Evaluating the adoption of voice recognition technology for real-time dictation in a rural healthcare system: A retrospective analysis of dragon medical one
Open accessAbstract
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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Tools like Nuance's Dragon Medical One use LLM technology to accurately convert speech to text, allowing doctors to focus more on patient care and less on paperwork.
“Tools like Nuance's Dragon Medical One (Onitilo et al. 2023) use LLM technology to accurately convert speech to text, allowing doctors to focus more on patient care and less on paperwork.”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Onitilo et al. 2023
The Introduction identifies Nuance’s Dragon Medical One as a voice-recognition system for real-time dictation and reports a trend of reduced documentation time. It does not say the system uses LLM technology or establish that its speech-to-text output is accurate; in fact, it says dictation accuracy remains a concern. The claimed shift toward patient care is an inference, not a finding reported here. (LLM, conf=0.88)
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- Authors
- Onitilo, A. A., Shour, A. R., Puthoff, D. S., Tanimu, Y., Joseph, A., & Sheehan, M. T.
- Affiliations
- Marshfield Clinic (US)
- Marshfield Clinic Research Foundation (US)
- Lagos University Teaching Hospital (NG)
- Corresponding author
- Onitilo, Adedayo A.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2023-03-23
- Volume
- 18
- Issue
- 3
- Pages
- e0272545
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC10035815
- OpenAlex
- W4360609479
- Cited by
- 21
- References
- 26
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 3 · 2023: 4
- FWCI
- 4.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Telemedicine and Telehealth Implementation
- Keywords
- voice recognition, technology adoption, rural health care, electronic health records, clinical documentation, physician characteristics, international medical graduates, telemedicine, telehealth utilization, barriers to telehealth, telehealth access
- MeSH terms
- Voice Recognition, Ambulatory Care Facilities, Delivery of Health Care, Humans, Physicians, Retrospective Studies
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