Thematic Analysis of Hospice Mentions in the Health Records of Veterans with Advanced Kidney Disease
Bibliographic record
Abstract
Significance Statement Little is known regarding how decisions about hospice referral among patients with advanced kidney disease unfold in real-world clinical settings. The authors identified three dominant themes in their qualitative analysis of documentation pertaining to hospice in the electronic medical records of members of a national sample of veterans with advanced kidney disease. First, hospice and usual care seemed to function as conflicting rather than complementary models of care. Second, patients were usually referred to hospice late in the course of illness after all other options had been exhausted. Third, patients’ complex care needs sometimes complicated transitions to hospice, stretched the limits of home hospice, and fostered reliance on the acute medical system. These findings highlight the need to improve hospice transitions for patients with advanced kidney disease. Background Patients with advanced kidney disease are less likely than many patients with other types of serious illness to enroll in hospice. Little is known about real-world clinical decision-making related to hospice for members of this population. Methods We used a text search tool to conduct a thematic analysis of documentation pertaining to hospice in the electronic medical record system of the Department of Veterans Affairs, for a national sample of 1000 patients with advanced kidney disease between 2004 and 2014 who were followed until October 8, 2019. Results Three dominant themes emerged from our qualitative analysis of the electronic medical records of 340 cohort members with notes containing hospice mentions: ( 1 ) hospice and usual care as antithetical care models: clinicians appeared to perceive a sharp demarcation between services that could be provided under hospice versus usual care and were often uncertain about hospice eligibility criteria. This could shape decision-making about hospice and dialysis and made it hard to individualize care; ( 2 ) hospice as a last resort: patients often were referred to hospice late in the course of illness and did not so much choose hospice as accept these services after all treatment options had been exhausted; and ( 3 ) care complexity: patients’ complex care needs at the time of hospice referral could complicate transitions to hospice, stretch the limits of home hospice, and promote continued reliance on the acute care system. Conclusions Our findings underscore the need to improve transitions to hospice for patients with advanced kidney disease as they approach the end of life.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".