Behavioral Economics Interventions in Clinical Decision Support Systems

Insook Cho; David W. Bates

doi:10.1055/s-0038-1641221

Yearbook of Medical Informatics, Table of Contents

CC BY-NC-ND 4.0 · Yearb Med Inform 2018; 27(01): 114-121
DOI: 10.1055/s-0038-1641221

Section 5: Decision Support

Survey

Georg Thieme Verlag KG Stuttgart

Behavioral Economics Interventions in Clinical Decision Support Systems

Authors

Insook Cho

¹Nursing Department, Inha University, Incheon, South Korea

²The Center for Patient Safety Research and Practice, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA
David W. Bates

²The Center for Patient Safety Research and Practice, Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA

³Harvard Medical School, Boston, MA, USA

⁴Partners Healthcare Systems, Inc., Wellesley, MA, USA

Abstract

Summary

Background: Clinical decision support (CDS) systems can improve safety and facilitate evidence-based practice. However, clinical decisions are often affected by the cognitive biases and heuristics of clinicians, which is increasing the interest in behavioral and cognitive science approaches in the medical field.

Objectives: This review aimed to identify decision biases that lead clinicians to exhibit irrational behaviors or responses, and to show how behavioral economics can be applied to interventions in order to promote and reveal the contributions of CDS to improving health care quality.

Methods: We performed a systematic review of studies published in 2016 and 2017 and applied a snowball citationsearch method to identify topical publications related to studies forming part of the BEARI (Application of Behavioral Economics to Improve the Treatment of Acute Respiratory Infections) multisite, cluster-randomized controlled trial performed in the United States.

Results: We found that 10 behavioral economics concepts with nine cognitive biases were addressed and investigated for clinician decision-making, and that the following five concepts, which were actively explored, had an impact in CDS applications: social norms, framing effect, status-quo bias, heuristics, and overconfidence bias.

Conclusions: Our review revealed that the use of behavioral economics techniques is increasing in areas such as antibiotics prescribing and preventive care, and that additional tests of the concepts and heuristics described would be useful in other areas of CDS. An improved understanding of the benefits and limitations of behavioral economics techniques is also still needed. Future studies should focus on successful design strategies and how to combine them with CDS functions for motivating clinicians.

Keywords

Clinical decision support systems - behavioral economics - cognitive science - decision bias

Full Text

References

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