Story Arcs in Serious Illness: Natural Language Processing features of Palliative Care Conversations.

Clicks: 511
ID: 81932
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #1 of 28 articles by views in Patient education and counseling

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Serious illness conversations are complex clinical narratives that remain poorly understood. Natural Language Processing (NLP) offers new approaches for identifying hidden patterns within the lexicon of stories that may reveal insights about the taxonomy of serious illness conversations.We analyzed verbatim transcripts from 354 consultations involving 231 patients and 45 palliative care clinicians from the Palliative Care Communication Research Initiative. We stratified each conversation into deciles of "narrative time" based on word counts. We used standard NLP analyses to examine the frequency and distribution of words and phrases indicating temporal reference, illness terminology, sentiment and modal verbs (indicating possibility/desirability).Temporal references shifted steadily from talking about the past to talking about the future over deciles of narrative time. Conversations progressed incrementally from "sadder" to "happier" lexicon; reduction in illness terminology accounted substantially for this pattern. We observed the following sequence in peak frequency over narrative time: symptom terms, treatment terms, prognosis terms and modal verbs indicating possibility.NLP methods can identify narrative arcs in serious illness conversations.Fully automating NLP methods will allow for efficient, large scale and real time measurement of serious illness conversations for research, education and system re-design.
Reference Key
ross2019storypatient Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ross, Lindsay;Danforth, Christopher M;Eppstein, Margaret J;Clarfeld, Laurence A;Durieux, Brigitte N;Gramling, Cailin J;Hirsch, Laura;Rizzo, Donna M;Gramling, Robert;
Journal Patient education and counseling
Year 2019
DOI
S0738-3991(19)30528-2
URL
Keywords
artificial intelligence Machine learning conversation communication natural language processing palliative care stories narrative analysis

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.