Evidence map›Paper›PMID 34051088›Full record

Observational studyJournal of the American Medical Informatics Association : JAMIA2021

Privacy-protecting, reliable response data discovery using COVID-19 patient observations.

Jihoon Kim, Larissa Neumann, Paulina Paul, Michele E Day, Michael Aratow, Douglas S Bell, Jason N Doctor, Ludwig C Hinske, Xiaoqian Jiang, Katherine K Kim and 9 more

Erratum issuedAbstract readMulticenter StudyObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Jihoon KimUC San Diego Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA.ORCID 0000-0002-5351-238X
Larissa NeumannInstitute for Medical Information Processing, Biometry, and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany.ORCID 0000-0001-6845-5413
Paulina PaulUC San Diego Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA.
Michele E DayUC San Diego Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA.
Michael AratowSan Mateo Medical Center, San Mateo, California, USA.
Douglas S BellBiomedical Informatics Program, UCLA Clinical and Translational Science Institute (CTSI), Los Angeles, California, USA.
Jason N DoctorUSC Schaeffer Center for Health Policy and Economics, Price School of Policy, University of Southern California, Los Angeles, California, USA.
Ludwig C HinskeInstitute for Medical Information Processing, Biometry, and Epidemiology, Ludwig Maximilian University of Munich, Munich, Germany.ORCID 0000-0001-7273-5899
Xiaoqian JiangSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.ORCID 0000-0001-9933-2205
Katherine K KimBetty Irene Moore School of Nursing, University of California Davis Medical Center, Sacramento, California, USA.
Michael E MathenyGRECC Tennessee Valley Healthcare System, Nashville, Tennessee, USA.
Daniella MeekerDepartment of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, California, USA.
Mark J PletcherDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA.
Lisa M SchillingData Science and Patient Value Program, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.
Spencer SooHooDivision of Informatics, Department of Biomedical Sciences, Cedars Sinai Medical Center, Los Angeles, California, USA.
Hua XuSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA.ORCID 0000-0002-5274-4672
Kai ZhengDepartment of Informatics, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, California, USA.
Lucila Ohno-MachadoUC San Diego Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA.ORCID 0000-0002-8005-7327
R2D2 Consortium

Funding

San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
Decentralized differentially-private methods for dynamic data release and analysisR01GM118609 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JIANG, XIAOQIAN, OHNO-MACHADO, LUCILA · 2017 to 2020
$2.3M
NIGMS NIH HHS R01 GM118609NIH HHS R01GM118609NIH HHS T15LM011271NLM NIH HHS T15 LM011271
6 · The paper itself

Abstract

objectiveTo utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND

methodsWe developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data.

resultsOur public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND

conclusionsWe present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.

Indexed as

AlgorithmsComputer Communication NetworksConfidentialityCOVID-19Electronic Health RecordsNatural Language ProcessingCommon Data ElementsFemaleHumansInformation Storage and RetrievalLogistic ModelsMaleRegistriescommon data elementsCOVID-19electronic health recordobservational studyregression analysis

Identifiers

PMID34051088
PMCPMC8194878

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.