Evidence map›Paper›PMID 30040113›Full record

ReviewApplied clinical informatics2018

Electronic Health Record Interactions through Voice: A Review.

Yaa A Kumah-Crystal, Claude J Pirtle, Harrison M Whyte, Edward S Goode, Shilo H Anders, Christoph U Lehmann

Open access · bronzeAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Applied clinical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 3 pooled it
15.4field-weighted citation impact, top 1% of its field
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

47 citing papers in PubMed, 3 syntheses or guidelines pooled it, 110 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Speech recognition for clinical documentation from 1990 to 2018: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2019
    Pooled it
  4. Trial
  5. Trial
  6. Article
  7. Article
  8. Review
  9. Article
  10. Review
  11. Observational
  12. Review
  13. Article
  14. Article
  15. Observational
  16. Article
  17. Article
  18. Article
  19. Article
  20. Review
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 at 2 institutions in 1 country.

Yaa A Kumah-CrystalDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, United States.
Claude J PirtleDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, United States.
Harrison M WhyteDepartment of Computer Science, Vanderbilt University College of Arts and Science, Vanderbilt University, Nashville, Tennessee, United States.
Edward S GoodeDepartment of Computer Science, Vanderbilt University College of Arts and Science, Vanderbilt University, Nashville, Tennessee, United States.
Shilo H AndersDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, United States.
Christoph U LehmannDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Vanderbilt University, Nashville, Tennessee, United States.
Vanderbilt University Medical Center · USVanderbilt University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUsability problems in the electronic health record (EHR) lead to workflow inefficiencies when navigating charts and entering or retrieving data using standard keyboard and mouse interfaces. Voice input technology has been used to overcome some of the challenges associated with conventional interfaces and continues to evolve as a promising way to interact with the EHR.

objectiveThis article reviews the literature and evidence on voice input technology used to facilitate work in the EHR. It also reviews the benefits and challenges of implementation and use of voice technologies, and discusses emerging opportunities with voice assistant technology.

methodsWe performed a systematic review of the literature to identify articles that discuss the use of voice technology to facilitate health care work. We searched MEDLINE and the Google search engine to identify relevant articles. We evaluated articles that discussed the strengths and limitations of voice technology to facilitate health care work. Consumer articles from leading technology publications addressing emerging use of voice assistants were reviewed to ascertain functionalities in existing consumer applications.

resultsUsing a MEDLINE search, we identified 683 articles that were reviewed for inclusion eligibility. The references of included articles were also reviewed. Sixty-one papers that discussed the use of voice tools in health care were included, of which 32 detailed the use of voice technologies in production environments. Articles were organized into three domains: Voice for (1) documentation, (2) commands, and (3) interactive response and navigation for patients. Of 31 articles that discussed usability attributes of consumer voice assistant technology, 12 were included in the review.

conclusionWe highlight the successes and challenges of voice input technologies in health care and discuss opportunities to incorporate emerging voice assistant technologies used in the consumer domain.

Indexed as

Electronic Health RecordsUser-Computer InterfaceVoiceDocumentationHumans

Identifiers

PMID30040113
PMCPMC6051768
OpenAlexW2883191466

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.