Evidence map›Paper›PMID 35679114›Full record

ArticleJMIR human factors2022

An Electronic Data Capture Tool for Data Collection During Public Health Emergencies: Development and Usability Study.

Joan Brown, Manas Bhatnagar, Hugh Gordon, Jared Goodner, J Perren Cobb, Karen Lutrick

Abstract read
In one paragraph

Article in JMIR human factors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
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

6 authors.

Joan Brown *Clinical Operations Business Intelligence, The Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0003-3492-2858
Manas BhatnagarDepartment of Surgery, The Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0001-6665-9578
Hugh GordonAkido Labs Inc, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-7856-6173
Jared GoodnerAkido Labs Inc, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0002-0174-746X
J Perren CobbDepartment of Surgery, The Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States.ORCID https://orcid.org/0000-0003-1335-7124
Karen LutrickDepartment of Family and Community Medicine, University of Arizona, Tucson, AZ, United States.ORCID https://orcid.org/0000-0003-3480-849X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Discovery Critical Care Research Network Program for Resilience and Emergency Preparedness (Discovery PREP) partnered with a third-party technology vendor to design and implement an electronic data capture tool that addressed multisite data collection challenges during public health emergencies (PHE) in the United States. The basis of the work was to design an electronic data capture tool and to prospectively gather data on usability from bedside clinicians during national health system stress queries and influenza observational studies.

objectiveThe aim of this paper is to describe the lessons learned in the design and implementation of a novel electronic data capture tool with the goal of significantly increasing the nation's capability to manage real-time data collection and analysis during PHE.

methodsA multiyear and multiphase design approach was taken to create an electronic data capture tool, which was used to pilot rapid data capture during a simulated PHE. Following the pilot, the study team retrospectively assessed the feasibility of automating the data captured by the electronic data capture tool directly from the electronic health record. In addition to user feedback during semistructured interviews, the System Usability Scale (SUS) questionnaire was used as a basis to evaluate the usability and performance of the electronic data capture tool.

resultsParticipants included Discovery PREP physicians, their local administrators, and data collectors from tertiary-level academic medical centers at 5 different institutions. User feedback indicated that the designed system had an intuitive user interface and could be used to automate study communication tasks making for more efficient management of multisite studies. SUS questionnaire results classified the system as highly usable (SUS score 82.5/100). Automation of 17 (61%) of the 28 variables in the influenza observational study was deemed feasible during the exploration of automated versus manual data abstraction. The creation and use of the Project Meridian electronic data capture tool identified 6 key design requirements for multisite data collection, including the need for the following: (1) scalability irrespective of the type of participant; (2) a common data set across sites; (3) automated back end administrative capability (eg, reminders and a self-service status board); (4) multimedia communication pathways (eg, email and SMS text messaging); (5) interoperability and integration with local site information technology infrastructure; and (6) natural language processing to extract nondiscrete data elements.

conclusionsThe use of the electronic data capture tool in multiple multisite Discovery PREP clinical studies proved the feasibility of using the novel, cloud-based platform in practice. The lessons learned from this effort can be used to inform the improvement of ongoing global multisite data collection efforts during the COVID-19 pandemic and transform current manual data abstraction approaches into reliable, real time, and automated information exchange. Future research is needed to expand the ability to perform automated multisite data extraction during a PHE and beyond.

Indexed as

clinical research designdesign tenetdisaster managementEDCTelectronic dataelectronic data captureinformaticspublic health emergenciespublic health emergencyreal time data

Identifiers

PMID35679114
PMCPMC9227656

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LicenceCC BY
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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.