Evidence map›Paper›PMID 30753666›Full record

SynthesisJournal of the American Medical Informatics Association : JAMIA2019

Speech recognition for clinical documentation from 1990 to 2018: a systematic review.

Suzanne V Blackley, Jessica Huynh, Liqin Wang, Zfania Korach, Li Zhou

Registry-linked trialAbstract readSystematic Review
In one paragraph

Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06792890 (A Randomized Controlled Trial of Two Ambient Artificial Intelligence Scribe Technologies to Improve Documentation Efficiency and Reduce Physician Burnout), which is not on this map. Cited by 42 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 5 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.

NCT06792890 nacompletednot on this mapstarted 2024, after this paper: background citation

A Randomized Controlled Trial of Two Ambient Artificial Intelligence Scribe Technologies to Improve Documentation Efficiency and Reduce Physician Burnout

TypeinterventionalSponsorUniversity of California, Los AngelesRan2024 to 2025Enrolled238ConditionsPhysician Workflow, Artificial Intelligence (AI)ArmsUse Nabla AI Scribe tool provided, Use AI Scribe tool provided by Vendor B
3 · Its place in the literature

Who cites it

42 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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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

5 authors.

Suzanne V BlackleyClinical and Quality Analysis, Information Systems, Partners HealthCare, Boston, Massachusetts, USA.
Jessica HuynhGeneral Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Liqin WangGeneral Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Zfania KorachGeneral Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Li ZhouGeneral Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA.

Funding

NLP to Improve Accuracy and Quality of Dictated Medical DocumentsR01HS024264 · AHRQ · BRIGHAM AND WOMEN'S HOSPITAL · PI ZHOU, LI · 2015 to 2017
$750k
AHRQ HHS R01 HS024264
6 · The paper itself

Abstract

objectiveThe study sought to review recent literature regarding use of speech recognition (SR) technology for clinical documentation and to understand the impact of SR on document accuracy, provider efficiency, institutional cost, and more. MATERIALS AND

methodsWe searched 10 scientific and medical literature databases to find articles about clinician use of SR for documentation published between January 1, 1990, and October 15, 2018. We annotated included articles with their research topic(s), medical domain(s), and SR system(s) evaluated and analyzed the results.

resultsOne hundred twenty-two articles were included. Forty-eight (39.3%) involved the radiology department exclusively and 10 (8.2%) involved emergency medicine; 10 (8.2%) mentioned multiple departments. Forty-eight (39.3%) articles studied productivity; 20 (16.4%) studied the effect of SR on documentation time, with mixed findings. Decreased turnaround time was reported in all 19 (15.6%) studies in which it was evaluated. Twenty-nine (23.8%) studies conducted error analyses, though various evaluation metrics were used. Reported percentage of documents with errors ranged from 4.8% to 71%; reported word error rates ranged from 7.4% to 38.7%. Seven (5.7%) studies assessed documentation-associated costs; 5 reported decreases and 2 reported increases. Many studies (44.3%) used products by Nuance Communications. Other vendors included IBM (9.0%) and Philips (6.6%); 7 (5.7%) used self-developed systems.

conclusionDespite widespread use of SR for clinical documentation, research on this topic remains largely heterogeneous, often using different evaluation metrics with mixed findings. Further, that SR-assisted documentation has become increasingly common in clinical settings beyond radiology warrants further investigation of its use and effectiveness in these settings.

Indexed as

EfficiencySpeech Recognition SoftwareBiomedical ResearchDocumentationElectronic Health RecordsHumansRadiology Information SystemsTime and Motion StudiesTime Factorsclinical documentationclinical document qualitydictationnatural language processingspeech recognition software

Identifiers

PMID30753666
PMCPMC7647182

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.