A Cost-Utility Analysis of Remote Pulse-Oximetry Monitoring of Patients With COVID-19

Abstract

Objectives: Since 2020, COVID-19 has infected tens of millions and caused hundreds of thousands of fatalities in the United States. Infection waves lead to increased emergency department utilization and critical care admission for patients with respiratory distress. Although many individuals develop symptoms necessitating a ventilator, some patients with COVID-19 can remain at home to mitigate hospital overcrowding. Remote pulse-oximetry (pulse-ox) monitoring of moderately ill patients with COVID-19 can be used to monitor symptom escalation and trigger hospital visits, as needed.

Methods: We analyzed the cost-utility of remote pulse-ox monitoring using a Markov model with a 3-week time horizon and daily cycles from a US health sector perspective. Costs (US dollar 2020) and outcomes were derived from the University Hospitals' real-world evidence and published literature. Costs and quality-adjusted life-years (QALYs) were used to determine the incremental cost-effectiveness ratio at a cost-effectiveness threshold of $100 000 per QALY. We assessed model uncertainty using univariate and probabilistic sensitivity analyses.

Results: Model results demonstrated that remote monitoring dominates current standard care, by reducing costs ($11 472 saved) and improving outcomes (0.013 QALYs gained). There were 87% fewer hospitalizations and 77% fewer deaths among patients with access to remote pulse-ox monitoring. The incremental cost-effectiveness ratio was not sensitive to uncertainty ranges in the model.

Conclusions: Patient with COVID-19 remote pulse-ox monitoring increases the specificity of those requiring follow-up care for escalating symptoms. We recommend remote monitoring adoption across health systems to economically manage COVID-19 volume surges, maintain patients' comfort, reduce community infection spread, and carefully monitor needs of multiple individuals from one location by trained experts.

Keywords: COVID-19; critical care; emergency department; infectious disease; intensive care unit; pulse oximetry; telemedicine.

Figure 1

A Markov model simulating treatment of moderate-to-severe patients with COVID-19 using telehealth monitoring with pulse oximetry compared with hospitalization. C indicates critical care; D, death; ED, emergency department; H, hospitalization; M, at-home monitoring; R, recovery.

Figure 2

Tornado diagram of univariate sensitivity analysis. Each parameter was varied by reported 95% confidence interval or ±20% if a reported confidence interval was not reported. Results are presented in terms of iNMB range. Black bars represent upper-bound estimates of the parameter, and white bars represent lower bound estimates. C indicates critical care; ED, emergency department; H, hospitalization; iNMB, incremental net monetary benefit; K, thousand; M, at-home monitoring; QALY, quality-adjusted life-year; R, recovery; WTP, willingness-to-pay.

Figure 3

Probabilistic sensitivity analysis. ICER scatterplot of 1000 Monte Carlo simulations. ICER indicates incremental cost-effectiveness ratio; QALY, quality-adjusted life-year; WTP, willingness-to-pay.