Implementing an Integrated Performance Management System: The Early Experience of The Ottawa Hospital
Bibliographic record
Abstract
This study is a mixed methods investigation, based on a case study of The Ottawa Hospital’s recent and ongoing implementation of an integrated performance management system (IPMS). It is the first empirical investigation to identify the reasons why Canadian healthcare leaders choose to implement an IPMS in a hospital setting, the core components of hospital-based IPMSs, the challenges that senior leaders face when implementing such systems, and how these challenges might be mitigated to increase the likelihood of a successful implementation. Key findings include the need for senior leaders to carefully consider organizational culture prior to fully implementing an IPMS, engaging physicians early in the journey, and coordinating the implementation so that knowledge, skill, and expertise, as it relates to the IPMS, are distributed across the organization in tightly knit waves. Recommendations for future research include the development of frameworks for the design, implementation, and use of IPMSs
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.031 | 0.014 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.003 | 0.008 |
| 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".