Evidence map›Paper›PMID 30815092›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018

Improving the Path from Diagnoses to Documentation: A Cognitive Review Tool for Clinical Notes and Administrative Records.

Yufan Guo, Joy Wu, Tyler Baldwin, David Beymer, Vandana V Mukherjee, Tanveer F Syeda-Mahmood

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. 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

6 authors.

Yufan GuoIBM Research - Almaden, San Jose, CA, US.
Joy WuIBM Research - Almaden, San Jose, CA, US.
Tyler BaldwinIBM Research - Almaden, San Jose, CA, US.
David BeymerIBM Research - Almaden, San Jose, CA, US.
Vandana V MukherjeeIBM Research - Almaden, San Jose, CA, US.
Tanveer F Syeda-MahmoodIBM Research - Almaden, San Jose, CA, US.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

EMR systems are intended to improve patient-centered care management and hospital administrative processing. However, the information stored in EMRs can be disorganized, incomplete, or inconsistent, creating problems at the patient and system level. We present a technology that reconciles inconsistencies between clinical diagnoses and administrative records by analyzing free-text notes, problem lists and recorded diagnoses in real time. A fully integrated pipeline has been developed for efficient, knowledge-driven extraction, normalization, and matching of disease terms among structured and unstructured data, with modular precision of 94-98% on over 1000 patients. This cognitive data review tool improves the path from diagnosis to documentation, facilitating accurate and timely clinical and administrative decision-making.

Indexed as

DiseaseElectronic Health RecordsTerminology as TopicAlgorithmsCognitionDiagnosisDocumentationHumansInformation Storage and Retrieval

Identifiers

PMID30815092
PMCPMC6371384

What OpenQuestion holds

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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.