From the back room to the front room: Combining clinical and financial information to support evidence-based decision making
Bibliographic record
Abstract
IntroductionDecisions in healthcare are not based on a single piece of evidence. Decision-makers consider a broad range of information, including patient, system and financial information. Canadian healthcare decision-makers now have access to linked clinical and financial data – at the patient level - via an online, private tool. Objectives and ApproachThe objectives of this presentation are to showcase the power of having linked inpatient and ambulatory care clinical and financial data, as presented in an online tool. More specifically, two separate scenarios will be worked through, demonstrating how key decisions can be impacted by having record-level clinical and financial information. For example, a hospital may make a different decision when looking at the price differential of performing some surgeries and keeping patients overnight, versus performing these same surgeries in day surgery context and sending patients home. Supporting drill-down detail and visualizations will also be showcased. ResultsThe presentation will focus on the importance of leveraging and integrating available information to better support decision-making. The presentation will emphasize how this tool, which uses linked clinical and financial data, is an example of the integration of new information sources into traditional decision-making practices. For example, with the availability of detailed cost estimates tied to clinical information, decision-makers have the ability to provide budgeting and costing estimates, by area, for different patient types. This is particularly important for health organizations that do not have a patient costing system in place. Conclusion/ImplicationsTools that integrate information in an easy to use format allow decision-makers to access important information quickly, thus facilitating more time to gather supplemental information and consider the information at hand, ultimately supporting evidence-based decision-making.
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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 | 0.081 | 0.228 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.024 | 0.014 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".