Evidence-Based Medicine in Otolaryngology, Part XI: Modeling and Analysis to Support Decisions

2020 ◽  
pp. 019459982094882
Author(s):  
Lisa Caulley ◽  
Myriam G. Hunink ◽  
Gregory W. Randolph ◽  
Jennifer J. Shin
Keyword(s):  
Decision Trees ◽  
Evidence Based Medicine ◽  
Markov Models ◽  
Clinical Care ◽  
Clinical Decision ◽  
Decision Models ◽  
Data Driven ◽  
Evidence Based ◽  
Modeling And Analysis ◽  
Based Medicine

Objective To provide a resource to educate clinical decision makers about the analyses and models that can be employed to support data-driven choices. Data Sources Published studies and literature regarding decision analysis, decision trees, and models used to support clinical decisions. Review Methods Decision models provide insights into the evidence and its implications for those who make choices about clinical care and resource allocation. Decision models are designed to further our understanding and allow exploration of the common problems that we face, with parameters derived from the best available evidence. Analysis of these models demonstrates critical insights and uncertainties surrounding key problems via a readily interpretable yet quantitative format. This 11th installment of the Evidence-Based Medicine in Otolaryngology series thus provides a step-by-step introduction to decision models, their typical framework, and favored approaches to inform data-driven practice for patient-level decisions, as well as comparative assessments of proposed health interventions for larger populations. Conclusions Information to support decisions may arise from tools such as decision trees, Markov models, microsimulation models, and dynamic transmission models. These data can help guide choices about competing or alternative approaches to health care. Implications for Practice Methods have been developed to support decisions based on data. Understanding the related techniques may help promote an evidence-based approach to clinical management and policy.

Bringing the best evidence to the point of care

2010 ◽  
pp. 22-27
Author(s):  
Paul P. Glasziou
Keyword(s):  
Patient Care ◽  
Evidence Based Medicine ◽  
Research Evidence ◽  
Point Of Care ◽  
Clinical Care ◽  
Clinical Decision ◽  
Evidence Based ◽  
Based Medicine ◽  
Quality Of Research

You must always be students, learning and unlearning till your life’s end. Joseph Lister Neither our memories nor our textbooks are complete and up to date with all the research relevant to the patients we will see today. The scattering of necessary research across a vast ocean of literature makes it inaccessible at the point of clinical decision. The consequences for patient care have given rise to the discipline of evidence-based medicine (EBM), whose two central concerns are with the quality of research evidence and with its appropriate usage in clinical care....

scholarly journals Tips for teaching evidence-based medicine in a clinical setting: lessons from adult learning theory. Part one

2008 ◽  
Vol 101 (10) ◽  
pp. 493-500 ◽  
Author(s):  
Kausik Das ◽  
Sadia Malick ◽  
Khalid S Khan
Keyword(s):  
Evidence Based Medicine ◽  
Clinical Decision Making ◽  
Adult Learning Theory ◽  
Clinical Decision ◽  
Evidence Based ◽  
Educational Training ◽  
Practice Teaching ◽  
Approach To Teaching ◽  
Indispensable Tool ◽  
Based Medicine

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.

scholarly journals Tips for teaching evidence-based medicine in a clinical setting: lessons from adult learning theory. Part two

2008 ◽  
Vol 101 (11) ◽  
pp. 536-543 ◽  
Author(s):  
Sadia Malick ◽  
Kausik Das ◽  
Khalid S Khan
Keyword(s):  
Adult Learning ◽  
Evidence Based Medicine ◽  
Clinical Decision Making ◽  
Adult Learning Theory ◽  
Clinical Decision ◽  
Evidence Based ◽  
Clinical Use ◽  
Evidence Based Healthcare ◽  
Based Medicine ◽  
Oriented Research

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.

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
Keyword(s):  
Machine Learning ◽  
Evidence Based Medicine ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Therapeutic Interventions ◽  
Evidence Based ◽  
Rigorous Approach ◽  
Common Purpose ◽  
Future Outcomes ◽  
Based Medicine

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?

The ‘Number Needed to Treat’ and the ‘Adjusted NNT’ in Health Care Decision-Making

1998 ◽  
Vol 3 (1) ◽  
pp. 44-49 ◽  
Author(s):  
Jack Dowie
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Evidence Based Medicine ◽  
Clinical Decision Making ◽  
Treatment Options ◽  
Clinical Decision ◽  
Population Based ◽  
Number Needed To Treat ◽  
Evidence Based ◽  
Based Medicine

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.

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