Difference between revisions of "Speech Recognition Technology and the EHR"

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7. Terry, Ken. (1999) “Instant Patient Records and All You Have to Do Is Talk.” Medical Economics 76, no.19: 101–102, 107–108, 111–112.
 
7. Terry, Ken. (1999) “Instant Patient Records and All You Have to Do Is Talk.” Medical Economics 76, no.19: 101–102, 107–108, 111–112.
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Submitted by Jose Gude
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Revision as of 13:43, 6 March 2009

Speech Recognition Technology and the EHR: The Future of Data Entry?


Introduction

The dismal adoption rate of the EHR in the United States (about 20% overall) is multifactorial. One of these factors may be related to the difficulty related to human-computer interface and the ability (or lack thereof) to easily input data and navigate the system. Speech Recognition Technology (SRT) may have a role as an increasingly viable solution to help improve EHR adoption rates by improving clinical documentation efficiency and quality.


Background

A basic assumption regarding the implementation of the EHR is that the technology will improve patient care and that it will not get in the way of time-tested clinician work-flows or increase the time burdens of an overloaded medical staff. Some clinicians have argued that narratives are essential to a patient’s episode of illness, and that poor communication is more often detrimental to patients than lack of knowledge. Additionally, computers should enable clinicians to capture narratives easily, and the structure of the patient’s record strongly should enhance the ease of clinical documentation and information retrieval.

Usability is clearly a critical factor on the front end success of EHR clinician adoptability in its early implementation stages. Given its affordability and relatively minimal training requirements, SRT has the potential to improve efficiency, work-flow processes, and increase the quality of patient care documentation, as well as reducing transcription costs.


Why use SRT?

As EHR implementation becomes more widespread, handwritting for clinical data and order entry will become obsolete. For most clinicians (who are usually not expert “touch-typists”) , marginal typing skills can lead to self-editing while trying to document a narrative, therefore ultimately compromising the completeness and accuracy of patient provided information.

Dictation with transcription is more likely to result in a legible and comprehensive document, but still requires a qualified transcriptionist to perform the job with high fidelity; even so, the turnaround can vary greatly, transcription errors can result in propagated inaccuracies (or blank information) in the medical record leading to delays in posting the final corrected and authenticated note on the EHR. Entry of coded data is unnatural, could be cumbersome, awkward, and time-consuming, and may not convey an accurate meaning of the patient's problem or condition, which may reduce the ability to precisely communicate complex nuanced clinical information among clinicians or to patients.


Benefits

As a result of dramatic improvements in SRT accuracy rate and ease-of-use, its demand has been growing steadily among physicians in search of tools to improve both work-flow processes and quality of documentation. Other benefits include reduced transcription costs, faster per-dictation turnaround time, increased accuracy and error reduction, and fast electronic capture of free-form text on complex cases beyond the scope of traditional EHR templates. Extremely complete medical vocabularies, acceptable accuracy rates, and voice macro creation capabilities (which ease and standardize complex documentation entry by the use of easily-editable templates or sequential order entry mouse/keyboard-driven navigation) have been lauded features of SRT applications that have enhanced their adoptability.


Risks and drawbacks

“Stylistic preference” may be a challenge for initial adoption. In general, clinicians may be initially unwilling to change their data input style, the main issues being how comfortable one is with the concept of speech recognition as a relatively new technology, and how tolerant of errors and how willing to correct them one may initially be.

Accuracy may be seen as a drawback by some; even with 98 percent accuracy, one of every 50 words is misrecognized; therefore, edits are required to ensure accuracy of the final clinical document. Some physicians find microphone and program set-up time-consuming in a busy clinical situation. Lack of software support on consumer SRT products, inadequate computer and audio hardware to optimally utilize and operate SRT, licensing agreement issues, and institutional ROI, are some of the other potential barriers.


Future Challenges

As SRT and other human-computer interaction technologies continue to evolve, the input of complex narrative clinical data, complex multi-screen navigation that is common in most of today’s EHR products will hopefully become more simplified and user-friendly with the aid of improved user interface modalities. Simplicity, usability, and adaptability to a clinician’s workflow will likely determine future user satisfaction at the EHR point of data entry, which will hopefully influence long term overall EHR adoption rates.


Conclusion

As with all technology, SRT and the hardware that supports it have improved and will continue to improve, progressively making it a viable option to a more computer-literate generation of healthcare providers and HIM professionals. Technical, personal, and institutional barriers still exist, and more current research is needed to identify specific challenges so that improvements can be targeted in the future, but the trend is one of technological improvement, ease of use, and increased clinician adoption as a useful adjunct to EHR interaction. From a clinician’s standpoint, anything that will save time, make documentation and communication more robust and more immediate, and any tools that will diminish the awareness of computer interaction and complement work-flow and ultimately improve quality of patient care, time at the bedside and clinical outcomes will eventually be embraced, as we continue to strive for better and more seamless interaction with information systems in the future.


References

1.Walsh, S. (2004). “The clinician’s perspective on electronic health records and how they can affect patient care”. BMJ; 328: 1184-7

2. Beats, J. et al. (2003). “Speech Recognition in the Electronic Health Record (AHIMA Practice Brief)”. Web site: http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_022192.hcsp?dDocName=bok1_022192

3. Gainer, C. “Voice Recognition: With Improved Technology, Efficiencies Are Clear.” Physicians Practice: The Business Journal for Physicans 13, no. 2 (2203):82-84.

4. Ury, A. (2007). “Practices can speed productivity and reduce costs by adding speech recognition to their EHRs” . Healthcare Informatics, September 2007. Web site: http://www.healthcare-informatics.com/ME2/dirmod.asp?sid=&nm=&type=Publishing&mod=Publications%3A%3AArticle&mid=8F3A7027421841978F18BE895F87F791&tier=4&id=62287833C9004652BA62EA9CCE046DC8

5. McGee, M. (2007). “Voice Recognition helps doctors get more out of e-health”. Information Week, September 2007. Web site: http://www.informationweek.com/news/internet/showArticle.jhtml?articleID=201806232

6. Glaser C. et al. (2005). “Speech recognition: impact on workflow and report availability”. Radiologue, Aug;45(8):735-42.

7. Terry, Ken. (1999) “Instant Patient Records and All You Have to Do Is Talk.” Medical Economics 76, no.19: 101–102, 107–108, 111–112.


Submitted by Jose Gude