Evidence map›Paper›PMID 41053419›Full record

ArticleScientific reports2025

Building a coronavirus disease 2019 healthcare registry in an evolving pandemic.

Indumathi Venkatachalam, Edwin Philip Conceicao, Jean Xiang Ying Sim, Shalvi Arora, Yue Yang, Jin Yee Shoon, Yingqi Xu, Marcus Eng Hock Ong, Sze Ling Chan

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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

9 authors.

Indumathi VenkatachalamDepartment of Infection Prevention and Epidemiology, Singapore General Hospital, Singapore, Singapore. indumathi.venkatachalam@singhealth.com.sg.ORCID http://orcid.org/0000-0001-6869-1360
Edwin Philip ConceicaoDepartment of Infection Control, Changi General Hospital, Singapore, Singapore.
Jean Xiang Ying SimDepartment of Infection Prevention and Epidemiology, Singapore General Hospital, Singapore, Singapore.
Shalvi AroraDepartment of Infection Prevention and Epidemiology, Singapore General Hospital, Singapore, Singapore.
Yue YangDepartment of Infection Prevention and Epidemiology, Singapore General Hospital, Singapore, Singapore.
Jin Yee ShoonDepartment of Infection Prevention and Epidemiology, Singapore General Hospital, Singapore, Singapore.
Yingqi XuHealth Services Research Centre, SingHealth, Singapore, Singapore.
Marcus Eng Hock OngHealth Services Research Centre, SingHealth, Singapore, Singapore.
Sze Ling ChanHealth Services Research Centre, SingHealth, Singapore, Singapore.

Funding

SingHealth Academic Medicine General Fund 03/FY2020/G2/01-A92
6 · The paper itself

Abstract

COVID-19 emerged in Wuhan, China in December 2019 and was declared a pandemic on March 11, 2020. As there were many unknowns about COVID-19, an immediate priority early in the pandemic was to collect high-quality data to understand disease characteristics and epidemiology to inform healthcare resource management. A COVID-19 registry was developed by Singapore Health Services (SingHealth) to support clinical, operational, and research needs. We describe the development process of the SingHealth COVID-19 registry. Patients diagnosed with COVID-19 and managed by the Singapore Health Services (SingHealth), the largest of three public healthcare clusters in Singapore that includes acute hospitals, national specialty centres, subacute hospitals, and primary health centres, were included in the registry. A combination of laboratory test results and nationally administered electronic disease tags were used to identify COVID-19 patients. A dashboard was built in the Electronic Health Intelligence System (eHIntS), the data repository within SingHealth, linking variables in a minimum data set (MDS) using patient identifiers. Healthcare utilization in different ward types was computed, and 19 comorbidities and 14 complications were derived and coded from raw data sources within eHints for hospitalized patients. The COVID-19 registry contains more than 100 variables across 15 data domains, including raw data in long format (e.g. problem list, movement within hospital) and derived variables (e.g. comorbidities, complications, outcomes). As of December 31, 2023, the COVID-19 registry comprises of 156,262 unique patients of whom, 27,630 were admitted for COVID-19 at least once at one of the SingHealth hospitals. It has provided data for operational, clinical, and research purposes. The SingHealth COVID-19 registry is a model of how a low-cost, automated database can be developed amidst an evolving pandemic, leveraging on pandemic-necessitated standardized clinical workflows for pandemic operations, clinical management and research.

Indexed as

COVID-19RegistriesDelivery of Health CareFemaleHumansMaleMiddle AgedPandemicsSARS-CoV-2SingaporeAutomated surveillanceCOVID-19Disease registry

Identifiers

PMID41053419
PMCPMC12500988

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.