Practitioners Of Evidence-based Medicine Research Articles

Prevagen®: Analysis of Clinical Evidence and Its Designation as a "#1 Pharmacist Recommended Brand".

Prevagen® is a dietary supplement that is marketed to help with mild memory loss associated with older people. The manufacturer of the product notes that clinical evidence supports this use. Furthermore, the manufacturer notes that Prevagen® is a "#1 Pharmacist Recommended Brand." The authors' search of the literature identified one clinical study that evaluated the efficacy and safety of Prevagen®; however, this study possesses significant limitations and therefore one must question the merits of such clinical evidence. Prevagen®'s designation as a "#1 Pharmacist Recommended Brand" is based on a survey facilitated by Pharmacy Times® that is designed to identify the brand name over-the-counter products that pharmacists recommend most frequently. Because of the limited clinical data supporting Prevagen®'s efficacy, it is likely that the survey results reflect pharmacists' familiarity with this product, which may be influenced by extensive advertising techniques. As practitioners of evidence-based medicine, pharmacists should not recommend a product with limited evidence to support its use. Furthermore, pharmacists should proactively educate their patients, especially those who are most vulnerable, about the rational use of all pharmacologically active substances, including dietary supplements.

Query-oriented evidence extraction to support evidence-based medicine practice

BackgroundEvidence-based medicine practice requires medical practitioners to rely on the best available evidence, in addition to their expertise, when making clinical decisions. The medical domain boasts a large amount of published medical research data, indexed in various medical databases such as MEDLINE. As the size of this data grows, practitioners increasingly face the problem of information overload, and past research has established the time-associated obstacles faced by evidence-based medicine practitioners. In this paper, we focus on the problem of automatic text summarisation to help practitioners quickly find query-focused information from relevant documents. MethodsWe utilise an annotated corpus that is specialised for the task of evidence-based summarisation of text. In contrast to past summarisation approaches, which mostly rely on surface level features to identify salient pieces of texts that form the summaries, our approach focuses on the use of corpus-based statistics, and domain-specific lexical knowledge for the identification of summary contents. We also apply a target-sentence-specific summarisation technique that reduces the problem of underfitting that persists in generic summarisation models. ResultsIn automatic evaluations run over a large number of annotated summaries, our extractive summarisation technique statistically outperforms various baseline and benchmark summarisation models with a percentile rank of 96.8%. A manual evaluation shows that our extractive summarisation approach is capable of selecting content with high recall and precision, and may thus be used to generate bottom-line answers to practitioners’ queries. ConclusionsOur research shows that the incorporation of specialised data and domain-specific knowledge can significantly improve text summarisation performance in the medical domain. Due to the vast amounts of medical text available, and the high growth of this form of data, we suspect that such summarisation techniques will address the time-related obstacles associated with evidence-based medicine.

Lessons from Evidence-Based Medicine: What Healthcare Designers Can Learn from the Medical Field

This paper explores the history of evidence-based medicine (EBM) in comparison to the relatively short history of evidence-based design (EBD). Throughout its development, EBM has encountered a number of challenges, including dealing with initial objections to the concept, creating safeguards to ensure rigor in research, supporting practitioners in their efforts to stay current with a growing body of research data, applying the literature to real-life situations, making decisions when literature to inform decision making is absent, and educating students and professionals to become EBM practitioners. The ways in which the field of EBM has handled these challenges provides a number of lessons for the relatively young field of EBD.

Misunderstandings, misperceptions, and mistakes

Discussions about evidence-based medicine (EBM) have engendered both positive and negative reactions from clinicians, researchers, and policymakers since the term was first coined in the early 1990s.1,2 These discussions were...

Are We Ready for Patient-Based Effect Sizes in Clinical-Trials Research?

M clinical trial results relevant to decision makers has been a theme in the Society for Medical Decision Making and this journal since their inception. Sample size calculations, ethical design, and the use of research results are also issues of long-standing interest to MDM and its readers. The “sufficiently important differences” (SID) proposed by Barrett and colleagues falls within this decades-long conversation and provides a potential solution that is patient oriented and decision-analytically coherent. To realize this potential, 3 methodologic issues need to be examined more closely: the relationship between SID and reporting of clinical trial results, the relationship between SID and the design of clinical trial results, and potential problems in elicitation. SID and reporting is straightforward. SID falls within the framework of providing methods to help individuals to make decisions that are well founded and coherent with their values and beliefs, which is a major goal of MDM. Based on 40 years of literature and a demonstration project, Lehmann and Goodman listed several desiderata for clinical-trial reporting that would employ a Bayesian and decision-analytic framework. They called for an interaction that would engage the reader’s prior knowledge and desired treatment threshold and convey the treatment effect’s size and variability. SID satisfies these goals handily. Lehmann and Goodman further raised the issue of communities of prior beliefs and communities of thresholds, which Barrett and colleagues address with their attention to the distribution of SIDs. Studies now in progress are heeding the call of Barrett and colleagues. For instance, DeBaun and colleagues, in a clinical trial of blood transfusion therapy for sickle-cell disease, are performing utility assessments and standard gambles for detecting SIDs prospectively on patients and providers in what they call a “risk-benefit analysis.” Implicit in Barrett and colleagues’ references but unstated in the article’s text, practitioners of evidencebased medicine often look to the number needed to treat (NNT) as a method of conveying the clinical significance of results, after the fact. In the terms used by Barrett and colleagues, a threshold maximum NNT is related to the minimum important difference. Similarly, for an adverse outcome, the number needed to harm (NNH) would lead to the maximum acceptable harm threshold, which Barrett and colleagues represent as a vertical line rising out of the x-axis in their Figure 2. Two points follow. First, threshold NNTs and NNHs lead to a quadrant of acceptability, and it is precisely the authors’ point that a truncated region better captures the thresholds. The 2nd, more subtle point is that NNT and NNH represent the perspective of a clinician or public health decision maker. It is not clear that patients understand the NNTs and the NNHs in the same way that clinicians do. Using SIDs in the design phase is a more contentious idea. Two recent books illuminate the high financial and medical stakes of the drug-approval process and also how, in this process, demonstrating that a new agent is more effective than the standard therapy is low on the list of concerns. (Evaluating whether it is as good at satisfying patients’ desires may be an even lower priority.) From a methodological perspective, it is crucial to choose a single outcome measure in a study, and a single threshold for that single outcome. If, in monitoring a trial, it becomes clear that the effect size is greater than the threshold, then it becomes unethical to continue that trial. On the other hand, if the effect size is so small in interim analyses that it would be futile to continue, it would likewise be unethical to continue the study. If an SID is used, then at least 2 outcomes must

Evidence-based medicine, medical decision analysis, and pathology

Recent advances in molecular pathology and other technologies such as proteomics present pathologists with the challenge of integrating the new information generated with high-throughput methods with current diagnostic models based mostly on histopathology and clinicopathologic correlations. Parallel developments in the field of medical informatics and bioinformatics provide the technical and mathematical methods to approach these problems in a rational manner. However, it remains unclear whether pathologists or other medical specialists will become primarily responsible for the development and maintenance of these multivariate and multidisciplinary diagnostic and prognostic models that are hoped to provide more accurate, individualized patient-based information. Evidence-based medicine (EBM) and medical decision analysis (MDA) are relatively new disciplines that use quantitative methods to assess the value of information, differentiate fact from myth, and integrate so-called best evidence into multivariate models for the assessment of prognosis, response to therapy, selection of laboratory tests, and other complex problems that influence individual patient care. We review from an epistemological viewpoint the current approach to information in pathology and describe some of the concepts developed by the practitioners of EBM and MDA.

Evidence-Based Laboratory Medicine: From Principles to Outcomes. Christopher P. Price and Robert H. Christenson. Washington, DC: AACC Press, 2003, 296 pp., $65.00 ($52.00 AACC members). ISBN 1-890883-90-5.

Practitioners of evidence-based medicine (EBM) strive to integrate, through a process of life-long learning, the best research evidence, clinical experience, and patient values as a basis for their clinical decisions. This book addresses the implications of this philosophy for the community of laboratory medicine. The book comprises 13 chapters dealing with issues such as the diagnostic process, testing and measures of outcome, study design and presentation of data, literature research and systematic reviewing, clinical guidelines and audits, …

Proof and policy from medical research evidence.

When judging the benefits and harms of health care and predicting patient prognosis, clinicians, researchers, and others must consider many types of evidence. Medical research evidence is part of the required knowledge base, and practitioners of evidence-based medicine must attempt to integrate the best available clinical evidence from systematic research with health professionals' expertise and patients' rights to be informed about diagnostic and therapeutic options available to them. Judging what constitutes sound evidence can be difficult because of, among other things, the sheer quantity, diversity, and complexity of medical evidence available today; the various scientific methods that have been advanced for assembling, evaluating, and interpreting such information; and the guides for applying medical research evidence to individual patients' situations. Recommendations based on sound research can then be brought forward as either guidelines or standards, and criteria exist by which valid guidelines and standards can be developed and promulgated. Nonetheless, gaps and deficiencies exist in current guidelines and in the methods for finding and synthesizing evidence. Interpreting and judging medical research involves subjective, not solely explicit, processes. Thus, developments in evidence-based medicine are an aid, but not a panacea, for definitively establishing benefits and harms of medical care, and the contributions that medical research evidence can make in any clinical or legal situation must be understood in a context in which judgment and values, understanding of probability, and tolerance for uncertainty all play a role.

10 Decision analysis

In this paper I have described a number of examples of the use of clinical decision analysis for guiding both individual patient decisions, and decisions for populations. The former is the only systematic way in which patients' preferences can be formally included in the decision. This is not always appropriate and it would be foolish to use decision analysis for many everyday clinical decisions. However, for difficult decisions, there are good reasons to believe that it will result in decisions that accord better with patient preferences than intuitive methods. Practitioners of evidence-based medicine will be looking for evidence from randomized controlled trials.