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Identifying Connectional Silence in Palliative Care Consultations: A Tandem Machine-Learning and Human Coding Method
Brigitte N Durieux1, Cailin J Gramling1, Viktoria Manukyan2
1School of Arts and Sciences, University of Vermont, Burlington, Vermont.
Journal of Palliative Medicine
|October 18, 2018
Summary
A new machine-learning and human coding (ML-HC) method reliably and efficiently identifies "Connectional Silence" in serious illness conversations. This approach enhances the study of palliative care communication quality.
Area of Science:
- Medical Communication
- Computational Linguistics
- Palliative Care Research
Background:
- Systematic measurement of conversational features in serious illness communication is crucial for improving care quality.
- Machine-learning (ML) speech classification offers a scalable complement to human coding (HC) for analyzing clinical conversations.
- Identifying specific communication elements like 'Connectional Silence' requires reliable and efficient measurement tools.
Purpose of the Study:
- To evaluate the reliability, efficiency, and sensitivity of a combined ML-HC method for detecting 'Connectional Silence' in serious illness conversations.
- To assess the performance of an ML algorithm in screening for potential 'Connectional Silence' episodes.
- To compare the time efficiency of the tandem ML-HC approach versus HC alone.
Main Methods:
- A cross-sectional analysis of 354 audio-recorded inpatient palliative care consultations.
- Creation of 1000 audio clips of pauses (≥2 seconds) identified by an ML algorithm, including surrounding speech.
- Independent evaluation of clips by two human coders (HCs) for 'Connectional Silence' and assessment of ML algorithm's missed detections.
Main Results:
- 'Connectional Silences' were identified as rare (5.5%) among longer pauses in palliative care conversations.
- The tandem ML-HC method demonstrated strong reliability (kappa = 0.62).
- The ML-HC method was 61% more time-efficient than HC alone, with no 'Connectional Silences' missed by the ML screening algorithm.
Conclusions:
- Tandem ML-HC methods provide a reliable, efficient, and sensitive approach for identifying 'Connectional Silence' in serious illness communication.
- This integrated methodology supports large-scale analysis of critical communication features in palliative care.
- The findings support the use of ML-assisted tools to enhance the study and quality of serious illness conversations.
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