Unexpected Journeys: Investigating Urgent and Emergency Care Trends of Patients Attending a Community Specialist Team for Older Adults
Bibliographic record
Abstract
Abstract Background Transitions of care for older people via ICPOP and Urgent and Emergency Care (UEC) pathways are becoming more complex. Demand for unscheduled care for older adults with complex care needs will increase with an ageing society, requiring proactive case management and practical realisation of integrated care pathways. Methods Retrospective analysis of UEC presentations of older patients attending a community specialist team (CST) who underwent Comprehensive Geriatric Assessment (CGA) between January-July 2023. Demographics and functional status [Clinical Frailty Score (CFS), Barthel Index (BI), Lawton-Brody Score (IADL), Montreal Cognitive Assessment (MOCA)] were extracted, along with UEC presentations post-CGA until March 2024. Results 356 patients underwent CGA and 42% (n=148) had subsequent unscheduled care attendance(s). Of those with an UEC attendance 36% (n=54) lived alone. Median functional scores were: CFS 5, BI 17, IADL 3, MOCA 17. Median time to UEC presentation was 100 days since CGA. There were 283 UEC interactions in total. 39% (n=58) had >1 interaction. 73% (n=206) of interactions resulted in acute hospital admissions, 21% (n=58) were ED visits, and 6% (n=19) were Pathfinder interactions. Falls (31%) and infection (19%) were the leading causes for presentation. Of 206 admissions, 61% were admitted under medical specialties, 26% under Geriatric Medicine, and 13% under Surgery. Average hospital stay was 13 days. 65% (n=134), were discharged home, 11% (n=22) to transitional care beds, 7% (n=15) to rehabilitation, 7% (n=14) to nursing home, 3% (n=5) transferred to other hospitals, 7%(n=15) died. Conclusion Over half of patients attending a CST had an UEC presentation, reflecting the complexity of this cohort and their level of healthcare utilisation. Case management roles that complement and pull together existing elements of the older persons pathway are lacking across healthcare settings and are fundamental to successful implementation of a patient-centred age friendly health system.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".