Abstract
Medical errors have a huge impact on clinical practice in terms of economic and human costs. As a result, technology-based solutions, such as those grounded in artificial intelligence (AI) or collective intelligence (CI), have attracted increasing interest as a means of reducing error rates and their impacts. Previous studies have shown that a combination of individual opinions based on rules, weighting mechanisms, or other CI solutions could improve diagnostic accuracy with respect to individual doctors. We conducted a study to investigate the potential of this approach in cardiology and, more precisely, in electrocardiogram (ECG) reading. To achieve this aim, we designed and conducted an experiment involving medical students, recent graduates, and residents, who were asked to annotate a collection of 10 ECGs of various complexity and difficulty. For each ECG, we considered groups of increasing size (from three to 30 members) and applied three different CI protocols. In all cases, the results showed a statistically significant improvement (ranging from 9% to 88%) in terms of diagnostic accuracy when compared to the performance of individual readers; this difference held for not only large groups, but also smaller ones. In light of these results, we conclude that CI approaches can support the tasks mentioned above, and possibly other similar ones as well. We discuss the implications of applying CI solutions to clinical settings, such as cases of augmented ‘second opinions’ and decision-making.
Highlights
Diagnostic and therapeutic errors are far from rare; it is estimated that 10–15% of all reported diagnoses are incorrect Graber (2013), and this entails millions of diagnostic errors each year in healthcare systems throughout the world Newman-Toker et al (2020)
In terms of loose accuracy (LA), we report an improvement in all the ECGs for all three of the considered collective intelligence (CI) protocols
We studied the application of CI to the task of ECG reading and discussed the more general implications of this approach in medicine for diagnostic practices and more general decision-making tasks
Summary
Diagnostic and therapeutic errors are far from rare; it is estimated that 10–15% of all reported diagnoses are incorrect Graber (2013), and this entails millions of diagnostic errors each year in healthcare systems throughout the world Newman-Toker et al (2020). Medical errors are not necessarily a manifestation of malpractice or negligence Gruver and Freis (1957), as they mostly depend on the complexity of the context in which health workers have to operate every day, which places them under conditions of great strain and uncertainty For this reason, there has always been great hope in the role of technology in reducing variability, helping to cope with uncertainty, and creating new working conditions and forms of collaboration among health practitioners. There has always been great hope in the role of technology in reducing variability, helping to cope with uncertainty, and creating new working conditions and forms of collaboration among health practitioners To achieve these broad aims, IT-based solutions Topol (2019) have been proposed to mitigate error rates in clinical practice. These solutions can be implemented either by using artificial intelligence (AI) tools to support cognitive tasks and decision-making Quer et al (2017), or by leveraging collaborative tools to support knowledge sharing or its combination, as in the case of collective intelligence (CI) solutions Tucker et al (2019)
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