About this event
The idea that the division of labor increases productivity is central to economic analyses of countries, industries, social structures, and occupations within organizations. We study the division of labor within an online primary healthcare organization, where an algorithm assigns patient cases between two clinician occupations: nurses and doctors. We compare a knowledge hierarchy in which nurses resolve some cases themselves and escalate others to doctors, with a direct-to-expert approach where cases are assigned directly to doctors. We use approximately 500,000 cases and an identification strategy that leverages temporary congestion which increases the odds of an assignment to the nurse-initiated knowledge hierarchy. We find that nurses resolve 70% of the cases and send the rest to doctors. Although the knowledge hierarchy slightly reduces the rates of meaningful diagnosis and prescription, it does not adversely impact patient satisfaction, acute care utilization, labor earnings, or mortality. The knowledge hierarchy reduces total costs to the healthcare system by approximately 8% without compromising health outcomes. Finally, we explore how the comparative advantage of the knowledge hierarchy varies between case types and assess the extent to which the allocation of tasks aligns with this comparative advantage.
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This event will take place in SAL 2.04, 2nd Floor Conference Room, Sir Arthur Lewis Building, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH.
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