The use by physicians of the current best scientific evidence in making decisions about the medical care of patients. Often there are gaps between an individual doctor's practice and the latest research about particular conditions, often because the research is not easily available. Increasingly, systems are being developed, including powerful electronic databases, to enable physicians to identify up-to-date information on the causes, diagnosis, and treatment of diseases, and help them make decisions about how best to manage their patients.
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."
Overview
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.
Evidence-based medicine has demoted ex cathedra statements of the "medical expert" to the least valid form of evidence. The explicit methodologies used to determine "best evidence" were largely established by the McMaster University research group led by David Sackett and Gordon Guyatt.
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.
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.
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. Level B: At least fair scientific evidence suggests that the benefits of the clinical service outweights the potential risks. 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. Level D: At least fair scientific evidence suggests that the risks of the clinical service outweighs potential benefits. 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.The Oxford Centre for Evidence-based Medicine uses these levels of evidence (LOE) and "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; 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.
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 — that is, 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."
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. 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. 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. 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.
A particular difficulty, which in essence is related to many of the problems above, is that evidence-based guidelines do not remove the problem of induction. Furthermore, skepticism about results may always be extended to areas not explicitly covered: for example a drug may influence a "intermediate endpoint" such as as test result (blood pressure, glucose, or cholesterol levels), or even a clinical endpoint such as number of heart attacks or tumor size, without having the power to show that it decreases overall mortality in a population. In such cases, skeptics who demand that an expensive treatment not be used until it can be shown to save lives may argue that crucial evidence is lacking.
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. For example, if an older generic statin drug has been shown to reduce mortality, is this enough evidence for use of a much more expensive newer statin drug which lowers cholesterol more effectively, but for which mortality reductions have not had time enough to be shown?
Evidence-based medicine has also been criticized for using a very specific, econometrically derived form of evidence.
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