Evidence map›Paper›PMID 35136670›Full record

ArticleJournal of pathology informatics2022

Creating Surveillance Data Infrastructure Using Laboratory Analytics: Leveraging Visiun and Epic Systems to Support COVID-19 Pandemic Response.

Mehrvash Haghighi, Dayanandan Adhimoolam, Ricky Kwan, Melissa Gitman, Maria McGuire, Damodara R Mendu, Adolfo Firpo-Betancourt, Russell B McBride, Carlos Cordon-Cardo, Catherine K Craven

Abstract read
In one paragraph

Article in Journal of pathology informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Mehrvash HaghighiDepartment of Pathology, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.
Dayanandan AdhimoolamDepartment of Pathology, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.
Ricky KwanDepartment of Pathology, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.
Melissa GitmanDepartment of Pathology, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.
Maria McGuireDepartment of Pathology, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.
Damodara R MenduDepartment of Pathology, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.
Adolfo Firpo-BetancourtDepartment of Pathology, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.
Russell B McBrideDepartment of Pathology, The Institute for Translational Epidemiology, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.
Carlos Cordon-CardoDepartment of Pathology, Icahn School of Medicine at Mount Sinai, New York City, New York, USA.
Catherine K CravenDepartment of Population Health Sciences, Joe R. & Teresa Lozano Long School of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPandemics are unpredictable and can rapidly spread. Proper planning and preparation for managing the impact of outbreaks is only achievable through continuous and systematic collection and analysis of health-related data. We describe our experience on how to comply with required reporting and develop a robust platform for surveillance data during an outbreak. MATERIALS AND

methodsAt Mount Sinai Health System, New York City, we applied Visiun, a laboratory analytics dashboard, to support main response activities. Epic System Inc.'s SlicerDicer application was used to develop clinical and research reports. We followed World Health Organization (WHO); federal and state guidelines; departmental policies; and expert consultation to create the framework.

resultsThe developed dashboard integrated data from scattered sources are used to seamlessly distribute reports to key stakeholders. The main report categories included federal, state, laboratory, clinical, and research. The first two groups were created to meet government and state reporting requirements. The laboratory group was the most comprehensive category and included operational reports such as performance metrics, technician performance assessment, and analyzer metrics. The close monitoring of testing volumes and lab operational efficiency was essential to manage increasing demands and provide timely and accurate results. The clinical data reports were valuable for proper managing of medical surge requirements, such as healthcare workforce and medical supplies. The reports included in the research category were highly variable and depended on healthcare setting, research priorities, and available funding. We share a few examples of queries that were included in the designed framework for research projects.

conclusionWe reviewed here the key components of a conceptual surveillance framework required for a robust response to COVID-19 pandemics. We demonstrated leveraging a lab analytics dashboard, Visiun, combined with Epic reporting tools to function as a surveillance system. The framework could be used as a generic template for possible future outbreak events.

Indexed as

COVID-19lab analyticspandemicsurveillancevisiun

Identifiers

PMID35136670
PMCPMC8794025

What OpenQuestion holds

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Registered trials

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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.