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Expanding the Functionality of Speech Recognition in Radiology: Creating a Real-Time Methodology for Measurement and Analysis of Occupational Stress and Fatigue

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Abstract

While occupational stress and fatigue have been well described throughout medicine, the radiology community is particularly susceptible due to declining reimbursements, heightened demands for service deliverables, and increasing exam volume and complexity. The resulting occupational stress can be variable in nature and dependent upon a number of intrinsic and extrinsic stressors. Intrinsic stressors largely account for inter-radiologist stress variability and relate to unique attributes of the radiologist such as personality, emotional state, education/training, and experience. Extrinsic stressors may account for intra-radiologist stress variability and include cumulative workload and task complexity. The creation of personalized stress profiles creates a mechanism for accounting for both inter- and intra-radiologist stress variability, which is essential in creating customizable stress intervention strategies. One viable option for real-time occupational stress measurement is voice stress analysis, which can be directly implemented through existing speech recognition technology and has been proven to be effective in stress measurement and analysis outside of medicine. This technology operates by detecting stress in the acoustic properties of speech through a number of different variables including duration, glottis source factors, pitch distribution, spectral structure, and intensity. The correlation of these speech derived stress measures with outcomes data can be used to determine the user-specific inflection point at which stress becomes detrimental to clinical performance.

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References

  1. Halsted MJ, Froehlee M: Design, implementation, and assessment of a radiology workflow management system. AJR 191:321–327, 2008

    Article  PubMed  Google Scholar 

  2. White KS: Speech recognition implementation in radiology. Pediatr Radiol 35:841–846, 2005

    Article  PubMed  Google Scholar 

  3. Koivikko MP, Kauppinen T, Ahovuo J: Improvement of report workflow and productivity using speech recognition—a follow up study. J Digit Imaging 21:378–382, 2008

    Article  PubMed  Google Scholar 

  4. Reiner BI, Siegel EL, Knight N: Radiology reporting: past, present, and future: the radiologist perspective. J Am Coll Radiol 5:313–319, 2007

    Article  Google Scholar 

  5. Reiner B, Kruspinski EA: The insidious problem of fatigue in medical imaging practice. J Digit Imaging 1:3–6, 2012

    Article  Google Scholar 

  6. Leape LL: Errors in medicine. JAMA 272:1851–1857, 1994

    Article  PubMed  CAS  Google Scholar 

  7. Kohn LT, Corrigan J, Donaldson MS: To Err Is Human: Building a Safer Health System. National Academy, Washington DC, 2000. Contributor: Institute of Medicine (U.S.). Committee on Quality of Health Care in America

    Google Scholar 

  8. Reiner B, Kruspinski EA: Innovation strategies for combating occupational stress and fatigue in medical imaging. J Digit Imaging 25:445–448, 2012

    Article  PubMed  Google Scholar 

  9. Helmreich RL: On error management: lessons from aviation. BMJ 32:781–785, 2000

    Article  Google Scholar 

  10. Institute of Medicine: Crossing the Quality Chasm: a New Health System for the 21st Century. National Academy, Washington DC, 2001

    Google Scholar 

  11. Reiner BI, Siegel EL, Siddiqui K: Evolution of the digital revolution: a radiologist perspective. J Digit Imaging 16:324–330, 2003

    Article  PubMed  Google Scholar 

  12. Krupinski EA, Reiner B: Real-time occupational stress and fatigue measurement in medical imaging practice. J Digit Imaging 25:319–324, 2012

    Article  PubMed  Google Scholar 

  13. Vertinsky T, Forster B: Prevalence of eye strain among radiologists: influence of viewing variables on symptoms. AJR 184:681–686, 2005

    PubMed  Google Scholar 

  14. Krupinski EA: Medical image perception issues for PACS deployment. Semin Roentgenol 38:231–243, 2003

    Article  PubMed  Google Scholar 

  15. Krupinski EA, Berbaum KS, Caldwell RT, et al: Long radiology workdays reduce detection and accommodation accuracy. J Am Coll Radiol 7:698–704, 2010

    Article  PubMed  Google Scholar 

  16. Hollien H, Scwartz R: Speaker identification utilizing noncontemporary speech. Journal of Forensic Sciences 46:63–67, 2001

    PubMed  CAS  Google Scholar 

  17. Kuenzel H: On the problem of speaker identification by victims and witnesses. Forensic Linguistics 1:45–51, 1994

    Google Scholar 

  18. Stevens KN: Sources of inter and intra-speaker variability in the acoustic properties of speech sounds. Proceedings of the 7th International Cons. Phonetics Sciences, Montreal, 1971, pp 206–232

  19. Cummings K, Clements M: Analysis of global waveforms across stress styles. Proceedings of the IEEE, ICASSP 2847:369–372, 1990

    Google Scholar 

  20. Hollien H, Saletto JA, Miller SK: Psychological stress in voice: a new approach. Sturdia Phonetica Posnaniensa 4:5–17, 1993

    Google Scholar 

  21. Williams CE, Stevens KN: Emotions and speech: some acoustical correlates. Journal of the Associated Society of America 2:1238–1250, 1972

    Google Scholar 

  22. http://www.voicestress.org/voicestress (Florida Study). Pdf

  23. Brockway BF, Plummer OB, Lowe BM: The effects of two types of nursing reassurance upon patient vocal stress levels as measured by a new tool, the PSE. Nurs Res 25:440–446, 1976

    Article  PubMed  CAS  Google Scholar 

  24. Van der Car DH, Greaner J, Hibler N, et al: A description and analysis of the operation and validity of the psychological stress evaluator. J Forensic Sci 25:174–188, 1980

    Google Scholar 

  25. Williams CE, Stevens KN: Emotions and speech: some acoustic correlates. J Acoustic Soc Am 52:1238–1250, 1972

    Article  CAS  Google Scholar 

  26. Streeter LA, Macdonald NH, Apple W, et al: Acoustic and perceptual indicators of emotional stress. J Acoustic Soc Am 73:1354–1360, 1983

    Article  CAS  Google Scholar 

  27. Rostolland D: Acoustic features of shouted voice part 1. Acustica 50:118–125, 1982

    Google Scholar 

  28. Lieberman P, Michaels S: Some aspects of fundamental frequency and envelope amplitude as related to the emotional content of speech. J Acoustic Soc Am 7:922–927, 1962

    Article  Google Scholar 

  29. Hopkins CS, Ratley RJ, Benincasa DS, et al: Evaluation of voice stress analysis technology. Proceedings of the 38th Annual Hawaii International Conference on System Sciences, 2005, pp 1–10

  30. Zhou G, Hansen JHL, Kaiser JF: Nonlinear feature based classification of speech under stress. IEEE Transactions on Speech and Audio Processing 9:201–216, 2001

    Article  Google Scholar 

  31. Baber C, Mellor B, Graham R, et al: Workload and the use of automated speech recognition: the effects of time and resource demands. Speech Commun 20:37–54, 1996

    Article  Google Scholar 

  32. Chen Y: Cepstral domain talker stress compensation for robust speech recognition. IEEE Trans Acoust, Speech, Signal Processing 36:433–439, 1988

    Article  Google Scholar 

  33. Murray IR, Baber C, South A: Toward a definition and working model of stress and its effects on speech. Speech Commun 20:3–12, 1996

    Article  Google Scholar 

  34. Whitmore J, Fisher S: Speech during sustained operations. Speech Commun 20:55–70, 1996

    Article  Google Scholar 

  35. Kuroda I, Fujiwara O, Okamura N, et al: Method for determining pilot stress through analysis of voice communication. Aviation, Space, and Environmental Medicine 47:528–533, 1976

    PubMed  CAS  Google Scholar 

  36. Anderson TR, Moore TJ, McKinley RL: Issues in the development and use of speech recognition database for military cockpit environments. Proc of Speech Tech’85, Media Dimensions, 1985, pp. 172–176

  37. Reiner B, Kruspinski EA: Demystifying occupational stress and fatigue through the creation of an adaptive end-user profiling system. J Digit Imaging 2:201–205, 2012

    Article  Google Scholar 

  38. Reiner B: One size (doesn’t) fit all. J Am Coll Radiol 4:567–570, 2008

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Radiology, Veterans Affairs Maryland Healthcare System, 10 North Greene Street, Baltimore, MD, 21201, USA

    Bruce I. Reiner

  2. 11402 Newport Bay Drive, Berlin, MD, 21811, USA

    Bruce I. Reiner

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  1. Bruce I. Reiner
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Correspondence to Bruce I. Reiner.

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Reiner, B.I. Expanding the Functionality of Speech Recognition in Radiology: Creating a Real-Time Methodology for Measurement and Analysis of Occupational Stress and Fatigue. J Digit Imaging 26, 5–9 (2013). https://doi.org/10.1007/s10278-012-9540-0

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  • DOI: https://doi.org/10.1007/s10278-012-9540-0

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