The experiences of patients who leave hospital against medical advice: analysis of survey data
Bibliographic record
Abstract
Background: Historically, when patients leave hospital against medical advice (LAMA), the focus has often been on non-compliance or other patient-level factors, rather than on how services may be designed to better support these patients. Efforts to better understand why patients LAMA could strengthen the provision of patient-centered care that is responsive to individual needs and values. This study aimed to explore the experiences of Albertan adults who LAMA by examining patient-centered quality indicators (PC-QIs) derived from survey data. We sought to identify actionable insights that may inform service improvements and reduce the risks associated with LAMA discharges. Methods: We analyzed seven years of survey data, encompassing hospital discharges from April 2016 to March 2023. A random sample of respondents completed the Canadian Patient Experiences - Inpatient Care (CPES-IC) instrument by telephone within six weeks of hospital discharge. From the data, we assessed ten patient-centred quality indicators (PC-QI) which were previously co-created with patient advisors, researchers, and health system administrators. Survey responses/PC-QIs were reported as percent in "top box", as represented by the most positive answer choice. Differences between patients who LAMA and other medical/surgical discharges were assessed. Results: A total of 144,480 surveys were successfully linked with inpatient records and included for analysis. This included 1,177 (0.9%) respondents who LAMA. In our sample, those who LAMA were predominantly male, younger, had a lower level of educational attainment, and were living with a greater number of comorbid health conditions. They also had lower self-reported levels of physical and mental health and had a longer average length of stay. The LAMA group had significantly lower top-box percentages on all ten of the PC-QIs which we examined. This difference ranged from 20.7% (communicating test results; 51.6% LAMA group vs. 71.3% others) to 29.2% (patient involvement in decisions about their care and treatment; 39.8% vs. 69.0% respectively). Conclusion: Patients who LAMA reported lower ratings of patient experience across all PC-QIs studied. Our findings may provide actionable, service-related insights into the reasons why patients LAMA. This is important as those who do so may place themselves at increased risk for future unplanned healthcare events, mortality, and morbidity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".