Evidence map›Paper›PMID 35582521›Full record

Article...IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies2021

VitalCore: Analytics and Support Dashboard for Medical Device Integration.

Hyonyoung Choi, Amanda Lor, Mike Megonegal, Xiayan Ji, Amanda Watson, James Weimer, Insup Lee

Abstract read
In one paragraph

Article in ...IEEE...International Conference on Connected Health: Applications, Systems and Engineering Technologies. IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Hyonyoung ChoiDept. of Computer and Information Science, University of Pennsylvania.
Amanda LorPenn Medicine, University of Pennsylvania.
Mike MegonegalPenn Medicine, University of Pennsylvania.
Xiayan JiDept. of Computer and Information Science, University of Pennsylvania.
Amanda WatsonDept. of Computer and Information Science, University of Pennsylvania.
James WeimerDept. of Computer and Information Science, University of Pennsylvania.
Insup LeeDept. of Computer and Information Science, University of Pennsylvania.

Funding

CPS: AutoWean: Foundations of Autonomous Medical CPS for Mechanical Ventilation Weaning R01EB029767 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI FUCHS, BARRY D, LEE, INSUP · 2019 to 2021
$1.1M
SCH: Monitoring safety and adherence of pain management though remote opioidR01EB029363 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI LEE, INSUP, WANG, PING · 2019 to 2022
$902k
NIBIB NIH HHS R01 EB029363NIBIB NIH HHS R01 EB029767
6 · The paper itself

Abstract

Medical professionals spend extensive time collecting, validating, reviewing, and analyzing medical device data. These devices use vendor-specific applications with lengthy troubleshooting times, causing extended downtimes where medical professionals have to manually document patient data in the electronic health record (EHR). Manual logging of this data creates delays and leaves it vulnerable to errors, manipulation, and omissions. In this paper, we present VitalCore, a medical device integration platform that supports access to medical device data in real-time. We deploy VitalCore in three applications at Penn Medicine: Medical Device Dashboard, Ventilation Alert, and Anomaly Detector. In the Medical Device Dashboard, we reduced, by up to six times, the amount of time required of medical professionals, clinical engineers, and IT analysts by simplifying the troubleshooting workflow, thus decreasing downtimes and increasing clinical productivity. In Ventilation Alert, we demonstrated the ability to assist medical professionals by alerting them to newly ventilated patients. In Anomaly Detector, we showed that we could predict anomalous patterns in our data with 93% accuracy.

Identifiers

PMID35582521
PMCPMC9109605

What OpenQuestion holds

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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.