Automated Detection of Conversational Pauses from Audio Recordings of Serious Illness Conversations in Natural Hospital Settings

Manukyan, Viktoria; Durieux, Brigitte N.; Gramling, Cailin J.; Clarfeld, Laurence A.; Rizzo, Donna M.; Eppstein, Margaret J.; Gramling, Robert

doi:10.1089/jpm.2018.0269

Cited by 8 publications

(5 citation statements)

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“…Clinical notes and electronic medical records were the most common primary data sources, used in 57 studies (69.5%). 21,23–27,29,30,33–36,40,42–46,48–52,54,55,57–64,66–68,70–73,75,79–93,95 Other primary sources included audio recordings ( n = 6, 7.3%), 6,28,32,38,39,65 administrative data ( n = 5, 6.1%), 37,47,53,77,...…”

Section: Resultsmentioning

confidence: 99%

“…We identified a trend in recent years where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings, particularly by the same research group (Gramling et al). 6,28,32,38,39,65 These in-depth analyses included concepts like conversational stories, conversational pauses, shapes of stories, connectional silence, analysis of prosody, turn length, turn-taking, and analyses of the lexicon of uncertainty.…”

Section: Discussionmentioning

confidence: 99%

“…Brizzi et al, 25 Kern et al, 49 Lau et al, 53 Lee et al, 54,55 Lindvall et al, 58 Poort et al, 71 Udelsman et al 84 Conversation analysis/conversational dynamics during serious illness conversations 7 (8.5) van den Broek-Altenburg et al, 6 Clarfeld et al, 28 Durieux et al, 32 Gramling et al, 38,39 Manukyan et al, 65 Ross et al 72 Symptom identification 6 (7.3) DiMartino et al, 30 Forsyth et al, 35 Heintzelman et al, 43 López-Torrecilla et al, 60 Taggart et al, 83 Yang et al 91 Decision-making 4 (4.8) Barrett et al, 24 Ouchi et al, 70 Saeed et al, 73 Almasalha et al 85 Identification of patients likely to benefit from palliative care or palliative needs or palliative status 4 (4.8) Murphree et al, 68 Sandham et al, 75 Song et al, 79 Zhang et al 97 Clinical phenotypes identification 3 (3.6) Ernecoff et al, 34 Jay et al, 45 natural language processing software to complete data analysis. This suggests that each software has its own strengths, and that weaknesses may be overcome by the combined use of more than one software solution to achieve the desired goal of the analyses.…”

Section: Discussionmentioning

confidence: 99%

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The use of natural language processing in palliative care research: A scoping review

et al. 2022

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Background: Natural language processing has been increasingly used in palliative care research over the last 5 years for its versatility and accuracy. Aim: To evaluate and characterize natural language processing use in palliative care research, including the most commonly used natural language processing software and computational methods, data sources, trends in natural language processing use over time, and palliative care topics addressed. Design: A scoping review using the framework by Arksey and O’Malley and the updated recommendations proposed by Levac et al. was conducted. Sources: PubMed, Web of Science, Embase, Scopus, and IEEE Xplore databases were searched for palliative care studies that utilized natural language processing tools. Data on study characteristics and natural language processing instruments used were collected and relevant palliative care topics were identified. Results: 197 relevant references were identified. Of these, 82 were included after full-text review. Studies were published in 48 different journals from 2007 to 2022. The average sample size was 21,541 (median 435). Thirty-two different natural language processing software and 33 machine-learning methods were identified. Nine main sources for data processing and 15 main palliative care topics across the included studies were identified. The most frequent topic was mortality and prognosis prediction. We also identified a trend where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings. Conclusions: We found 82 papers on palliative care using natural language processing methods for a wide-range of topics and sources of data that could expand the use of this methodology. We encourage researchers to consider incorporating this cutting-edge research methodology in future studies to improve published palliative care data.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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The use of natural language processing in palliative care research: A scoping review

et al. 2022

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“…The latter, for example, has been shown to relate to dogmatism, among others [ 19 ]. The content and addressing uncertainty [ 9 ] is important, but using conversational pauses [ 20 ] and connectional silence [ 21 ] have also been proven to be effective tools in PC communication. Quality of PC consultations is primarily focused on aligning prognosis communication with patient’s personal values and beliefs to optimize understanding.…”

Section: Introductionmentioning

confidence: 99%

Using natural language processing to explore heterogeneity in moral terminology in palliative care consultations

et al. 2021

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Background High quality serious illness communication requires good understanding of patients’ values and beliefs for their treatment at end of life. Natural Language Processing (NLP) offers a reliable and scalable method for measuring and analyzing value- and belief-related features of conversations in the natural clinical setting. We use a validated NLP corpus and a series of statistical analyses to capture and explain conversation features that characterize the complex domain of moral values and beliefs. The objective of this study was to examine the frequency, distribution and clustering of morality lexicon expressed by patients during palliative care consultation using the Moral Foundations NLP Dictionary. Methods We used text data from 231 audio-recorded and transcribed inpatient PC consultations and data from baseline and follow-up patient questionnaires at two large academic medical centers in the United States. With these data, we identified different moral expressions in patients using text mining techniques. We used latent class analysis to explore if there were qualitatively different underlying patterns in the PC patient population. We used Poisson regressions to analyze if individual patient characteristics, EOL preferences, religion and spiritual beliefs were associated with use of moral terminology. Results We found two latent classes: a class in which patients did not use many expressions of morality in their PC consultations and one in which patients did. Age, race (white), education, spiritual needs, and whether a patient was affiliated with Christianity or another religion were all associated with membership of the first class. Gender, financial security and preference for longevity-focused over comfort focused treatment near EOL did not affect class membership. Conclusions This study is among the first to use text data from a real-world situation to extract information regarding individual foundations of morality. It is the first to test empirically if individual moral expressions are associated with individual characteristics, attitudes and emotions.

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“…Automated speech analysis and communication feedback will likely take years to manifest because not only do technical and logistical barriers remain (e.g., lack of adequate high-quality SIC recordings to accurately assess non-linguistic features) 28 , but also greater consensus is needed to define and measure basic communication quality and outcomes 29 . Researchers are currently utilizing NLP to analyze audio recordings of SIC to characterize and understand the naturally occurring features of these complex conversations [30][31][32] , such as identifying intentional pauses that foster empathy, compassion, and understanding, aka "Connectional Silences. 30 " This type of research will guide future efforts to develop ways of automating the measurement of SIC quality in real time, allowing for immediate feedback to improve clinician performance.…”

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confidence: 99%

Enhancing serious illness communication using artificial intelligence

2022

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Delivery of serious illness communication (SIC) is necessary to ensure that all seriously ill patients receive goal-concordant care. However, the current SIC delivery process contains barriers that prevent the delivery of timely and effective SIC. In this paper, we describe the current bottlenecks of the traditional SIC workflow and explore how a hybrid artificial intelligence-human workflow may improve the efficiency and effectiveness of SIC delivery in busy practice settings.

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Automated Detection of Conversational Pauses from Audio Recordings of Serious Illness Conversations in Natural Hospital Settings

Abstract: ML is a valid method for automatically identifying Conversational Pauses in the natural acoustic setting of inpatient serious illness conversations.

Cited by 8 publications

References 28 publications

The use of natural language processing in palliative care research: A scoping review

The use of natural language processing in palliative care research: A scoping review

Using natural language processing to explore heterogeneity in moral terminology in palliative care consultations

Enhancing serious illness communication using artificial intelligence

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