Lexical associations can characterize clinical documentation trends related to palliative care and metastatic cancer.

Journal: Scientific reports
PMID: 40383724

Abstract

Palliative care is known to improve quality of life in advanced cancer. Natural language processing offers insights to how documentation around palliative care in relation to metastatic cancer has changed. We analyzed inpatient clinical notes using unsupervised language models that learn how words related to metastatic cancer (e.g. "mets", "metastases") and palliative care (e.g. "palliative care", "pal care") appear relationally and change over time. We included any note from adults hospitalized at the University of California, San Francisco system. The primary outcome was how similarly terms related to metastatic cancer and palliative care appeared in notes using a mathematical approach (cosine similarity). We used word2vec to model language numerically as vectors. Relational data between vectors was captured using cosine similarity. We performed linear regression to identify changes in these relationships of terms over time. As a sensitivity analysis, we performed the same analysis per year restricted only to patients with an ICD-9/10 diagnosis code for metastatic cancer. Metastatic cancer and palliative care terms appeared in similar contexts in clinical notes each year, suggesting a close relationship in documentation. However, over time, this relationship weakened, with these terms becoming less commonly used together as measured by cosine similarities. We found similar trends when we retrained models just on patients with a diagnosis code for metastatic cancer. Text in clinical notes offers unique insights into how medical providers document palliative care in patients with advanced malignancies and how these documentation practices evolve over time.

Authors

  • Hao Yuan Yang
    Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
  • Karthik Raghunathan
    Department of Anesthesia and Perioperative Care, Duke University, Durham, NC.
  • Eric Widera
    Division of Geriatrics, San Francisco VA Health Care System, San Francisco, CA, USA.
  • Steven Z Pantilat
    Division of Palliative Medicine, University of California San Francisco, San Francisco, CA, USA.
  • Teva Brender
    Department of Internal Medicine, University of California San Francisco, San Francisco, CA, USA.
  • Timothy A Heintz
    School of Medicine, University of California, San Diego, San Diego, CA.
  • Edie Espejo
    Division of Geriatrics, University of California, San Francisco, San Francisco, CA.
  • John Boscardin
    Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
  • Hunter Mills
    Bakar Computation Health Sciences Institute, University of California, San Francisco, San Francisco, California.
  • Albert Lee
    JC School of Public Health and Primary Care, The Chinese University of Hong Kong, Shatin, China.
  • Jacob Berchuck
    Division of Oncology, Winship Cancer Institute, Emory University, Atlanta, GA, USA.
  • Julien Cobert
    Anesthesia Service, San Francisco VA Health Care System, University of California, San Francisco, San Francisco, CA; Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA. Electronic address: Julien.cobert@ucsf.edu.

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