Integrating Narrative Inquiry and Thermal Comfort Simulation for Empathy-Driven Healthcare Design: A Case Study
Bibliographic record
Abstract
Designers have an important role to play in shaping healthcare environments, not only by addressing functional needs but also by creating experiences that reflect empathy and care for patients, families, and staff.This paper draws on a case study that employed narrative inquiry as a design approach to explore how empathy can be embedded into the design process.The study was conducted in a design hospital focusing on a patient care center, where to engage with a variety of narrative techniques, listening to stories, creating visual and written accounts, and reflecting on these through design.In addition, the CBE Thermal Comfort Tool was used to evaluate and simulate indoor environmental conditions, ensuring that thermal comfort parameters complied with ASHRAE Standard 55:2023.The process revealed that empathy-driven approaches not only encouraged engagement but also led to creative and often unexpected design solutions that sought to support the patient as a whole person.The thermal comfort assessment using the CBE Tool confirmed that maintaining operative temperatures between 21-24℃ and relative humidity of 40-60% fostered a sense of comfort, calmness, and trust conditions that align with empathetic and patient-centered care.The study suggests that placing empathy at the forefront of the design process allows healthcare spaces to move closer to genuinely patient-centered care by advancing an operational design framework that systematically links narrative-based empathy exploration with measurable environmental performance evaluation.Narrative analysis revealed three core experiential needs recurring across the narratives: (1) patient empowerment and control, (2) emotional restoration and stress relief, and (3) support for relational and family presence.These needs were systematically translated through a Design Translation Matrix into specific interior strategies, including empowerment zones replacing conventional waiting areas, interior healing gardens integrated with treatment circulation, and configurable treatment spaces supporting private and semi-private care scenarios.Thermal comfort simulations conducted across six finalized design proposals demonstrated consistent compliance with ASHRAE Standard 55:2023, with all schemes maintaining operative temperatures within 21-24℃, relative humidity between 40-60%, and PMV values approaching neutrality (mean PMV = -0.07,PPD ≈ 5%).
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".