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Per-Patient Illness Trajectory Analyses.

Juliet Jacobsen1,2, Karin Boo Hammas3, Mikael Segerlantz1,4,5

  • 1Department of Clinical Sciences Lund, Medical Oncology, Lund University, Lund, Sweden.

Journal of Palliative Medicine
|November 12, 2024
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Summary

Per-patient illness trajectory analysis visualizes end-of-life care, preserving individual patient experiences. This method aids in assessing palliative care needs and quality for better healthcare improvements.

Keywords:
illness experienceillness trajectorypatient centered

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Area of Science:

  • Palliative Care
  • Health Services Research
  • Data Visualization

Background:

  • Traditional summary statistics obscure individual patient suffering in end-of-life care.
  • Quality improvement efforts are hindered by the lack of individual patient perspectives in aggregated data.

Purpose of the Study:

  • To present the end-of-life healthcare experience of a population while retaining individual patient details.
  • To introduce a novel data display method for analyzing patient trajectories.

Main Methods:

  • Developed and tested 'per-patient illness trajectory analysis' using a cohort of 192 cancer patients.
  • Utilized chart reviews to gather detailed information on illness trajectory events, focusing on unplanned hospitalizations.
  • Created per-patient timelines from diagnosis to death on a logarithmic scale to enhance end-of-life time resolution.

Main Results:

  • The logarithmic scale effectively expands time resolution towards the end of life compared to linear scales.
  • The method demonstrated feasibility and promise for analyzing populations up to 200 individuals.
  • Per-patient analysis facilitates assessment of unmet palliative care needs and quality at individual and group levels.

Conclusions:

  • Per-patient illness trajectory analysis is a feasible and promising method for evaluating end-of-life care.
  • This approach can improve the assessment of palliative care quality and unmet needs.
  • The method can be extended to larger populations through random sampling.