Open Access
Subscription Access
A cognitive model of converting scientific evidence on treatment effects into prognostic information in clinical practice
Abstract
Rationale, aims and objectives: Little is known about the cognitive processes of how population-based scientific evidence is utilized in the treatment of individual patients. The present work aims to outline a formalized framework of converting scientific evidence on treatment effects into prognostic information in clinical practice and describes a pilot study for illustration of how the framework can be used in research.
Method:The proposed theoretical framework considers the human mind as a limited capacity information processing system. This system codes external stimuli into internal inputs, which are then processed through several steps towards an evidence-informed clinical output. A small-scale pilot study including 130 laypersons was performed. Participants were asked to make probabilistic prognostic statements based on a fictional study (“evidence”) and a fictional case vignette (“clinical context”).
Results: Several cognitive processes were defined, including coding of the attributes of the evidence and the clinical context, integration of the information in a similarity-dissimilarity matrix and calculating the prognostic output. Frequently required operations, such as extrapolation, individualization, particularization, generalization and absolutization of the information were outlined. The presented framework was shown to be useful in analyzing and interpreting the pilot study data.
Conclusions:The postulated model holds great potential for investigating cognitive processes included in evidence-informed clinical care.
Keywords
References
Asimov, I. (2010). Foundation, Foundation and Empire, Second Foundation. New York, NY: Everyman’s Library.
Rosenberg, W. & Donald, A. (1995). Evidence based medicine: an approach to clinical problem-solving. British Medical Journal (Clinical Research Ed.) 310 (6987) 1122-1126.
Sackett, D.L. (1995). Applying overviews and meta-analyses at the bedside. Journal of Clinical Epidemiology 48 (1) 61-66.
Dans, A.L., Dans, L.F., Guyatt, G.H. & Richardson, S. (1998). Users’ guides to the medical literature: XIV. How to decide on the applicability of clinical trial results to your patient. Journal of the American Medical Association 279 (7) 545-549.
Glasziou, P., Guyatt, G.H., Dans, A.L., Dans, L.F., Straus, S. & Sackett, D.L. (1998). Applying the results of trials and systematic reviews to individual patients. ACP Journal Club 129 (3) A15-A16.
Atkins, D., Chang, S.M., Gartlehner, G., Buckley, D.I., Whitlock, E.P., Berliner, E. & Matchar, D. (2011). Assessing applicability when comparing medical interventions: AHRQ and the Effective Health Care Program. Journal of Clinical Epidemiology 64 (11) 1198-1207.
Kriston, L. & Yildiz, A. (2015). Traditional and novel research synthesis approaches to support evidence-based treatment decisions. In: Bipolar Disorder: Millenium Update. Yildiz ,A., Nemeroff, C. & Ruiz, P. (Eds). New York, NY: Oxford University Press USA.
Howick, J., Glasziou, P. & Aronson, J.K. (2013). Problems with using mechanisms to solve the problem of extrapolation. Theoretical Medicine and Bioethics 34 (4) 275-291.
Croft, P., Altman, D.G., Deeks, J.J., Dunn, K.M., Hay, A.D., Hemingway, H., LeResche, L., Peat, G., Perel, P., Petersen, S.E., Riley, R.D., Roberts, I., Sharpe, M., Stevens, R.J., Van Der Windt, D.A., Von Korff, M. & Timmis, A. (2015). The science of clinical practice: disease diagnosis or patient prognosis? Evidence about “what is likely to happen” should shape clinical practice. BMC Medicine 13, 20.
Kriston, L. (2013). Dealing with clinical heterogeneity in meta-analysis. Assumptions, methods, interpretation. International Journal of Methods in Psychiatric Research 22 (1) 1-15.
Dobrow, M.J., Goel, V. & Upshur, R.E.G. (2004). Evidence-based health policy: context and utilisation. Social Science & Medicine 58 (1) 207-217.
Engebretsen, E., Vøllestad, N.K., Wahl, A.K., Robinson, H.S. & Heggen, K. (2015). Unpacking the process of interpretation in evidence-based decision making. Journal of Evaluation in Clinical Practice 21 (3) 529-531.
Kriston, L. & Meister, R. (2014). Incorporating uncertainty regarding applicability of evidence from meta-analyses into clinical decision making. Journal of Clinical Epidemiology 67 (3) 325-334.
Miles, A. & Loughlin, M. (2011). Models in the balance: evidence-based medicine versus evidence-informed individualized care. Journal of Evaluation in Clinical Practice 17 (4) 531-536.
Hofmeijer, J. (2014). Evidence-based medical knowledge: the neglected role of expert opinion. Journal of Evaluation in Clinical Practice 20 (6) 803-808.
Upshur, R.E.G. (2013). A short note on probability in clinical medicine. Journal of Evaluation in Clinical Practice 19 (3) 463-466.
Haynes, R.B., Devereaux, P.J. & Guyatt, G.H. (2002). Clinical expertise in the era of evidence-based medicine and patient choice. Evidence Based Medicine 7 (2) 36-38.
Anderson, J.R. (2009). Cognitive Psychology and its Implications (Seventh Edition). New York, NY: Worth Publishers.
Howick, J., Glasziou, P. & Aronson, J.K. (2013). Can understanding mechanisms solve the problem of extrapolating from study to target populations (the problem of “external validity”)? Journal of the Royal Society of Medicine 106 (3) 81-86.
Grant, R.L. (2014). Converting an odds ratio to a range of plausible relative risks for better communication of research findings. British Medical Journal 348, f7450.
Kahneman, D. & Tversky, A. (1973). On the psychology of prediction. Psychological Review 80 (4) 237-251.
Tversky, A. & Kahneman, D. (1974). Judgment under uncertainty: heuristics and biases. Science 185 (4157) 1124-1131.
R Core Team. (2014). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
National Prescribing Centre. (2011). Making decisions better. MeReC Bulletin 22 (1) 1-8.
DOI: http://dx.doi.org/10.5750/ejpch.v3i4.1028
Refbacks
- There are currently no refbacks.