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SPECIAL FEATURE

the simulated impact of differing test sensitivities and specificities on hos- pital charges for suspected COVID-19 patients in the population. Exhibit 1 lists the assumptions that

were used to perform deterministic simulation analysis and calculate pre- dicted charge estimates.

Cost analysis of COVID-19 patients Between the months of April 1st–June 30th, 2020, 170 patients treated in the ED had the ICD-10 code for COVID-19 (U07.1). Of the 170 patients, 153 were discharged from an ED within 24 hours after a negative test result, and the remaining 17 were admitted for observation or treatment after being confirmed positive for COVID-19 by rtPCR. Exhibit 2 compares the charges/LOS and LOS for COVID-19 patients treated in the ED and dis- charged with those admitted as inpa- tients. The charges for the 153 patients discharged from the ED ranged from \$364 - \$10,130, with a median of \$3,208. Expectedly, charges for patients admit- ted to COVID-19 wards were signifi- cantly higher, with a median of \$7,815 per day of hospitalization, ranging from \$4,596 - \$8,446. Median LOS for a COVID-19 patient was found to be 3.4 days with a minimum and maxi- mum of 2 and 10 days. Based on these median LOS and charges, estimated charges for a TP, FP, TN and FN patient were \$26,571 (3.4 days x \$7,815.0), \$ 15,630 (2 days x \$7,815.0), \$3,208 and \$29,779 (charge for TP + charge for TN). Exhibit 3 graphs that with an ideal

test at a sensitivity and specificity of 100%, the total financial burden of hospital charges would be \$132.8 million dollars per annum if disease prevalence was maintained at 0.2%. However, if the prevalence increased to 10%, there would be a 68% increase in charges to \$226.9 million dollars.

False negative and false positive impacts For a diagnostic test to accurately identify a patient as having, or not having, a disease is clinically impor- tant. A false positive result can cause anxiety and result in patients under- going treatment for a condition they do not have, incurring all the risks and expenses involved. Alternatively, a false negative can result in timely intervention being missed, worsen- ing disease, requiring more resource intense intervention, disability or

Exhibit 3: Effect of test sensitivity and specificity on total charges for COVID-19.

A. Assuming low disease prevalence (0.2%)

B. Assuming high disease prevalence (10%)

even death. In the pandemic, test results drive not only patient treat- ment decisions but also isolation and quarantine requirements of patients and their contacts. False negatives carry the additional burden of indi- viduals remaining in the community and infecting others who will then require treatment. Results demonstrate that, even at a single healthcare system, the hospital charges for a declining positive predic- tive value are substantial —whether driven by charges associated with treating false positives (such as declin- ing specificity in high or low disease prevalence), or by the costs of later patient admissions with false negative results, plus the people they infected (as in the case of declining sensitivity

in high disease prevalence). To maxi- mize patient benefit and avoid unnec- essary spending, test characteristics, as well as disease prevalence, need to be considered when choosing an appropriate test and/or when inter- preting results and deciding whether a patient should be considered as hav- ing COVID-19 for contact tracing and disease containment purposes. Current rtPCR test sensitivity ranges

from 60% to 90% and specificity of 99.0 – 99.7%. Improving test sensitiv- ity from 60% to 99% would result in savings of \$0.96 and \$47.38 million dollars for low- and high-prevalence scenarios in one year at this single tertiary-care medical center. However, improving test specificity from 60% - 99.7% would result in bigger savings of

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