Evidence map›Paper›PMID 29888069›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2018

Provider Adoption of Speech Recognition and its Impact on Satisfaction, Documentation Quality, Efficiency, and Cost in an Inpatient EHR.

Kshitij Saxena, Robert Diamond, Reid F Conant, Terri H Mitchell, Ir Guido Gallopyn, Kristin E Yakimow

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
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  5. Preferences to improve rounding efficiency amongst hospitalists: a survey analysis.Journal of community hospital internal medicine perspectives · 2021
    Article
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  7. 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.

Kshitij SaxenaHealth Quest, Lagrangeville, New York.
Robert DiamondHealth Quest, Lagrangeville, New York.
Reid F ConantNuance® Communications, Inc., Burlington, Massachusetts.
Terri H MitchellNuance® Communications, Inc., Burlington, Massachusetts.
Ir Guido GallopynNuance® Communications, Inc., Burlington, Massachusetts.
Kristin E YakimowNuance® Communications, Inc., Burlington, Massachusetts.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study utilizes qualitative and quantitative methods to measure the adoption of speech recognition (SR) and its impact ON provider satisfaction, documentation quality, efficiency, and cost when used for clinical documentation within the electronic health record (EHR). Qualitative surveys gauged providers' expectations and experiences regarding documentation before and after SR implementation. A new methodology was developed to measure SR adoption as a proportion of total documentation volume. Quantitative data was collected from the EHR, medical transcription and SR solutions to measure SR adoption and cost savings. Study results revealed significant improvements in satisfaction, documentation quality, and efficiency among providers as a direct result of SR use. An improved provider experience correlated to an 81% reduction in monthly medical transcription costs, an increase from 20% to 77% in electronic clinical documentation adoption, and a 74% SR adoption rate.

Indexed as

clinical documentation improvementelectronic health recordselectronic provider documentationhealth information technologymedical transcriptionprovider satisfactionspeech recognition

Identifiers

PMID29888069
PMCPMC5961784

What OpenQuestion holds

Textmetadata
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.