Voice Recognition in the Electronic Health Record Diane Luedtke
Nursing Informatics, NSG600INA November, 2010
Slide 3
Speech Recognition Definition The process of converting an
acoustic signal, captured by a microphone or a telephone to a set
of words.
Slide 4
History 1952 - Recognition of single digits 1964 Device
exhibited at NY Worlds Fair 1980s 1,000 to 20,000 word vocabularies
Early 90s Accuracy 10% to 50% and discrete voice recognition 1997
Recognition of normal speech Early 2000s Accuracy 80%
Slide 5
Types of Speech Recognition Isolated - pause between words
Continuous no pause between words Spontaneous extemporaneous most
difficult to recognize
Slide 6
Properties Speaker enrollment Speaker independent Finite state
network General language models Perplexity External parameters
Slide 7
Variables Phonemes Acoustic variables Within speaker variables
Across speaker variables Zue, V., Cole, R., Ward, W. Speech
recognition. Retrieved from
http://cslu.cse.ogi.edu/HLTsurvey/ch1node4.html on
10/6/2010.http://cslu.cse.ogi.edu/HLTsurvey/ch1node4.html
Speech Recognition in Health Care Earliest users radiologists
Most successful early users radiologists, pathologists and
emergency physician s Photo
source:www.google.com/imgres?imgurl=http://www.rsna.org/Publications/RSNAnews/November
-2010/images_speech_recognition_1.jpg
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Other Healthcare Settings Primary care clinicians Psychiatrists
IV nurses - AccuNurse
http://www.google.com/imgres?imgurl=http://1stprovidersc
hoice.com/images/Medical-Voice-Recognition-Software.jpg
Slide 11
Primary Care Trial at US Army Medical Command in 2009 10,000
copies of voice recognition software Installed 42 healthcare
facilities Software tutorial and face-to-face training offered
Champions trained Accuracy rated 90% by all participants Not used
with patient in exam room, but used immediately after seeing
patient by majority of users Hoyt, R., & Yoshihashi, A. (2010,
Winter). Lessons learned from implementation of voice recognition
for documentation in the military electronic health record system.
Perspectives in Health Information Management, 7(Winter). Retrieved
from
http://www.ncbe.nlm.nih.gov/pmc/articles/PMC2805557/?tool=pubmed.
Slide 12
Primary Care Clinic from Wellspan Health implemented electronic
health records with voice recognition included Voice recognition
treated as component of EHR Used in exam room with patient Baker,
R.H. (2010, May). Voice recognition assists clinicians. Health
Management Technology. Retrieved from
http://healthmgttech.com.
Slide 13
The VA Early trial in late 1990s Cost $2,000 per work station
Compare 3 word recognition systems using 12 physicians Evaluation
from scripted charting Error rate ranged from 6.6% to 14.6%
Estimate current use by 7000 nurses and physicians Devine, E.G.,
Gaehde, S.A., & Curtis, A.C. (2000, Sept-Oct). Comparative
evaluation of three continuous speech recognition software packages
in the generation of medical reports. Journal of the American
Medical Informatics Association, 7(5), 462-468.
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Psychiatry Health record includes dense narrative In mandatory
implementation, providers who do not type notes more inclined to
accept voice recognition Providers would not dictate in front of
patient Providers found no perceived benefit in speech recognition
Half of the evaluators favored the use of speech recognition
Derman, Y.D., Arenovich, T, Straus, J. (2010). Speech recognition
software and electronic psychiatric progress notes: physicians
ratings and preferences. BMC Medical Informatics and Decision
Making, 10:44. Retrieved from
http://www.biomedcentral.com/1472-6947/10/44.
Slide 15
IV Nurses Used at Butler Memorial Hospital, Butler, PA Pilot
project with 3 IV nurses Lightweight headset and pocket sized
wireless device Computer entry of IV needs sent to nurses headset
On completion of patient care, nurse uses voice recognition system
to record what was done in patients record Receive next order over
headset for next patient McGee, Marianne Kolbasuk. (2009, September
17). Voice recognition tools make rounds at hospitals.
InformationWeek Healthcare. Retrieved from
http://www.informationweek.com/news/healthcare/EMR
Slide 16
Patient Interactive Voice Response System Automated telephone
calls made to patients on day following surgery Patients respond to
questions via speech Speech recognition software updates database
based on to patients response If response indicates follow-up
telephone call by nurse, nurses will be prompted to complete
contact System reported to be 97% accurate Foster, AJ; LaBranche,
R; McKim, R; Faught, JW; Feasby, TE; Janes-Kelley, S; Shojania, KG;
van Walraven, C. (2008). Automated patient assessments after
outpatient surgery using an interactive voice response system. The
American Journal of Managed Care, 14(7), 429-36.
Slide 17
Benefits of Speech Recognition Reduction of transcription
expense Improved patient care Reduction in time documenting care
Increase per patient revenue Allows physician to dictate in their
own words Does not add recurring labor costs
Slide 18
Barriers to Speech Recognition Capital cost of EHR with speech
recognition Cost in time (users) Security or confidentiality issues
Costs to maintain EHR Interference with doctor-patient relationship
Difficulty with learning new technology Lack of tech support Lack
of perceived benefit
Slide 19
Problems with Speech Recognition Accuracy rate approximating
90% requires editing Upgrade of processor speed and/or random
access memory may be required Change in method of documenting
encounter notes Not all users receiving appropriate training