer programs and information techniques such as data mining. Increased use of information technology turns large volumes of information into practical guides. Finally, Evidence-based medicine can be understood as a medical 'movement', where advocates work to popularize the method and usefulness of the practice in the public, patient communities, educational institutions, and continuing education of practicing professionals.
Evidence-based medicine has demoted ex cathedra statements of the "medical expert" to the least valid form of evidence. All "experts" are now expected to reference their pronouncements to scientific studies.
Although testing medical interventions for efficacy has existed for several hundred years, and arguably more, it was only in the 20th century that this effort evolved to impact almost all fields of health care and policy. Professor Archie Cochrane , a Scottish epidemiologist whose book Effectiveness and Efficiency: Random Reflections on Health Services (1972) and subsequent advocacy, caused increasing acceptance of the concepts behind evidence-based practice. Cochrane's work was honoured through the naming of centres of evidence-based medical research ' Cochrane Centres ' and an international organisation, the Cochrane Collaboration. The explicit methodologies used to determine 'best evidence' were largely established by the McMaster University research group led by David Sackett and Gordon Guyatt. The term "evidence-based medicine" first appeared in the medical literature in 1992 in a paper by Guyatt et al.
Qualification of evidence
Evidence-based medicine categorizes different types of clinical evidence and ranks them according to the strength of their freedom from the various biases that beset medical research. For example, the strongest evidence for therapeutic interventions is provided by randomized, double-blind, placebo-controlled trials involving a homogeneous patient population and medical condition. In contrast, patient testimonials, case reports, and even expert opinion have little value as proof because of the placebo effect, the biases inherent in observation and reporting of cases, difficulties in ascertaining who is an expert, and more.
Practising evidence-based medicine implies not only clinical expertise, but expertise in retrieving, interpreting, and applying the results of scientific studies, and in communicating the risks and benefit of different courses of action to patients.
The concept of number needed to treat (NNT) is increasingly part of evidence-based medicine. NNT is a numerical indicator of the effectiveness of a therapy. For example, an NNT of 4 means if 4 patients are treated, only one would respond. An NNT of 1 is the most effective and means each patient treated responds, eg in comparing antibiotics with placebo in the eradication of Helicobacter pylori. An NNT of 2 or 3 indicates that a treatment is quite effective (with one patient in 2 or 3 responding to the treatment). An NNT of 20 to 40 can still be considered clinically effective.
Systems to stratify evidence by quality have been developed, such as this one by the U.S. Preventive Services Task Force:
- Level I: Evidence obtained from at least one properly designed randomized controlled trial.
- Level II-1: Evidence obtained from well-designed controlled trials without randomization.
- Level II-2: Evidence obtained from well-designed cohort or case-control analytic studies, preferably from more than one center or research group.
- Level II-3: Evidence obtained from multiple time series with or without the intervention. Dramatic results in uncontrolled trials might also be regarded as this type of evidence.
- Level III: Opinions of respected authorities, based on clinical experience, descriptive studies, or reports of expert committees.
Categories of recommendations
In guidelines and other publications, recommendation for a clinical service is classified by the balance of risk versus benefit of the service and the level of evidence on which this information is based. The U.S. Preventive Service Task Force uses:
- Level A: Good scientific evidence suggests that the benefits of the clinical service substantially outweighs the potential risks. Clinicians should discuss the service with eligible patients.
- Level B: At least fair scientific evidence suggests that the benefits of the clinical service outweights the potential risks. Clinicians should discuss the service with eligible patients.
- Level C: At least fair scientific evidence suggests that there are benefits provided by the clinical service, but the balance between benefits and risks are too close for making general recommendations. Clinicians need not offer it unless there are individual considerations.
- Level D: At least fair scientific evidence suggests that the risks of the clinical service outweighs potential benefits. Clinicans should not routinely offer the service to asymptomatic patients.
- Level I: Scientific evidence is lacking, of poor quality, or conflicting, such that the risk versus benefit balance cannot be assessed. Clinicians should help patients understand the uncertainty surrounding the clinical service.
This is a distinct and conscious improvement on older fashions in recommendation and the interpretation of recommendations where it was less clear which parts of a guideline were most firmly established.
The Oxford Centre for Evidence-based Medicine uses these "grades of recommendations" according to the study designs and critical appraisal of prevention, diagnosis, prognosis, therapy, and harm studies:
- Level A: consistent Randomised Controlled Clinical Trial, Cohort Study, All or None, Clinical Decision Rule validated in different populations.
- Level B: consistent Retrospective Cohort, Exploratory Cohort, Ecological Study, Outcomes Research, Case-Control Study; or extrapolations from level A studies.
- Level C: Case-series Study or extrapolations from level B studies
- Level D: Expert opinion without explicit critical appraisal, or based on physiology, bench research or first principles
"Extrapolations" are where data is used in a situation which has potentially clinically important differences than the original study situation.
Limitations of available evidence
It is recognised that not all evidence is made accessible, that this can limit the effectiveness of any approach, and that effort to reduce various publication and retrieval biases is required.
Failure to publish negative trials is the most obvious gap, and moves to register all trials at the outset, and then to pursue their results are underway. Changes in publication methods, particularly related to the Web should reduce the difficulty of getting a paper on a trial that concludes it did not prove anything new, including its starting hypothesis, published.
Treatment effectiveness reported from clinical studies may be higher than that achieved in later routine clinical practice due to the closer patient monitoring during trials that leads to much higher compliance rates.
Criticism of evidence-based medicine
Critics of EBM say lack of evidence and lack of benefit are not the same, and that the more data are pooled and aggregated, the more difficult it is to compare the patients in the studies with the patient in front of the doctor, i.e. EBM applies to populations, not necessarily to individuals. In The limits of evidence-based medicine, Tonelli argues that "the knowledge gained from clinical research does not directly answer the primary clinical question of what is best for the patient at hand." Tonelli suggests that proponents of evidence-based medicine discount the value of clinical experience.
Although evidence-based medicine is quickly becoming the "gold standard" for clinical practice and treatment guidelines, there are a number of reasons why most current medical and surgical practices do not have a strong literature base supporting them. First, in some cases, conducting randomized controlled trials would be unethical--such as in open-heart surgery--although observational studies are designed to address these problems to some degree. Second, certain groups have been historically under-researched (women, racial minorities, people with many co-morbid diseases) and thus the literature is sparse in areas that do not allow for generalizeability. Third, the types of trials considered 'gold standard' (i.e. randomized double-blind placebo-controlled trials) may be expensive and thus funding sources play a role in what gets investigated. For example, public authorities may tend to fund preventive medicine studies to improve public health as a whole, while pharmaceutical companies fund studies intended to demonstrate the efficacy and safety of particular drugs. Fourth, the studies that are published in medical journals may not be representative of all the studies that are completed on a given topic (published and unpublished) or may be misleading due to conflicts of interest (i.e. publication bias). Thus the array of evidence available on particular therapies may not be well-represented in the literature. Since the 2004 statement by the International Committee of Medical Journal Editors that they will refuse to publish clinical trial results if the trial was not recorded publicly at its outset, this may become less of a problem. Fifth, the quality of studies performed varies, making it difficult to generalize about the results, although well conducted meta-analyses remove poor quality studies from influencing data.
Large randomized controlled trials are useful for examining discrete interventions for carefully defined medical conditions. The more complex the patient population, the conditions, and the intervention, the more difficult it is to separate the treatment effect from random variation. Because of this, a number of studies obtain non-significant results, either because there is insufficient power to show a difference, or because the groups are not well-enough 'controlled'.
In managed healthcare systems evidence-based guidelines have been used as a basis for denying insurance coverage for some treatments which are held by the physicians involved to be effective, but of which randomized controlled trials have not yet been published.