Background

Coronavirus disease-2019 (COVID-19) is a novel disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that rapidly spread around the globe. The dramatic increase in the number of cases and deaths have placed tremendous strain on health care systems worldwide. As health care workers and society adjust to focus treatment and prevention of COVID-19, other facets of the health care enterprise are affected, particularly surgical volume and revenue. The purpose of this study was to describe the financial impact of COVID-19 on an academic neurosurgery department.

Methods

A retrospective review of weekly average daily work relative value units (wRVUs) were compared before and after COVID-19 in the fiscal year 2020. A comparative time period of the same months in the year prior was also included for review. We also review strategies for triaging neurosurgical disease as needing emergent, urgent, or routine operative treatment.

Results

Daily average wRVU after COVID-19 dropped significantly with losses in all weeks examined. Of the 7 weeks in the current post-COVID period, the weekly daily average wRVU was 173 (range, 128-363). The mean decline was 51.4% compared with the pre-COVID era. Both inpatient and outpatient revenue was affected.

Conclusions

COVID-19 had a profound detrimental effect on surgical productivity and revenue generation.

The Gene Expression Omnibus (GEO) contains more than two million digital samples from functional genomics experiments amassed over almost two decades. However, individual sample meta-data remains poorly described by unstructured free text attributes preventing its largescale reanalysis. We introduce the Search Tag Analyze Resource for GEO as a web application (http://STARGEO.org) to curate better annotations of sample phenotypes uniformly across different studies, and to use these sample annotations to define robust genomic signatures of disease pathology by meta-analysis. In this paper, we target a small group of biomedical graduate students to show rapid crowd-curation of precise sample annotations across all phenotypes, and we demonstrate the biological validity of these crowd-curated annotations for breast cancer. STARGEO.org makes GEO data findable, accessible, interoperable and reusable (i.e., FAIR) to ultimately facilitate knowledge discovery. Our work demonstrates the utility of crowd-curation and interpretation of open 'big data' under FAIR principles as a first step towards realizing an ideal paradigm of precision medicine.