Usage of primary and administrative data to measure the economic impact of quality improvement projects
Bibliographic record
Abstract
In healthcare, quality improvement (QI) aims to improve patient outcomes and fight inefficiencies.1 2 Inefficiency is associated with limited access to care and premature death.3 QI is not a costless endeavour,1 so healthcare providers and decision-makers must quantify both potential health and economic impacts that result from QI projects. Economic analysis supports decision-makers by estimating the value associated with a programme. In QI, this includes studying the costs of implementing a project as well as evaluating any incremental changes in healthcare costs that occur as a result of the project. In tandem with an evaluation of the project’s impact on patient and provider outcomes, economic analysis provides another value dimension. Demonstrating added value can further incentivise resourcing for the implementation of a QI project to decision-makers. Despite the potential benefits of performing economic analyses on QI projects, little guidance exists on how to empirically evaluate their potential costs.4 Primary and administrative data each have unique and complementary strengths. Primary data, collected during a QI project, measures processes and outcomes which are important for evaluation, and are often not captured in administrative data.5 In contrast, administrative data represents a secondary data source which, although routinely collected for purposes other than research, can serve as a source of readily available information that lends itself to further analyses. Primary data are collected immediately, without the delays inherent in accessing and analysing administrative data. Administrative data offers linkable, comprehensive records of health system resource utilisation at both the population and the individual level,6 and provides a way to follow patients over time and outside the facility conducting the project. Measures of performance (eg, changes in the utilisation of healthcare services) can be operationalised to quantify the impact of the project. The objective of this paper is to describe …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.085 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".