Alignment factors between client needs and design solutions during the project definition: Case study of a Canadian mega-hospital using Lean-led Design
Bibliographic record
Abstract
Project definition refers to the first stages of a project life cycle (i.e. planning, programming, and preliminary design) in which client needs are identified and a conceptual design solution is developed. Defining and formalizing client needs are complex tasks especially in complex projects such as hospitals since naturally multiple clients such as managers, the government, users, clinicians, patients, and staff members among others, are involved. Each client has their own needs and interests that could sometimes be conflicting with those of the others.However, traditional methods of project definition management have been proved to be inadequate. In the traditional approach, users are rarely consulted, and the focus is more on technical issues and less on functional aspects, which impacts the future work environment and may consequently lead to increased hospital-acquired infections or patient mortality. Participative approaches such as Lean-led design, in which users including patients are involved in the process of project definition, are proposed to address this problem. However, little is discussed in the literature regarding the value of such approaches in terms of better alignment of projects with client needs.This research first identified the factors that can impact the alignment between needs and design solutions during the project definition via a systematic literature review. Based on these factors, a framework was provided to assess and improve the alignment. The validity of the framework was then empirically evaluated and revised based on a longitudinal mega-hospital case study that had implemented the Lean-led design approach the objective of which was to ensure a harmony between needs and requirements as a result of integrating two hospitals. The main assumption here is that better alignment provides more value to end users and the main contribution of this research is a framework that can help researchers and managers to assess and evaluate alignment during the project definition stage.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".