Lean Healthcare and Ontario Case Costing : An Examination of Strategic Change and Management Control System
Bibliographic record
Abstract
In this paper we examine how two different strategic changes influence the Management Control Systems in six Ontario based hospitals. These changes are: 1. new funding regimes based on Ontario Case Costing and, 2. Lean Healthcare initiatives in Ontario Hospitals.The Ontario Case Costing (OCC) approach is based on the traditional MAS assumption that more accurate costs will lead to lower costs through better management decisions of some sort. In contrast, Lean Healthcare is premised on the assumption that the primary path to better performance is realized by individual employees, who serve as the leading actors in a daily process of waste removal and efficiency improvement.Based on the MA literature and on information about both the OCC and Lean Healthcare initiatives, we expect there to be significant frictions inside the financial management staff as well as between the Lean initiative and the financial management staff related to the OCC mandate. This study examines this set of interactions to learn how these issues impact the employees involved. In particular, the Ontario context allows us to examine the variety of responses to the interaction of Lean healthcare strategy and a traditional MAS such as OCC.We find that the MASs of hospitals in our sample are too loosely coupled to develop the frictions indicated by traditional MAS theory and propose modifications in application of the theory to accommodate this observation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".