Has the implementation of time-based-targets for emergency department length-of-stay influenced the quality of care for patients? A systematic review of qualitative literature
Bibliographic record
Abstract
abstract Background Time-based-targets for emergency department length-of-stay were introduced in England in 2000; followed by Canada, Ireland, New Zealand, and Australia after emergency department crowding was associated with poor quality of care and increased mortality. Objectives The aim of the systematic review was to evaluate qualitative literature to investigate how implementing time-based-targets for emergency department length-of-stay has influenced the quality of care of patients. Methods Systematic review of qualitative studies that described knowledge, attitudes to or experiences regarding a time-based-target for emergency department length-of-stay. Searches were conducted in Cochrane library, Medline, Embase, CInAHL, Emerald, ABI/Inform, and Informit. Individual studies were evaluated using the Critical Appraisal Skills Programme tool. Individual study findings underwent thematic analysis. Confidence in findings was assessed using the Confidence in the Evidence from Reviews of Qualitative research approach. Results The review included thirteen studies from four countries, incorporating 617 interviews. Themes identified were: quality of care, access block and overcrowding, patient experience, staff morale and workload, intrahospital and interdepartmental relationships, clinical education and training, gaming, and enablers and barriers to achieving targets. The confidence in findings is moderate or high for most themes. More patient and junior doctor perspectives are needed. Conclusions Emergency time-based-targets have impacted on the quality of emergency patient care. The impact can be both positive and negative and successful implementation depends on whole hospital resourcing and engagement with targets. Funding The Australasian College for Emergency Medicine provided administrative support for the study, no funding was received. Registration PROSPERO CRD42019107755 (prospective)
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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 | 0.074 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".