A Health Care Project Management Office's Strategies for Continual Change and Continuous Improvement
Bibliographic record
Abstract
Health care organizations need project and change management support in order to achieve successful transformations. A project management office (PMO) helps support the organizations through their transformations along with increasing their capabilities in project and change management. The aim of the present study was to extend understanding of the continuous improvement mechanisms used by PMOs and to describe PMO's strategies for continual change and continuous improvement in the context of major transformation in health care. This study is a descriptive case study design with interviews conducted from October to December 2015 with PMO's members (3 managers and 1 director) and 3 clients working with the PMO after a major redevelopment project ended (transition to the new facility). Participants suggested a number of elements including carefully selecting the members of the PMO, having a clear mandate for the PMO, having a method and a discipline at the same time as allowing openness and flexibility, clearly prioritizing projects, optimizing collaboration, planning for everything the PMO will need, not overlooking organizational culture, and retaining the existing support model. This study presents a number of factors ensuring the sustainability of changes.
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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 | 0.042 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".