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303 Preliminary development of the clinician spoken plain language measure using a secondary analysis of clinical encounters
  1. Renata W Yen1,2,
  2. Marie-Anne Durand1,3,4,
  3. Joanna Leyenaar1,5,
  4. A James O’malley1,6,
  5. Edward Rego7,
  6. Rachel C Forcino8,
  7. Catherine H Saunders1,5,
  8. Talia Isaacs9,
  9. Glyn Elwyn1
  1. 1The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth College, Lebanon, New Hampshire, USA
  2. 2Center for Technology and Behavioral Health, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
  3. 3Unisanté, Lausanne, Switzerland
  4. 4Université Toulouse III Paul Sabatier, Toulouse, France
  5. 5Dartmouth Health, Lebanon, New Hampshire, USA
  6. 6Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth College, Lebanon, NH, USA
  7. 7Duke University School of Medicine, Durham, NC, USA
  8. 8Department of Population Health, University of Kansas School of Medicine, Kansas City, KS, USA
  9. 9IOE-UCL’s Faculty of Education and Society, University College London, London, UK


Background Shared decision making relies on communication skills. Measuring verbal communication in clinical encounters is cumbersome and feedback is rarely given to clinicians. We aimed to develop a preliminary version of a spoken plain language (SPL) measure that is practical and could potentially be used for feedback and improvement purposes.

Methods We researched a set of initial variables across five constructs: turn length, language complexity (including Flesch-Kincaid grade level), turn speed, medical terminology use (with and without explanation), and common word use. Then, using encounter transcripts from an SDM trial,1 we assessed variables for each turn (unit of speech in a dialogue) overall and by turn topic (e.g., greetings/goodbyes, medical information delivery). We conducted a factor analysis to isolate the best variables for measure development.

Results We determined 21 initial variables. From 287 encounters across 13 clinicians, medical information delivery turns were longer than turns overall (55.46 words/turn, SD=26.02 vs 30.89 words/turn, SD=12.68, p<0.001) and more complex (Flesch-Kincaid=5.75, SD=1.36 vs 4.80, SD=1.06 p<0.001). Clinicians used 0.88 unexplained medical terms/minute (SD=0.56) and 0.69 explained medical terms/minute (SD=0.43). Results varied by clinician. In the factor analysis, 11 variables for four constructs: turn speed, turn length, word complexity, and turn complexity.

Discussion Clinician SPL can potentially be measured using a set of 11 variables. In future research, we will refine the SPL measure, explore sources of clinician variation, and determine how to deliver results to clinicians, as well as whether patient factors predict the level of SPL used.

Conclusion There is strong potential to develop a feasible measure of SPL using a set of key variables nested into four SPL constructs.

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