Data for Improvement and Clinical Excellence: a report of an interrupted time series trial of feedback in home care
Bibliographic record
Abstract
There is substantial evidence about the effectiveness of audit with feedback, but none that we know have been conducted in home care settings. The primary purpose of the Data for Improvement and Clinical Excellence – Home Care (DICE-HC) project was to evaluate the effects of an audit and feedback delivered to care providers on home care client outcomes. The objective of this paper is to report the effects of feedback on four specific quality indicators: pain, falls, delirium, and hospital visits. A 10-month audit with feedback intervention study was conducted with care providers in seven home care offices in Alberta, Canada, which involved delivery of four quarterly feedback reports consisting of data derived from the Resident Assessment Instrument – Home Care (RAI-HC). The primary evaluation employed an interrupted time series design using segmented regression analysis to assess the effects of feedback reporting on the four quality indicators: pain, falls, delirium, and hospitalization. Changes in level and trend of the quality indicators were measured before, during, and after the implementation of feedback reports. Pressure ulcer reporting was analyzed as a comparator condition not included in the feedback report. Care providers were surveyed on responses to feedback reporting which informed a process evaluation. At initiation of feedback report implementation, the percentage of clients reporting pain and falls significantly increased. Though the percentage of clients reporting pain and falls tended to increase and reporting of delirium and hospital visits tended to decrease relative to the pre-intervention period, there was no significant effect of feedback reporting on quality indicators during the 10-month intervention. The percentage of clients reporting falls, delirium, and hospital visits significantly increased in the 6-month period following feedback reporting relative to the intervention period. About 50% of the care providers that read and understand the feedback reports found the reports useful to make changes to the way clients are cared for. Routinely collected data used over time for feedback is feasible in home care settings. A high proportion of care providers find feedback reports useful for informing how they care for clients. Since reporting on the frequency of quality indicators increased in the post-intervention period, this study suggests that ongoing use of audit with feedback to enhance health outcomes in home care may promote improved reporting on standardized instruments.
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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 | 0.058 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".