During COVID, the issue of healthcare crowd-out is critical even in non-hotspot areas. The alpha-reserve capacity reallocation through tele-medicine could mitigate this.

There are lots of anecdotic evidence and news articles talking about large amount of healthcare needs have remained unmet during COVID-19, including whether COVID-related care may have displaced non-COVID care (so-called crowd-out effect). If so, efficient healthcare resource allocation is required during such a critical period. We aim to understand the extent healthcare demand may be shifted and can be rearranged. The non-availability of timely and reliable medical claim data during a public health crisis like COVID, makes this task difficult. To surmount this practical challenge, we leverage the dataset of online drug retailing transactions across geographic regions in Mainland China during the pandemic’s first wave. The key data aspect that allows us to infer crowd-out regards demand differences between prescription (Rx) and over-the-counter (OTC) drugs. Specifically, because a provider-written prescription is required for Rx but not for OTC purchases, relative Rx/OTC demand changes reflect changes in the amount of used medical consultations. Since most sample drugs target non-C19 symptoms, changes in the amount of used non-COVID care can be inferred from this variation.

We embed the above insights in a differences-in-differences-in-differences (DDD) identification framework. We find COVID-fueled relative surge of online Rx/OTC demand in lower-capacity regions. Given that the Chinese system implicitly bundles utilization with offline Rx demand, this suggests that patients from lower-capacity regions were less likely to receive care as demand for COVID-related care strained the system. At the pandemic’s peak, this crowd-out effect is equivalent to 10% decrease of non-C19 care. We propose and evaluate an alpha-reserve capacity reallocation policy. Relying on tele-health infrastructure, this policy would reallocate healthcare supply across regions on a spot basis, aiming to minimize aggregate crowd-out. Significant crowd-out reduction is achieved without drastically undercutting any region’s healthcare capacity. This provides meaningful managerial and policy suggestions to lessen the adverse impacts of such a public health crisis.

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