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The Potential of Collective Intelligence in Emergency Medicine: Pooling Medical Students’ Independent Decisions Improves Diagnostic Performance

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  • Juliane E. Kämmer
  • Wolf E. Hautz
  • Stefan M. Herzog
  • Olga Kunina-Habenicht
  • Ralf H. J. M. Kurvers

Abstract

Background. Evidence suggests that pooling multiple independent diagnoses can improve diagnostic accuracy in well-defined tasks. We investigated whether this is also the case for diagnostics in emergency medicine, an ill-defined task environment where diagnostic errors are rife. Methods. A computer simulation study was conducted based on empirical data from 2 published experimental studies. In the computer experiments, 285 medical students independently diagnosed 6 simulated patients arriving at the emergency room with dyspnea. Participants’ diagnoses (n = 1,710), confidence ratings, and expertise levels were entered into a computer simulation. Virtual groups of different sizes were randomly created, and 3 collective intelligence rules (follow-the-plurality rule, follow-the-most-confident rule, and follow-the-most-senior rule) were applied to combine the independent decisions into a final diagnosis. For different group sizes, the performance levels (i.e., percentage of correct diagnoses) of the 3 collective intelligence rules were compared with each other and against the average individual accuracy. Results. For all collective intelligence rules, combining independent decisions substantially increased performance relative to average individual performance. For groups of 4 or fewer, the follow-the-most-confident rule outperformed the other rules; for larger groups, the follow-the-plurality rule performed best. For example, combining 5 independent decisions using the follow-the-plurality rule increased diagnostic accuracy by 22 percentage points. These results were robust across case difficulty and expertise level. Limitations of the study include the use of simulated patients diagnosed by medical students. Whether results generalize to clinical practice is currently unknown. Conclusion. Combining independent decisions may substantially improve the quality of diagnoses in emergency medicine and may thus enhance patient safety.

Suggested Citation

  • Juliane E. Kämmer & Wolf E. Hautz & Stefan M. Herzog & Olga Kunina-Habenicht & Ralf H. J. M. Kurvers, 2017. "The Potential of Collective Intelligence in Emergency Medicine: Pooling Medical Students’ Independent Decisions Improves Diagnostic Performance," Medical Decision Making, , vol. 37(6), pages 715-724, August.
    • Handle: RePEc:sae:medema:v:37:y:2017:i:6:p:715-724
    DOI: 10.1177/0272989X17696998
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    References listed on IDEAS

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    1. Robert T. Clemen & Robert L. Winkler, 1985. "Limits for the Precision and Value of Information from Dependent Sources," Operations Research, INFORMS, vol. 33(2), pages 427-442, April.
    2. Robert L. Winkler & Roy M. Poses, 1993. "Evaluating and Combining Physicians' Probabilities of Survival in an Intensive Care Unit," Management Science, INFORMS, vol. 39(12), pages 1526-1543, December.
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    1. Elaine C. Khoong & Sarah S. Nouri & Delphine S. Tuot & Shantanu Nundy & Valy Fontil & Urmimala Sarkar, 2022. "Comparison of Diagnostic Recommendations from Individual Physicians versus the Collective Intelligence of Multiple Physicians in Ambulatory Cases Referred for Specialist Consultation," Medical Decision Making, , vol. 42(3), pages 293-302, April.

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