Evidence map›Paper›PMID 37090509›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Development & Deployment of a Real-time Healthcare Predictive Analytics Platform.

Aaron Boussina, Supreeth Shashikumar, Fatemeh Amrollahi, Hayden Pour, Michael Hogarth, Shamim Nemati

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. 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

5 · Who and what money

Authors and funding

6 authors.

Aaron BoussinaDivision of Biomedical Informatics, University of California San Diego, San Diego, USA.
Supreeth ShashikumarDivision of Biomedical Informatics, University of California San Diego, San Diego, USA.
Fatemeh AmrollahiDivision of Biomedical Informatics, University of California San Diego, San Diego, USA.
Hayden PourDivision of Biomedical Informatics, University of California San Diego, San Diego, USA.
Michael HogarthDivision of Biomedical Informatics, University of California San Diego, San Diego, USA.
Shamim NematiDivision of Biomedical Informatics, University of California San Diego, San Diego, USA.

Funding

San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2012 to 2026
$9.7M
VentNet: A Real-Time Multimodal Data Integration Model for Prediction of Respiratory Failure in Patients with COVID-19R01HL157985 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MALHOTRA, ATUL, NEMATI, SHAMIM · 2022 to 2025
$2.9M
GeneRAlizable Sepsis Phenotyping (GRASP) using Electronic Health Records and Continuous Monitoring SensorsR35GM143121 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI NEMATI, SHAMIM · 2021 to 2025
$2.2M
RAAB-AI: Reducing Automation and Anchoring Bias in AI SystemsR01LM013998 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SHAMIM NEMATI · 2022 to 2026
$1.7M
NHLBI NIH HHS R01 HL157985NIGMS NIH HHS R35 GM143121NLM NIH HHS R01 LM013998NLM NIH HHS T15 LM011271
6 · The paper itself

Abstract

The deployment of predictive analytic algorithms that can safely and seamlessly integrate into existing healthcare workflows remains a significant challenge. Here, we present a scalable, cloud-based, fault-tolerant platform that is capable of extracting and processing electronic health record (EHR) data for any patient at any time following admission and transferring results back into the EHR. This platform has been successfully deployed within the UC San Diego Health system and utilizes interoperable data standards to enable portability.

Identifiers

PMID37090509
PMCPMC10120788

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.