Related Experiment Video
Updated: Aug 4, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
The Variable Impact of Clinical Risk-Adjustment Models to Evaluate Hospital Design
Mitchell Mead1,2,3, Upali Nanda3,4, Andrew M Ibrahim1,2,3,5
1Department of Surgery, University of Michigan, Ann Arbor, MI, USA.
Objectives:
To identify the impact of clinical risk adjustment models for evaluating pain medication consumption differences between private rooms and a multibed ward.
Background:
Views of nature are reported to reduce anxiety and pain for patients. This often leads to prioritizing large windows with views for patient rooms; however, it is not clear how other factors influencing pain (e.g., patient demographics) may confound evaluations of room design.
Methods:
We identified 1,284 patients at the University of Michigan undergoing thyroidectomy where patients recovered in one of the two locations: a private room with a view to nature or a multibed ward with no windows. We used pain medication data from the electronic medical record and risk adjustment models to evaluate pain medication consumption between the room types.
Results:
Private room patients did not use more pain medications when measured using unadjusted morphine milligram equivalents (18.3 vs. 15.3 mg, p = .06). Risk adjusting for age, gender, comorbidities, opioid history, and procedure subtype resulted in private room patients demonstrating higher consumption of morphine milliequivalents (17.5 vs. 15.5 mg, p < .01). In contrast, risk adjusting for age, gender, opioid history, and selected comorbidities estimated higher pain medication consumption for multibed ward patients relative to private rooms (16.27 vs. 15.51 mg, p < .05).
Conclusion:
Estimated differences of pain medication consumption for patients in differently designed rooms varied depending on the risk adjustment model. These findings underscore the importance of understanding appropriate clinical measurement and risk adjustment strategies to accurately estimate the impact of design, before applying research into practice.
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