Evidence-based medicine and machine learning: a partnership with a common purpose

2020 ◽  
pp. bmjebm-2020-111379
Author(s):  
Ian Scott ◽  
David Cook ◽  
Enrico Coiera

From its origins in epidemiology, evidence-based medicine has promulgated a rigorous approach to assessing the validity, impact and applicability of hypothesis-driven empirical research used to evaluate the utility of diagnostic tests, prognostic tools and therapeutic interventions. Machine learning, a subset of artificial intelligence, uses computer programs to discover patterns and associations within huge datasets which are then incorporated into algorithms used to assist diagnoses and predict future outcomes, including response to therapies. How do these two fields relate to one another? What are their similarities and differences, their strengths and weaknesses? Can each learn from, and complement, the other in rendering clinical decision-making more informed and effective?

2008 ◽  
Vol 101 (10) ◽  
pp. 493-500 ◽  
Author(s):  
Kausik Das ◽  
Sadia Malick ◽  
Khalid S Khan

Summary Evidence-based medicine (EBM) is an indispensable tool in clinical practice. Teaching and training of EBM to trainee clinicians is patchy and fragmented at its best. Clinically integrated teaching of EBM is more likely to bring about changes in skills, attitudes and behaviour. Provision of evidence-based health care is the most ethical way to practice, as it integrates up-to-date, patient-oriented research into the clinical decision making process, thus improving patients' outcomes. In this article, we aim to dispel the myth that EBM is an academic and statistical exercise removed from practice by providing practical tips for teaching the minimum skills required to ask questions and critically identify and appraise the evidence and presenting an approach to teaching EBM within the existing clinical and educational training infrastructure.


2008 ◽  
Vol 101 (11) ◽  
pp. 536-543 ◽  
Author(s):  
Sadia Malick ◽  
Kausik Das ◽  
Khalid S Khan

Summary Evidence-based medicine (EBM) is the clinical use of current best available evidence from relevant, valid research. Provision of evidence-based healthcare is the most ethical way to practise as it integrates up-to-date patient-oriented research into the clinical decision-making to improve patients' outcomes. This article provides tips for teachers to teach clinical trainees the final two steps of EBM: integrating evidence with clinical judgement and bringing about change.


1998 ◽  
Vol 3 (1) ◽  
pp. 44-49 ◽  
Author(s):  
Jack Dowie

Within ‘evidence-based medicine and health care’ the ‘number needed to treat’ (NNT) has been promoted as the most clinically useful measure of the effectiveness of interventions as established by research. Is the NNT, in either its simple or adjusted form, ‘easily understood’, ‘intuitively meaningful’, ‘clinically useful’ and likely to bring about the substantial improvements in patient care and public health envisaged by those who recommend its use? The key evidence against the NNT is the consistent format effect revealed in studies that present respondents with mathematically-equivalent statements regarding trial results. Problems of understanding aside, trying to overcome the limitations of the simple (major adverse event) NNT by adding an equivalent measure for harm (‘number needed to harm’ NNH) means the NNT loses its key claim to be a single yardstick. Integration of the NNT and NNH, and attempts to take into account the wider consequences of treatment options, can be attempted by either a ‘clinical judgement’ or an analytical route. The former means abandoning the explicit and rigorous transparency urged in evidence-based medicine. The attempt to produce an ‘adjusted’ NNT by an analytical approach has succeeded, but the procedure involves carrying out a prior decision analysis. The calculation of an adjusted NNT from that analysis is a redundant extra step, the only action necessary being comparison of the results for each option and determination of the optimal one. The adjusted NNT has no role in clinical decision-making, defined as requiring patient utilities, because the latter are measurable only on an interval scale and cannot be transformed into a ratio measure (which the adjusted NNT is implied to be). In any case, the NNT always represents the intrusion of population-based reasoning into clinical decision-making.


1997 ◽  
Vol 14 (3) ◽  
pp. 83-84 ◽  
Author(s):  
John Geddes

Over the last five years the adjective ‘evidence-based’ has become difficult to avoid. Indeed, a MEDLINE search for articles containing the phrase ‘evidence-based medicine’ in their titles or abstracts reveals one mention in 1992, rapidly increasing to 53 in 1996. So great has been the increase that the National Library of Medicine now includes ‘evidence-based medicine’ as a MeSH heading for indexing papers.But what is evidence-based medicine (EBM)? First and foremost, EBM is a set of strategies designed to help the clinician keep up-to-date and to base his clinical decision making on the best available external evidence. EBM has been espoused by policymakers, purchasers and others — and, although the approach is open to misuse by these groups as a cost-cutting exercise, there are refreshing signs that they will be able to use the approach to help produce real improvements in patient care. However, the essential focus of EBM is on assisting doctors and other clinicians make decisions about individual patients. The steps involved in EBM include: a precise definition of the clinical problem (a crucial first step — in medical practice it will usually include making a diagnosis), an efficient search for the best available evidence, critical appraisal of the evidence and integration of the research findings with clinical expertise. Finally, the clinician assesses the outcome of the process and continues to improve his EBM skills.


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