SBIR Phase I: Architectural Epidemiology: Leveraging Machine Learning and Spatial Data at Scale to Understand Health Outcomes

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the ability to leverage architecture as a new tool for advancing health and wellbeing. A growing body of evidence demonstrates that hospital architecture systemically affects patient health, operational efficiency, and patient satisfaction. However, architects often lack the tools to reliably predict how design choices will impact building operations. Often hospitals and clinics designs fall short with building owners not able to achieve organizational metrics and goals, and patients can see poorer quality of care, increased medical errors, and even higher mortality rates. The commercial impact will stem from a software platform that equips architects with no-code workflows necessary to deploy data-driven design while managing project timelines and internal costs. Although the Phase I project focuses on healthcare architecture, the broader impact will be that architects will be able to design all buildings to optimize health, productivity, and organizational goals.This Small Business Innovation Research (SBIR) Phase I project will enable development of 1) algorithms that generate structured architectural data capturing spatial qualities that affect occupant health, 2) data visualization interfaces for exploring and validating spatial data and health data jointly, and 3) statistical and machine learning models for identifying meaningful relationships between architectural characteristics and health outcomes across large-scale datasets. These tools will be tested and evaluated by conducting a proof of concept study with a large health system. The goal of the study is to evaluate the efficacy of untested statistical machine learning methods to identify meaningful relationships between architecture and health outcomes. The proposed research will provide new types of insight into architecture's role in affecting health outcomes, providing a novel approach to data-driven design. Though the focus of this proposal is on architectural data, the principles explored will push boundaries of current limits by 1) incorporating qualitative and quantitative data in machine learning models, 2) assessing new modes of interpretability via data visualization, and 3) advancing methods for working with sparse but rich datasets.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.