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Improving the detection and documentation of patients’ symptoms in an integrated gastrointestinal oncology and palliative care clinic.

2024· article· en· W4402987136 on OpenAlexaboutno aff
Vinay Rao, Stephanie Trexler, Caroline Boaz, Duncan E. Phillips, Laura Van Metre Baum

Bibliographic record

VenueJCO Oncology Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsnot available
Fundersnot available
KeywordsDocumentationMedicinePalliative careOncologyInternal medicineIntensive care medicineNursingComputer science

Abstract

fetched live from OpenAlex

336 Background: Patients with gastrointestinal (GI) malignancies experience a high burden of disease-related symptoms and treatment side effects. We established an integrated GI oncology and palliative care (PC) clinic in which a PC physician is co-located with the oncology team and can provide timely response to patient distress and facilitate communication with the interdisciplinary palliative care team. Yet, even in the integrated clinic, patient symptoms were unreliably assessed, leading to missed opportunities to address patient suffering. Between October 2023 and January 2024 for patients seen in-person in the integrated clinic for oncology or PC visits, the median rate of symptom documentation was 45%. Emotional, financial, and spiritual distress documentation rates were lowest. We hypothesized that by implementing a systematic, comprehensive patient-reported symptom assessment tool, we could increase the detection and documentation of symptoms, and in turn, improve communication around patient distress and timely response to symptoms. Methods: We measured the rate of symptom documentation (primary outcome) and the use of a patient-reported symptom assessment tool for adults with GI cancers seen in-person in the integrated clinic in Spring 2024. Based on the Edmonton Symptom Assessment Scale (ESAS) and expert consensus, we determined which symptoms were most important to identify. Documentation rates were assessed via manual chart review of visit notes and flowsheet data. Prospective post-intervention data (March to May 2024) were compared to retrospective pre-intervention baseline data (October 2023 to January 2024). Interventions included modification of the ESAS to include GI oncology-specific symptoms, implementation of the tool in paper format to encourage patient-clinician communication, frequent staff education and feedback sessions, creation and dissemination of a resource guide for clinicians to respond to identified distress, and patient satisfaction surveys. Results: 126 patients were included in the study (31 pre-intervention and 95 post-intervention). During the post-intervention period, the median rate of symptom documentation increased from 45% to 83% and the median rate of ESAS survey completion increased from 9% to 68%. Documentation of financial and spiritual distress increased from 13% to 71%, for both, and documentation of emotional distress increased from 35% to 72%. On satisfaction surveys (N=20), most patients reported the tool was helpful in sharing their symptoms (70%), was easy to read and understand (100%), and that their clinician was engaged in addressing their symptoms (67%). Conclusions: The implementation of a patient-reported symptom assessment tool in an integrated GI oncology/PC clinic increased documentation of important symptoms, potentially increasing clinician awareness of patient distress.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.074
GPT teacher head0.464
Teacher spread0.391 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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