Footnote:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments May 5, 2022 erstellt
Description:
Background: Traffic represents a key operational input, especially in healthcare, as many aspects of medicine cannot happen from a distance. Though serious consequences follow delayed or omitted care, with the emergence of Covid-19 in March of 2020, traffic to many healthcare clinics fell dramatically overnight.Aim: Other literature has shown stay-at-home orders alone cannot fully explain the drops in clinic traffic. Using aggregate measures of patient mobility, this study seeks to characterize factors which explain the drops in traffic and so enable better traffic prediction going forward.Methods: Our empirical analysis combines observations from healthcare clinics across 15 US states, anonymized cellphone mobility data, Covid-19 severity measures, and stay-at-home orders. A Lasso-based procedure from machine learning selects instruments and generates county-level measures of individual mobility.Results: We find that in the early stages of the pandemic, clinic traffic returns with rising mobility, but the size of returns depends on individual mobility preferences, signals of environmental safety, and the value of care which patients seek. For example, a 1% increase in mobility yields more than a 2% return to more-valuable traffic but only a 0.5% return to less-valuable traffic.Conclusions: During our study period, though patients exercised discretion in many negative ways (e.g., foregoing care), we find they prioritized important services and shouldered some of the burden of their own wellness. By studying such decisions, we characterize powerful predictors of healthcare traffic, and we encourage researchers to explore the role of patient discretion in the co-production of wellness