Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening. (17th June 2020)

• Record Type: Journal Article
• Authors: Kashyap, Sehj; Gombar, Saurabh; Yadlowsky, Steve; Callahan, Alison; Fries, Jason; Pinsky, Benjamin A; Shah, Nigam H
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 7(2020)
• Page Start: 1026
• Page End: 1131
• Publication Date: 2020-06-17
• Subjects: COVID-19 -- surge models -- capacity planning -- electronic health records; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa076
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Responding to the COVID-19 pandemic requires accurate forecasting of health system capacity requirements using readily available inputs. We examined whether testing and hospitalization data could help quantify the anticipated burden on the health system given shelter-in-place (SIP) order. Materials and Methods: 16, 103 SARS-CoV-2 RT-PCR tests were performed on 15, 807 patients at Stanford facilities between March 2 and April 11, 2020. We analyzed the fraction of tested patients that were confirmed positive for COVID-19, the fraction of those needing hospitalization, and the fraction requiring ICU admission over the 40 days between March 2nd and April 11th 2020. Results: We find a marked slowdown in the hospitalization rate within ten days of SIP even as cases continued to rise. We also find a shift towards younger patients in the age distribution of those testing positive for COVID-19 over the four weeks of SIP. The impact of this shift is a divergence between increasing positive case confirmations and slowing new hospitalizations, both of which affects the demand on health systems. Conclusion: Without using local hospitalization rates and the age distribution of positive patients, current models are likely to overestimate the resource burden of COVID-19. It is imperative that health systems start using these data to quantify effects of SIP and aid reopening planning.