Abstract
Employing more than 2 million emergency department (ED) records, we combine machine learning and regression discontinuity to document novel distortions in triage nurses’ assessments of patients’ conditions and investigate the short- and medium-term consequences for patients. We show that triage nurses progressively become more lenient during their shifts, and identical ED patients arriving just after a shift change are thus assigned a lower priority. We show that these patients receive lower levels of care and require additional emergency care afterward. We conclude that distortions in nurses’ initial assessments of urgency bias’ medical staff’s perceptions.
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