[P3–508]: EXPLORING QUALITY INDICATORS FOR OLDER PERSONS’ TRANSITIONS IN CARE: A SYSTEMATIC REVIEW AND DELPHI PROCESS
Bibliographic record
Abstract
Nearly 47 million people live with dementia worldwide today, a number projected to continue growing (Prince et al., 2016). Those with dementia are at increased risk for transitions in care and experience many negative consequences as a result (Callahan et al., 2012). There is a need for quality indicators (QIs) to aid health care personnel in all sectors of the healthcare transition to improve care. Using a systematic review and Delphi process, we catalogued established QIs to evaluate the quality of care provided to older persons during transitions to and from emergency departments (ED). Our search included articles examining development and testing of quality of care measures for older persons’ transitions across the following settings: residential seniors’ facilities, homes, emergency transport services, emergency departments (EDs), and hospitals. Two reviewers independently screened abstracts and full text articles for indicators using predefined inclusion and exclusion criteria. In preparation for Delphi rounds, extracted indicators were coded by setting, Donabedian framework domain, and Institute of Medicine (IOM) Domains of Quality. From 10,487 unique records screened, 41 met inclusion criteria. We digitally searched the grey literature for organization websites that generated reports of quality indicators, yielding an additional 12 reports. Overall, 326 QIs (n= 266 established and n= 60 developing) were identified, including 35 (11%) structure, 212 (65%) process, and 79 (25%) outcome indicators. This included indicators categorized into Timeliness (25%), Safety (21%), Effectiveness (n=24%), Patient-centeredness (19%), Efficiency (10%) and Equity (<1%). These indicators will be evaluated in two rounds of electronic surveys for relevance, feasibility, and scientific soundness using a Delphi process. This will allow expert panellists to categorize indicators into “maintain”, “consider” or “discard” groups. QIs provide benchmarks for monitoring and decision-making on quality improvement in healthcare systems. By identifying indicators and knowledge gaps that exist in quality measurement, policy makers, knowledge-users and researchers can collaborate to improve care for vulnerable older persons across settings.
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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 | 0.173 | 0.349 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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".