Using Applied Conversation Analysis in Patient Education

Halpin, Sean N.; Konomos, Michael; Roulson, Kathryn

doi:10.1177/23333936211012990

Cited by 3 publications

(1 citation statement)

References 45 publications

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“…The field lacks descriptive studies of actual practice. This is a general gap in the wider patient education field ( Halpin et al, 2021 ). In the previous literature reviews, medical information-sharing strategies that have been tested, such as information structuring or teach back, were indeed selected as general, top-down, common sense strategies ( Lie et al, 2021 ; Menichetti et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

How Do Physicians Frame Medical Information in Talks With Their Patients? An Inductive Microanalysis

Menichetti,

Gulbrandsen,

Landmark

et al. 2023

Qual Health Res

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During medical consultations, physicians need to share a substantial amount of information with their patients. How this information is framed can be crucial for patient understanding and outcomes, but little is known about the details of how physicians frame information in practice. Using an inductive microanalysis approach in the study of videotaped medical interactions, we aimed to identify the information frames (i.e., higher-level ways of organizing and structuring information to reach a particular purpose) and the information-framing devices (i.e., any dialogic mechanism used to present information in a particular way that shapes how the patient might perceive and interpret it) physicians use spontaneously and intuitively while sharing information with their patients. We identified 66 different information-framing devices acting within nine information frames conveying: (1) Do we agree that we share this knowledge?, (2) I don’t like where I (or where you are) am going with this, (3) This may be tricky to understand, (4) You may need to think, (5) This is important, (6) This is not important, (7) This comes from me as a doctor, (8) This comes from me as a person, and (9) This is directed to you as a unique person. The kaleidoscope of information-framing devices described in this study reveals the near impossibility for neutrality and objectivity in the information-sharing practice of medical care. It also represents an inductively derived starting point for further research into aspects of physicians’ information-sharing praxis.

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Section: Introductionmentioning

confidence: 99%

How Do Physicians Frame Medical Information in Talks With Their Patients? An Inductive Microanalysis

Menichetti,

Gulbrandsen,

Landmark

et al. 2023

Qual Health Res

View full text Add to dashboard Cite

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Is the patient speaking or the nurse? Automatic speaker type identification in patient–nurse audio recordings

Zolnoori,

Vergez,

Sridharan

et al. 2023

Journal of the American Medical Informatics Association

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Objectives Patient–clinician communication provides valuable explicit and implicit information that may indicate adverse medical conditions and outcomes. However, practical and analytical approaches for audio-recording and analyzing this data stream remain underexplored. This study aimed to 1) analyze patients’ and nurses’ speech in audio-recorded verbal communication, and 2) develop machine learning (ML) classifiers to effectively differentiate between patient and nurse language. Materials and Methods Pilot studies were conducted at VNS Health, the largest not-for-profit home healthcare agency in the United States, to optimize audio-recording patient–nurse interactions. We recorded and transcribed 46 interactions, resulting in 3494 “utterances” that were annotated to identify the speaker. We employed natural language processing techniques to generate linguistic features and built various ML classifiers to distinguish between patient and nurse language at both individual and encounter levels. Results A support vector machine classifier trained on selected linguistic features from term frequency-inverse document frequency, Linguistic Inquiry and Word Count, Word2Vec, and Medical Concepts in the Unified Medical Language System achieved the highest performance with an AUC-ROC = 99.01 ± 1.97 and an F1-score = 96.82 ± 4.1. The analysis revealed patients’ tendency to use informal language and keywords related to “religion,” “home,” and “money,” while nurses utilized more complex sentences focusing on health-related matters and medical issues and were more likely to ask questions. Conclusion The methods and analytical approach we developed to differentiate patient and nurse language is an important precursor for downstream tasks that aim to analyze patient speech to identify patients at risk of disease and negative health outcomes.

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Utilizing patient-nurse verbal communication in building risk identification models: the missing critical data stream in home healthcare

Zolnoori,

Sridharan,

Zolnour

et al. 2023

Journal of the American Medical Informatics Association

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Background In the United States, over 12 000 home healthcare agencies annually serve 6+ million patients, mostly aged 65+ years with chronic conditions. One in three of these patients end up visiting emergency department (ED) or being hospitalized. Existing risk identification models based on electronic health record (EHR) data have suboptimal performance in detecting these high-risk patients. Objectives To measure the added value of integrating audio-recorded home healthcare patient-nurse verbal communication into a risk identification model built on home healthcare EHR data and clinical notes. Methods This pilot study was conducted at one of the largest not-for-profit home healthcare agencies in the United States. We audio-recorded 126 patient-nurse encounters for 47 patients, out of which 8 patients experienced ED visits and hospitalization. The risk model was developed and tested iteratively using: (1) structured data from the Outcome and Assessment Information Set, (2) clinical notes, and (3) verbal communication features. We used various natural language processing methods to model the communication between patients and nurses. Results Using a Support Vector Machine classifier, trained on the most informative features from OASIS, clinical notes, and verbal communication, we achieved an AUC-ROC = 99.68 and an F1-score = 94.12. By integrating verbal communication into the risk models, the F-1 score improved by 26%. The analysis revealed patients at high risk tended to interact more with risk-associated cues, exhibit more “sadness” and “anxiety,” and have extended periods of silence during conversation. Conclusion This innovative study underscores the immense value of incorporating patient-nurse verbal communication in enhancing risk prediction models for hospitalizations and ED visits, suggesting the need for an evolved clinical workflow that integrates routine patient-nurse verbal communication recording into the medical record.

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Using Applied Conversation Analysis in Patient Education

Cited by 3 publications

References 45 publications

How Do Physicians Frame Medical Information in Talks With Their Patients? An Inductive Microanalysis

How Do Physicians Frame Medical Information in Talks With Their Patients? An Inductive Microanalysis

Is the patient speaking or the nurse? Automatic speaker type identification in patient–nurse audio recordings

Utilizing patient-nurse verbal communication in building risk identification models: the missing critical data stream in home healthcare

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