The impact of implementing speech recognition technology on the accuracy and efficiency (time to complete) clinical documentation by nurses: A systematic review

2020 ◽  
Vol 29 (13-14) ◽  
pp. 2125-2137
Author(s):  
Joseph Joseph ◽  
Zena E. H. Moore ◽  
Declan Patton ◽  
Tom O'Connor ◽  
Linda Elizabeth Nugent
2015 ◽  
Vol 23 (e1) ◽  
pp. e169-e179 ◽  
Author(s):  
Tobias Hodgson ◽  
Enrico Coiera

Abstract Objective To review literature assessing the impact of speech recognition (SR) on clinical documentation. Methods Studies published prior to December 2014 reporting clinical documentation using SR were identified by searching Scopus, Compendex and Inspect, PubMed, and Google Scholar. Outcome variables analyzed included dictation and editing time, document turnaround time (TAT), SR accuracy, error rates per document, and economic benefit. Twenty-three articles met inclusion criteria from a pool of 441. Results Most studies compared SR to dictation and transcription (DT) in radiology, and heterogeneity across studies was high. Document editing time increased using SR compared to DT in four of six studies (+1876.47% to –16.50%). Dictation time similarly increased in three of five studies (+91.60% to –25.00%). TAT consistently improved using SR compared to DT (16.41% to 82.34%); across all studies the improvement was 0.90% per year. SR accuracy was reported in ten studies (88.90% to 96.00%) and appears to improve 0.03% per year as the technology matured. Mean number of errors per report increased using SR (0.05 to 6.66) compared to DT (0.02 to 0.40). Economic benefits were poorly reported. Conclusions SR is steadily maturing and offers some advantages for clinical documentation. However, evidence supporting the use of SR is weak, and further investigation is required to assess the impact of SR on documentation error types, rates, and clinical outcomes.


Author(s):  
Maree Johnson ◽  
Samuel Lapkin ◽  
Vanessa Long ◽  
Paula Sanchez ◽  
Hanna Suominen ◽  
...  

2019 ◽  
Vol 26 (4) ◽  
pp. 324-338 ◽  
Author(s):  
Suzanne V Blackley ◽  
Jessica Huynh ◽  
Liqin Wang ◽  
Zfania Korach ◽  
Li Zhou

Abstract Objective The 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 Methods We 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. Results One 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. Conclusion Despite 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.


2015 ◽  
Vol 1 (1) ◽  
pp. 91-100
Author(s):  
Banumathi A C ◽  
Chandra E

Speech Recognition means converting Speech into Text. This Emerging Technology makes all the field of use as more sophisticated one. The impact of this revolutionary Technology has shown its wide range of usage in all tasks. Almost all the Technical devices use the Speech recognition as their part of their project. Speech Recognition Technology used in fields like computers, artificial Intelligence, Medical , Healthcare, Smart Phones, etc., This paper provides a glimpse of the challenges that is faced by the speech recognition systems in many applications and the approaches taken to fulfill it.


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