A review of computerized hospital layout modelling techniques and their ethical implications
Bibliographic record
Abstract
This paper reviews an area of interdisciplinary collaboration in the design of healthcare facilities that attempts to optimize hospital space-planning using automated statistical techniques from the discipline of Operations Research (OR). This review articulates Facility Layout Problems (FLPs) as a general class of OR problems. Furthermore, the review highlights limitations of these techniques, which necessitate an ethical and participatory engagement with computerized processes of healthcare architecture. An in-depth critical review was carried out, which revealed a number of common themes, collectively theorized as metamodeling processes, or models of models, through which various FLP modelling techniques can be challenged and debated in terms of their architectural viability, and ethical ramifications. This review provides a methodological basis for the further evaluation of computational models. It was found that most of the reviewed studies are functionally focused on flow efficiency and, in general, do not consider broader contextual, relational, social, or salutogenic design values. This review is the first on the subject written from an architectural perspective. It can be used by a broad range of readers as its critical review of past and present hospital layout modelling techniques discusses their capabilities and limitations. As such, it also enables them to consider ethical values while critiquing the epistemology of computational processes hidden beneath algorithmic outputs.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".