Evidence map›Paper›PMID 33417660›Full record

ArticleActa informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH2020

Rapid Development and Utilization of a Clinical Intelligence Dashboard for Frontline Clinicians to Optimize Critical Resources During Covid-19.

Halah Ibrahim, Sara Sorrell, Satish Chandrasekhar Nair, Ahmed Al Romaithi, Shamma Al Mazrouei, Ashraf Kamour

Abstract read
In one paragraph

Article in Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. VIEWER: an extensible visual analytics framework for enhancing mental healthcare.Journal of the American Medical Informatics Association : JAMIA · 2026
    Article
  4. Article
  5. Understanding epidemic spread patterns: a visual analysis approach.Health systems (Basingstoke, England) · 2024
    Article
  6. Article
  7. Characteristics and specifications of dashboards developed for the COVID-19 pandemic: a scoping review.Zeitschrift fur Gesundheitswissenschaften = Journal of public health · 2023
    Review
  8. Article
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  10. Article
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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.

Halah IbrahimDepartment of Medicine, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates.
Sara SorrellDepartment of Medicine, Khalifa University College of Medicine and Health Sciences, Abu Dhabi, United Arab Emirates.
Satish Chandrasekhar NairDepartment of Academic Affairs, Tawam Hospital Johns Hopkins Medicine, College of Medicine and Health Sciences, UAE University, Al Ain, United Arab Emirates.
Ahmed Al RomaithiOperations Department, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates.
Shamma Al MazroueiOperations Department, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates.
Ashraf KamourDepartment of Medicine, Sheikh Khalifa Medical City, Abu Dhabi, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe COVID-19 pandemic has created an unprecedented situation where sudden and prolonged surges of critically ill patients have disrupted healthcare systems worldwide A major concern for hospitals worldwide is how to best manage large numbers of COVID-19 infected and non-infected patients, while still maintaining high-quality clinical care.

aimThis manuscript describes the system development, collaborative efforts and the challenges encountered in developing an in-house clinical intelligence dashboard.

methodsThrough a longitudinal, interdepartmental collaboration, a COVID-19 clinical intelligence dashboard was created using Microsoft Power BI and Cerner Computer Language (CCL) to demonstrate clinical severity of patients and patient location in a single screen. A color-coding schema was applied to produce a red highlight for patients whose condition is deteriorating, whether due to increasing oxygen demand or worsening laboratory values. An additional function enabled users to drill down into the patient's clinical and laboratory parameters for the past 5 days, and ultimately to the respective patient chart for further assessment.

resultsThe development of an in-house clinical intelligence dashboard is a feasible, effective tool to allow frontline clinicians to monitor patient status in multiple wards and proactively intervene as clinically necessary and transfer patients to the appropriate level of care. Comparing the 30 days before and 30 days after the implementation of the dashboard, the percentage of patients who required urgent intubation or cardiac resuscitation on the general medical ward, rather than a critical care setting, declined by over 50% (8 out of 34, 33% vs. 7 out of 55, 13%; two-tailed p < 0.05 by Fisher's exact test; OR 3.43; CI 1.07 to 10.95).

conclusionThe dashboard has enabled physicians to efficiently assess patient volumes and case severity to prioritize clinical care and appropriately allocate scarce resources. The dashboard can be replicated by developing healthcare systems that are continuing to grapple with the pandemic.

Indexed as

clinical intelligence dashboardCOVID-19informaticspatient careresource allocation

Identifiers

PMID33417660
PMCPMC7780821

What OpenQuestion holds

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LicenceCC BY-NC
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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.