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Per-Patient Illness Trajectory Analyses

Jacobsen, Juliet LU ; Boo Hammas, Karin LU ; Segerlantz, Mikael LU ; Ekstrand, Joakim LU ; Mahajan, Sanjoy and Klintman, Jenny LU (2024) In Journal of Palliative Medicine
Abstract

Background: Summary statistics often hide individual patients' suffering, thereby impeding quality improvement efforts. Objectives: We aimed to show the experience of a population with health care toward the end of life while preserving the experience of the individual. Design: We developed a data display method called per-patient illness trajectory analysis. We tested it using a demonstration cohort of 192 patients with cancer referred to a regional Swedish specialized home-based palliative care practice. Chart review provided detailed information about illness trajectory events with a focus on unplanned hospitalization. Results: We created per-patient timelines spanning from cancer diagnosis until death and using a logarithmic scale:... (More)

Background: Summary statistics often hide individual patients' suffering, thereby impeding quality improvement efforts. Objectives: We aimed to show the experience of a population with health care toward the end of life while preserving the experience of the individual. Design: We developed a data display method called per-patient illness trajectory analysis. We tested it using a demonstration cohort of 192 patients with cancer referred to a regional Swedish specialized home-based palliative care practice. Chart review provided detailed information about illness trajectory events with a focus on unplanned hospitalization. Results: We created per-patient timelines spanning from cancer diagnosis until death and using a logarithmic scale: Compared with a conventional, linear timescale, this scale expands the time resolution toward the end of life. The method fosters the assessment of unmet palliative care need and care quality for individuals, small high-need groups, and populations. Conclusion: In populations of up to 200 people, per-patient illness trajectory analysis is feasible and promising. Using random sampling, it could be extended to larger populations.

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author
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organization
publishing date
type
Contribution to journal
publication status
epub
subject
in
Journal of Palliative Medicine
publisher
Mary Ann Liebert, Inc.
external identifiers
  • pmid:39530126
  • scopus:85209764211
ISSN
1096-6218
DOI
10.1089/jpm.2024.0181
language
English
LU publication?
yes
id
94b1d66f-3fae-4f10-bcc2-37e17367b07d
date added to LUP
2024-11-14 17:49:05
date last changed
2025-06-03 16:14:10
@article{94b1d66f-3fae-4f10-bcc2-37e17367b07d,
  abstract     = {{<p>Background: Summary statistics often hide individual patients' suffering, thereby impeding quality improvement efforts. Objectives: We aimed to show the experience of a population with health care toward the end of life while preserving the experience of the individual. Design: We developed a data display method called per-patient illness trajectory analysis. We tested it using a demonstration cohort of 192 patients with cancer referred to a regional Swedish specialized home-based palliative care practice. Chart review provided detailed information about illness trajectory events with a focus on unplanned hospitalization. Results: We created per-patient timelines spanning from cancer diagnosis until death and using a logarithmic scale: Compared with a conventional, linear timescale, this scale expands the time resolution toward the end of life. The method fosters the assessment of unmet palliative care need and care quality for individuals, small high-need groups, and populations. Conclusion: In populations of up to 200 people, per-patient illness trajectory analysis is feasible and promising. Using random sampling, it could be extended to larger populations.</p>}},
  author       = {{Jacobsen, Juliet and Boo Hammas, Karin and Segerlantz, Mikael and Ekstrand, Joakim and Mahajan, Sanjoy and Klintman, Jenny}},
  issn         = {{1096-6218}},
  language     = {{eng}},
  month        = {{11}},
  publisher    = {{Mary Ann Liebert, Inc.}},
  series       = {{Journal of Palliative Medicine}},
  title        = {{Per-Patient Illness Trajectory Analyses}},
  url          = {{http://dx.doi.org/10.1089/jpm.2024.0181}},
  doi          = {{10.1089/jpm.2024.0181}},
  year         = {{2024}},
}