{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T01:30:21Z","timestamp":1774402221097,"version":"3.50.1"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T00:00:00Z","timestamp":1592352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000092","name":"National Library of Medicine","doi-asserted-by":"publisher","award":["R01LM011369-07"],"award-info":[{"award-number":["R01LM011369-07"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NHS"},{"name":"Stanford Health CEO Innovation Fund"},{"name":"Debra and Mark Leslie endowment for AI in Healthcare"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocaa076","type":"journal-article","created":{"date-parts":[[2020,4,25]],"date-time":"2020-04-25T15:08:24Z","timestamp":1587827304000},"page":"1026-1131","source":"Crossref","is-referenced-by-count":14,"title":["Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0276-2183","authenticated-orcid":false,"given":"Sehj","family":"Kashyap","sequence":"first","affiliation":[{"name":"Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saurabh","family":"Gombar","sequence":"additional","affiliation":[{"name":"Department of Pathology and Medicine, Stanford University School of Medicine, Stanford, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steve","family":"Yadlowsky","sequence":"additional","affiliation":[{"name":"Deptartment of Electrical Engineering, Stanford University, Stanford, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alison","family":"Callahan","sequence":"additional","affiliation":[{"name":"Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason","family":"Fries","sequence":"additional","affiliation":[{"name":"Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin A","family":"Pinsky","sequence":"additional","affiliation":[{"name":"Department of Pathology and Medicine, Stanford University School of Medicine, Stanford, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nigam H","family":"Shah","sequence":"additional","affiliation":[{"name":"Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,6,17]]},"reference":[{"key":"2020110613114881000_ocaa076-B1","article-title":"IHME COVID-19 Health Service Utilization Forecasting Team, Murray CJL. Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months","journal-title":"medRxiv"},{"key":"2020110613114881000_ocaa076-B2","author":"Goh"},{"key":"2020110613114881000_ocaa076-B3","article-title":"A model to forecast regional demand for COVID-19 related hospital beds","author":"Ferstad","journal-title":"medRxiv"},{"key":"2020110613114881000_ocaa076-B4"},{"key":"2020110613114881000_ocaa076-B5","author":"Flaxman"},{"key":"2020110613114881000_ocaa076-B6","article-title":"Estimation of SARS-CoV-2 Infection Prevalence in Santa Clara County","author":"Yadlowsky","journal-title":"medRxiv"},{"key":"2020110613114881000_ocaa076-B7","author":"Shah"},{"key":"2020110613114881000_ocaa076-B8","author":"Malhi"},{"key":"2020110613114881000_ocaa076-B9","article-title":"See which states and cities have told residents to stay at home","author":"Mervosh","journal-title":"The New York Times"},{"key":"2020110613114881000_ocaa076-B10"},{"key":"2020110613114881000_ocaa076-B11","author":"Kennedy"},{"key":"2020110613114881000_ocaa076-B12"},{"key":"2020110613114881000_ocaa076-B13"},{"issue":"16","key":"2020110613114881000_ocaa076-B14","doi-asserted-by":"crossref","first-page":"1612","DOI":"10.1001\/jama.2020.4326","article-title":"Characteristics and outcomes of 21 critically ill patients with COVID-19 in Washington State","volume":"323","author":"Arentz","year":"2020","journal-title":"JAMA"},{"key":"2020110613114881000_ocaa076-B15","doi-asserted-by":"crossref","first-page":"343","DOI":"10.15585\/mmwr.mm6912e2","article-title":"Severe outcomes among patients with coronavirus disease 2019 (COVID-19) - United States, February 12-March 16, 2020","volume":"69","year":"2020","journal-title":"MMWR Morb Mortal Wkly Rep"},{"issue":"16","key":"2020110613114881000_ocaa076-B16","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1001\/jama.2020.4031","article-title":"Critical care utilization for the COVID-19 outbreak in Lombardy, Italy: early experience and forecast during an emergency response","volume":"323","author":"Grasselli","year":"2020","journal-title":"JAMA"},{"issue":"13","key":"2020110613114881000_ocaa076-B17","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1001\/jama.2020.2648","article-title":"Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention","volume":"323","author":"Wu","year":"2020","journal-title":"JAMA"},{"key":"2020110613114881000_ocaa076-B18","article-title":"Incidence, clinical outcomes, and transmission dynamics of hospitalized 2019 coronavirus disease among 9,596,321 individuals residing in California and Washington, United States: a prospective cohort study","author":"Lewnard","journal-title":"medRxiv 10.1101\/2020"},{"key":"2020110613114881000_ocaa076-B19","article-title":"A model to estimate bed demand for COVID-19 related hospitalization","author":"Zhang","journal-title":"medRxiv"},{"key":"2020110613114881000_ocaa076-B20","author":"Koseff"},{"key":"2020110613114881000_ocaa076-B21","author":"Roy"}],"container-title":["Journal of the American Medical Informatics Association"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/jamia\/article-pdf\/27\/7\/1026\/34153797\/ocaa076.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/jamia\/article-pdf\/27\/7\/1026\/34153797\/ocaa076.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,6]],"date-time":"2020-11-06T14:37:39Z","timestamp":1604673459000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamia\/article\/27\/7\/1026\/5858301"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,17]]},"references-count":21,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,6,17]]},"published-print":{"date-parts":[[2020,7,1]]}},"URL":"https:\/\/doi.org\/10.1093\/jamia\/ocaa076","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.04.19.20072017","asserted-by":"object"}]},"ISSN":["1527-974X"],"issn-type":[{"value":"1527-974X","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2020,7]]},"published":{"date-parts":[[2020,6,17]]}}}