Exploring the sources and mechanisms of cognitive errors in medical diagnosis with associative memory models

Andrés Pomi

doi:10.1515/dx-2017-0024

Article

Exploring the sources and mechanisms of cognitive errors in medical diagnosis with associative memory models

Andrés Pomi

Published/Copyright: November 21, 2017

Published by

Become an author with De Gruyter Brill

Submit Manuscript Author Information Explore this Subject

From the journal Diagnosis Volume 4 Issue 4

Abstract

Background:

One of the central challenges of third millennium medicine is the abatement of medical errors. Among the most frequent and hardiest causes of misdiagnosis are cognitive errors produced by faulty medical reasoning. These errors have been analyzed from the perspectives of cognitive psychology and empirical medical studies. We introduce a neurocognitive model of medical diagnosis to address this issue.

Methods:

We construct a connectionist model based on the associative nature of human memory to explore the non-analytical, pattern-recognition mode of diagnosis. A context-dependent matrix memory associates signs and symptoms with their corresponding diseases. The weights of these associations depend on the frequencies of occurrence of each disease and on the different combinations of signs and symptoms of each presentation of that disease. The system receives signs and symptoms and by a second input, the degree of diagnostic uncertainty. Its output is a probabilistic map on the set of possible diseases.

Results:

The model reproduces different kinds of well-known cognitive errors in diagnosis. Errors in the model come from two sources. One, dependent on the knowledge stored in memory, varies with the accumulated experience of the physician and explains age-dependent errors and effects such as epidemiological masking. The other is independent of experience and explains contextual effects such as anchoring.

Conclusions:

Our results strongly suggest that cognitive biases are inevitable consequences of associative storage and recall. We found that this model provides valuable insight into the mechanisms of cognitive error and we hope it will prove useful in medical education.

Keywords: associative memories; cognitive errors; medical diagnosis

Corresponding author: Andrés Pomi, MD, PhD, Group of Cognitive Systems Modeling, Sección Biofísica, Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo 11400, Uruguay

Acknowledgments

I am indebted to Prof. Eduardo Mizraji for his persistent but gentle persuasion to publish these results.

Author contributions: The author has accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: I am grateful to the Program for the Development of Basic Sciences (PEDECIBA) of Uruguay, for partial support.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

References

1. Makary MA, Daniel M. Medical error – the third leading cause of death in the US. Br Med J 2016;353:i2139.10.1136/bmj.i2139Search in Google Scholar

2. Elstein AS. Clinical reasoning in medicine. In: Higgs J, Jones MA, editors. Clinical reasoning in the health professions. Mass: Butterworth-Heinemann, 1995:49–59.Search in Google Scholar

3. Shojania KG, Burton EC, McDonald KM, Goldman L. Changes in rates of autopsy-detected diagnostic errors over time. J Am Med Assoc 2003;289:2849–56.10.1001/jama.289.21.2849Search in Google Scholar

4. Graber ML, Franklin N, Gordon R. Diagnostic error in internal medicine. Arch Intern Med 2005;165:1493–9.10.1001/archinte.165.13.1493Search in Google Scholar PubMed

5. Bornstein BH, Emler AC. Rationality in medical decision making: a review of the literature on doctors’ decision-making biases. J Eval Clin Pract 2001;7:97–107.10.1046/j.1365-2753.2001.00284.xSearch in Google Scholar PubMed

6. McDonald CJ. Medical heuristics: the silent adjudicators of clinical practice. Ann Intern Med 1996;124:56–62.10.7326/0003-4819-124-1_Part_1-199601010-00009Search in Google Scholar PubMed

7. Tversky A, Kahneman D. Judgment under uncertainty: heuristics and biases. Science 1974;185:1124–31.10.1126/science.185.4157.1124Search in Google Scholar PubMed

8. Kassirer JP, Kopelman RI. Cognitive errors in diagnosis: instantiation, classification, and consequences. Am J Med 1989;86:433–41.10.1016/0002-9343(89)90342-2Search in Google Scholar PubMed

9. Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them. Acad Med 2003;78: 775–80.10.1097/00001888-200308000-00003Search in Google Scholar PubMed

10. Croskerry P. Achieving quality in clinical decision making: cognitive strategies and detection of bias. Acad Emerg Med 2002;9:1184–204.10.1111/j.1553-2712.2002.tb01574.xSearch in Google Scholar PubMed

11. Croskerry P. Bias: a normal operating characteristic of the diagnosing brain. Diagnosis 2014;1:23–7.10.1515/dx-2013-0028Search in Google Scholar PubMed

12. Klein JG. Five pitfalls in decisions about diagnosis and prescribing. Br Med J 2005;330:781–3.10.1136/bmj.330.7494.781Search in Google Scholar

13. Gigerenzer G. How to make cognitive illusions disappear: beyond “Heuristics and Biases”. In: Stroebe W, Hewstone M, editors. European review of social psychology (Vol. 2). Chichester: Wiley, 1991:83–115.10.1080/14792779143000033Search in Google Scholar

14. Graber ML, Gordon R, Franklin N. Reducing diagnostic errors in medicine: what’s the goal? Acad Med 2002;77: 981–92.10.1097/00001888-200210000-00009Search in Google Scholar PubMed

15. Baxt WG. Application of artificial neural networks to clinical medicine. Lancet 1995;346:1135–38.10.1016/S0140-6736(95)91804-3Search in Google Scholar PubMed

16. Lisboa PJ. A review of evidence of health benefit from artificial neural network in health intervention. Neural Netw 2002;15: 11–39.10.1016/S0893-6080(01)00111-3Search in Google Scholar

17. Spitzer M. The mind within the net. Cambridge, MA: MIT Press, 1999.10.7551/mitpress/4632.001.0001Search in Google Scholar

18. Hoffman RE, Rapaport J, Ameli R, McGlashan TH, Harcherik D, Servan-Schreiber D. A neural network simulation of hallucinated voices and associated speech perception impairments in schizophrenia patients. J Cogn Neurosci 1995;7:479–97.10.1162/jocn.1995.7.4.479Search in Google Scholar PubMed

19. Valle-Lisboa JC, Pomi A, Cabana A, Elvevåg B, Mizraji E. A modular approach to language production: models and facts. Cortex 2014;55:61–76.10.1016/j.cortex.2013.02.005Search in Google Scholar PubMed

20. Gradin VB, Pomi A. The role of hippocampal atrophy in depression: a neurocomputational approach. J Biol Phys 2008;34:107–20.10.1007/s10867-008-9099-7Search in Google Scholar PubMed PubMed Central

21. Norman GR, Brooks LR. The non-analytical basis of clinical reasoning. Adv Health Sci Educ 1997;2:173–84.10.1023/A:1009784330364Search in Google Scholar

22. Papa FJ, Li F. Evidence of the preferential use of disease prototypes over case exemplars among early year one medical students prior to and following diagnostic training. Diagnosis 2015;2:217–25.10.1515/dx-2015-0024Search in Google Scholar

23. Cooper LN. A possible organization of animal memory and learning. In: Lundquist B, Lundquist S, editors. Proceedings of the Nobel symposium on collective properties of physical systems. New York: Academic Press, 1973:252–64.10.1016/B978-0-12-460350-9.50043-6Search in Google Scholar

24. Anderson JA. Introduction to neural networks. Cambridge, MA: MIT Press, 1995.10.7551/mitpress/3905.001.0001Search in Google Scholar

25. Friston KJ. Functional and effective connectivity: a review. Brain Connect 2011;1:13–36.10.1089/brain.2011.0008Search in Google Scholar PubMed

26. Mizraji E. Context-dependent associations in linear distributed memories. Bull Math Biol 1989;51:195–205.10.1007/BF02458441Search in Google Scholar PubMed

27. Mizraji E, Pomi A, Alvarez F. Multiplicative contexts in associative memories. Biosystems 1994;32:145–61.10.1016/0303-2647(94)90038-8Search in Google Scholar PubMed

28. Pomi A, Olivera F. Context-sensitive autoassociative memories as expert systems in medical diagnosis. BMC Med Inf Decis Mak 2006;6:39.10.1186/1472-6947-6-39Search in Google Scholar PubMed PubMed Central

29. Leblanc VR, Brooks LR, Norman GR. Believing is seeing: the influence of a diagnostic hypothesis on the interpretation of clinical features. Acad Med 2002;77:S67–9.10.1097/00001888-200210001-00022Search in Google Scholar PubMed

30. Hobus PP, Schmidt HG, Boshuizen HP, Patel VL. Contextual factors in the activation of first diagnostic hypotheses: expert-novices differences. Med Educ 1987;21:471–6.10.1111/j.1365-2923.1987.tb01405.xSearch in Google Scholar PubMed

31. Eva KW. The aging physician: changes in cognitive processing and their impact on medical practice. Acad Med 2002;77:S1–6.10.1097/00001888-200210001-00002Search in Google Scholar PubMed

32. Ericsson KA. Deliberate practice and the acquisition and maintenance of expert performance in medicine and related domains. Acad Med 2004;79:S70–81.10.1097/00001888-200410001-00022Search in Google Scholar PubMed

33. Ladouceur R. Should older family physicians retire? [Editorial] Can Fam Physician 2012;58:11.Search in Google Scholar

34. Kuhn GJ. Diagnostic errors. Acad Emerg Med 2002;9:740–50.10.1111/j.1553-2712.2002.tb02155.xSearch in Google Scholar PubMed

Received: 2017-5-29

Accepted: 2017-9-28

Published Online: 2017-11-21

Published in Print: 2017-11-27

You are currently not able to access this content.

Articles in the same Issue

https://doi.org/10.1515/dx-2017-0024

Keywords for this article

associative memories; cognitive errors; medical diagnosis