Fluency—Not Competency or Expertise—Is Needed to Incorporate Evidence Into Practice
Bibliographic record
Abstract
Twenty years ago, the Journal of the American Medical Association published an article describing a “new approach to teaching the practice of medicine”: evidence-based medicine (EBM).1 Today, the ability to apply research to practice is a program accreditation requirement at both the undergraduate and postgraduate levels in the United States and Canada as well as an ongoing practice competency requirement. The EBM process—assessing the patient, asking the question, acquiring the evidence, assessing and applying that evidence, and finally evaluating the whole process—seeks to provide a framework for the integration of evidence into clinical practice. Much of the training around EBM has focused on building expertise in literature searching and critical appraisal of resources; however, as Moore’s2 response to the 2011 Question of the Year indicated, these efforts have not been successful in practice. Perhaps one of the most effective ways to ensure that those who work and learn in medical schools and teaching hospitals can develop to their full potential is to shift from the perspective that these individuals must become experts in EBM to the view that they should become comfortable with the tools which will allow them to be fluent users of the evidence. To that end, we propose four levels of performance: Literacy—knowing and understanding the EBM concepts; Competency—being able to apply these concepts in controlled conditions; Fluency—having a comfort level with incorporating the concepts into daily practice; and Expertise—having the high level of skill needed to create and demonstrate the tools that translate research into practice. There is simplicity with building competency around a skill set that can be defined, taught, and evaluated through assignments, objective structured clinical examinations, or licensing examinations. But what is required to move a medical student or physician from a level of competency to one of fluency? Increasing their understanding of clinical information systems, both organizational and technological, is key. Building on Marcum’s3 position that fluency can only be achieved in the workplace, the focus must be on providing training and support for processes that can be readily integrated into regular practice routines. For medical students, this would include determining realistic competencies for the clinical practice environment and simulating that environment to train them to integrate the skills and clinical tools and resources required. Just as fluency in language is best developed through immersion in the native culture, fluency in the use of health information is best developed through immersion in the culture of EBM. Of particular importance is the identification and support of physician role models. For residents and clinicians to become fluent, it is necessary to integrate clinical practice tools and resources into the clinical interface and to ensure that ongoing support and training are available at the point of need. Librarians and informaticians play crucial roles in fostering EBM fluency in medical schools and teaching hospitals. The ideal, as outlined by Moore,2 is the integration of these specialists into health care teams. When this is not possible due to issues of availability, scalability, or sustainability, a level of self-sufficiency is required on the part of health care practitioners. In conjunction with physicians and informaticians, librarians are developing and supporting health information technology initiatives that integrate point-of-care resources into the clinical interface. Librarians are responsible for ensuring that point-of-care resources are licensed and linked at as granular a level as possible, as well as for providing ongoing support and training in the use of those resources. They also play a major role in building medical students’ EBM skills, particularly those related to the effective search for and use of high-quality, best-evidence resources. Librarians must collaborate with medical educators and informaticians in the development and utilization of simulated clinical interfaces to present patient data and to integrate best-evidence resources into those interfaces. When this is accomplished, EBM skills will come to be seen as a seamless part of the clinical process, with a resultant increase in students’ confidence and fluency. While the effective implementation of electronic health records and clinical information systems is challenging, using technology to deliver and integrate EBM resources is the best chance of attaining Garrity’s4 vision of focusing on “evidence” as part of treatment and care to ensure that “clinicians are given just the information they need to advance health, to provide each person the best care, at the right time, every time.”
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.050 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".