“We’ve got the home care data, what do we do with it?”: understanding data use in decision making and quality improvement
Bibliographic record
Abstract
BACKGROUND: In the past decade the provision of home care services in Canada has doubled; with this increase there has been a growing interest in improving quality and safety. National-level data are captured in the Home Care Reporting System (HCRS), using the interRAI-HC tools. Data in the HCRS provide decision-makers and administrators with actionable evidence to inform quality and safety improvement initiatives. The objective of this study was to determine how providers and administrators were using these data, and if there were any opportunities to enhance their use. METHODS: We collaborated with the Canadian Patient Safety Institute to conduct this qualitative study. To understand data use in home care, we recruited participants in management and/or data analysis roles from home care organizations across Canada. We conducted three individual interviews and three focus group interviews with a total of eleven participants from five Canadian provinces. Individual and focus group interviews were audio recorded, transcribed, and analysed using a combination of emergent coding and thematic analysis. RESULTS: Several participants provided powerful examples of how interRAI data have been used to guide patient safety and quality improvement initiatives; however, all participants recognized challenges in using these data. Leveraging interRAI data in the complex home care environment is limited by several factors: the general "newness" of these data in many jurisdictions; the sheer volume of data; limited capacity and resources to interpret and analyse the data; and connectivity issues in rural areas. Participants recognized and appreciated the training that has been provided, and made several recommendations for additional training. CONCLUSIONS: Mandated clinical datasets, such as the suite of interRAI tools, have the potential to improve quality and inform decision-making. However, in order to utilize these data, home care agencies require additional education, personnel and supports. Additional training and resources for these organizations could improve the use of available data by home care agencies, as well as outcomes for individuals receiving home care services.
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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 | 0.175 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.029 | 0.097 |
| Scholarly communication | 0.032 | 0.041 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.008 | 0.013 |
| 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".