Physician experience with speech recognition software in psychiatry: usage and perspective

Fernandes, John J.; Brunton, I. D.; Strudwick, Gillian; Banik, Suman; Strauss, John S.

doi:10.1186/s13104-018-3790-y

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…28 Prior work has demonstrated a reduction in users of speech recognition technology after initial adoption to a more stable core set of users, corresponding with a "trough of disillusionment" in the adoption of new technologies. 29 This suggests that the learning curve associated with use of voice recognition technologies could be a focus for technological innovation and training.…”

Section: Discussionmentioning

confidence: 99%

Physician Note Composition Patterns and Time on the EHR Across Specialty Types: a National, Cross-sectional Study

Rotenstein

Apathy

Holmgren³

et al. 2022

J GEN INTERN MED

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BACKGROUND:The burden of clinical documentation in electronic health records (EHRs) has been associated with physician burnout. Numerous tools (e.g., note templates and dictation services) exist to ease documentation burden, but little evidence exists regarding how physicians use these tools in combination and the degree to which these strategies correlate with reduced time spent on documentation. OBJECTIVE: To characterize EHR note composition strategies, how these strategies differ in time spent on notes and the EHR, and their distribution across specialty types. DESIGN: Secondary analysis of physician-level measures of note composition and EHR use derived from Epic Systems' Signal data warehouse. We used k-means clustering to identify documentation strategies, and ordinary least squares regression to analyze the relationship between documentation strategies and physician time spent in the EHR, on notes, and outside scheduled hours. PARTICIPANTS: A total of 215,207 US-based ambulatory physicians using the Epic EHR between September 2020 and May 2021. MAIN MEASURES: Percent of note text derived from each of five documentation tools: SmartTools, copy/paste, manual text, NoteWriter, and voice recognition and transcription; average total and after-hours EHR time per visit; average time on notes per visit. KEY RESULTS: Six distinct note composition strategies emerged in cluster analyses. The most common strategy was predominant SmartTools use (n=89,718). In adjusted analyses, physicians using primarily transcription and dictation (n=15,928) spent less time on notes than physicians with predominant Smart Tool use. (b=−1.30, 95% CI=−1.62, −0.99, p<0.001; average 4.8 min per visit), while those using mostly copy/paste (n=23,426) spent more time on notes (b=2.38, 95% CI=1.92, 2.84, p<0.001; average 13.1 min per visit). CONCLUSIONS: Physicians' note composition strategies have implications for both time in notes and after-hours EHR use, suggesting that how physicians use EHR-based documentation tools can be a key lever for institutions investing in EHR tools and training to reduce documentation time and alleviate EHR-associated burden.

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Section: Discussionmentioning

confidence: 99%

Physician Note Composition Patterns and Time on the EHR Across Specialty Types: a National, Cross-sectional Study

Rotenstein

Apathy

Holmgren³

et al. 2022

J GEN INTERN MED

View full text Add to dashboard Cite

show abstract

“…Furthermore, the majority of studies did not describe user characteristics (14)(15)(16)(17)(18)(19)(20)(21). Due to user homogeneity, the studies that did analyze user characteristics such as age, gender, or native language lacked statistical power to examine the effect of user characteristics on VR adoption (13,22,23).…”

Section: Discussionmentioning

confidence: 99%

Evaluating the Adoption of Voice Recognition Technology for Real-Time Dictation in a Rural Healthcare System: A Retrospective Analysis of Dragon Medical One

Onitilo

Shour

Puthoff

et al. 2022

Preprint

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Background In 2013, Marshfield Clinic Health System (MCHS) implemented the Dragon Medical One (DMO) system provided by Nuance Management Center (NMC) for Real-Time Dictation (RTD), embracing the idea of streamlined clinic workflow, reduced dictation hours, and improved documentation legibility. Since then, MCHS has observed a trend of reduced time in documentation, however, the target goal of 100% adoption of voice recognition (VR)-based RTD has not been met. Objective To evaluate the uptake/adoption of VR technology for RTD in MCHS, between 2018-2020. Methods DMO data for 1,373 MCHS providers from 2018-2020 were analyzed. The study outcome was VR uptake, defined as the number of hours each provider used VR technology to dictate patient information, used as continuous, and classified as no/yes. Covariates included sex, age, US-trained/international medical graduates, trend, specialty, and facility. Descriptive statistics and unadjusted and adjusted logistic regression analyses were performed. Stata/SE.version.17 was used for analyses. P-values less than/equal to 0.05 were considered statistically significant. Results Of the 1,373 MCHS providers, the mean (SD) age was 48.3 (12.4) years. VR uptake was higher than no uptake (72.0% vs. 28.0%). In both unadjusted and adjusted analyses, VR uptake was 4.3 times and 7.7 times higher in 2019-2020 compared to 2018, respectively (OR:4.30,95%CI:2.44-7.46 and AOR:7.74,95%CI:2.51-23.86). VR uptake was 0.5 and 0.6 times lower among US-trained physicians compared to internationally-trained physicians (OR:0.53,95%CI:0.37-0.76 and AOR:0.58,95%CI:0.35-0.97). Uptake was 0.2 times lower among physicians aged 60/above than physicians aged 29/less (OR:0.20,95%CI:0.10-0.59, and AOR:0.17,95%CI:0.27-1.06). Conclusion Since 2018, VR adoption has increased significantly across MCHS. However, it was lower among US-trained physicians than among internationally-trained physicians (although internationally physicians were in minority) and lower among more senior physicians than among younger physicians. These findings provide critical information about VR trends, physician factors, and which providers could benefit from additional training to increase VR adoption in healthcare systems.

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“…focused on VR use in specific hospital units, such as the emergency department and radiology, while a few have looked at the impact of VR systems across healthcare systems [3,12,13]. Furthermore, the majority of studies did not describe user characteristics [14][15][16][17][18][19][20][21]. Due to user homogeneity, the studies that did analyze user characteristics such as age, gender, or native language lacked statistical power to examine the effect of user characteristics on VR adoption [13,22,23].…”

Section: Plos Onementioning

confidence: 99%

Evaluating the adoption of voice recognition technology for real-time dictation in a rural healthcare system: A retrospective analysis of dragon medical one

et al. 2023

View full text Add to dashboard Cite

Background In 2013, Marshfield Clinic Health System (MCHS) implemented the Dragon Medical One (DMO) system provided by Nuance Management Center (NMC) for Real-Time Dictation (RTD), embracing the idea of streamlined clinic workflow, reduced dictation hours, and improved documentation legibility. Since then, MCHS has observed a trend of reduced time in documentation, however, the target goal of 100% adoption of voice recognition (VR)-based RTD has not been met. Objective To evaluate the uptake/adoption of VR technology for RTD in MCHS, between 2018–2020. Methods DMO data for 1,373 MCHS providers from 2018–2020 were analyzed. The study outcome was VR uptake, defined as the median number of hours each provider used VR technology to dictate patient information, and classified as no/yes. Covariates included sex, age, US-trained/international medical graduates, trend, specialty, and facility. Descriptive statistics and unadjusted and adjusted logistic regression analyses were performed. Stata/SE.version.17 was used for analyses. P-values less than/equal to 0.05 were considered statistically significant. Results Of the 1,373 MCHS providers, the mean (SD) age was 48.3 (12.4) years. VR uptake was higher than no uptake (72.0% vs. 28.0%). In both unadjusted and adjusted analyses, VR uptake was 4.3 times and 7.7 times higher in 2019–2020 compared to 2018, respectively (OR:4.30,95%CI:2.44–7.46 and AOR:7.74,95%CI:2.51–23.86). VR uptake was 0.5 and 0.6 times lower among US-trained physicians compared to internationally-trained physicians (OR:0.53,95%CI:0.37–0.76 and AOR:0.58,95%CI:0.35–0.97). Uptake was 0.2 times lower among physicians aged 60/above than physicians aged 29/less (OR:0.20,95%CI:0.10–0.59, and AOR:0.17,95%CI:0.27–1.06). Conclusion Since 2018, VR adoption has increased significantly across MCHS. However, it was lower among US-trained physicians than among internationally-trained physicians (although internationally physicians were in minority) and lower among more senior physicians than among younger physicians. These findings provide critical information about VR trends, physician factors, and which providers could benefit from additional training to increase VR adoption in healthcare systems.

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Physician experience with speech recognition software in psychiatry: usage and perspective

Cited by 9 publications

References 17 publications

Physician Note Composition Patterns and Time on the EHR Across Specialty Types: a National, Cross-sectional Study

Physician Note Composition Patterns and Time on the EHR Across Specialty Types: a National, Cross-sectional Study

Evaluating the Adoption of Voice Recognition Technology for Real-Time Dictation in a Rural Healthcare System: A Retrospective Analysis of Dragon Medical One

Evaluating the adoption of voice recognition technology for real-time dictation in a rural healthcare system: A retrospective analysis of dragon medical one

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