Quality of care assessment in geriatric evaluation and management units: construction of a chart review tool for a tracer condition
Bibliographic record
Abstract
BACKGROUND: The number of elderly people requiring hospital care is growing, so, quality and assessment of care for elders are emerging and complex areas of research. Very few validated and reliable instruments exist for the assessment of quality of acute care in this field. This study's objective was to create such a tool for Geriatric Evaluation and Management Units (GEMUs). METHODS: The methodology involved a reliability and feasibility study of a retrospective chart review on 934 older inpatients admitted in 49 GEMUs during the year 2002-2003 for fall-related trauma as a tracer condition. Pertinent indicators for a chart abstraction tool, the Geriatric Care Tool (GCT), were developed and validated according to five dimensions: access to care, comprehensiveness, continuity of care, patient-centred care and appropriateness. Consensus methods were used to develop the content. Participants were experts representing eight main health care professions involved in GEMUs from 19 different sites. Items associated with high quality of care at each step of the multidisciplinary management of patients admitted due to falls were identified. The GCT was tested for intra- and inter-rater reliability using 30 medical charts reviewed by each of three independent and blinded trained nurses. Kappa and agreement measures between pairs of chart reviewers were computed on an item-by-item basis. RESULTS: Three quarters of 169 items identifying the process of care, from the case history to discharge planning, demonstrated good agreement (kappa greater than 0.40 and agreement over 70%). Indicators for the appropriateness of care showed less reliability. CONCLUSION: Content validity and reliability results, as well as the feasibility of the process, suggest that the chart abstraction tool can gather standardized and pertinent clinical information for further evaluating quality of care in GEMU using admission due to falls as a tracer condition. However, the GCT should be evaluated in other models of acute geriatric units and new strategies should be developed to improve reliability of peer assessments in characterizing the quality of care for elderly patients with complex conditions.
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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 | 0.067 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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 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".