Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening.
Identifieur interne : 000463 ( Main/Exploration ); précédent : 000462; suivant : 000464Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening.
Auteurs : Sehj Kashyap [États-Unis] ; Saurabh Gombar [États-Unis] ; Steve Yadlowsky [États-Unis] ; Alison Callahan [États-Unis] ; Jason Fries [États-Unis] ; Benjamin A. Pinsky [États-Unis] ; Nigam H. Shah [États-Unis]Source :
- Journal of the American Medical Informatics Association : JAMIA [ 1527-974X ] ; 2020.
Descripteurs français
- KwdFr :
- Adolescent (MeSH), Adulte (MeSH), Adulte d'âge moyen (MeSH), Betacoronavirus (MeSH), Capacité hospitalière (statistiques et données numériques), Dossiers médicaux électroniques (MeSH), Enfant (MeSH), Enfant d'âge préscolaire (MeSH), Femelle (MeSH), Hospitalisation (statistiques et données numériques), Humains (MeSH), Infections à coronavirus (diagnostic), Infections à coronavirus (épidémiologie), Jeune adulte (MeSH), Modèles statistiques (MeSH), Mâle (MeSH), Pandémies (MeSH), Planification en santé (MeSH), Pneumopathie virale (diagnostic), Pneumopathie virale (épidémiologie), Prévision (MeSH), Quarantaine (MeSH), Répartition par âge (MeSH), Sujet âgé (MeSH), Sujet âgé de 80 ans ou plus (MeSH), États-Unis (épidémiologie).
- MESH :
- diagnostic : Infections à coronavirus, Pneumopathie virale.
- statistiques et données numériques : Capacité hospitalière, Hospitalisation.
- épidémiologie : Infections à coronavirus, Pneumopathie virale, États-Unis.
- Adolescent, Adulte, Adulte d'âge moyen, Betacoronavirus, Dossiers médicaux électroniques, Enfant, Enfant d'âge préscolaire, Femelle, Humains, Jeune adulte, Modèles statistiques, Mâle, Pandémies, Planification en santé, Prévision, Quarantaine, Répartition par âge, Sujet âgé, Sujet âgé de 80 ans ou plus.
- Wicri :
- geographic : États-Unis.
English descriptors
- KwdEn :
- Adolescent (MeSH), Adult (MeSH), Age Distribution (MeSH), Aged (MeSH), Aged, 80 and over (MeSH), Betacoronavirus (MeSH), COVID-19 (MeSH), Child (MeSH), Child, Preschool (MeSH), Coronavirus Infections (diagnosis), Coronavirus Infections (epidemiology), Electronic Health Records (MeSH), Female (MeSH), Forecasting (MeSH), Health Planning (MeSH), Hospital Bed Capacity (statistics & numerical data), Hospitalization (statistics & numerical data), Humans (MeSH), Male (MeSH), Middle Aged (MeSH), Models, Statistical (MeSH), Pandemics (MeSH), Pneumonia, Viral (diagnosis), Pneumonia, Viral (epidemiology), Quarantine (MeSH), SARS-CoV-2 (MeSH), United States (epidemiology), Young Adult (MeSH).
- MESH :
- geographic , epidemiology : United States.
- diagnosis : Coronavirus Infections, Pneumonia, Viral.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- statistics & numerical data : Hospital Bed Capacity, Hospitalization.
- Adolescent, Adult, Age Distribution, Aged, Aged, 80 and over, Betacoronavirus, COVID-19, Child, Child, Preschool, Electronic Health Records, Female, Forecasting, Health Planning, Humans, Male, Middle Aged, Models, Statistical, Pandemics, Quarantine, SARS-CoV-2, Young Adult.
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.
DOI: 10.1093/jamia/ocaa076
PubMed: 32548636
PubMed Central: PMC7337779
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening.</title><author><name sortKey="Kashyap, Sehj" sort="Kashyap, Sehj" uniqKey="Kashyap S" first="Sehj" last="Kashyap">Sehj Kashyap</name><affiliation wicri:level="4"><nlm:affiliation>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author><author><name sortKey="Gombar, Saurabh" sort="Gombar, Saurabh" uniqKey="Gombar S" first="Saurabh" last="Gombar">Saurabh Gombar</name><affiliation wicri:level="2"><nlm:affiliation>Department of Pathology and Medicine, Stanford University School of Medicine, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Pathology and Medicine, Stanford University School of Medicine, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Yadlowsky, Steve" sort="Yadlowsky, Steve" uniqKey="Yadlowsky S" first="Steve" last="Yadlowsky">Steve Yadlowsky</name><affiliation wicri:level="4"><nlm:affiliation>Deptartment of Electrical Engineering, Stanford University, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Deptartment of Electrical Engineering, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author><author><name sortKey="Callahan, Alison" sort="Callahan, Alison" uniqKey="Callahan A" first="Alison" last="Callahan">Alison Callahan</name><affiliation wicri:level="4"><nlm:affiliation>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author><author><name sortKey="Fries, Jason" sort="Fries, Jason" uniqKey="Fries J" first="Jason" last="Fries">Jason Fries</name><affiliation wicri:level="4"><nlm:affiliation>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author><author><name sortKey="Pinsky, Benjamin A" sort="Pinsky, Benjamin A" uniqKey="Pinsky B" first="Benjamin A" last="Pinsky">Benjamin A. Pinsky</name><affiliation wicri:level="2"><nlm:affiliation>Department of Pathology and Medicine, Stanford University School of Medicine, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Pathology and Medicine, Stanford University School of Medicine, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Shah, Nigam H" sort="Shah, Nigam H" uniqKey="Shah N" first="Nigam H" last="Shah">Nigam H. Shah</name><affiliation wicri:level="4"><nlm:affiliation>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2020">2020</date><idno type="RBID">pubmed:32548636</idno><idno type="pmid">32548636</idno><idno type="doi">10.1093/jamia/ocaa076</idno><idno type="pmc">PMC7337779</idno><idno type="wicri:Area/Main/Corpus">000589</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000589</idno><idno type="wicri:Area/Main/Curation">000589</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000589</idno><idno type="wicri:Area/Main/Exploration">000589</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening.</title><author><name sortKey="Kashyap, Sehj" sort="Kashyap, Sehj" uniqKey="Kashyap S" first="Sehj" last="Kashyap">Sehj Kashyap</name><affiliation wicri:level="4"><nlm:affiliation>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author><author><name sortKey="Gombar, Saurabh" sort="Gombar, Saurabh" uniqKey="Gombar S" first="Saurabh" last="Gombar">Saurabh Gombar</name><affiliation wicri:level="2"><nlm:affiliation>Department of Pathology and Medicine, Stanford University School of Medicine, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Pathology and Medicine, Stanford University School of Medicine, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Yadlowsky, Steve" sort="Yadlowsky, Steve" uniqKey="Yadlowsky S" first="Steve" last="Yadlowsky">Steve Yadlowsky</name><affiliation wicri:level="4"><nlm:affiliation>Deptartment of Electrical Engineering, Stanford University, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Deptartment of Electrical Engineering, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author><author><name sortKey="Callahan, Alison" sort="Callahan, Alison" uniqKey="Callahan A" first="Alison" last="Callahan">Alison Callahan</name><affiliation wicri:level="4"><nlm:affiliation>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author><author><name sortKey="Fries, Jason" sort="Fries, Jason" uniqKey="Fries J" first="Jason" last="Fries">Jason Fries</name><affiliation wicri:level="4"><nlm:affiliation>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author><author><name sortKey="Pinsky, Benjamin A" sort="Pinsky, Benjamin A" uniqKey="Pinsky B" first="Benjamin A" last="Pinsky">Benjamin A. Pinsky</name><affiliation wicri:level="2"><nlm:affiliation>Department of Pathology and Medicine, Stanford University School of Medicine, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Pathology and Medicine, Stanford University School of Medicine, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Shah, Nigam H" sort="Shah, Nigam H" uniqKey="Shah N" first="Nigam H" last="Shah">Nigam H. Shah</name><affiliation wicri:level="4"><nlm:affiliation>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region><settlement type="city">Stanford (Californie)</settlement></placeName><orgName type="university">Université Stanford</orgName></affiliation></author></analytic><series><title level="j">Journal of the American Medical Informatics Association : JAMIA</title><idno type="eISSN">1527-974X</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent (MeSH)</term><term>Adult (MeSH)</term><term>Age Distribution (MeSH)</term><term>Aged (MeSH)</term><term>Aged, 80 and over (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>COVID-19 (MeSH)</term><term>Child (MeSH)</term><term>Child, Preschool (MeSH)</term><term>Coronavirus Infections (diagnosis)</term><term>Coronavirus Infections (epidemiology)</term><term>Electronic Health Records (MeSH)</term><term>Female (MeSH)</term><term>Forecasting (MeSH)</term><term>Health Planning (MeSH)</term><term>Hospital Bed Capacity (statistics & numerical data)</term><term>Hospitalization (statistics & numerical data)</term><term>Humans (MeSH)</term><term>Male (MeSH)</term><term>Middle Aged (MeSH)</term><term>Models, Statistical (MeSH)</term><term>Pandemics (MeSH)</term><term>Pneumonia, Viral (diagnosis)</term><term>Pneumonia, Viral (epidemiology)</term><term>Quarantine (MeSH)</term><term>SARS-CoV-2 (MeSH)</term><term>United States (epidemiology)</term><term>Young Adult (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adolescent (MeSH)</term><term>Adulte (MeSH)</term><term>Adulte d'âge moyen (MeSH)</term><term>Betacoronavirus (MeSH)</term><term>Capacité hospitalière (statistiques et données numériques)</term><term>Dossiers médicaux électroniques (MeSH)</term><term>Enfant (MeSH)</term><term>Enfant d'âge préscolaire (MeSH)</term><term>Femelle (MeSH)</term><term>Hospitalisation (statistiques et données numériques)</term><term>Humains (MeSH)</term><term>Infections à coronavirus (diagnostic)</term><term>Infections à coronavirus (épidémiologie)</term><term>Jeune adulte (MeSH)</term><term>Modèles statistiques (MeSH)</term><term>Mâle (MeSH)</term><term>Pandémies (MeSH)</term><term>Planification en santé (MeSH)</term><term>Pneumopathie virale (diagnostic)</term><term>Pneumopathie virale (épidémiologie)</term><term>Prévision (MeSH)</term><term>Quarantaine (MeSH)</term><term>Répartition par âge (MeSH)</term><term>Sujet âgé (MeSH)</term><term>Sujet âgé de 80 ans ou plus (MeSH)</term><term>États-Unis (épidémiologie)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>United States</term></keywords><keywords scheme="MESH" qualifier="diagnosis" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="diagnostic" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Pneumonia, Viral</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Hospital Bed Capacity</term><term>Hospitalization</term></keywords><keywords scheme="MESH" qualifier="statistiques et données numériques" xml:lang="fr"><term>Capacité hospitalière</term><term>Hospitalisation</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Infections à coronavirus</term><term>Pneumopathie virale</term><term>États-Unis</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Age Distribution</term><term>Aged</term><term>Aged, 80 and over</term><term>Betacoronavirus</term><term>COVID-19</term><term>Child</term><term>Child, Preschool</term><term>Electronic Health Records</term><term>Female</term><term>Forecasting</term><term>Health Planning</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Models, Statistical</term><term>Pandemics</term><term>Quarantine</term><term>SARS-CoV-2</term><term>Young Adult</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Betacoronavirus</term><term>Dossiers médicaux électroniques</term><term>Enfant</term><term>Enfant d'âge préscolaire</term><term>Femelle</term><term>Humains</term><term>Jeune adulte</term><term>Modèles statistiques</term><term>Mâle</term><term>Pandémies</term><term>Planification en santé</term><term>Prévision</term><term>Quarantaine</term><term>Répartition par âge</term><term>Sujet âgé</term><term>Sujet âgé de 80 ans ou plus</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>États-Unis</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>OBJECTIVE</b></p><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.</p></div><div type="abstract" xml:lang="en"><p><b>MATERIALS AND METHODS</b></p><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.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><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.</p></div><div type="abstract" xml:lang="en"><p><b>CONCLUSION</b></p><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.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">32548636</PMID><DateCompleted><Year>2020</Year><Month>08</Month><Day>04</Day></DateCompleted><DateRevised><Year>2020</Year><Month>12</Month><Day>18</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1527-974X</ISSN><JournalIssue CitedMedium="Internet"><Volume>27</Volume><Issue>7</Issue><PubDate><Year>2020</Year><Month>07</Month><Day>01</Day></PubDate></JournalIssue><Title>Journal of the American Medical Informatics Association : JAMIA</Title><ISOAbbreviation>J Am Med Inform Assoc</ISOAbbreviation></Journal><ArticleTitle>Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening.</ArticleTitle><Pagination><MedlinePgn>1026-1131</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/jamia/ocaa076</ELocationID><Abstract><AbstractText Label="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.</AbstractText><AbstractText Label="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.</AbstractText><AbstractText Label="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.</AbstractText><AbstractText Label="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.</AbstractText><CopyrightInformation>© The Author(s) 2020. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Kashyap</LastName><ForeName>Sehj</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Gombar</LastName><ForeName>Saurabh</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Department of Pathology and Medicine, Stanford University School of Medicine, Stanford, California, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Yadlowsky</LastName><ForeName>Steve</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Deptartment of Electrical Engineering, Stanford University, Stanford, California, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Callahan</LastName><ForeName>Alison</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Fries</LastName><ForeName>Jason</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Pinsky</LastName><ForeName>Benjamin A</ForeName><Initials>BA</Initials><AffiliationInfo><Affiliation>Department of Pathology and Medicine, Stanford University School of Medicine, Stanford, California, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Shah</LastName><ForeName>Nigam H</ForeName><Initials>NH</Initials><AffiliationInfo><Affiliation>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>R01 LM011369</GrantID><Acronym>LM</Acronym><Agency>NLM NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D052061">Research Support, N.I.H., Extramural</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>J Am Med Inform Assoc</MedlineTA><NlmUniqueID>9430800</NlmUniqueID><ISSNLinking>1067-5027</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000293" MajorTopicYN="N">Adolescent</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D017677" MajorTopicYN="N">Age Distribution</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000368" MajorTopicYN="N">Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000369" MajorTopicYN="N">Aged, 80 and over</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="Y">Betacoronavirus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="N">COVID-19</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002648" MajorTopicYN="N">Child</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002675" MajorTopicYN="N">Child, Preschool</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D057286" MajorTopicYN="N">Electronic Health Records</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005544" MajorTopicYN="N">Forecasting</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006285" MajorTopicYN="Y">Health Planning</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006742" MajorTopicYN="N">Hospital Bed Capacity</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006760" MajorTopicYN="N">Hospitalization</DescriptorName><QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D006801" MajorTopicYN="N">Humans</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008297" MajorTopicYN="N">Male</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D008875" MajorTopicYN="N">Middle Aged</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="N">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D058873" MajorTopicYN="N">Pandemics</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011024" MajorTopicYN="N">Pneumonia, Viral</DescriptorName><QualifierName UI="Q000175" MajorTopicYN="N">diagnosis</QualifierName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D011790" MajorTopicYN="N">Quarantine</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D000086402" MajorTopicYN="N">SARS-CoV-2</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014481" MajorTopicYN="N" Type="Geographic">United States</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">COVID-19</Keyword><Keyword MajorTopicYN="Y">capacity planning</Keyword><Keyword MajorTopicYN="Y">electronic health records</Keyword><Keyword MajorTopicYN="Y">surge models</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2020</Year><Month>04</Month><Day>17</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2020</Year><Month>04</Month><Day>21</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2020</Year><Month>04</Month><Day>25</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2020</Year><Month>6</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>8</Month><Day>5</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2020</Year><Month>6</Month><Day>18</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">32548636</ArticleId><ArticleId IdType="pii">5858301</ArticleId><ArticleId IdType="doi">10.1093/jamia/ocaa076</ArticleId><ArticleId IdType="pmc">PMC7337779</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>JAMA. 2020 Feb 24;:</Citation><ArticleIdList><ArticleId IdType="pubmed">32091533</ArticleId></ArticleIdList></Reference><Reference><Citation>JAMA. 2020 Mar 13;:</Citation><ArticleIdList><ArticleId IdType="pubmed">32167538</ArticleId></ArticleIdList></Reference><Reference><Citation>JAMA. 2020 Mar 19;:</Citation><ArticleIdList><ArticleId IdType="pubmed">32191259</ArticleId></ArticleIdList></Reference><Reference><Citation>MMWR Morb Mortal Wkly Rep. 2020 Mar 27;69(12):343-346</Citation><ArticleIdList><ArticleId IdType="pubmed">32214079</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Californie</li></region><settlement><li>Stanford (Californie)</li></settlement><orgName><li>Université Stanford</li></orgName></list><tree><country name="États-Unis"><region name="Californie"><name sortKey="Kashyap, Sehj" sort="Kashyap, Sehj" uniqKey="Kashyap S" first="Sehj" last="Kashyap">Sehj Kashyap</name></region><name sortKey="Callahan, Alison" sort="Callahan, Alison" uniqKey="Callahan A" first="Alison" last="Callahan">Alison Callahan</name><name sortKey="Fries, Jason" sort="Fries, Jason" uniqKey="Fries J" first="Jason" last="Fries">Jason Fries</name><name sortKey="Gombar, Saurabh" sort="Gombar, Saurabh" uniqKey="Gombar S" first="Saurabh" last="Gombar">Saurabh Gombar</name><name sortKey="Pinsky, Benjamin A" sort="Pinsky, Benjamin A" uniqKey="Pinsky B" first="Benjamin A" last="Pinsky">Benjamin A. Pinsky</name><name sortKey="Shah, Nigam H" sort="Shah, Nigam H" uniqKey="Shah N" first="Nigam H" last="Shah">Nigam H. Shah</name><name sortKey="Yadlowsky, Steve" sort="Yadlowsky, Steve" uniqKey="Yadlowsky S" first="Steve" last="Yadlowsky">Steve Yadlowsky</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Sante/explor/CovidStanfordV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000463 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000463 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Sante
|area= CovidStanfordV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= pubmed:32548636
|texte= Measure what matters: Counts of hospitalized patients are a better metric for health system capacity planning for a reopening.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i -Sk "pubmed:32548636" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd \
| NlmPubMed2Wicri -a CovidStanfordV1
| This area was generated with Dilib version V0.6.38. |  |