Voice recognition software: effect on radiology report turnaround time at an academic medical center.
OBJECTIVE
Previous studies have documented reductions in turnaround time after implementation of voice recognition software in the generation of radiology reports. Our preliminary observations suggested that improvement in report turnaround time varies among users. The purpose of this study was to analyze the effect of work habits and caseload on such variations.


METHODS
Data were collected for 9 months before and after the implementation of voice recognition after a 6-month training period. Thirty faculty members were ranked according to their report turnaround time before and after implementation of voice recognition and according to their percentage reduction in report turnaround time. The report turnaround times before and after implementation of voice recognition for faculty were compared with the number of verified reports and work habit type.


RESULTS
The average report turnaround time for the department before implementation of voice recognition was 28 hours. After implementation of voice recognition, the average turnaround time was 12.7 hours, and the volume of verified reports increased 5% between the two study periods. The improvement in report turnaround time for individual faculty members ranged from -33% to +93%, and the rank order did not change significantly (Spearman coefficient, 0.58; p < 0.05). Faculty members' ranks in report turnaround time did not correlate significantly with volume rank before and after implementation of voice recognition (Spearman coefficients, 0.341 and 0.346; p > 0.05). Faculty members who had type 1 work habits, that is, reviewed, revised, and finalized reports at the time of image review, benefited the most from use of voice recognition.


CONCLUSIONS
Use of voice recognition software decreased report turnaround time for the department and for 28 of 30 individual faculty members. Improvement in report turnaround time does not correlate with workload but does correlate with work habits, suggesting human behavior may play a role in determining the outcome of adopting a productivity-enhancing technology.
Krishnaraj
Arun
A
Department of Radiology, University of North Carolina Hospitals, Chapel Hill, 27599, USA. akrishna@unch.unc.edu <akrishna@unch.unc.edu>
Lee
Joseph K T
JK
Laws
Sandra A
SA
Crawford
T Jay
TJ
D000046:Academic Medical Centers; D000704:Analysis of Variance; D017598:Efficiency, Organizational; D006801:Humans; D011872:Radiology Department, Hospital / Q000458:organization & administration*; D011873:Radiology Information Systems; D049250:Speech Recognition Software; D013997:Time Factors