Evidence-based medicine (EBM) emphasizes the use of evidence from well designed and conducted research in healthcare decision-making. The term was originally used to describe an approach to teaching the practice of medicine and improving decisions by individual physicians. Use of the term rapidly expanded to include a previously described approach that emphasized the use of evidence in the design of guidelines and policies that apply to populations ("evidence-based practice policies"). It has subsequently spread to describe an approach to decision making that is used at virtually every level of the healthcare system.
Whether applied to medical education, decisions about individuals, guidelines and policies applied to populations, or administration of health services in general, evidence-based medicine advocates that to the greatest extent possible, decisions and policies should be based on evidence, not just the beliefs of practitioners, experts, or administrators. It promotes the use of formal, explicit methods to analyze evidence and make it available to decision makers. It promotes programs to teach the methods to medical students, practitioners, and policy makers.
- 1 Background, history and definition
- 2 Process and progress
- 3 Assessing the quality of evidence
- 4 Statistical measures
- 5 Quality of clinical trials
- 6 Limitations and criticism
- 7 Assessing the teaching of evidence-based medicine
- 8 EBM and ethics of experimental or risky treatments
- 9 Application of the evidence based model on other public policy matters
- 10 In psychiatry
- 11 See also
- 12 References
- 13 Bibliography
- 14 External links
Background, history and definition
In its broadest form, evidence-based medicine is the application of the scientific method into healthcare decision-making. Medicine has a long tradition of both basic and clinical research that dates back at least to Avicenna. However until recently, the process by which research results were incorporated in medical decisions was highly subjective. Called "clinical judgment" and "the art of medicine", the traditional approach to making decisions about individual patients depended on having each individual physician determine what research evidence, if any, to consider, and how to merge that evidence with personal beliefs and other factors . In the case of decisions that applied to populations, the guidelines and policies would usually be developed by committees of experts, but there was no formal process for determining the extent to which research evidence should be considered or how it should be merged with the beliefs of the committee members. There was an implicit assumption that decision makers and policy makers would incorporate evidence in their thinking appropriately, based on their education, experience, and ongoing study of the applicable literature.
Beginning in the late 1960s, several flaws became apparent in the traditional approach to medical decision-making. Alvan Feinstein's publication of Clinical Judgment in 1967 focused attention on the role of clinical reasoning and identified biases that can affect it. In 1972, Archie Cochrane published Effectiveness and Efficiency, which described the lack of controlled trials supporting many practices that had previously been assumed to be effective. In 1973, John Wennberg began to document wide variations in how physicians practiced. Through the 1980s, David Eddy described errors in clinical reasoning and gaps in evidence. In the mid 1980s, Alvin Feinstein, David Sackett and others published textbooks on clinical epidemiology, which translated epidemiological methods to physician decision making. Toward the end of the 1980s, a group at RAND showed that large proportions of procedures performed by physicians were considered inappropriate even by the standards of their own experts. These areas of research increased awareness of the weaknesses in medical decision making at the level of both individual patients and populations, and paved the way for the introduction of evidence based methods.
The term "evidence-based medicine", as it is currently used, has two main 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 individual patient-level decision-making.
The term "evidence-based" was first used by David M. Eddy in the context of population-level policies such as clinical practice guidelines and insurance coverage of new technologies. He first began to use the term "evidence-based" in 1987 in workshops and a manual commissioned by the Council of Medical Specialty Societies to teach formal methods for designing clinical practice guidelines. The manual was widely available in unpublished form in the late 1980s and 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 laid out the principles of evidence-based guidelines and population-level policies, which Eddy described as "explicitly describing the available evidence that pertains to a policy and tying the policy to evidence. Consciously anchoring a policy, not to current practices or the beliefs of experts, but to experimental evidence. The policy must be consistent with and supported by evidence. The pertinent evidence must be identified, described, and analyzed. The policymakers must determine whether the policy is justified by the evidence. A rationale must be written." He discussed "evidence-based" policies in several other papers published in JAMA in the spring of 1990. Those papers were part of a series of 28 published in JAMA between 1990 and 1997 on formal methods for designing population-level guidelines and policies.
The term "evidence-based medicine" was first used slightly later, in the context of medical education. This branch of evidence-based medicine has its roots in clinical epidemiology. In the autumn of 1990, Gordon Guyatt used it in an unpublished description of a program at McMaster University for prospective or new medical students. Guyatt and others first published the term two years later (1992) to describe a new approach to teaching the practice of medicine. In 1996, David Sackett and colleagues clarified the definition of this tributary of evidence-based medicine as "the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients. ... [It] means integrating individual clinical expertise with the best available external clinical evidence from systematic research." This branch of evidence-based medicine aims to make individual decision making more structured and objective by better reflecting the evidence from research. It requires the application of population-based data to the care of an individual patient, while respecting the fact that practitioners have clinical expertise reflected in effective and efficient diagnosis and thoughtful identification and compassionate use of individual patients' predicaments, rights, and preferences. This tributary of evidence-based medicine had its foundations in clinical epidemiology, a discipline that teaches medical students and physicians how to apply clinical and epidemiological research studies to their practices. The methods were published to a broad physician audience in a series of 25 "Users’ Guides to the Medical Literature" published in JAMA between 1993 and 2000 by the Evidence based Medicine Working Group at McMaster University. Other definitions for individual level evidence-based medicine have been put forth. For example, in 1995 Rosenberg and Donald defined it as "the process of finding, appraising, and using contemporaneous research findings as the basis for medical decisions." In 2010 by Greenhalgh used a definition that emphasized the use of quantitative methods: "the use of mathematical estimates of the risk of benefit and harm, derived from high-quality research on population samples, to inform clinical decision-making in the diagnosis, investigation or management of individual patients." Many other definitions have been offered for individual level evidence-based medicine, but the one by Sackett and colleagues is the most commonly cited.
The two original definitions highlight important differences in how evidence-based medicine is applied to populations versus individuals. When designing policies such as guidelines that will be applied to large groups of people in settings where there is relatively little opportunity for modification by individual physicians, evidence-based policymaking stresses that there be good evidence documenting that the effectiveness of the test or treatment under consideration. In the setting of individual decision-making there is additional information about the individual patients. Practitioners can be given greater latitude in how they interpret research and combine it with their clinical judgment. Recognizing the two branches of EBM, in 2005 Eddy offered an umbrella definition: "Evidence-based medicine is a set of principles and methods intended to ensure that to the greatest extent possible, medical decisions, guidelines, and other types of policies are based on and consistent with good evidence of effectiveness and benefit."
Both branches of evidence-based medicine spread rapidly. On the evidence-based guidelines and policies side, explicit insistence on evidence of effectiveness was introduced by the American Cancer Society in 1980. The U.S. Preventive Services Task Force (USPSTF) began issuing guidelines for preventive interventions based on evidence-based principles in 1984. In 1985, the Blue Cross Blue Shield Association applied strict evidence-based criteria for covering new technologies. Beginning in 1987, specialty 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, began an evidence based guidelines program. In 1991, Richard Smith wrote an editorial in the British Medical Journal and introduced the ideas of evidence-based policies in the UK. In 1993, the Cochrane Collaboration created a network of 13 countries to produce of systematic reviews and guidelines. In 1997, the US Agency for Healthcare Research and Quality (then known as the Agency for Health Care Policy and Research, or AHCPR) established Evidence-based Practice Centers (EPCs) to produce evidence reports and technology assessments to support the development of guidelines. In the same year, a National Guideline Clearinghouse that followed the principles of evidence based policies was created by AHRQ, the AMA, and the American Association of Health Plans (now America's Health Insurance Plans). In 1999, the National Institute for Clinical Excellence (NICE) was created in the UK.
On the medical education side, programs to teach evidence-based medicine have been created in medical schools in Canada, the US, the UK, Australia, and other countries. A 2009 study of UK programs found the more than half of UK medical schools offered some training in evidence-based medicine, although there was considerable variation in the methods and content, and EBM teaching was restricted 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, Up-to-date was created in the early 1990s. The Cochrane Center began publishing evidence reviews in 1993. BMJ Publishing Group launched a 6-monthly periodical in 1995 called Clinical Evidence that provided brief summaries of the current state of evidence about important clinical questions for clinicians. Since then many other programs have been developed to make evidence more accessible to practitioners.
The term evidence-based medicine is now applied to both the programs that are designing evidence-based guidelines and the programs that teach evidence-based medicine to practitioners. By 2000, "evidence-based medicine" had become an umbrella term for the emphasis on evidence in both population-level and individual-level decisions. In subsequent years, use of the term "evidence-based" had extended to other levels of the health care system. An example is "evidence-based health services", which seek to increase the competence of health service decision makers and the practice of evidence-based medicine at the organizational or institutional level. The concept has also spread outside of healthcare; for example, in his 1996 inaugural speech as President of the Royal Statistical Society, Adrian Smith proposed that "evidence-based policy" should be established for education, prisons and policing policy and all areas of government work.
The multiple tributaries of evidence based medicine share an emphasis on the importance of incorporating evidence from formal research in medical policies and decisions. However they differ on the extent to which they require good evidence of effectiveness before promulgating a guideline or payment policy, and they differ on the extent to which it is feasible to incorporate individual-level information in decisions. Thus, evidence-based guidelines and policies may not readily 'hybridise' with experience-based practices orientated towards ethical clinical judgement, and can lead to contradictions, contest, and unintended crises. The most effective 'knowledge leaders' (managers and clinical leaders) use a broad range of management knowledge in their decision making, rather than just formal evidence. Evidence-based guidelines may provide the basis for governmentality in health care and consequently play a central role in the distant governance of contemporary health care systems.
Process and progress
Five steps of EBM in practice were first described in 1992 and the experience of delegates attending the 2003 Conference of Evidence-Based Health Care Teachers and Developers was summarized into five steps and published in 2005. This five step process can broadly be categorized as:
- Translation of uncertainty to an answerable question and includes critical questioning, study design and levels of evidence
- Systematic retrieval of the best evidence available
- Critical appraisal of evidence for internal validity that can be broken down into aspects regarding:
- Systematic errors as a result of selection bias, information bias and confounding
- Quantitative aspects of diagnosis and treatment
- The effect size and aspects regarding its precision
- Clinical importance of results
- External validity or generalizability
- Application of results in practice
- Evaluation of performance
Using techniques from science, engineering and statistics, such as the systematic review of medical literature, meta-analysis, risk-benefit analysis, and randomized controlled trials (RCTs), EBM aims for the ideal that healthcare professionals should make "conscientious, explicit, and judicious use of current best evidence" in their everyday practice. Ex cathedra statements by the "medical expert" are considered to be the least valid form of evidence. All "experts" are now expected to reference their pronouncements to scientific studies.
The systematic review of published research studies is a major method used for evaluating particular treatments. The Cochrane Collaboration is one of the best-known, respected examples of systematic reviews. Like other collections of systematic reviews, it requires authors to provide a detailed and repeatable plan of their literature search and evaluations of the evidence. Once all the best evidence is assessed, treatment is categorized as (1) likely to be beneficial, (2) likely to be harmful, or (3) evidence did not support either benefit or harm.
A 2007 analysis of 1,016 systematic reviews from all 50 Cochrane Collaboration Review Groups found that 44% of the reviews concluded that the intervention was likely to be beneficial, 7% concluded that the intervention was likely to be harmful, and 49% concluded that evidence did not support either benefit or harm. 96% recommended further research. A 2001 review of 160 Cochrane systematic reviews (excluding complementary treatments) in the 1998 database revealed that, according to two readers, 41.3% concluded positive or possibly positive effect, 20% concluded evidence of no effect, 8.1% concluded net harmful effects, and 21.3% of the reviews concluded insufficient evidence. A review of 145 alternative medicine Cochrane reviews using the 2004 database revealed that 38.4% concluded positive effect or possibly positive (12.4%) effect, 4.8% concluded no effect, 0.69% concluded harmful effect, and 56.6% concluded insufficient evidence.
Assessing the quality of evidence
Evidence quality can be assessed based on the source type (from meta-analyses and systematic reviews of triple-blind randomized clinical trials with concealment of allocation and no attrition at the top end, down to conventional wisdom at the bottom), as well as other factors including statistical validity, clinical relevance, currency, and peer-review acceptance. Evidence-based medicine categorizes different types of clinical evidence and rates or grades 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 systematic review of randomized, triple-blind, placebo-controlled trials with allocation concealment and complete follow-up involving a homogeneous patient population and medical condition. In contrast, patient testimonials, case reports, and even expert opinion (however some critics have argued that expert opinion "does not belong in the rankings of the quality of empirical evidence because it does not represent a form of empirical evidence" and continue that "expert opinion would seem to be a separate, complex type of knowledge that would not fit into hierarchies otherwise limited to empirical evidence alone.") have little value as evidence because of the placebo effect, the biases inherent in observation and reporting of cases, difficulties in ascertaining who is an expert and more.
U.S. Preventive Services Task Force (USPSTF)
Systems to stratify evidence by quality have been developed, such as this one by the United States Preventive Services Task Force for ranking evidence about the effectiveness of treatments or screening:
- 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 designs 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.
Oxford CEBM Levels of Evidence (UK)
Most of the evidence ranking schemes grade evidence for therapy and prevention, but not for diagnostic tests, prognostic markers, or harm. The Oxford CEBM Levels of Evidence addresses this issue and provides 'Levels' of evidence for claims about prognosis, diagnosis, treatment benefits, treatment harms, and screening. The original CEBM Levels was first released in September 2000 for Evidence-Based On Call to make the process of finding evidence feasible and its results explicit. In 2011 the Oxford CEBM Levels were redesigned by international team including Jeremy Howick, Sir Iain Chalmers, Paul Glasziou (chair), Trish Greenhalgh, Carl Heneghan, Alessandro Liberati, Ivan Moschetti, Bob Phillips, and Hazel Thornton (and help from Olive Goddard and Mary Hodgkinson) re-designed the Oxford CEBM Levels to make it more understandable and to take into account recent developments in evidence ranking schemes. The Oxford CEBM Levels of Evidence have been used by patients, clinicians 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 the BCLC staging system for diagnosing and monitoring hepatocellular carcinoma in Canada.
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 Services Task Force uses:
- Level A: Good scientific evidence suggests that the benefits of the clinical service substantially outweigh 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 outweighs 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. Clinicians 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.
GRADE working group
||This article lends undue weight to certain ideas, incidents, or controversies. (July 2014)|
A system was developed by the GRADE working group and takes into account more dimensions than just the quality of medical research. It requires users of GRADE (short for Grading of Recommendations Assessment, Development and Evaluation) who are performing an assessment of the quality of evidence, usually as part of a systematic review, to consider the impact of different factors on their confidence in the results. Authors of GRADE tables, grade the quality of evidence into four levels, on the basis of their confidence in the observed effect(a numerical value) being close to what the true effect is. The confidence value is based on judgements assigned in five different domains in a structured manner. The GRADE working group defines 'quality of evidence' and 'strength of recommendations' based on the quality as two different concepts which are commonly confused with each other.
Systematic reviews may include Randomized Controlled trials that have low risk of bias, or, observational studies that have high risk of bias. In the case of Randomized controlled trials, the quality of evidence is high, but can be downgraded in five different domains.
- Risk of bias: Is a judgement made on the basis of the chance that bias in included studies have influenced the estimate of effect.
- Imprecision: Is a judgement made on the basis of the chance that the observed estimate of effect could change completely.
- Indirectness: Is a judgement made on the basis of the differences in characteristics of how the study was conducted and how the results are actually going to be applied.
- Inconsistency: Is a judgement made on the basis of the variability of results across the included studies.
- Publication bias: Is a judgement made on the basis of the question whether all the research evidence has been taken to account.
In the case of observational studies, the quality of evidence starts of lower and may be upgraded in three domains in addition to being subject to downgrading.
- Large effect: This is when methodologically strong studies show that the observed effect is so large that the probability of it changing completely is less likely.
- Plausible confounding would change the effect: This is when despite the presence of a possible confounding factor which is expected to reduce the observed effect, the effect estimate still shows significant effect.
- Dose response gradient: This is when the intervention used becomes more effective with increasing dose. This suggests that a further increase will likely bring about more effect.
Meaning of the levels of Quality of evidence as per GRADE
- High Quality Evidence: The authors are very confident that the estimate that is presented lies very close to the true value. One could interpret it as: there is very low probability of further research completely changing the presented conclusions.
- Moderate Quality Evidence: The authors are confident that the presented estimate lies close to the true value, but it is also possible that it may be substantially different. One could also interpret it as: further research may completely change the conclusions.
- Low Quality Evidence: The authors are not confident in the effect estimate and the true value may be substantially different. One could interpret it as: further research is likely to change the presented conclusions completely.
- Very low quality Evidence: The authors do not have any confidence in the estimate and it is likely that the true value is substantially different from it. One could interpret it as: New research will most probably change the presented conclusions completely.
Guideline panelists may make Strong or Weak recommendations on the basis of further criteria. Some of the important criteria are:
- Balance between desirable and undesirable effects (not considering cost)
- Quality of the evidence
- Values and preferences
- Costs (resource utilization)
Despite the differences between systems, the purposes are the same: to guide users of clinical research information on which studies are likely to be most valid. However, the individual studies still require careful critical appraisal.
Evidence-based medicine attempts to express clinical benefits of tests and treatments using mathematical methods. Tools used by practitioners of evidence-based medicine include:
The pre-test odds of a particular diagnosis, multiplied by the likelihood ratio, determines the post-test odds. (Odds can be calculated from, and then converted to, the [more familiar] probability.) This reflects Bayes' theorem. The differences in likelihood ratio between clinical tests can be used to prioritize clinical tests according to their usefulness in a given clinical situation.
The area under the receiver operating characteristic curve (AUC-ROC) reflects the relationship between sensitivity and specificity for a given test. High-quality tests will have an AUC-ROC approaching 1, and high-quality publications about clinical tests will provide information about the AUC-ROC. Cutoff values for positive and negative tests can influence specificity and sensitivity, but they do not affect AUC-ROC.
Number needed to treat / harm
Number needed to treat or Number needed to harm are ways of expressing the effectiveness and safety of an intervention in a way that is clinically meaningful. In general, NNT is always computed with respect to two treatments A and B, with A typically a drug and B a placebo (in our example above, A is a 5-year treatment with the hypothetical drug, and B is no treatment). A defined endpoint has to be specified (in our example: the appearance of colon cancer in the 5 year period). If the probabilities pA and pB of this endpoint under treatments A and B, respectively, are known, then the NNT is computed as 1/(pB-pA). If it is said that the NNT for breast mammography is 285 this means that 285 mammograms need to be performed to diagnose one case of breast cancer. As another 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, e.g., 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.
Quality of clinical trials
Evidence-based medicine attempts to objectively evaluate the quality of clinical research by critically assessing techniques reported by researchers in their publications.
- Trial design considerations. High-quality studies have clearly defined eligibility criteria and have minimal missing data.
- Generalizability considerations. Studies may only be applicable to narrowly defined patient populations and may not be generalizable to other clinical contexts.
- Follow-up. Sufficient time for defined outcomes to occur can influence the prospective study outcomes and the statistical power of a study to detect differences between a treatment and control arm.
- Power. A mathematical calculation can determine if the number of patients is sufficient to detect a difference between treatment arms. A negative study may reflect a lack of benefit, or simply a lack of sufficient quantities of patients to detect a difference.
Limitations and criticism
Although evidence-based medicine is regarded as the gold standard of conventional clinical practice, there are a number of limitations and criticisms of its use, many of which remain unresolved despite nearly two centuries of debate.
- EBM produces quantitative research, especially from randomized controlled trials (RCTs). Accordingly, results may not be relevant for all treatment situations.
- RCTs are expensive, influencing research topics according to the sponsor's interests.
- There is a lag between when the RCT is conducted and when its results are published.
- There is a lag between when results are published and when these are properly applied.
- Certain population segments have been historically under-researched (racial minorities and people with co-morbid diseases), and thus the RCT restricts generalizing.
- Not all evidence from an RCT is made accessible. Treatment effectiveness reported from RCTs may be different than that achieved in routine clinical practice.
- Published studies may not be representative of all studies completed on a given topic (published and unpublished) or may be unreliable due to the different study conditions and variables.
- EBM applies to groups of people but this does not preclude clinicians from using their personal experience in deciding how to treat each patient. One author advises that "the knowledge gained from clinical research does not directly answer the primary clinical question of what is best for the patient at hand" and suggests that evidence-based medicine should not discount the value of clinical experience. Another author stated that "the practice of evidence-based medicine means integrating individual clinical expertise with the best available external clinical evidence from systematic research".
- Hypocognition (the absence of a simple, consolidated mental framework that new information can be placed into) can hinder the application of EBM.
Assessing the teaching of evidence-based medicine
EBM and ethics of experimental or risky treatments
Insurance companies in the United States and public insurers in other countries usually wait for drug use approval based on evidence-based guidelines before funding a treatment. Where approval for a drug has been given, and subsequent evidence-based findings indicating that a drug may be less safe than originally anticipated, some insurers in the U.S. have reacted very cautiously and withdrawn funding. For example, an older generic statin drug had been shown to reduce mortality, but a newer and much more expensive statin drug was found to lower cholesterol more effectively. However, evidence came to light about safety concerns with the new drug which caused some insurers to stop funding it even though marketing approval was not withdrawn.
Some people are willing to take their chances to gamble their health on the success of new drugs or old drugs in new situations which may not yet have been fully tested in clinical trials. However insurance companies are reluctant to take on the job of funding such treatments, preferring instead to take the safer route of awaiting the results of clinical testing and leaving the funding of such trials to the manufacturer seeking a license.
Sometimes caution errs in the other direction. Kaiser Permanente did not change its methods of evaluating whether or not new therapies were too "experimental" to be covered until it was successfully sued twice: once for delaying in vitro fertilization treatments for two years after the courts determined that scientific evidence of efficacy and safety had reached the "reasonable" stage; and in another case where Kaiser refused to pay for liver transplantation in infants when it had already been shown to be effective in adults, on the basis that use in infants was still "experimental." Here again, the problem of induction plays a key role in arguments.
Application of the evidence based model on other public policy matters
There has been discussion of applying what has been learned from evidence-based medicine to public policy. In his 1996 inaugural speech as President of the Royal Statistical Society, Adrian Smith held out evidence-based medicine as an exemplar for all public policy. He proposed that "evidence-based policy" should be established for education, prisons and policing policy and all areas of government work.
Standard descriptions about mental illnesses, such as the Diagnostic and Statistical Manual of Mental Disorders (DSM), have been criticized as incompletely justified by scientific evidence. In many cases, it is unknown whether a particular "disease" has one, several, or no underlying biological causes, with controversy arising over whether some diseases are merely an artifact of the attempt to construct a unified classification scheme, rather than a "real" disease.
While some experts point to statistics in support of the idea that a lack of adoption of research findings results in suboptimal treatment for psychiatric patients, others emphasize the skill of the practitioner and customization of the treatment to fit individual needs. There is some controversy over whether mental illnesses are too complex for broad population studies to be helpful.
Examples of using evidence to support general interpretations in psychiatry:
- The rate of incidence of mental illness has increased from 0.3% (1 in 300) in 1955 to 18.6% today (2012).
- As reported in 2006 the seriously mentally ill died about twenty five years younger than the population as a whole, often due to cardiovascular disease, substance abuse, obesity, or a combination. In previous research, it was believed the mentally ill had a ten year shorter life span (page 11 of report).
- Studies document progressive brain volume reductions in psychiatric patients, when historically there was no noticeable pathological difference between a normal brain and a schizophrenic one.
- Reports show evidence that not all psychiatric patients need treatment with antipsychotics.
- Clinical decision support system (CDSS)
- Clinical epidemiology
- Consensus (medical)
- Evidence-based dentistry
- Evidence-based design
- Evidence-based management
- Evidence-based nursing
- Personalized medicine
- Policy-based evidence making
- Precision medicine
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