Assessing quality of older persons’ emergency transitions between long-term and acute care settings: a proof-of-concept study
Bibliographic record
Abstract
BACKGROUND: Long-term care (LTC) residents frequently experience transitions in the location of more advanced care delivery, including receiving emergency department (ED) care. In this proof-of-concept study, we aimed to determine if we could identify measures in quality of care across transitions from LTC to the ED, via emergency medical services and back, by applying Institute of Medicine (IOM) Quality of Care Domains to an existing dataset. METHODS: In the Older Persons' Transitions in Care (OPTIC) study, we collected information on residents' transitions in two Western Canadian cities. We applied the IOM's Quality of Care Domains to the OPTIC data to create binary measures of transition quality. We report the median (MED) per cent and IQR of measures met within each domain of quality. RESULTS: We tracked 637 transitions over a 12-month period, with data collected from each setting. We developed 19 safety measures, 20 measures of resident-centred care, 3 measures of timely care and 5 measures of effective care. We were unable to develop measures for equitable care at an individual transfer level. Domain scores varied across individual transitions, with the highest scores in safety (MED 79%, IQR: 63-95), efficiency (66%; IQR: 66-99), and resident-centred (45%; IQR: 25-65), followed by effectiveness (36%; IQR: 16-56), and timeliness (0%; IQR: 0-50). CONCLUSIONS: Our results show variation in scores across the domains of quality suggesting that it is possible to track quality of transitions for individuals across all settings, and not only within settings. We recommend that future work in tracking quality of care be performed at several levels (LTC, region, health authority, province). Such tracking is necessary to evaluate and improve overall quality of care.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".