The steep slope of this curve during the pilot, and especially during the implementation, indicates a rapid provider adoption of SR. Results, demonstrated in Figure 4 , show the evolution of medical transcription cost and financial savings. The financial savings were a direct consequence of the strong decline observed in medical transcription volume as SR was adopted by providers.
This is the first study of its kind, to our knowledge, based on the following: First, both qualitative and quantitative methods were utilized concurrently to measure the adoption of speech recognition and its impact on provider satisfaction, documentation quality, efficiency, and medical transcription cost when used for clinical documentation within the electronic health record.
Second, over a period of 31 months, quantitative data was collected from the EHR, transcription and SR solutions to measure adoption and cost savings. Third, a new methodology was developed and used in this study to determine which provider notes within the EHR were created with SR, to facilitate accurate measurement of note volume evolution per input modality.
Fourth, preliminary analysis indicated there may be positive effects on satisfaction, efficiency, and workflow of documentation reviewers, beyond providers, but this will require additional analysis and review to confirm. Validated results revealed significant improvements in satisfaction, documentation quality, and efficiency among providers as a direct result of SR use.
This, coupled with the studies by Alapetite, et al. Furthermore, an interesting finding in this current study is that while expectations were high before the introduction of SR, expectations were significantly exceeded after the introduction of SR, which presents an opportunity for additional review and future research.
Study findings also revealed statistically significant improvement in documentation quality and completeness required for optimal care and a borderline statistically significant reduction in time spent answering questions and clarifications from CDI, nursing, medical records, quality, and coding staff. Much of the previous research for SR quality and completeness focused heavily on comparing errors or recognition rates versus the quality and completeness provided by supporting clinical narrative capture, reduced time answering questions and clarifications, and its impact to quality and efficiency.
This greatly exceeded expectations reported prior to SR implementation and showed remarkable progress. Prior studies by Alapetite, et. These findings are also consistent with the Vogel, et al.
Time savings, when using SR, can be challenging to compare and quantify due to the variation in user utilization of speech-enabled Smart Templates and auto-texts, differences among specialties, and variations in note type utilized.
Yet, this presents an interesting future research opportunity regarding implementation best practices. This is consistent with survey findings regarding provider satisfaction with SR. These observations could be attributed to many factors, including but not limited to effective integration, implementation, training, SR accuracy, leadership buy-in, and provider satisfaction with the new solution.
Furthermore, these findings suggest that SR is indeed a factor in the adoption rate of electronic provider documentation and presents an interesting future research opportunity regarding implementation best practices. While not completed for this study, the opportunity exists to augment this study, and for future research studies, to correlate provider utilization data by input method and per note type to efficiency, workflow, quality, and provider satisfaction.
Another interesting research question is the adoption rate of electronic provider documentation without SR compared to with SR. Preliminary analysis indicated, though not presented in this paper, there may be positive effects on satisfaction, efficiency, and workflow of other documentation reviewers beyond providers.
This requires additional analysis to confirm, presenting an opportunity to expand this study and future studies. Hodgson and Coiera 10 identified some form of economic evaluation in seven previous clinical documentation SR studies.
Of no surprise, this current study denoted a significant decline of Furthermore, the extent and speed of the decline in medical transcription costs observed in the study, within only a few months, is noteworthy. These observations could be attributed to many factors, including but not limited to effective integration; implementation; training, which leveraged lessons learned from the pilot; leadership buy-in; and provider satisfaction with the new SR solution. A hard return on investment is difficult to calculate due to the variability involved.
Variables, among others, can include the following: the impact on timely care transitions, discharges and care quality, resulting from immediate access of speech-enabled provider notes that can affect clinical and financial outcomes; financial impact from the potential reduction of delays for coding and billing through timely access to accurate, complete encounter information; efficiency gains of non-providers as a result of less time spent seeking or clarifying information, which was validated in this study as providers felt they spent less time answering questions and clarifications; and potential reduction of provider burnout and turnover rates through improved satisfaction.
Several factors could hinder this study from being generalized to other clinical settings or information systems. Other EHR and SR systems might differ in performance and different approaches to integrating and implementing the two could also lead to varying results. Furthermore, this study was not designed to evaluate clinical effectiveness nor the direct impact on clinical outcomes of one input modality versus another.
We were unable to collect note length information to measure the effect of SR input modality on note length. The methodology to interlink EHR notes with SR is binary, and, as such, does not provide measurement as to how much SR was used as opposed to keyboard and mouse.
No time-motion studies were done with providers, which may have provided greater detail around the actual decrease in documentation time, reduced clarifications, and improved documentation quality when using SR versus other input modalities.
For many organizations, the search for the most efficient method of capturing clinical documentation, optimizing the EHR, enhancing usability and improving provider satisfaction remains a strategic priority.
Given the substantial cost of documentation, in terms of clinician time and varying input modalities, the foundational act of interacting with an electronic health record was the primary focus of this study. By linking provider notes within the EHR to SR, and evaluating both qualitative and quantitative data inputs concurrently, this study created a baseline for the impact of SR use on provider adoption, satisfaction, documentation quality, efficiency, and cost.
This new methodology, and its associated findings, have contributed to the current field of research regarding best practices for integrating and implementing SR within the EHR. However, as SR technologies continue to extend into different healthcare settings and advance to include more robust narrative and clinical intelligence capabilities, additional studies are needed to augment this research. Furthermore, the healthcare field should evolve the understanding of the ways SR will support providers and downstream documentation users within their workflow, and how improved documentation translates to better clinical and financial outcomes.
The authors indicated with the number 2 are employed by Nuance Communications Inc. National Center for Biotechnology Information , U. Guido Gallopyn , 2 and Kristin E. Reid F. Terri H. Guido Gallopyn. Kristin E. Author information Copyright and License information Disclaimer. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose.
This article has been cited by other articles in PMC. Abstract This study utilizes qualitative and quantitative methods to measure the adoption of speech recognition SR and its impact ON provider satisfaction, documentation quality, efficiency, and cost when used for clinical documentation within the electronic health record EHR. Keywords: speech recognition, medical transcription, electronic health records, health information technology, electronic provider documentation, clinical documentation improvement, provider satisfaction.
Qualitative Survey Method, Instrument, Participants, and Procedure An electronic survey questionnaire was developed and deployed to measure expectations and experience regarding clinical documentation prior to SR implementation and to evaluate the post-implementation documentation experience including SR. Table 1: Survey Questions. What about in the future? Trending Posts. What is a Mobile Driver's License?
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The purpose of the cookie is to determine if the user's browser supports cookies. Used to track the information of the embedded YouTube videos on a website. Chatbot Voice recognition. Written by Cem Dilmegani. Common Applications Voice recognition is a maturing technology and users seem to trust it for the most basic functionality like search or playing music. User adoption of voice interfaces is still low for applications with more significant implications like buying things or controlling smart devices, Source: PWC Voice Search This is the most common use of speech recognition.
Voice to text Speech recognition enables hands free computing. Writing emails For instance, you can use these voice typing and voice command features in Google Docs if you are using the Google Chrome browser. Automatic subtitling with speech recognition YouTube Automatic translation Voice commands to smart home devices Smart home applications are mostly designed to take a certain action after the user gives voice commands.
Business function applications Customer service This is one of the most important AI applications in customer service. Interactive Voice Response IVR : It is one of the oldest speech recognition applications and allows customer to reach the right agents or resolve their problem via voice commands. Analytics : Transcription of thousands of phone calls between customers and agents help identify common call patterns and issues.
Pre-sales We have all sat through calls with Sales Development Reps SDRs that took us through a series of questions to identify if we are a good fit for their product. Industry applications Automotive In-car speech recognition systems have become a standard feature for most new vehicles. Legaltech Applications include Court reporting Realtime Speech Writing eDiscovery Legal discovery If you are looking for a voice bot platform, feel free to check our transparent vendor lists where sorting vendors by AIMultiple score shows the best solutions based on maturity, popularity, and satisfaction.
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