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Evidence-based library and information practice (EBLIP) or evidence-based librarianship (EBL) is the use of evidence-based practices (EBP) in the field of library and information science (LIS). This means that all practical decisions made within LIS should 1) be based on research studies and 2) that these research studies are selected and interpreted according to some specific norms characteristic for EBP. Typically such norms disregard theoretical studies and qualitative studies and consider quantitative studies according to a narrow set of criteria of what counts as evidence. If such a narrow set of methodological criteria are not applied, it is better instead to speak of research based library and information practice.
== Characteristics ==
Evidence-based practice in general has been characterised as a positivist approach; EBLIP is therefore also a positivist approach to LIS. As such, EBLIP is an approach in contrast to other approaches to LIS. The use of statistical approaches known as meta-analysis to conclude what evidence has been reported in the literature is one among other methods which is typical for the evidence-based approach.
In 2002, Booth noted the three schools of EBILP had some commonalities, including the context of day-to-day decision-making, an emphasis on improving the quality of professional practice, a pragmatic focus on the 'best available evidence', incorporation of the user perspective, the acceptance of a broad range of quantitative and qualitative research designs, and access, either first-hand or second-hand, to the (process of) evidence-based
practice and its products. He added one more, that EBILP is concerned with getting the best value for money.
== The role of library and information science in EBP ==
Evidence-based practice in general is based on a very thorough search of the scientific literature and a very thorough selection and analysis of the retrieved literature. A close familiarity with database searching is needed, and library and information professionals have important roles to play in this respect. Therefore LIS professionals should be well suited to help professionals in other disciplines doing EBP. EBLIP is the application of this approach on LIS itself. It should be mentioned, however, that EBP started in medicine as evidence-based medicine (EBM) from which it spread to other fields. Only slowly and to a limited extent has EBP moved on to LIS. The EBLIP process can be applied to a variety of scenarios in LIS, including customer service, collection development, library management and information literacy instruction. In general, quantitative methods are used in LIS research.
A 2010 study revealed five categories that capture the different ways library and information professionals experience evidence-based practice:
Evidence-based practice is experienced as irrelevant;
Evidence-based practice is experienced as learning from published research;
Evidence-based practice is experienced as service improvement;
Evidence-based practice is experienced as a way of being;
Evidence-based practice is experienced as a weapon.
== See also ==
Evidence Based Library and Information Practice (journal)
Evidence-based practices
Meta-analysis
Systematic review
== References ==
== Further reading ==
Booth, A. & Brice, A. (Eds.) (2004). Evidence-Based Practice for Information Professionals: A Handbook. London: Facet Publishing.
Burrows, S.C.; Tylman, V. (1999). "Evaluating medical student searches of MEDLINE for evidence-based information: process and application of results". Bulletin of the Medical Library Association. 87 (4): 471–476.
Dalrymple, Prudence W. (2010) Evidence-Based Practice, Encyclopedia of Library and Information Sciences, Third Edition, Vol. III, 1790-1796.
Eldredge, J.D. (2000). "Evidence-based librarianship: an overview". Bulletin of the Medical Library Association. 88 (4): 289–302. PMC 35250. PMID 11055296.
Eldredge, J (2002). "Cohort studies in health sciences librarianship". Journal of the Medical Library Association. 90 (4): 380–392.
Kloda, LA; Koufogiannakis, D.; Mallan, K. (2011). "Transferring evidence into practice: what evidence summaries of library and information studies research tell practitioners". Information Research. V (16) 465.
Koufogiannakis, Denise; Brettle, Alison, eds. (2016). Being evidence based in library and information practice. London, UK: Facet Publishing. ISBN 9781783300716.
Lewis, S (2011). "Evidence based library and information practice in Australia: defining skills and knowledge". Health Information and Libraries Journal. 28 (2): 152–155.
Urquhart, C (2010). "Systematic reviewing, meta-analysis and meta-synthesis for evidence-based library and information science". Information Research. 15 (3, S) colis708.

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Evidence-based management (EBMgt) is an emerging movement to explicitly use the current, best evidence in management and decision-making. It is part of the larger movement towards evidence-based practices.
== Overview ==
Evidence-based management entails managerial decisions and organizational practices informed by the best available evidence. As with other evidence-based practice, this is based on the three following principles:
1) published peer-reviewed (often in management or social science journals) research evidence that bears on whether and why a particular management practice works;
2) judgement and experience from contextual management practice, to understand the organization and interpersonal dynamics in a situation and determine the risks and benefits of available actions;
3) the preferences and values of those affected.
While, like its counterparts in medicine, and education EBMgt considers the circumstances and ethical concerns managerial decisions involve, it tends not to make extensive use of behavioral science relevant to effective management practice. Evidence-based management proceeds from the premise that using better, deeper logic and employing facts to the extent possible permits leaders to do their jobs better.
== Practice ==
An important part of EBMgt is educating current and future managers in evidence-based practices. The EBMgt website maintained at Stanford University provides a repository of syllabi, cases, and tools that can inform the teaching of evidence-based management.
Efforts to promote EBMgt face greater challenges than other evidence-based initiatives. In medicine, there is more consensus as to what constitutes best evidence than in the social sciences more generally, and management in particular. Unlike medicine, nursing, education, and law enforcement, "management", alone, is not a regulated profession. Management, however, is a learned discipline applied in practice in all types of professions, and professional disciplines essentially require professional management knowledge. There are no established legal or cultural requirements regarding education or knowledge for an individual to become a manager. Nevertheless, there are professional management organizations that do provide well-vetted and generally accepted professional certifications for managers who have been found knowledgeable, experienced, and tested through management certification examinations. Managers have diverse disciplinary backgrounds. An undergraduate college degree is typically required to enter MBA programs – but not to be a manager. No "regulated" body of shared knowledge characterizes managers, making it unlikely that peer pressure will be exerted to promote the use of evidence by any manager who refuses to do so. Little shared language or terminology exists, making it difficult for managers to hold discussions of evidence or evidence-based practices. For this reason, the adoption of evidence-based practices is likely to be organization-specific, where leaders take the initiative to build an evidence-based culture. Organizations successfully pursuing evidence-based management typically go through cycles of experimentation and redesign of their practices to create an evidence-based culture consistent with their values and mission.
Practices indicative of an evidence-based organizational culture include:
systematic accumulation and analysis of organizational data;
problem-based reading and discussion of research summaries; and,
making decisions informed by best available research and organizational information.
Organizations adopting agile approaches in their product development, often find they need to make changes in other areas to reap the full benefits of the changes (the growing field of business agility and agile transformation). Evidence-based management provides a more structured approach to working through such change in short-cycles; to focus investments in areas that will bring the greatest value soonest; and to provide a framework for evaluating their success.
Some advocates of EBMgt argue that it is more likely to be adopted in knowledge-intensive organizations. A study of six leading healthcare organizations found that managers and clinical leaders used a variety of forms of knowledge including drawing on academic research, experiential knowledge and respected colleagues. The researchers concluded that skillful 'knowledge leadership' is crucial in translating EBMgt and other academic research into practice in ways that are relevant and can be mobilized in specific organizational contexts.
== Alternatives and objections ==
The weak form alternatives to evidence-based anything include hearsay, opinion, rhetoric, discourse, advice (opinion), self deception, bias, belief, fallacy, or advocacy. The stronger forms include concerns about what counts as evidence, types of evidence, what evidence is available, sought or possible, who decides and pays for what evidence to be collected, and that evidence needs to be interpreted. Also there are the limitations to empiricism as well argued in the historical debate between empiricism and rationalism which is usually assumed to be resolved by Immanuel Kant by saying the two are inextricably interwoven. We reason what evidence is fair and what the evidence means (Critique of Practical Reason). Critical theorists have raised objections to the claims made by those promoting evidence-based management. From this perspective, what counts as "evidence" is considered as intrinsically problematic and contested because there are different ways of looking at social problems. Furthermore, in line with perspectives from critical management studies, "management" is not necessarily an automatic good thing—it often involves the exercise of power and the exploitation of others. One response is to include a balanced treatment of such issues in reviewing and interpreting the research literature for practice. Another response is to reconsider EBMgt in terms of cybernetic theory, whereby the "requisite variety" of evidence compiled across decision-makers is critical because "compiling more evidence does not necessarily imply compiling a wider range of knowledge types" To that end, a promising alternative to the "evidence-based" approach would be the use of dialectic, argument, or public debate (argument is not to be confused with advocacy or quarreling). Aristotle, in works like Rhetoric, reasons that the way to test knowledge claims is to set up an inquiry method where a sceptical audience is encouraged to question evidence and its assumptions. To win an argument, convincing evidence is required. Calls for argumentative inquiry, or the argumentative turn may be fairer, safer and more creative than calls for evidence-based approaches.
== Supporting research ==
Some of the publications in this area are Evidence-Based Management, Harvard Business Review, and Hard Facts, Dangerous Half-Truths and Total Nonsense: Profiting From Evidence-Based Management. Some of the people conducting research on the effects of evidence-based management are Jeffrey Pfeffer, Robert I. Sutton, and Tracy Allison Altman. Pfeffer and Sutton also have a website dedicated to EBMgt.
Evidence-based management is also being applied in specific industries and professions, including software development. Other areas are crime prevention (Sherman et al. (2002), public management, and manufacturing.
== See also ==
Argumentation theory
Evidence-based policy
Evidence-based practices
Outline of management
Test and Learn
== References ==

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Meta-analysis is a method of synthesis of quantitative data from multiple independent studies addressing a common research question. An important part of this method involves computing a combined effect size across all of the studies. As such, this statistical approach involves extracting effect sizes and variance measures from various studies. By combining these effect sizes the statistical power is improved and can resolve uncertainties or discrepancies found in individual studies. Meta-analyses are integral in supporting research grant proposals, shaping treatment guidelines, and influencing health policies. They are also pivotal in summarizing existing research to guide future studies, thereby cementing their role as a fundamental methodology in metascience. Meta-analyses are often, but not always, important components of a systematic review.
== History ==
The term "meta-analysis" was coined in 1976 by the statistician Gene Glass, who stated "Meta-analysis refers to the analysis of analyses". Glass's work aimed at describing aggregated measures of relationships and effects. While Glass is credited with authoring the first modern meta-analysis, a paper published in 1904 by the statistician Karl Pearson in the British Medical Journal collated data from several studies of typhoid inoculation and is seen as the first time a meta-analytic approach was used to aggregate the outcomes of multiple clinical studies. Numerous other examples of early meta-analyses can be found including occupational aptitude testing, and agriculture.
The first model meta-analysis was published in 1978 on the effectiveness of psychotherapy outcomes by Mary Lee Smith and Gene Glass. After publication of their article there was pushback on the usefulness and validity of meta-analysis as a tool for evidence synthesis. The first example of this was by Hans Eysenck who in a 1978 article in response to the work done by Mary Lee Smith and Gene Glass called meta-analysis an "exercise in mega-silliness". Later Eysenck would refer to meta-analysis as "statistical alchemy". Despite these criticisms the use of meta-analysis has only grown since its modern introduction. By 1991 there were 334 published meta-analyses; this number grew to 9,135 by 2014.
The field of meta-analysis expanded greatly since the 1970s and touches multiple disciplines including psychology, medicine, and ecology. Further the more recent creation of evidence synthesis communities has increased the cross pollination of ideas, methods, and the creation of software tools across disciplines.
== Literature search ==
One of the most important steps of a meta-analysis is data collection. For an efficient database search, appropriate keywords and search limits need to be identified. The use of Boolean operators and search limits can assist the literature search. A number of databases are available (e.g., PubMed, Embase, PsychInfo), however, it is up to the researcher to choose the most appropriate sources for their research area. Indeed, many scientists use duplicate search terms within two or more databases to cover multiple sources. The reference lists of eligible studies can also be searched for eligible studies (i.e., snowballing). The initial search may return a large volume of studies. Quite often, the abstract or the title of the manuscript reveals that the study is not eligible for inclusion, based on the pre-specified criteria. These studies can be discarded. However, if it appears that the study may be eligible (or even if there is some doubt) the full paper can be retained for closer inspection. The references lists of eligible articles can also be searched for any relevant articles. These search results need to be detailed in a PRIMSA flow diagram which details the flow of information through all stages of the review. Thus, it is important to note how many studies were returned after using the specified search terms and how many of these studies were discarded, and for what reason. The search terms and strategy should be specific enough for a reader to reproduce the search. The date range of studies, along with the date (or date period) the search was conducted should also be provided.
A data collection form provides a standardized means of collecting data from eligible studies. For a meta-analysis of correlational data, effect size information is usually collected as Pearson's r statistic. Partial correlations are often reported in research, however, these may inflate relationships in comparison to zero-order correlations. Moreover, the partialed out variables will likely vary from study-to-study. As a consequence, many meta-analyses exclude partial correlations from their analysis. As a final resort, plot digitizers can be used to scrape data points from scatterplots (if available) for the calculation of Pearson's r. Data reporting important study characteristics that may moderate effects, such as the mean age of participants, should also be collected. A measure of study quality can also be included in these forms to assess the quality of evidence from each study. There are more than 80 tools available to assess the quality and risk of bias in observational studies reflecting the diversity of research approaches between fields. These tools usually include an assessment of how dependent variables were measured, appropriate selection of participants, and appropriate control for confounding factors. Other quality measures that may be more relevant for correlational studies include sample size, psychometric properties, and reporting of methods.
A final consideration is whether to include studies from the gray literature, which is defined as research that has not been formally published. This type of literature includes conference abstracts, dissertations, and pre-prints. While the inclusion of gray literature reduces the risk of publication bias, the methodological quality of the work is often (but not always) lower than formally published work. Reports from conference proceedings, which are the most common source of gray literature, are poorly reported and data in the subsequent publication is often inconsistent, with differences observed in almost 20% of published studies.
== Methods and assumptions ==

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=== Approaches ===
In general, two types of evidence can be distinguished when performing a meta-analysis: individual participant data (IPD), and aggregate data (AD). The aggregate data can be direct or indirect.
AD is more commonly available (e.g. from the literature) and typically represents summary estimates such as odds ratios or relative risks. This can be directly synthesized across conceptually similar studies using several approaches. On the other hand, indirect aggregate data measures the effect of two treatments that were each compared against a similar control group in a meta-analysis. For example, if treatment A and treatment B were directly compared vs placebo in separate meta-analyses, we can use these two pooled results to get an estimate of the effects of A vs B in an indirect comparison as effect A vs Placebo minus effect B vs Placebo.
IPD evidence represents raw data as collected by the study centers. This distinction has raised the need for different meta-analytic methods when evidence synthesis is desired, and has led to the development of one-stage and two-stage methods. In one-stage methods the IPD from all studies are modeled simultaneously whilst accounting for the clustering of participants within studies. Two-stage methods first compute summary statistics for AD from each study and then calculate overall statistics as a weighted average of the study statistics. By reducing IPD to AD, two-stage methods can also be applied when IPD is available; this makes them an appealing choice when performing a meta-analysis. Although it is conventionally believed that one-stage and two-stage methods yield similar results, recent studies have shown that they may occasionally lead to different conclusions.
=== Statistical models for aggregate data ===
==== Fixed effect model ====
The fixed effect model provides a weighted average of a series of study estimates. The inverse of the estimates' variance is commonly used as study weight, so that larger studies tend to contribute more than smaller studies to the weighted average. Consequently, when studies within a meta-analysis are dominated by a very large study, the findings from smaller studies are practically ignored. Most importantly, the fixed effects model assumes that all included studies investigate the same population, use the same variable and outcome definitions, etc. This assumption is typically unrealistic as research is often prone to several sources of heterogeneity.
If we start with a collection of independent effect size estimates, each estimate a corresponding effect size
i
=
1
,
…
,
k
{\displaystyle i=1,\ldots ,k}
we can assume that
y
i
=
θ
i
+
e
i
{\textstyle y_{i}=\theta _{i}+e_{i}}
where
y
i
{\displaystyle y_{i}}
denotes the observed effect in the
i
{\displaystyle i}
-th study,
θ
i
{\displaystyle \theta _{i}}
the corresponding (unknown) true effect,
e
i
{\displaystyle e_{i}}
is the sampling error, and
e
i
∼
N
(
0
,
v
i
)
{\displaystyle e_{i}\thicksim N(0,v_{i})}
. Therefore, the
y
i
{\displaystyle y_{i}}
's are assumed to be unbiased and normally distributed estimates of their corresponding true effects. The sampling variances (i.e.,
v
i
{\displaystyle v_{i}}
values) are assumed to be known.
==== Random effects model ====
Most meta-analyses are based on sets of studies that are not exactly identical in their methods and/or the characteristics of the included samples. Differences in the methods and sample characteristics may introduce variability ("heterogeneity") among the true effects. One way to model the heterogeneity is to treat it as purely random. The weight that is applied in this process of weighted averaging with a random effects meta-analysis is achieved in two steps:
Step 1: Inverse variance weighting
Step 2: Un-weighting of this inverse variance weighting by applying a random effects variance component (REVC) that is simply derived from the extent of variability of the effect sizes of the underlying studies.
This means that the greater this variability in effect sizes (otherwise known as heterogeneity), the greater the un-weighting and this can reach a point when the random effects meta-analysis result becomes simply the un-weighted average effect size across the studies. At the other extreme, when all effect sizes are similar (or variability does not exceed sampling error), no REVC is applied and the random effects meta-analysis defaults to simply a fixed effect meta-analysis (only inverse variance weighting).
The extent of this reversal is solely dependent on two factors:

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Heterogeneity of precision
Heterogeneity of effect size
Since neither of these factors automatically indicates a faulty larger study or more reliable smaller studies, the re-distribution of weights under this model will not bear a relationship to what these studies actually might offer. Indeed, it has been demonstrated that redistribution of weights is simply in one direction from larger to smaller studies as heterogeneity increases until eventually all studies have equal weight and no more redistribution is possible.
Another issue with the random effects model is that the most commonly used confidence intervals generally do not retain their coverage probability above the specified nominal level and thus substantially underestimate the statistical error and are potentially
overconfident in their conclusions. Several fixes have been suggested but the debate continues on. A further concern is that the average treatment effect can sometimes be even less conservative compared to the fixed effect model and therefore misleading in practice. One interpretational fix that has been suggested is to create a prediction interval around the random effects estimate to portray the range of possible effects in practice. However, an assumption behind the calculation of such a prediction interval is that trials are considered more or less homogeneous entities and that included patient populations and comparator treatments should be considered exchangeable and this is usually unattainable in practice.
There are many methods used to estimate between studies variance with restricted maximum likelihood estimator being the least prone to bias and one of the most commonly used. Several advanced iterative techniques for computing the between studies variance exist including both maximum likelihood and restricted maximum likelihood methods and random effects models using these methods can be run with multiple software platforms including Excel, Stata, SPSS, and R.
Most meta-analyses include between 2 and 4 studies and such a sample is more often than not inadequate to accurately estimate heterogeneity. Thus it appears that in small meta-analyses, an incorrect zero between study variance estimate is obtained, leading to a false homogeneity assumption. Overall, it appears that heterogeneity is being consistently underestimated in meta-analyses and sensitivity analyses in which high heterogeneity levels are assumed could be informative. These random effects models and software packages mentioned above relate to study-aggregate meta-analyses and researchers wishing to conduct individual patient data (IPD) meta-analyses need to consider mixed-effects modelling approaches./
==== Quality effects model ====
Doi and Thalib originally introduced the quality effects model. They introduced a new approach to adjustment for inter-study variability by incorporating the contribution of variance due to a relevant component (quality) in addition to the contribution of variance due to random error that is used in any fixed effects meta-analysis model to generate weights for each study. The strength of the quality effects meta-analysis is that it allows available methodological evidence to be used over subjective random effects, and thereby helps to close the damaging gap which has opened up between methodology and statistics in clinical research. To do this a synthetic bias variance is computed based on quality information to adjust inverse variance weights and the quality adjusted weight of the ith study is introduced. These adjusted weights are then used in meta-analysis. In other words, if study i is of good quality and other studies are of poor quality, a proportion of their quality adjusted weights is mathematically redistributed to study i giving it more weight towards the overall effect size. As studies become increasingly similar in terms of quality, re-distribution becomes progressively less and ceases when all studies are of equal quality (in the case of equal quality, the quality effects model defaults to the IVhet model – see previous section). A recent evaluation of the quality effects model (with some updates) demonstrates that despite the subjectivity of quality assessment, the performance (MSE and true variance under simulation) is superior to that achievable with the random effects model. This model thus replaces the untenable interpretations that abound in the literature and a software is available to explore this method further.
==== Network meta-analysis methods ====
Indirect comparison meta-analysis methods (also called network meta-analyses, in particular when multiple treatments are assessed simultaneously) generally use two main methodologies. First, is the Bucher methodwhich is a single or repeated comparison of a closed loop of three-treatments such that one of them is common to the two studies and forms the node where the loop begins and ends. Therefore, multiple two-by-two comparisons (3-treatment loops) are needed to compare multiple treatments. This methodology requires that trials with more than two arms have two arms only selected as independent pair-wise comparisons are required. The alternative methodology uses complex statistical modelling to include the multiple arm trials and comparisons simultaneously between all competing treatments. These have been executed using Bayesian methods, mixed linear models and meta-regression approaches.

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===== Bayesian framework =====
Specifying a Bayesian network meta-analysis model involves writing a directed acyclic graph (DAG) model for general-purpose Markov chain Monte Carlo (MCMC) software such as WinBUGS. In addition, prior distributions have to be specified for a number of the parameters, and the data have to be supplied in a specific format. Together, the DAG, priors, and data form a Bayesian hierarchical model. To complicate matters further, because of the nature of MCMC estimation, overdispersed starting values have to be chosen for a number of independent chains so that convergence can be assessed. Recently, multiple R software packages were developed to simplify the model fitting (e.g., metaBMA and RoBMA) and even implemented in statistical software with graphical user interface (GUI): JASP. Although the complexity of the Bayesian approach limits usage of this methodology, recent tutorial papers are trying to increase accessibility of the methods. Methodology for automation of this method has been suggested but requires that arm-level outcome data are available, and this is usually unavailable. Great claims are sometimes made for the inherent ability of the Bayesian framework to handle network meta-analysis and its greater flexibility. However, this choice of implementation of framework for inference, Bayesian or frequentist, may be less important than other choices regarding the modeling of effects (see discussion on models above).
===== Frequentist multivariate framework =====
On the other hand, the frequentist multivariate methods involve approximations and assumptions that are not stated explicitly or verified when the methods are applied (see discussion on meta-analysis models above). For example, the mvmeta package for Stata enables network meta-analysis in a frequentist framework. However, if there is no common comparator in the network, then this has to be handled by augmenting the dataset with fictional arms with high variance, which is not very objective and requires a decision as to what constitutes a sufficiently high variance. The other issue is use of the random effects model in both this frequentist framework and the Bayesian framework. Senn advises analysts to be cautious about interpreting the 'random effects' analysis since only one random effect is allowed for but one could envisage many. Senn goes on to say that it is rather naıve, even in the case where only two treatments are being compared to assume that random-effects analysis accounts for all uncertainty about the way effects can vary from trial to trial. Newer models of meta-analysis such as those discussed above would certainly help alleviate this situation and have been implemented in the next framework.
===== Generalized pairwise modelling framework =====
An approach that has been tried since the late 1990s is the implementation of the multiple three-treatment closed-loop analysis. This has not been popular because the process rapidly becomes overwhelming as network complexity increases. Development in this area was then abandoned in favor of the Bayesian and multivariate frequentist methods which emerged as alternatives. Very recently, automation of the three-treatment closed loop method has been developed for complex networks by some researchers as a way to make this methodology available to the mainstream research community. This proposal does restrict each trial to two interventions, but also introduces a workaround for multiple arm trials: a different fixed control node can be selected in different runs. It also utilizes robust meta-analysis methods so that many of the problems highlighted above are avoided. Further research around this framework is required to determine if this is indeed superior to the Bayesian or multivariate frequentist frameworks. Researchers willing to try this out have access to this framework through a free software.
==== Diagnostic test accuracy meta-analysis ====
Diagnostic test accuracy (DTA) meta-analyses differ methodologically from those assessing intervention effects, as they aim to jointly synthesize pairs of sensitivity and specificity values. These parameters are typically analyzed using hierarchical models that account for the correlation between them and between-study heterogeneity. Two commonly used models are the bivariate random-effects model and the hierarchical summary receiver operating characteristic (HSROC) model. These approaches are recommended by the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy and are widely used in reviews of screening tests, imaging tools, and laboratory diagnostics.
Beyond the standard hierarchical models, other approaches have been developed to address various complexities in diagnostic accuracy synthesis. These include methods that incorporate differences in threshold effects, account for covariates through meta-regression, or improve applicability by considering test setting and clinical variation. Some frameworks aim to adapt the synthesis to reflect intended use conditions more directly. These extensions are part of an evolving body of methodology that reflects growing experience in the field and increasing demands from clinical and policy decision-makers.
==== Aggregating IPD and AD ====
Meta-analysis can also be applied to combine IPD and AD. This is convenient when the researchers who conduct the analysis have their own raw data while collecting aggregate or summary data from the literature. The generalized integration model (GIM) is a generalization of the meta-analysis. It allows that the model fitted on the individual participant data (IPD) is different from the ones used to compute the aggregate data (AD). GIM can be viewed as a model calibration method for integrating information with more flexibility.

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=== Validation of meta-analysis results ===
The meta-analysis estimate represents a weighted average across studies and when there is heterogeneity this may result in the summary estimate not being representative of individual studies. Qualitative appraisal of the primary studies using established tools can uncover potential biases, but does not quantify the aggregate effect of these biases on the summary estimate. Although the meta-analysis result could be compared with an independent prospective primary study, such external validation is often impractical. This has led to the development of methods that exploit a form of leave-one-out cross validation, sometimes referred to as internal-external cross validation (IOCV). Here each of the k included studies in turn is omitted and compared with the summary estimate derived from aggregating the remaining k- 1 studies. A general validation statistic, Vn based on IOCV has been developed to measure the statistical validity of meta-analysis results. For test accuracy and prediction, particularly when there are multivariate effects, other approaches which seek to estimate the prediction error have also been proposed.
== Challenges ==
A meta-analysis of several small studies does not always predict the results of a single large study. Some have argued that a weakness of the method is that sources of bias are not controlled by the method: a good meta-analysis cannot correct for poor design or bias in the original studies. This would mean that only methodologically sound studies should be included in a meta-analysis, a practice called 'best evidence synthesis'. Other meta-analysts would include weaker studies, and add a study-level predictor variable that reflects the methodological quality of the studies to examine the effect of study quality on the effect size. However, others have argued that a better approach is to preserve information about the variance in the study sample, casting as wide a net as possible, and that methodological selection criteria introduce unwanted subjectivity, defeating the purpose of the approach. More recently, and under the influence of a push for open practices in science, tools to develop "crowd-sourced" living meta-analyses that are updated by communities of scientists in hopes of making all the subjective choices more explicit.
=== Publication bias: the file drawer problem ===
Another potential pitfall is the reliance on the available body of published studies, which may create exaggerated outcomes due to publication bias, as studies which show negative results or insignificant results are less likely to be published. For example, pharmaceutical companies have been known to hide negative studies and researchers may have overlooked unpublished studies such as dissertation studies or conference abstracts that did not reach publication. This is not easily solved, as one cannot know how many studies have gone unreported.
This file drawer problem characterized by negative or non-significant results being tucked away in a cabinet, can result in a biased distribution of effect sizes thus creating a serious base rate fallacy, in which the significance of the published studies is overestimated, as other studies were either not submitted for publication or were rejected. This should be seriously considered when interpreting the outcomes of a meta-analysis.
The distribution of effect sizes can be visualized with a funnel plot which (in its most common version) is a scatter plot of standard error versus the effect size. It makes use of the fact that the smaller studies (thus larger standard errors) have more scatter of the magnitude of effect (being less precise) while the larger studies have less scatter and form the tip of the funnel. If many negative studies were not published, the remaining positive studies give rise to a funnel plot in which the base is skewed to one side (asymmetry of the funnel plot). In contrast, when there is no publication bias, the effect of the smaller studies has no reason to be skewed to one side and so a symmetric funnel plot results. This also means that if no publication bias is present, there would be no relationship between standard error and effect size. A negative or positive relation between standard error and effect size would imply that smaller studies that found effects in one direction only were more likely to be published and/or to be submitted for publication.
Apart from the visual funnel plot, statistical methods for detecting publication bias have also been proposed. These are controversial because they typically have low power for detection of bias, but also may make false positives under some circumstances. For instance small study effects (biased smaller studies), wherein methodological differences between smaller and larger studies exist, may cause asymmetry in effect sizes that resembles publication bias. However, small study effects may be just as problematic for the interpretation of meta-analyses, and the imperative is on meta-analytic authors to investigate potential sources of bias.
The problem of publication bias is not trivial as it is suggested that 25% of meta-analyses in the psychological sciences may have suffered from publication bias. However, low power of existing tests and problems with the visual appearance of the funnel plot remain an issue, and estimates of publication bias may remain lower than what truly exists.
Most discussions of publication bias focus on journal practices favoring publication of statistically significant findings. However, questionable research practices, such as reworking statistical models until significance is achieved, may also favor statistically significant findings in support of researchers' hypotheses.
=== Problems related to studies not reporting non-statistically significant effects ===
Studies often do not report the effects when they do not reach statistical significance. For example, they may simply say that the groups did not show statistically significant differences, without reporting any other information (e.g. a statistic or p-value). Exclusion of these studies would lead to a situation similar to publication bias, but their inclusion (assuming null effects) would also bias the meta-analysis.

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=== Problems related to the statistical approach ===
Other weaknesses are that it has not been determined if the statistically most accurate method for combining results is the fixed, IVhet, random or quality effect models, though the criticism against the random effects model is mounting because of the perception that the new random effects (used in meta-analysis) are essentially formal devices to facilitate smoothing or shrinkage and prediction may be impossible or ill-advised. The main problem with the random effects approach is that it uses the classic statistical thought of generating a "compromise estimator" that makes the weights close to the naturally weighted estimator if heterogeneity across studies is large but close to the inverse variance weighted estimator if the between study heterogeneity is small. However, what has been ignored is the distinction between the model we choose to analyze a given dataset, and the mechanism by which the data came into being. A random effect can be present in either of these roles, but the two roles are quite distinct. There's no reason to think the analysis model and data-generation mechanism (model) are similar in form, but many sub-fields of statistics have developed the habit of assuming, for theory and simulations, that the data-generation mechanism (model) is identical to the analysis model we choose (or would like others to choose). As a hypothesized mechanisms for producing the data, the random effect model for meta-analysis is silly and it is more appropriate to think of this model as a superficial description and something we choose as an analytical tool – but this choice for meta-analysis may not work because the study effects are a fixed feature of the respective meta-analysis and the probability distribution is only a descriptive tool.
=== Problems arising from agenda-driven bias ===
The most severe fault in meta-analysis often occurs when the person or persons doing the meta-analysis have an economic, social, or political agenda such as the passage or defeat of legislation. People with these types of agendas may be more likely to abuse meta-analysis due to personal bias. For example, researchers favorable to the author's agenda are likely to have their studies cherry-picked while those not favorable will be ignored or labeled as "not credible". In addition, the favored authors may themselves be biased or paid to produce results that support their overall political, social, or economic goals in ways such as selecting small favorable data sets and not incorporating larger unfavorable data sets. The influence of such biases on the results of a meta-analysis is possible because the methodology of meta-analysis is highly malleable.
A 2011 study done to disclose possible conflicts of interests in underlying research studies used for medical meta-analyses reviewed 29 meta-analyses and found that conflicts of interests in the studies underlying the meta-analyses were rarely disclosed. The 29 meta-analyses included 11 from general medicine journals, 15 from specialty medicine journals, and three from the Cochrane Database of Systematic Reviews. The 29 meta-analyses reviewed a total of 509 randomized controlled trials (RCTs). Of these, 318 RCTs reported funding sources, with 219 (69%) receiving funding from industry (i.e. one or more authors
having financial ties to the pharmaceutical industry). Of the 509 RCTs, 132 reported author conflict of interest disclosures, with 91 studies (69%) disclosing one or more authors having financial ties to industry. The information was, however, seldom reflected in the meta-analyses. Only two (7%) reported RCT funding sources and none reported RCT author-industry ties. The authors concluded "without acknowledgment of COI due to industry funding or author industry financial ties from RCTs included in meta-analyses, readers' understanding and appraisal of the evidence from the meta-analysis may be compromised."
For example, in 1998, a US federal judge found that the United States Environmental Protection Agency had abused the meta-analysis process to produce a study claiming cancer risks to non-smokers from environmental tobacco smoke (ETS) with the intent to influence policy makers to pass smoke-free–workplace laws.
=== Comparability and validity of included studies ===
Meta-analysis may often not be a substitute for an adequately powered primary study, particularly in the biological sciences.
Heterogeneity of methods used may lead to faulty conclusions. For instance, differences in the forms of an intervention or the cohorts that are thought to be minor or are unknown to the scientists could lead to substantially different results, including results that distort the meta-analysis' results or are not adequately considered in its data. Vice versa, results from meta-analyses may also make certain hypothesis or interventions seem nonviable and preempt further research or approvals, despite certain modifications – such as intermittent administration, personalized criteria and combination measures – leading to substantially different results, including in cases where such have been successfully identified and applied in small-scale studies that were considered in the meta-analysis. Standardization, reproduction of experiments, open data and open protocols may often not mitigate such problems, for instance as relevant factors and criteria could be unknown or not be recorded.
There is a debate about the appropriate balance between testing with as few animals or humans as possible and the need to obtain robust, reliable findings. It has been argued that unreliable research is inefficient and wasteful and that studies are not just wasteful when they stop too late but also when they stop too early. In large clinical trials, planned, sequential analyses are sometimes used if there is considerable expense or potential harm associated with testing participants. In applied behavioural science, "megastudies" have been proposed to investigate the efficacy of many different interventions designed in an interdisciplinary manner by separate teams. One such study used a fitness chain to recruit a large number participants. It has been suggested that behavioural interventions are often hard to compare [in meta-analyses and reviews], as "different scientists test different intervention ideas in different samples using different outcomes over different time intervals", causing a lack of comparability of such individual investigations which limits "their potential to inform policy".

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=== Weak inclusion standards lead to misleading conclusions ===
Meta-analyses in education are often not restrictive enough in regards to the methodological quality of the studies they include. For example, studies that include small samples or researcher-made measures lead to inflated effect size estimates. However, this problem also troubles meta-analysis of clinical trials. The use of different quality assessment tools (QATs) lead to including different studies and obtaining conflicting estimates of average treatment effects.
== Applications in modern science ==
Modern statistical meta-analysis does more than just combine the effect sizes of a set of studies using a weighted average. It can test if the outcomes of studies show more variation than the variation that is expected because of the sampling of different numbers of research participants. Additionally, study characteristics such as measurement instrument used, population sampled, or aspects of the studies' design can be coded and used to reduce variance of the estimator (see statistical models above). Thus some methodological weaknesses in studies can be corrected statistically. Other uses of meta-analytic methods include the development and validation of clinical prediction models, where meta-analysis may be used to combine individual participant data from different research centers and to assess the model's generalisability, or even to aggregate existing prediction models.
Meta-analysis can be done with single-subject design as well as group research designs. This is important because much research has been done with single-subject research designs. Considerable dispute exists for the most appropriate meta-analytic technique for single subject research.
Meta-analysis leads to a shift of emphasis from single studies to multiple studies. It emphasizes the practical importance of the effect size instead of the statistical significance of individual studies. This shift in thinking has been termed "meta-analytic thinking". The results of a meta-analysis are often shown in a forest plot.
Results from studies are combined using different approaches. One approach frequently used in meta-analysis in health care research is termed 'inverse variance method'. The average effect size across all studies is computed as a weighted mean, whereby the weights are equal to the inverse variance of each study's effect estimator. Larger studies and studies with less random variation are given greater weight than smaller studies. Other common approaches include the Mantel–Haenszel method and the Peto method.
Seed-based d mapping (formerly signed differential mapping, SDM) is a statistical technique for meta-analyzing studies on differences in brain activity or structure which used neuroimaging techniques such as fMRI, VBM or PET.
Different high throughput techniques such as microarrays have been used to understand Gene expression. MicroRNA expression profiles have been used to identify differentially expressed microRNAs in particular cell or tissue type or disease conditions or to check the effect of a treatment. A meta-analysis of such expression profiles was performed to derive novel conclusions and to validate the known findings.
Meta-analysis of whole genome sequencing studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Some methods have been developed to enable functionally informed rare variant association meta-analysis in biobank-scale cohorts using efficient approaches for summary statistic storage.
Sweeping meta-analyses can also be used to estimate a network of effects. This allows researchers to examine patterns in the fuller panorama of more accurately estimated results and draw conclusions that consider the broader context (e.g., how personality-intelligence relations vary by trait family).
== Software ==
R Package: metafor & meta, RevMan, JASP, Jamovi,
StatsDirect,
MetaEssential,
Comprehensive meta-analysis
== See also ==
== Sources ==
This article incorporates text by Daniel S. Quintana available under the CC BY 4.0 license.
This article incorporates text by Wolfgang Viechtbauer available under the CC BY 3.0 license.
== References ==

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Model for assessment of telemedicine (MAST) is a framework for assessment of the value of telemedicine.
== Description ==
Telemedicine services may have many different types of outcomes and can be studied in many ways. In order for those who develop new telemedicine services to produce the information that healthcare managers need for making decisions on investment in telemedicine, a model for assessment of telemedicine (MAST) was developed. This work was done in 2010 through stakeholder workshops and on the basis of a systematic literature review.
If the goal of evaluating telemedicine applications is to assess their effectiveness and impact on the quality of care, and to provide a foundation for decision-making, then according to MAST, the appropriate assessment framework is a multidisciplinary process. This process involves summarizing and evaluating information concerning medical, social, economic, and ethical considerations related to telemedicine in a systematic, impartial, and rigorous manner.
This statement is based on the definition of Health technology assessment (HTA) in the EUnetHTA project. Key concepts are "multidisciplinary" and "systematic, unbiased and robust". The first concept implies that the assessments should include all important outcomes of the applications for patients, clinicians, healthcare institutions and society in general. The others imply that assessments should be based on scientific studies and methods, scientific criteria for quality of evidence and scientific standards for reporting of results, e.g. as described in EQUATOR Network.
== Steps ==
In practice the use of MAST includes three steps:
Preceding assessment
Multidisciplinary assessment
Transferability assessment
Firstly, the assessment must start with preceding considerations in order to determine whether it is relevant for an institution at a given point in time to carry out the assessment. This step involves mainly assessment of the maturity of the technology and the organization planning to use it. If the technology is not matured and have not been tested in practice, then pilot studies must be carried out to mature the technology before a multidisciplinary study is initiated.
Secondly, after the preceding considerations, the multidisciplinary assessment is carried out in order to describe and assess the different outcomes of the telemedicine application. This involves assessment of outcomes within the following seven domains:
Domain 1: Health problem and characteristics of the application
Domain 2: Safety
Domain 3: Clinical effectiveness
Domain 4: Patient perspectives
Domain 5: Economic aspects
Domain 6: Organizational aspects
Domain 7: Socio-cultural, ethical and legal aspects
Thirdly, in relation to the description of the outcomes, an assessment should also be made of the transferability of the results to other settings or countries.
== Use ==
MAST is the most widely used framework for assessment of telemedicine in Europe. The model is used in large EU funded telemedicine project like Renewing Health, United4Health, Smartcare and inCASA. These projects include more than 20.000 patients and more than 18 randomised controlled trials. A large number of individual telemedicine projects also use MAST e.g. Patient@home, Durand-Zaleski (2013) and Campos et al. (2013)
The number of publications of studies using MAST is still limited, but growing. The first clinical studies have been reported by Sorknæs et al. (2013), Karhula et al. (2015) and Rasmussen et al. (2015). Recently a study of the organizational outcomes of implementation of telemedicine was published by Rasmussen et al. (2015).
MAST has also been recommended as a usable structure for assessment of outcomes of telemedicine by the association of Danish Regions Telemedicine strategy, by the British Thoracic Society statement on telemedicine (2014) and within the field of wound care by Angel et al. (2015).
== Difference between MAST and EUnetHTA Core model ==
MAST is based on HTA and the EUnetHTA Core model, but whereas the core model includes 9 domains, MAST only includes 7 domains. This is done by combining the content of several domains into one. MAST has also a separate domain describing the impact of telemedicine on patient perception and thereby underlining the importance of the patients' view of this type of health care technology. In addition the three steps in MAST underline that the assessment of outcomes should be seen in the light of the maturity of the technology and the transferability of the results to other countries.
== References ==
== External links ==
EUropean network for Health Technology Assessment (EUnetHTA): http://www.eunethta.eu/
Renewing Health project: http://www.renewinghealth.eu/en/
United4Health project: http://united4health.eu/
Patient@home project: http://www.en.patientathome.dk/
Smart Care project: http://www.pilotsmartcare.eu/home/
InCASA: http://www.incasa-project.eu/news.php
MAST manual
Methotelemed
Videos on MAST

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The National Registry of Evidence-based Programs and Practices (NREPP) was a searchable online database of interventions designed to promote mental health or to prevent or treat substance abuse and mental disorders. The registry was funded and administered by the Substance Abuse and Mental Health Services Administration (SAMHSA), part of the U.S. Department of Health and Human Services. The goal of the Registry was to encourage wider adoption of evidence-based interventions and to help those interested in implementing an evidence-based intervention to select one that best meets their needs.
The NREPP website was phased out in 2018. The included phaseout section has more information.
== Overview ==
In the behavioral health field, there is an ongoing need for researchers, developers, evaluators, and practitioners to share information about what works to improve outcomes among individuals coping with, or at risk for, mental disorders and substance abuse. Discussing how this need led to the development of NREPP, Brounstein, Gardner, and Backer (2006) wrote:
It is important to note that not all prevention programs work. Still other programs have no empirically based support regarding their effectiveness. […] Many others have empirical support, but the methods used to generate that support are suspect. This is another reason to highlight the need for and use of scientifically defensible, effective prevention programs. These are programs that clearly demonstrate that the program was well implemented, well evaluated, and produced a consistent pattern of positive results.
The focus of NREPP was on delivering an array of standardized, comparable information on interventions that are evidence based, as opposed to identifying programs that are "effective" or ranking them in effectiveness. Its peer reviewers used specific criteria to rate the quality of an intervention's evidence base as well as the intervention's suitability for broad adoption. In addition, NREPP provided contextual information about the intervention, such as the population served, implementation history, and cost data to encourage a realistic and holistic approach to selecting prevention interventions.
As of 2010, the interventions reviewed by NREPP had been implemented successfully in more than 229,000 sites, in all 50 States and more than 70 countries, and with more than 107 million clients. Versions of its review process and rating criteria had been adopted by the National Cancer Institute and the Administration on Aging.
The information NREPP provided was subject to certain limitations. It was not an exhaustive repository of all tested mental health interventions; submission was a voluntary process, and limited resources may preclude the review of some interventions even though they meet minimum requirements for acceptance. The NREPP home page prominently stated that "inclusion in the registry does not constitute an endorsement."
== Submission process ==
NREPP held an open submission period that ran November 1 through February 1. For an intervention to be eligible for review, it was required to meet four minimum criteria:
The intervention has produced one or more positive behavioral outcomes (p ≤ 0.05) in mental health, mental disorders, substance abuse, or substance use disorders use among individuals, communities, or populations.
Evidence of these outcomes has been demonstrated in at least one study using an experimental or quasi-experimental design.
The results of these studies have been published in a peer-reviewed journal or other professional publication, or documented in a comprehensive evaluation report.
Implementation materials, training and support resources, and quality assurance procedures have been developed and are ready for use by the public.
Once reviewed and added to the Registry, interventions were invited to undergo a new review four or five years after their initial review.
== Review process ==
The NREPP review process consisted of two parallel and simultaneous review tracks, one for the intervention's Quality of Research (QOR) and another for the intervention's Readiness for Dissemination (RFD). The materials used in a QOR review are generally published research articles, although unpublished final evaluation reports could also be included. The materials used in an RFD review included implementation materials and process documentation, such as manuals, curricula, training materials, and written quality assurance procedures.
The reviews were conducted by expert consultants who had received training on NREPP's review process and rating criteria. Two QOR and two RFD reviewers were assigned to each review. Reviewers worked independently, rating the same materials. Their ratings were averaged to generate final scores.
While the review process was ongoing, NREPP staff worked with the intervention's representatives to collect descriptive information about the intervention, such as the program goals, types of populations served, and implementation history.
The QOR ratings on a scale of 0.0 to 4.0, indicated the strength of the evidence supporting the outcomes of the intervention. Higher scores indicated stronger, more compelling evidence. Each outcome was rated separately because interventions could target multiple outcomes (e.g., alcohol use, marijuana use, behavior problems in school), and the evidence supporting the different outcomes could vary. The QOR rating criteria were:
Reliability of measures
Validity of measures
Intervention fidelity
Missing data and attrition
Potential confounding variables
Appropriateness of analysis
The RFD ratings were also given on a scale of 0.0 to 4.0, indicating the amount and quality of the resources available to support the use of the intervention. Higher scores indicated that resources were readily available and of high quality. These ratings applied to the intervention as a whole. The RFD criteria were:
Availability of implementation materials
Availability of training and support resources
Availability of quality assurance procedures
=== Reviewers ===
QOR reviewers were required to have a doctoral-level degree and a strong background and understanding of current methods of evaluating prevention and treatment interventions. RFD reviewers were selected from two categories: direct services experts (including both providers and consumers of services), or experts in the field of implementation. Direct services experts must have had previous experience evaluating prevention or treatment interventions and knowledge of mental health or substance abuse prevention or treatment content areas.
=== Products and publications ===
NREPP published an intervention summary for each intervention in the Registry. The summaries, accessed through the Registry's search engine, contained standardized information:

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A brief description of the reviewed intervention, including targeted goals and theoretical basis
Study populations (age, gender, race/ethnicity)
Study settings and geographical locations
Implementation history
Funding information
Comparative evaluation research conducted with the intervention
Adaptations
Adverse effects
List of studies and materials reviewed
List of outcomes
Description of measures and key findings for each outcome
Research design of the studies reviewed
Quality of Research and Readiness for Dissemination ratings
Reviewer comments (Strengths and Weaknesses)
Costs
Replication studies
Contact information
NREPP maintained an online Learning Center that included learning modules on implementation and preparing for NREPP submission; a research paper on evidence-based therapy relationships; and links to screening and assessment tools for mental health and substance use.
== Predecessor system ==
The registry originated in 1997 as the National Registry of Effective Prevention Programs (later renamed the National Registry of Effective Programs and Practices) and has gone through several changes. This predecessor to the NREPP was developed by SAMHSA's Center for Substance Abuse Prevention as part of the Model Programs initiative. Procedures under this earlier registry were developed to review, rate, and designate programs as Model, Effective, or Promising. Based on extensive input from scientific communities, service providers, expert panels, and the public, the procedures were revised. Reviews using the new NREPP system began in 2006, and the redesigned Web site debuted in March 2007.
== Phase out in 2018 ==
According to an email from SAMHSA:
"SAMHSA is committed to advancing the adoption of evidence-based interventions related to mental health and substance use. Consistent with the January 2018 announcement from the Assistant Secretary for Mental Health and Substance Use related to discontinuing the National Registry of Evidence-based Programs and Practices (NREPP), SAMHSA has now phased out the NREPP website, which has been in existence since 1997. In April 2018, SAMHSA launched the Evidence-Based Practices Resource Center (Resource Center) that aims to provide communities, clinicians, policy makers, and others in the field with the information and tools they need to incorporate evidence-based practices into their communities or clinical settings. The Resource Center contains a collection of science-based resources; however, it does not replace NREPP and does not contain all of the resources that were previously available on NREPP.
"The Resource Center is a component of SAMHSA’s new comprehensive approach to identify and disseminate clinically sound and scientifically based policy, practices, and programs. Under this new approach, we are continuing to develop and add additional resources to the Resource Center as they become available. In the meantime, please use our Resource Center as well as the SAMHSA Store to find information on evidence-based practices and other resources related to mental health and substance use. For products and resources not developed by SAMHSA, please contact the developers for more information."
== Further reading ==
Hennessy, K.; Finkbiner, R.; Hill, G. (2006). "The National Registry of Evidence-Based Programs and Practices: A decision-support tool to advance the use of evidence-based services". International Journal of Mental Health. 35 (2): 21–34. doi:10.2753/IMH0020-7411350202.{{cite journal}}: CS1 maint: deprecated archival service (link)
Brounstein, PJ; Gardner, SE; Backer, T (2006). "Research to practice: efforts to bring effective prevention to every community". J Prim Prev. 27: 91–109. doi:10.1007/s10935-005-0024-6. PMID 16421654.. These criteria and the accompanying rating anchors are unique to NREPP but share common elements with the types of standards used by other Federal agencies to assess evidence-based programs.
== External links ==
Official Web site - phased out in 2018 Archived 2015-11-15 at the Wayback Machine
SAMHSA Evidence-Based Practices Resource Center
Substance Abuse and Mental Health Services (SAMHSA) Archived 2021-03-21 at the Wayback Machine
== References ==

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The PICO process (or framework) is a mnemonic used in evidence-based practice (and specifically evidence-based medicine) to frame and answer a clinical or health care related question, though it is also argued that PICO "can be used universally for every scientific endeavour in any discipline with all study designs". The PICO framework is also used to develop literature search strategies, for instance in systematic reviews.
The PICO acronym has come to stand for:
P – Patient, problem, or population
I – Intervention
C – Comparison, control, or comparator
O – Outcome(s) (e.g. pain, fatigue, nausea, infections, death)
An application that covers clinical questions about interventions, as well as exposures, risk/ prognostic factors, and test accuracy, is:
P – Patient, problem, or population
I – Investigated condition (e.g. intervention, exposure, risk/ prognostic factor, or test result)
C – Comparison condition (e.g. intervention, exposure, risk/ prognostic factor, or test result respectively)
O – Outcome(s) (e.g. symptom, syndrome, or disease of interest)
Alternatives such as SPICE and PECO (among many others) can also be used. Some authors suggest adding T and S, as follows:
T - Timing (e.g. duration of intervention, or date of publication)
S - Study type (e.g. randomized controlled trial, cohort study, etc.)
== PICO as a universal technique ==
It was argued that PICO may be useful for every scientific endeavor even beyond clinical settings. This proposal is based on a more abstract view of the PICO mnemonic, equating them with four components that is inherent to every single research, namely (1) research object; (2) application of a theory or method; (3) alternative theories or methods (or the null hypothesis); and (4) the ultimate goal of knowledge generation.
This proposition would imply that the PICO technique could be used for teaching academic writing even beyond medical disciplines.
== Examples ==
Clinical question: "In children with headache, is paracetamol more effective than placebo against pain?"
Population = Children with headaches; keywords = children + headache
Intervention = Paracetamol; keyword = paracetamol
Compared with = Placebo; keyword = placebo
Outcome of interest = Pain; keyword = pain
PubMed (health research database) search strategy: children headache paracetamol placebo pain
Clinical question: "Is the risk of having breast cancer higher in symptom-free women with a positive mammography compared to symptom-free women with a negative mammography?"
Population = Women without a history of breast cancer
Investigated test result = Positive result on mammography
Comparator test result = Negative result on mammography
Outcome of interest = Breast cancer according to biopsy (or not)
== Similar Frameworks ==
The PICO framework was originally developed to frame interventional clinical questions. PICO inspired other frameworks such as PICOS, PICOT, PICOTT, PECO, PICOTS, PECODR, PEICOIS, PICOC, SPICE, PIPOH, EPICOT+, PESICO, PICo, and PS.
== References ==

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Patient safety is a specialized field focused on enhancing healthcare quality through the systematic prevention, reduction, reporting, and analysis of medical errors and preventable harm that can lead to negative patient outcomes. Although healthcare risks have long existed, patient safety only gained formal recognition in the 1990s following reports of alarming rates of medical error-related injuries in many countries. The urgency of the issue was underscored when the World Health Organization (WHO) identified that 1 in 10 patients globally experience harm due to healthcare errors, declaring patient safety an "endemic concern" in modern medicine.
Today, patient safety is a distinct healthcare discipline, supported by an ever evolving scientific framework. It is underpinned by a robust transdisciplinary body of theoretical and empirical research, with emerging technologies, such as mobile health applications, playing a pivotal role in its advancement.
== Prevalence of adverse events ==
Millennia ago, Hippocrates recognized the potential for injuries that arise from the well-intentioned actions of healers. Greek healers in the 4th century BC drafted the Hippocratic Oath, pledging to "prescribe regimens for the good of my patients according to my ability and my judgment and never do harm to anyone." Since then, the directive primum non nocere ("first, do no harm") has become a central tenet for contemporary medicine. However, despite an increasing emphasis on the scientific basis of medical practice in Europe and the United States in the late 19th century, data on adverse outcomes were hard to come by; the various studies commissioned collected mostly anecdotal events.
In April 1982, the ABC television program 20/20 entitled The Deep Sleep presented a rising problem in American hospitals. Showing accounts of anesthetic accidents, the producers stated that, every year, 6,000 Americans die or experience brain damage related to these mishaps. In 1983, the British Royal Society of Medicine and the Harvard Medical School jointly sponsored a symposium on anesthesia deaths and injuries, resulting in an agreement to share statistics and conduct studies. Attention was brought to medical errors in 1999 when the Institute of Medicine reported that about 98,000 deaths occur every year due to medical errors made in hospitals.
By 1984, the American Society of Anesthesiologists (ASA) had established the Anesthesia Patient Safety Foundation (APSF). The APSF marked the first use of the term "patient safety" in the name of a professional reviewing organization. Although anesthesiologists comprise only about 5% of physicians in the United States, anesthesiology became the leading medical specialty addressing issues of patient safety.
=== To Err is Human ===
In the United States, the full magnitude and impact of errors in health care were not appreciated until the 1990s, when several reports brought attention to this issue. In 1999, the Institute of Medicine (IOM) of the National Academy of Sciences released a report, To Err Is Human: Building a Safer Health System.
The IOM called for a broad national initiative focused on several key actions: creating a Center for Patient Safety, expanding the reporting of adverse events, implementing safety programs within healthcare organizations, and increasing involvement from regulators, healthcare purchasers, and professional societies. The majority of media attention, however, focused on the statistics: from 44,000 to 98,000 preventable deaths annually due to medical errors in hospitals, with 7,000 preventable deaths related to medication errors alone. Within 2 weeks of the report's release, Congress began hearings, and President Clinton ordered a government-wide study of the feasibility of implementing the report's recommendations. Initial criticisms of the methodology in the IOM estimates focused on the statistical methods of amplifying low numbers of incidents in the pilot studies to the general population.
To this day, there are only a few comprehensive studies on medical errors. A bibliometric analysis in 2020 revealed a steady growth of publications in this area. In 2016, Michael Daniels and Martin A. Makary published a piece in The British Medical Journal that claimed medical error was the third leading cause of death in America at nearly half a million deaths per year. This number has since been debunked, citing flawed and improper methodology in the paper. More recent analysis using data from the 2016 Global Burden of Diseases, Injuries, and Risk Factors (GBD) study obtained an estimate of 123,603 deaths in the United States from 1990 to 2016 due to adverse effects of medical treatment (AEMT), with the mortality rate decreasing over time despite an overall increase in the number of deaths.
The experience has been similar in other countries.
In 1992, an Australian study revealed 18,000 annual deaths from medical errors. Professor Bill Runciman, one of the study's authors and president of the Australian Patient Safety Foundation since its inception in 1989, reported himself a victim of a medical dosing error.
In June 2000, the Department of Health Expert Group estimated that over 850,000 incidents harm National Health Service hospital patients in the United Kingdom each year. On average, forty incidents a year contribute to patient deaths in each NHS institution.
In 2004, the Canadian Adverse Events Study found that adverse events occurred in more than 7% of hospital admissions and estimated that 9,000 to 24,000 Canadians die annually after an avoidable medical error.
These and other reports from New Zealand, Denmark and developing countries have led the World Health Organization to estimate that one in ten persons receiving health care will suffer preventable harm.
== Psychological safety ==
Psychological safety aims to provide an environment where patients and medical professionals feel comfortable sharing concerns and mistakes without fear of embarrassment or retribution. This enables increased reporting, as well as the sharing of new ideas and honest feedback. A wider variety of information is thus shared throughout the organization, allowing for creativity, innovation, and learning. Psychological safety is believed to lead to better outcomes by providing basis for more informed decisions.
Psychological safety has been found to play an important role in both patient safety culture and in enabling quality improvement in the health care setting.

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== Communication ==
Communication involves distributing relevant information across operational sites to ensure alignment. It also reduces administrative burden by using model-driven instructions, freeing up operational staff and easing procedural demands. This enables consistent execution with minimal but essential feedback, ensuring processes remain both efficient and controlled.
=== Effective and ineffective communication ===
The use of effective communication among patients and healthcare professionals is associated with a patient's health outcome. However, scientific patient safety research by Annegret Hannawa et al. has shown that ineffective communication can lead to patient harm. Communication regarding patient safety can be classified into two categories: the prevention of adverse events and the response to adverse events. Effective communication may help to prevent adverse events, whereas ineffective communication may contribute to their occurrence. If ineffective communication contributes to an adverse event, improved communication skills may be applied in response to achieve optimal outcomes for the patient's safety. There are different modes in which healthcare professionals can work to optimize the safety of patients which include both verbal and nonverbal communication, as well as the effective use of communication technologies.
Methods of effective verbal and nonverbal communication include treating patients with respect and showing empathy, clearly communicating with patients in a way that best fits their needs, practicing active listening skills, demonstrating cultural sensitivity and awareness, and respecting the privacy and confidentiality rights of the patient. To use appropriate communication technology, healthcare professionals must choose which channel of communication is best suited to benefit the patient. Some channels are more likely to result in communication errors than others, such as communicating through telephone or email (missing nonverbal messages which are an important element of understanding the situation). It is also the responsibility of the provider to know the advantages and limitations of using electronic health records, as they do not convey all the information necessary to understand patient needs. If a health care professional is not practicing these skills, they are not being an effective communicator which may affect patient outcomes.
The goal of a healthcare professional is to aid a patient in achieving their optimal health outcome, which entails that the patient's safety is not at risk. The practice of effective communication plays a crucial role in promoting and protecting patient safety.
=== Teamwork and communication ===
During complex situations, health professionals must communicate clearly and effectively. There are several techniques, tools, and strategies used to improve communication. Any team should have a clear purpose, and each member should be aware of their role and be involved accordingly. To increase the quality of communication between people involved, regular feedback should be provided. Strategies such as briefings allow the team to be set on their purpose and ensure that members not only share the goal but also the process they will follow to achieve it. Briefings reduce interruptions, prevent delays, and build stronger relationships, resulting in a strong patient safety environment.
Debriefing is another useful strategy. Healthcare providers meet to discuss a situation, record what they learned, and discuss how it might be better handled. Closed loop communication is another important technique used to ensure that the message that was sent is received and interpreted by the receiver. SBAR is a structured system designed to help team members communicate about the patient in the most convenient form possible. Communication between healthcare professionals not only helps achieve the best results for the patient but also prevents any unseen incidents.
=== Safety culture ===
As is the case in other industries, when a mistake or error is made, people look for someone to blame. This tendency creates a blame culture where who is more important than why or how. A just culture, also sometimes known as no blame or no fault, seeks to understand the root causes of an incident rather than just who was involved.
In health care, there is a move towards a patient safety culture. This applies the lessons learned from other industries, such as aviation, marine, and industrial, to a health care setting.
When assessing and analyzing an incident, individuals involved are much more likely to be forthcoming with their own mistakes if they know that their job is not at risk. This allows a much more complete and clearer picture to be formed of the facts of an event. From there, root cause analysis can occur. There are often multiple causative factors involved in an adverse or near-miss event. It is only after all contributing factors have been identified that effective changes can be made that will prevent a similar incident from occurring.
=== Disclosure of an incident ===
After an adverse event occurs, each country has its own way of dealing with the incident. In Canada, a quality improvement review is primarily used. A quality improvement review is an evaluation that is completed after an adverse event occurs with the intention to both fix the problem as well as prevent it from happening again. The individual provinces and territories have laws on whether it is required to disclose the quality improvement review to the patient. Healthcare providers have an obligation to disclose any adverse event to their patients because of ethical and professional guidelines. If more providers participate in the quality improvement review, it can increase interdisciplinary collaboration and can sustain relationships between departments and staff. In the US, clinical peer review is used: uninvolved medical staff review the event and work toward preventing further incidents.
The disclosure of adverse events is important in maintaining trust in the relationship between healthcare provider and patient. It is also important to learn how to avoid these mistakes in the future by conducting quality improvement reviews or clinical peer reviews. If the provider accurately handles the event and discloses it to the patient and their family, he/she can avoid getting punished, which includes lawsuits, fines, and suspension.
== Causes of healthcare error ==

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The simplest definition of a healthcare error is a preventable adverse effect of care, whether or not it is evident or harmful to the patient. Errors have been, in part, and/or attributed to:
=== Human factors ===
Variations in healthcare provider training and experience, fatigue, depression and burnout.
Diverse patients, unfamiliar settings, and time pressures.
Failure to acknowledge the prevalence and seriousness of medical errors.
Increasing working hours of healthcare personnel.
Mislabeling specimen or forgetting to label specimen.
States of anxiety and stress put on the healthcare provider.
=== Medical complexity ===
Complicated technologies, powerful drugs.
Intensive care, prolonged hospital stays.
=== System failures ===
Unsafe communication.
Unclear lines of authority or guidelines for physicians, nurses, and other care providers.
Complication increasing when the patient to nurse staffing ratio increases to a point where the patient rate is higher than the rate of staff.
Disconnected reporting systems within a hospital: fragmented systems in which numerous hand-offs of patients result in errors in examples such as coordination or other general reports due to even minor errors.
Drug names that look alike or sound alike.
The impression that action is being taken by other groups within the institution.
Reliance on automated systems to prevent error.
Inadequate systems to share information about errors hamper analysis of contributory causes and improvement strategies.
Cost-cutting measures by hospitals in response to reimbursement cutbacks.
Environment and design factors. In emergencies, patient care may be rendered in areas poorly suited for safe monitoring. The American Institute of Architects has identified concerns for the safe design and construction of healthcare facilities.
Infrastructure failure. According to the WHO, around 50% of medical equipment in developing countries is only partly usable due to a lack of skilled operators or parts. As a result, diagnostic procedures or treatments cannot be performed, leading to substandard treatment.
The Joint Commission's Annual Report on Quality and Safety 2007 found that inadequate communication between healthcare providers, between providers and the patient, and between providers and the patient's family members, was the root cause of over half the serious severe adverse events in accredited hospitals. Other leading causes included inadequate assessment of the patient's condition, poor leadership, and/or training.
Common misconceptions about adverse events are:
" 'Bad apples', or incompetent health care providers are a common cause for patient harm". Many of the errors are normal human slips or lapses, and not the result of poor judgment or recklessness.
"High-risk procedures or medical specialties are responsible for most avoidable adverse events". Although some mistakes, such as those in surgery, are easier to notice, errors occur at all levels of care. Even though complex procedures entail more risk, adverse outcomes are not usually due to error, but to the severity of the condition being treated. However, USP has reported that medication errors during the course of a surgical procedure are three times more likely to cause harm to a patient than those occurring in other types of hospital care.
"If a patient experiences an adverse event during the process of care, an error has occurred". Most medical care entails some level of risk, and there can be complications or side effects, even unforeseen ones, from the underlying condition or from the treatment itself.
=== Nursing burnout and patient safety ===
In the medical field, many things can lead to decreased patient safety. One significant influence on this is nurse burnout, leading to hundreds of thousands of deaths a year and billions of dollars spent when having to rectify a new problem; this is a real issue in the world. On average in the medical field, one out of 20 prescriptions filled contains an error, considering the billions of prescriptions that get filled every year there is a vital amount of error happening. With these errors, not only is there a likelihood of a prescription being wrong, but there is a $3.5 billion price tag that goes with it, covering the amount that people pay each year for litigation costs and extra days that patients need to stay in hospital beds because of mistakes from the hospital.
Burnout has been going on for years amongst nurses and other physicians, affecting nearly half of healthcare workers. Burnout has been going on for decades and the term was originally coined by Herbert Freudenberger. Freudenberger was working at a free clinic, and over time mentioned some of the effects that he had seen, such as "emotional depletion and accompanying psychosomatic symptoms... excessive demands on energy, strength, or resources". These burnout symptoms are commonly seen today in hospital settings as nurses feel like they are pushed to the edge. This emotion is not ideal nor wanted for everyone, especially for people who have to look after patients and take care of others who can be in very severe and mortally harmed states. Using what Freudenberger described, there was a scale created to measure the amount of burnout in the healthcare field. Known as Maslach's scale, this measures 1. Workload, 2. Control, 3. Reward, 4. Community, 5. Fairness, and 6. Values. All of these core points work together and the less you have of most of them, the more likely that burnout will occur and cause a major decrease in patient safety. Similarly to Maslach's scale, there is the Conservation of Resources Theory. This theory essentially states that if one of the four pillars are lost, so are safety and control. According to the Journal of Advanced Nursing, "Healthcare organizations and nursing administrations should develop strategies to protect nurses from the threat of resource loss to decreases in nurse burnout, which may improve nurse and patient safety." The amount of nursing professionals that have experienced burnout is said to be around 50%. This number leads to an increased risk of adverse events that should not happen, ranging from 26% to 70% of a higher risk that something negative will happen to the patient.
== Safety programs in industry ==

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=== Aviation safety ===
In the United States, two organizations contribute to one of the world's lowest aviation accident rates. Mandatory accident investigation is carried out by the National Transportation Safety Board, while the Aviation Safety Reporting System receives voluntary reports to identify deficiencies and provide data for planning improvements. The latter system is confidential and provides reports back to stakeholders without regulatory action. Similarities and contrasts have been noted between the "cultures of safety" in medicine and aviation. Pilots and medical personnel operate in complex environments, interact with technology, and are subject to fatigue, stress, danger, and loss of life and prestige as a consequence of error. Given the enviable record of aviation in accident prevention, a similar medical adverse event system would include both mandatory (for severe incidents) and voluntary non-punitive reporting, teamwork training, feedback on performance and an institutional commitment to data collection and analysis. The Patient Safety Reporting System (PSRS) is a program modeled upon the Aviation Safety Reporting System and developed by the Department of Veterans Affairs (VA) and the National Aeronautics and Space Administration (NASA) to monitor patient safety through voluntary, confidential reports. Required training in crew resource management (CRM), which focused on team dynamics both inside the cockpit and outside was introduced in the early 1980s after the tragic mishap of United Airlines 173. CRM is considered an effective means of improving safety in aviation and is utilized by the DoD, NASA, and almost all commercial airlines. Many of the tenets of this training have been incorporated into medicine under the guise of Team Stepps, which was introduced by the Agency for Healthcare Research and Quality (AHRQ). The AHRQ calls this program "an evidence-based teamwork system to improve communication and teamwork skills among health care professionals."
=== Near-miss reporting ===
A near miss is an unplanned event that did not result in injury, illness, or damage - but had the potential to do so. Reporting of near misses by observers is an established error reduction technique in aviation, and has been extended to private industry, traffic safety, and fire-rescue services with reductions in accidents and injury. AORN, a US-based professional organization of perioperative registered nurses, has put in effect a voluntary near-miss reporting system (SafetyNet), covering medication or transfusion reactions, communication or consent issues, wrong patient or procedures, communication breakdown or technology malfunctions. An analysis of incidents allows safety alerts to be issued to AORN members. AlmostME is another commercially offered solution for near miss reporting in healthcare.
=== Limits of the Industrial Safety Model ===
Unintended consequences may occur as improvements in safety are undertaken. It may not be possible to attain maximum safety goals in healthcare without adversely affecting patient care in other ways. An example is blood transfusion; in recent years, to reduce the risk of transmissible infection in the blood supply, donors with only a small probability of infection have been excluded. The result has been a critical shortage of blood for other lifesaving purposes, with a broad impact on patient care. Application of high-reliability theory and normal accident theory can help predict the organizational consequences of implementing safety measures.
== Technology in healthcare ==
=== Overview ===
According to a study by RAND Health, the U.S. healthcare system could save more than $81 billion annually, reduce adverse healthcare events, and improve the quality of care if health information technology (HIT) is widely adopted. The most immediate barrier to widespread adoption of technology is cost despite the patient benefit from better health, and payer benefit from lower costs. However, hospitals pay both higher costs for implementation and potentially lower revenues (depending on reimbursement scheme) due to reduced patient length of stay. The benefits provided by technological innovations also give rise to serious issues with the introduction of new and previously unseen error types.
=== Types of healthcare technology ===
Handwritten reports or notes, manual order entry, non-standard abbreviations, and poor legibility lead to substantial errors and injuries, according to the IOM (2000) report. The follow-up IOM report, Crossing the Quality Chasm: A New Health System for the 21st Century, advised rapid adoption of electronic patient records, and electronic medication ordering, with computer- and internet-based information systems to support clinical decisions. This section contains only the patient safety related aspects of HIT.
=== Electronic health record (EHR) ===
The electronic health record (EHR), previously known as the electronic medical record (EMR), reduces several types of errors, including those related to prescription drugs, emergency and preventive care, and to tests and procedures. Important features of modern EHR include automated drug-drug/drug-food interaction checks and allergy checks, standard drug dosages and patient education information. Drug Information at the point-of-care and drug dispensing points helps in reducing errors. Example: India, MedCLIK. Also, these systems provide recurring alerts to remind clinicians of intervals for preventive care and to track referrals and test results. Clinical guidelines for disease management have a demonstrated benefit when accessible within the electronic record during the process of treating the patient. Advances in health informatics and widespread adoption of interoperable electronic health records promise access to a patient's records at any health care site. Still, there may be a weak link because of physicians' deficiencies in understanding the patient safety features of e.g. government-approved software. Errors associated with patient misidentification may be exacerbated by EHR use, but inclusion of a prominently displayed patient photograph in the EHR can reduce errors and near misses.
Portable offline emergency medical record devices have been developed to provide access to health records during widespread or extended infrastructure failure, such as in natural disasters or regional conflicts.
=== Active RFID platform ===
These systems' basic security measures are based on sound identifying electronic tags to ensure that the patient details provided in different situations are always reliable. These systems offer three differently qualified options:

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Identification upon request of health care personnel, using scanners (similar to readers for passive RFID tags or scanners for barcode labels) to identify patients semi-automatically upon presentation of the patient with a tag to staff
Automatic identification upon entry of patient. An automatic identification check is carried out on each person with tags (primarily patients) entering the area to determine the presented patient in contrast to other patient earlier entered into reach of the used reader.
Automatic identification and range estimation upon approach to the most proximate patient, excluding reads from more distant tags of other patients in the same area
Any of these options may be applied whenever and wherever patient details are required in electronic form Such identifying is essential when the information concerned is critical. There are increasing numbers of hospitals that have an RFID system to identify patients, for instance: Hospital La Fe in Valencia, Spain; Wayne Memorial Hospital (US); Royal Alexandria Hospital (UK).
=== Computerized provider order entry (CPOE) ===
Prescribing errors are the largest identified source of preventable errors in hospitals (IOM, 2000; 2007). The IOM (2006) estimates that each hospitalized patient, on average, is exposed to one medication error each day. Computerized provider order entry (CPOE), formerly called computerized physician order entry, can reduce medication errors by 80% overall but more importantly decrease harm to patients by 55%. A Leapfrog (2004) survey found that 16% of US clinics, hospitals, and medical practices are expected to utilize CPOE within 2 years.
Complete safety medication system
A standardized bar code system for dispensing drugs might prevent 25% of drug errors. Despite ample evidence to reduce medication errors, complete medication delivery systems (barcoding and Electronic prescribing) have slow adoption by doctors and hospitals in the United States, due to concerns with interoperability and compliance with future national standards. Such concerns are not inconsequential; standards for electronic prescribing for Medicare Part D conflict with regulations in many US states.
==== Specific patient safety software ====
A standardized, modular technology system that allows a hospital, clinic, or health system to record their Incidents, including falls, medication errors, pressure ulcers, near misses, etc. These systems can be configured to specific workflows, and the analytics behind them will allow for reporting and dashboards to help learn from things that have gone wrong (and right). Some vendors include Datix, RL Solutions, Verge, Midas, and Quantros.
=== Technological Iatrogenesis ===
Technology-induced errors are significant and increasingly more evident in care delivery systems.
This idiosyncratic and potentially serious problem associated with HIT implementation has recently become a tangible concern for healthcare and information technology professionals. As such, the term technological iatrogenesis describes this new category of adverse events that are an emergent property resulting from technological innovation creating system and microsystem disturbances. Healthcare systems are complex and adaptive, meaning there are many networks and connections working simultaneously to produce certain outcomes. When these systems are under the increased stresses caused by the diffusion of new technology, unfamiliar and new process errors often result. If not recognized, over time these new errors can collectively lead to catastrophic system failures. The term "e-iatrogenesis" can be used to describe the local error manifestation. The sources for these errors include:
Prescriber and staff inexperience may lead to a false sense of security; that when technology suggests a course of action, errors are avoided.
Shortcut or default selections can override non-standard medication regimens for elderly or underweight patients, resulting in toxic doses.
CPOE and automated drug dispensing were identified as a cause of error by 84% of over 500 healthcare facilities participating in a surveillance system by the United States Pharmacopoeia.
Irrelevant or frequent warnings can interrupt workflow.
Solutions include ongoing changes in design to cope with unique medical settings, supervising overrides from automatic systems, and training (and re-training) all users.
=== Evidence-based medicine ===
Evidence-based medicine integrates an individual doctor's exam and diagnostic skills for a specific patient, with the best available evidence from medical research. The doctor's expertise includes both diagnostic skills and consideration of individual patients' rights and preferences in making decisions about his or her care. The clinician uses pertinent clinical research on the accuracy of diagnostic tests and the efficacy and safety of therapy, rehabilitation, and prevention to develop an individual plan of care. The development of evidence-based recommendations for specific medical conditions, termed clinical practice guidelines or "best practices", has accelerated in the past few years. In the United States, over 1,700 guidelines (see example image, right) have been developed as a resource for physicians to apply to specific patient presentations. The National Institute for Health and Clinical Excellence (NICE) in the United Kingdom provides detailed "clinical guidance" for both health care professionals and the public about specific medical conditions. National Guideline Agencies from all continents collaborate in the Guidelines International Network, which entertains the largest guideline library worldwide. The International Standard ISO 15189:2007 for Accreditation of Medical Laboratory requires laboratories to continuously monitor and improve the quality of their facilities.
Advantages:
Evidence-based medicine may reduce adverse events, especially those involving incorrect diagnosis, outdated or risky tests or procedures, or medication overuse.
Clinical guidelines provide a common framework for improving communication among clinicians, patients and non-medical purchasers of health care.
Errors related to changing shifts or multiple specialists are reduced by a consistent plan of care.
Information on the clinical effectiveness of treatments and services can help providers, consumers and purchasers of health care make better use of limited resources.
As medical advances become available, doctors and nurses can keep up with new tests and treatments as guidelines are improved.
Drawbacks:

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Managed care plans may attempt to limit "unnecessary" services to cut the costs of health care, despite evidence that guidelines are not designed for general screening, but rather as decision-making tools when an individual practitioner evaluates a specific patient.
The medical literature is evolving and often controversial; the development of guidelines requires consensus.
Implementing guidelines and educating the entire health care team within a facility costs time and resources (which may be recovered by future efficiency and error reduction).
Clinicians may resist evidence-based medicine as a threat to traditional relationships between patients, doctors, and other health professionals since any participant can influence decisions.
Failing to follow guidelines might increase the risk of liability or disciplinary action by regulators.
== Quality and Safety Initiatives in Community Pharmacy practice ==
Community pharmacy practice is making important advances in the quality and safety movement, despite the limited number of federal and state regulations that exist and in the absence of national accreditation organizations such as the Joint Commission - a driving force for performance improvement in health care systems. Community pharmacies are using automated drug dispensing devices (robots), computerized drug utilization review tools, and, most recently, the ability to receive electronic prescriptions from prescribers to decrease the risk of error and increase the likelihood of delivering high-quality care.
Quality Assurance (QA) in community practice is a relatively new concept. As of 2006, only 16 states have some form of legislation that regulates QA in community pharmacy practice. While most state QA legislation focuses on error reduction, North Carolina has recently approved legislation that requires the pharmacy QA program to include error reduction strategies and assessments of the quality of their pharmaceutical care outcomes and pharmacy services.
New technologies facilitate the traceability tools of patients and medications. This is particularly relevant for drugs that are considered high-risk and costly.
== Quality improvement and safety initiatives in pediatrics ==
Quality improvement and patient safety is a major concern in the pediatric world of health care. This next section will focus on quality improvement and patient safety initiatives in inpatient settings.
Over the last several years, pediatric groups have partnered to improve general understanding, reporting, process improvement methodologies, and quality of pediatric inpatient care. These collaborations have created a robust program of projects, bench-marking efforts, and research. Much of the research and focus on adverse events has been on medication errors–the most frequently reported adverse event for both adult and pediatric patients. It is also of interest to note that medication errors are also the most preventable type of harm that can occur within the pediatric population. It has been reported that when pediatric medication errors occur, these patients have a higher rate of death associated with the error than adult patients. A more recent review of potential pediatric safety issues conducted by Miller, Elixhauser, and Zhan found that hospitalized children who experienced a patient safety incident, compared with those who did not, had
Length of stay 2 to 6 times longer
Hospital mortality 2 to 18 times greater
Hospital charges 2 to 20 times higher
In order to reduce these errors, the attention to safety needs to concentrate on designing safe systems and processes. Slonim and Pollack point out that safety is critical to reducing medical errors and adverse events. These problems can range from diagnostic and treatment errors to hospital-acquired infections, procedural complications, and failure to prevent problems such as pressure ulcers. In addition to addressing quality and safety issues found in adult patients there are a few characteristics that are unique to the pediatric population:
Development: As children mature both cognitively and physically, their needs as consumers of health care goods and services change. Therefore, planning a unified approach to pediatric safety and quality is affected by the fluid nature of childhood development.
Dependency: Hospitalized children, especially those who are very young and/or nonverbal, are dependent on caregivers, parents, or other surrogates to convey key information associated with patient encounters. Even when children can accurately express their needs, they are unlikely to receive the same acknowledgment accorded to adult patients. In addition, because children are dependent on their caregivers, their care must be approved by parents or surrogates during all encounters.
Different epidemiology: Most hospitalized children require acute episodic care, not care for chronic conditions as with many adult patients. Planning safety and quality initiatives within a framework of "wellness, interrupted by acute conditions or exacerbations" presents distinct challenges and requires a new way of thinking.
Demographics: Children are more likely than other groups to live in poverty and experience racial and ethnic disparities in health care. Children are more dependent on public insurance, such as the State Children's Health Insurance Program (SCHIP) and Medicaid.
One of the main challenges faced by pediatric safety and quality efforts is that most of the work on patient safety to date has focused on adult patients. In addition, there is no standard nomenclature for pediatric patient safety that is widely used. However, a standard framework for classifying pediatric adverse events that offers flexibility has been introduced. Standardization provides consistency between interdisciplinary teams and can facilitate multi-site studies. If these large-scale studies are conducted, the findings could generate large-scale intervention studies conducted with a faster life cycle.
=== Leaders in pediatric safety and quality ===
The Agency for Healthcare Research and Quality (AHRQ) is the Federal authority for patient safety and quality of care and has been a leader in pediatric quality and safety. AHRQ has developed Pediatric Quality Indicators (PedQIs) with the goal to highlight areas of quality concern and to target areas for further analysis. Eighteen pediatric quality indicators are included in the AHRQ quality measure modules; based on expert input, risk adjustment, and other considerations. Thirteen inpatient indicators are recommended for use at the hospital level, and five are designated area indicators. Inpatient indicators are treatments or conditions with the greatest potential of an adverse event for hospitalized children.

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Possible additions to the dataset will address the patient's condition on admission and increase the understanding of how laboratory and pharmacy utilization impact patient outcomes. The goal of AHRQ is to refine the area-level indicators to improve outcomes for children receiving outpatient care and reduce the incidence of hospitalization for those defined conditions.
=== Collaborations for pediatric safety and quality ===
Numerous groups are engaged in improving pediatric care, quality, and safety. Each of these groups has a unique mission and membership. The following table details these groups' missions and websites.
=== Nurse staffing and pediatric outcomes ===
While the number of nurses providing patient care is recognized as an inadequate measure of nursing care quality, there is hard evidence that nurse staffing is directly related to patient outcomes. Studies by Aiken and Needleman have demonstrated that patient death, nosocomial infections, cardiac arrest, and pressure ulcers are linked to inadequate nurse-to-patient ratios. The presence or absence of registered nurses (RNs) impacts the outcome for pediatric patients requiring pain management and/or peripheral administration of intravenous fluids and/or medications. These two indicators of pediatric nursing care quality are sensitive measures of nursing care. Professional nurses play a key role in successful pain management, especially among pediatric patients unable to verbally describe pain. Astute assessment skills are required to intervene successfully and relieve discomfort.33 Maintenance of a patient's intravenous access is a clear nursing responsibility. Pediatric patients are at increased risk for intravenous infiltration and for significant complications of infiltration, should it occur.
The characteristics of effective indicators of pediatric nursing care quality include the following:
Scalable: The indicators are applicable to pediatric patients across a broad range of units and hospitals, in both intensive care and general care settings.
Feasible: Data collection does not pose undue burden on staff of participating units as the data is available from existing sources, such as the medical record or a quality improvement database and can be collected in real time.
Valid and reliable: Indicator measurement within and across participating sites is accurate and consistent over time.
=== Conclusions ===
Pediatric care is complex due to developmental and dependency issues associated with children. How these factors impact the specific processes of care is an area of science in which little is known. Throughout health care, providing safe and high-quality patient care continues to provide significant challenges. Efforts to improve the safety and quality of care are resource-intensive and take continued commitment not only by those who deliver care but also by agencies and foundations that fund this work. Advocates for children's health care must be at the table when key policy and regulatory issues are discussed. Only then will the voices of our most vulnerable groups of healthcare consumers be heard.
== Working hours of nurses and patient safety ==
A recent increase in work hours and overtime shifts of nurses has been used to compensate for the decrease in a number of registered nurses (RNs). Logbooks completed by nearly 400 RNs have revealed that about "40 percent of the 5,317 work shifts they logged exceeded twelve hours." Errors by hospital staff nurses are more likely when work shifts extend beyond 12 hours, or they work over 40 hours in one week. Studies have shown that overtime shifts have harmful effects on the quality of care provided to patients, but some researchers "who evaluated the safety of 12-hour shifts did not find increases in medication errors." The errors which these researchers found were "lapses of attention to detail, errors of omission, compromised problem solving, reduced motivation" due to fatigue as well as "errors in grammatical reasoning and chart reviewing." Overworked nurses are a serious safety concern for their patients' well-being. Working back-to-back shifts, or night shifts, is a common cause of fatigue in hospital staff nurses. "Less sleep, or fatigue, may lead to increased likelihood of making an error, or even the decreased likelihood of catching someone else's error." Limiting working hours and shift rotations could "reduce the adverse effects of fatigue" and increase the quality of patient care.
== Health literacy ==
Health literacy is a common and serious safety concern. A study of 2,600 patients at two hospitals determined that between 26% and 60% of patients could not understand medication directions, a standard informed consent, or basic health care materials. This mismatch between a clinician's level of communication and a patient's ability to understand can lead to medication errors and adverse outcomes.
The Institute of Medicine (2004) report found low health literacy levels negatively affect healthcare outcomes. In particular, these patients have a higher risk of hospitalization and longer hospital stays, are less likely to comply with treatment, are more likely to make errors with medication, and are more ill when they seek medical care.
== Pay for performance (P4P) ==
Pay for performance systems can improve patient safety by linking providers' compensation to measures of work quality or process goals. As of 2005, 75 percent of all U.S. companies connected at least part of an employee's pay to measures of performance, and in healthcare, over 100 private and federal pilot programs were underway. Methods of healthcare payment current at that time may actually have rewarded less-safe care, since some insurance companies will not pay for new practices to reduce errors, while physicians and hospitals can bill for additional services that are needed when patients are injured by mistakes. However, early studies showed little gain in quality for the money spent, as well as evidence suggesting unintended consequences, like the avoidance of high-risk patients, when payment was linked to outcome improvements. The 2006 Institute of Medicine report Preventing Medication Errors recommended "incentives...so that profitability of hospitals, clinics, pharmacies, insurance companies, and manufacturers (are) aligned with patient safety goals...(to) strengthen the business case for quality and safety."
There is widespread international interest in health care pay-for-performance programs in a range of countries, including Australia, Canada, Germany, the Netherlands, New Zealand, the United Kingdom, and the United States.

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=== United Kingdom ===
In the United Kingdom, the National Health Service (NHS) began an ambitious pay-for-performance initiative in 2004, known as the Quality and Outcomes Framework (QOF). General practitioners agreed to increases in existing income according to performance with respect to 146 quality indicators covering clinical care for 10 chronic diseases, organization of care, and patient experience. Unlike proposed quality incentive programs in the United States, funding for primary care was increased by 20% over previous levels. This allowed practices to invest in extra staff and technology; 90% of general practitioners use the NHS Electronic Prescription Service, and up to 50% use electronic health records for the majority of clinical care. Early analysis showed that substantially increasing physicians' pay based on their success in meeting quality performance measures is successful. The 8,000 family practitioners included in the study earned an average of $40,000 more by collecting nearly 97% of the points available.
A component of this program, known as exception reporting, allows physicians to use criteria to exclude individual patients from the quality calculations that determine physician reimbursement. There was initial concern that exception reporting would allow inappropriate exclusion of patients in whom targets were missed ("gaming"). However, a 2008 study has shown little evidence of widespread gaming.
=== United States ===
In the United States, Medicare has various pay-for-performance ("P4P") initiatives in offices, clinics, and hospitals, seeking to improve quality and avoid unnecessary healthcare costs. The Centers for Medicare and Medicaid Services (CMS) has several demonstration projects underway offering compensation for improvements:
Payments for better care coordination between home, hospital, and offices for patients with chronic illnesses. In April 2005, CMS launched its first value-based purchasing pilot or "demonstration" project- the three-year Medicare Physician Group Practice (PGP) Demonstration. The project involves ten large, multi-specialty physician practices caring for more than 200,000 Medicare fee-for-service beneficiaries. Participating practices will phase in quality standards for preventive care and the management of common chronic illnesses such as diabetes. Practices meeting these standards will be eligible for rewards from savings due to resulting improvements in patient management. The First Evaluation Report to Congress in 2006 showed that the model rewarded high quality, efficient provision of health care, but the lack of up-front payment for the investment in new systems of case management "have made for an uncertain future with respect for any payments under the demonstration."
A set of 10 hospital quality measures which, if reported to CMS, will increase the payments that hospitals receive for each discharge. By the third year of the demonstration, those hospitals that do not meet a threshold on quality will be subject to reductions in payment. Preliminary data from the second year of the study indicates that pay for performance was associated with a roughly 2.5% to 4.0% improvement in compliance with quality measures, compared with the control hospitals. Dr. Arnold Epstein of the Harvard School of Public Health commented in an accompanying editorial that pay-for-performance "is fundamentally a social experiment likely to have only modest incremental value." Unintended consequences of some publicly reported hospital quality measures have adversely affected patient care. The requirement to give the first antibiotic dose in the emergency department within 4 hours, if the patient has pneumonia, has caused an increase in pneumonia misdiagnosis.
Rewards to physicians for improving health outcomes by the use of health information technology in the care of chronically ill Medicare patients.
Disincentives: The Tax Relief & Health Care Act of 2006 required the HHS Inspector General to study ways that Medicare payments to hospitals could be recouped for "never events", as defined by the National Quality Forum, including hospital infections. In August 2007, CMS announced that it will stop payments to hospitals for several negative consequences of care that result in injury, illness or death. This rule, effective October 2008, would reduce hospital payments for eight serious types of preventable incidents: objects left in a patient during surgery, blood transfusion reaction, air embolism, falls, mediastinitis, urinary tract infections from catheters, pressure ulcer, and sepsis from catheters. Reporting of "never events" and creation of performance benchmarks for hospitals are also mandated. Other private health payers are considering similar actions; in 2005, HealthPartners, a Minnesota health insurer, chose not to cover 27 types of "never events". The Leapfrog Group has announced that they will work with hospitals, health plans, and consumer groups to advocate reducing payment for "never events", and will recognize hospitals that agree to certain steps when a serious avoidable adverse event occurs in the facility, including notifying the patient and patient safety organizations, and waiving costs. Physician groups involved in the management of complications, such as the Infectious Diseases Society of America, have voiced objections to these proposals, observing that "some patients develop infections despite the application of all evidence-based practices known to avoid infection", and that a punitive response may discourage further study and slow the dramatic improvements that have already been made.

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=== Complex illness ===
Pay-for-performance programs often target patients with serious and complex illnesses; such patients commonly interact with multiple healthcare providers and facilities. However, pilot programs now underway focus on simple indicators such as improvement in lab values or use of emergency services, avoiding areas of complexity such as multiple complications or several treating specialists. A 2007 study analyzing Medicare beneficiaries' healthcare visits showed that a median of two primary care physicians and five specialists provide care for a single patient. The authors doubt that pay-for-performance systems can accurately attribute responsibility for the outcome of care for such patients. The American College of Physicians Ethics has stated concerns about using a limited set of clinical practice parameters to assess quality, "especially if payment for good performance is grafted onto the current payment system, which does not reward robust comprehensive care...The elderly patient with multiple chronic conditions is especially vulnerable to this unwanted effect of powerful incentives." Present pay-for-performance systems measure good performance based on specified clinical measurements, such as glycohemoglobin for diabetic patients. Healthcare providers who are monitored by such limited criteria have a powerful incentive to deselect (dismiss or refuse to accept) patients whose outcome measures fall below the quality standard and therefore worsen the provider's assessment. Patients with low health literacy, inadequate financial resources to afford expensive medications or treatments, and ethnic groups traditionally subject to healthcare inequities may also be deselected by providers seeking improved performance measures.
== Public reporting ==
=== Mandatory reporting ===
==== Denmark ====
The Danish Act on Patient Safety passed Parliament in June 2003. On January 1, 2004, Denmark became the first country to introduce nationwide mandatory reporting. The Act obligates front line personnel to report adverse events to a national reporting system. Hospital owners are obligated to act on the reports and the National Board of Health is obligated to communicate the learning nationally. The reporting system is intended purely for learning and front line personnel cannot experience sanctions for reporting. This is stated in Section 6 of the Danish Act on Patient Safety (as of January 1, 2007: Section 201 of the Danish Health Act): "A front line person who reports an adverse event cannot as a result of that report be subjected to investigation or disciplinary action from the employer, the Board of Health or the Court of Justice." The reporting system and the Danish Patient Safety Database are described in further detail in a National Board of Health publication.
==== United Kingdom ====
The National Patient Safety Agency encourages voluntary reporting of health care errors but has several specific instances, known as "Confidential Enquiries", for which investigation is routinely initiated: maternal or infant deaths, childhood deaths to age 16, deaths in persons with mental illness, and perioperative and unexpected medical deaths. Medical records and questionnaires are requested from the involved clinician, and participation has been high, since individual details are confidential.
==== United States ====
The 1999 Institute of Medicine (IOM) report recommended "a nationwide mandatory reporting system ... that provides for ... collection of standardized information by state governments about adverse events that result in death or serious harm." Professional organizations, such as the Anesthesia Patient Safety Foundation, responded negatively: "Mandatory reporting systems, in general, create incentives for individuals and institutions to play a numbers game. If such reporting becomes linked to punitive action or inappropriate public disclosure, there is a high risk of driving reporting "underground", and of reinforcing the cultures of silence and blame that many believe are at the heart of the problems of medical error..."
Although 23 states established mandatory reporting systems for serious patient injuries or death by 2005, the national database envisioned in the IOM report was delayed by the controversy over mandatory versus voluntary reporting. Finally in 2005, the US Congress passed the long-debated Patient Safety and Quality Improvement Act, establishing a federal reporting database. Hospitals reports of serious patient harm are voluntary, collected by patient safety organizations under contract to analyze errors and recommend improvements. The federal government serves to coordinate data collection and maintain the national database. Reports remain confidential and cannot be used in liability cases. Consumer groups have objected to the lack of transparency, claiming it denies the public information on the safety of specific hospitals.
==== Sweden ====
According to the Patient Safety Law (Patientsäkerhetslagen) healthcare providers must report incidents that resulted or could have resulted in serious medical damage to Health and Social Care Inspectorate (known as Lex Maria notification).
=== Individual patient disclosures ===
For a healthcare institution, disclosing an unanticipated event should be made as soon as possible. Some healthcare organizations may have a policy regarding the disclosure of unanticipated events. The amount of information presented to those affected is dependent on the family's readiness and the organization's culture. The employee disclosing the event to the family requires support from risk management, patient safety officers, and senior leadership. Disclosures are objectively documented in the medical record.
=== Voluntary disclosure ===
In public surveys, a significant majority of those surveyed believe that health care providers should be required to report all serious medical errors publicly. However, reviews of the medical literature show little effect of publicly reported performance data on patient safety or the quality of care. Public reporting on the quality of individual providers or hospitals does not seem to affect selection of hospitals and individual providers. Some studies have shown that reporting performance data stimulates quality improvement activity in hospitals. As of 2012, only one in seven errors or accidents are reported, showing that most errors that happen are not reported.
==== United States ====

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===== Medical error =====
Ethical standards of the Joint Commission on Accreditation of Healthcare Organizations (JCAHO), the American Medical Association (AMA) Council on Ethical and Judicial Affairs, and the American College of Physicians Ethics Manual require disclosure of the most serious adverse events. However, many doctors and hospitals do not report errors under the current system because of concerns about malpractice lawsuits; this prevents collection of information needed to find and correct the conditions that lead to mistakes. As of 2008, 35 US states have statutes allowing doctors and health care providers to apologize and offer expressions of regret without their words being used against them in court, and 7 states have also passed laws mandating written disclosure of adverse events and bad outcomes to patients and families. In September 2005, US Senators Clinton and Obama introduced the National Medical Error Disclosure and Compensation (MEDiC) Bill, providing physicians protection from liability and a safe environment for disclosure, as part of a program to notify and compensate patients harmed by medical errors. It is now the policy of several academic medical centers, including Johns Hopkins, University of Illinois and Stanford, to promptly disclose medical errors, offering apologies and compensation. This national initiative, hoping to restore integrity to dealings with patients, make it easier to learn from mistakes, and avoid angry lawsuits, was modeled after a University of Michigan Hospital System program that has reduced the number of lawsuits against the hospital by 75% and has decreased the average litigation cost. The Veterans Health Administration requires the disclosure of all adverse events to patients, even those that are not obvious. However, as of 2008 these initiatives have only included hospitals that are self-insured and that employ their staffs, thus limiting the number of parties involved. Medical errors are the third leading cause of death in the US, after heart disease and cancer, according to research by Johns Hopkins University. Their study published in May 2016 concludes that more than 250,000 people die every year due to medical mix-ups. Other countries report similar results.
===== Performance =====
In April 2008, consumer, employer, and labor organizations announced an agreement with major physician organizations and health insurers on principles to measure and report doctors' performance on quality and cost.
==== United Kingdom ====
In the United Kingdom, whistleblowing is well recognized and is government-sanctioned, as a way to protect patients by encouraging employees to call attention to deficient services. Health authorities are encouraged to put local policies in place to protect whistleblowers.
== Studies of patient safety ==
Numerous organizations, government branches, and private companies conduct research studies to investigate the overall health of patient safety in America and across the globe. Despite the shocking and widely publicized statistics on preventable deaths due to medical errors in America's hospitals, the 2006 National Healthcare Quality Report assembled by the Agency for Healthcare Research and Quality (AHRQ) had the following sobering assessment:
Most measures of quality are improving, but the pace of change remains modest.
Quality improvement varies by setting and phase of care.
The rate of improvement accelerated for some measures while a few continued to show deterioration.
Variation in health care quality remains high.
A 2011 study of more than 1,000 patients with advanced colon cancer found that one in eight were treated with at least one drug regimen with specific recommendations against its use in the National Comprehensive Cancer Network guidelines. The study focused on three chemotherapy regimens that were not supported by evidence from prior clinical studies or clinical practice guidelines. One treatment was rated "insufficient data to support", one had been "shown to be ineffective", and one was supported by "no data, nor is there a compelling rationale." Many of the patients received multiple cycles of non-beneficial chemotherapy, and some received two or more unproven treatments. Potential side effects of the treatments included hypertension, heightened risk of bleeding and bowel perforation.
== Organizations advocating patient safety ==
Several authors of the 1999 Institute of Medicine report revisited the status of their recommendations and the state of patient safety, five years after "To Err is Human". Discovering that patient safety had become a frequent topic for journalists, health care experts, and the public, it was harder to see overall improvements on a national level. What was noteworthy was the impact on attitudes and organizations. Few healthcare professionals now doubt that preventable medical injuries are a serious problem. The central concept of the report—that bad systems and not bad people lead to most errors—became established in patients' safety efforts. A broad array of organizations now advances the cause of patient safety. For instance, in 2010 the principal European anaesthesiology organizations launched the Helsinki Declaration for Patient Safety in Anaesthesiology, which incorporates many of the principles described above.
== See also ==
== References ==
== External links ==
CIMIT Center for Integration of Medicine and Innovative Technology - Nonprofit organizations together advocating for Patient safety
Institute for safety in Office Based Surgery
Center for the Advancement of Healthcare Quality & Safety (CAHQS)
Safe communication video for the prevention of healthcare-induced harm
Health-EU Portal Patient Safety in the EU
Academic Center for Evidence-Based Practice (ACE)
Improvement Science Research Network (ISRN)
Beyond The Checklist: What Else Healthcare Can Learn From Aviation Teamwork and Safety
Institute of Medicine & Law

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Peer instruction is a teaching method popularized by Harvard Professor Eric Mazur in the early 1990s. Originally used in introductory undergraduate physics classes at Harvard University, peer instruction is used in various disciplines and institutions around the globe. It is a student-centered learning approach that involves flipping the traditional classroom. It expects students to prepare for class by exploring provided materials and then engage with a series of questions about the material in class.
== Method ==
Peer instruction as a learning system works by moving information transfer out and moving information assimilation, or application of learning, into the classroom. Students prepare to learn outside of class by doing pre-class readings and answering questions about those readings using another method, called Just in Time Teaching. Then, in class, the instructor engages students by posing prepared conceptual questions or ConcepTests that are based on student difficulties. The questioning procedure outlined by Eric Mazur is as follows:
Instructor poses question based on students' responses to their pre-class reading
Students reflect on the question
Students commit to an individual answer
Instructor reviews student responses
Students discuss their thinking and answers with their peers
Students then commit again to an individual answer
The instructor again reviews responses and decides whether more explanation is needed before moving on to the next concept.
Peer instruction has been used in a range of educational contexts around the globe and in many disciplines, including philosophy, psychology, geology, mathematics, computer science and engineering.
== Effectiveness ==
There is some research that supports the effectiveness of peer instruction over more traditional teaching methods, such as traditional lecture. The effectiveness of peer instruction can depend on prior student knowledge. A randomized controlled trial published in 2021 found no difference in total test scores for one laboratory exercise compared to traditional group work.
== References ==

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"Policy-based evidence making" is a pejorative term which refers to the commissioning of research in order to support a policy which has already been decided upon. It is the converse of evidence-based policy making.
As the name suggests, policy-based evidence making means working back from a predefined policy to produce underpinning evidence. Working from a conclusion to provide only supporting evidence is an approach which contradicts most interpretations of the scientific method; however, it should be distinguished from research into the effects of a policy where such research may provide either supporting or opposing evidence.
== Examples ==
In The Politics of Evidence: From evidence-based policy to the good governance of evidence, Justin Parkhurst quotes the following example from Professor Anne Glover, then Chief Scientific Officer to the European Commission:
Let's imagine a Commissioner over the weekend thinks, "Let's ban the use of credit cards in the EU because credit cards lead to personal debt". So that commissioner will come in on Monday morning and say to his or her Director General, "Find me the evidence that demonstrates that this is the case".
Similar reasoning has been advanced in respect of public policy on alcohol and narcotics.
== Mentions ==
In July 2006, Rebecca Boden and Debbie Epstein published a paper in which they wrote:
This need [for evidence] has been reified in the UK and elsewhere, as routines of "evidence-based policy"-making have been hardwired into the business of Government. Intuitively, basing policies that affect people's lives and the economy on rigorous academic research sounds rational and desirable. However, such approaches are fundamentally flawed by virtue of the fact that Government, in its broadest sense, seeks to capture and control the knowledge producing processes to the point where this type of "research" might best be described as "policy-based evidence".
The term "policy-based evidence making" was later referred to in a report of the United Kingdom House of Commons Select Committee on Science and Technology into Scientific Advice, Risk and Evidence Based Policy Making issued in October 2006. The committee stated:
[Ministers] should certainly not seek selectively to pick pieces of evidence which support an already agreed policy, or even commission research in order to produce a justification for policy: so-called "policy-based evidence making" (see paragraphs 95–6). Where there is an absence of evidence, or even when the Government is knowingly contradicting the evidence—maybe for very good reason—this should be openly acknowledged. [emphasis in original]
The term has been applied to climate policy. Oliver Geden, writing in Nature in 2015, said (p. 28):
Everyday politics is therefore dominated not by evidence-based policy-making but by attempts at 'policy-based evidence-making'.
The term has also been applied outside the strictly scientific arena, for example in a position paper for the Arts and Humanities Research Council.
== See also ==
== References ==