From Data to Design Intelligence: Building a Dashboard for Healthcare Decision-Making
Christian Beatty Student Intern
Architectural projects generate an enormous amount of data. The ways in which we gather, organize, and store this data over the course of a hectic project schedule can vary; our information is often fragmented across spreadsheets, models, formats, and project management systems. As our work grows in complexity, managing usable data is increasingly challenging
NCSU architecture student and LS3P intern Christian Beatty finds these challenges exciting. In working with data from a trove of LS3P healthcare projects, Beatty is seeking to transform information into a tool that designers can use to ask better questions. “If we can figure out how to manage data from healthcare projects, which automatically come with a lot of complexity, we should be able to translate this process to all kinds of project types,” he explains.
Working in collaboration with Innovation Leader and healthcare planner Espy Harper, Beatty set out to explore how a centralized dashboard could translate raw information into design intelligence, grounding complex healthcare decisions in evidence while remaining flexible enough to evolve over time.
Healthcare Market Leader and Associate Principal Jeff Mural envisioned a tool more dynamic than a conventional Excel-based workflow:
a centralized database designed to aggregate healthcare projects and, potentially, expand into a cross-sector superset. At the outset, the idea was intentionally open-ended and not fully defined, with no expectations beyond producing careful, thorough work. The broader goal, however, was to transform how project data is used, framing the effort as an opportunity to explore whether this problem could be meaningfully addressed given its potential to significantly improve efficiency and inform design decisionmaking across the firm.
The work began modestly, with what Beatty describes as “pretty much an Excel sheet” containing square footage data pulled from multiple sources. Early efforts focused on verification, reconciling information from Revit exports, Deltek, and legacy files where some of the inconsistencies in the numbers led to more questions. That first phase of analysis revealed a core truth that would shape the entire project: the dashboard would only be as useful as the data behind it. The initial task was less about visualization and more about understanding what information existed, what was missing, and how data from disparate systems could be aligned.
As the dataset grew, so did the ambition of the structure supporting it. What started as individual Excel tabs for each project evolved into a
single master framework that allows designers to view projects collectively. This shift made gaps in the data impossible to ignore, but it also opened new possibilities. Beatty began organizing information across multiple tiers, moving from project to floor, department, function, and finally room level data. “Every single tier is really one step deeper into the building,” he says. That hierarchical logic became the backbone of a system designed to scale across projects and, eventually, across markets.
Iteration defined the next phase of the work. As Beatty moved from Excel into anayltics tool Power BI, the process became a continuous push and pull between data and design intent. “It was really a conversation back and forth between working on the dashboard and then going back to the data and filling it back out,” he noted. Developed in collaboration with Senior Architect Ron Smith and the broader dashboard team, early dashboard drafts tested how much information was too much, when complexity hindered insight, and what story the dashboard was actually meant to tell. Rather than attempting to show everything at once, the team focused on creating a clear project overview that could serve as a starting point for deeper exploration.
Collaboration played a critical role in refining both the structure and the logic of the dashboard. Beatty worked
closely with members of the healthcare team to understand which metrics truly influenced design decisions, while conversations with knowledge management and data specialists helped resolve technical challenges. One breakthrough came through developing a system of unique keys that allowed data to connect cleanly across multiple levels. After reviewing the approach, one data expert told him, “I would never have thought to do that, but that is a super efficient way to do it.” Those moments reinforced the value of cross-disciplinary exchange.
Throughout the process, Beatty remained focused on usability. The dashboard needed to be dense enough for designers who knew the buildings intimately, yet legible enough for those encountering the data for the first time. “That’s a weird thing to balance,” he acknowledged, “when you’re trying to put a lot of information in one page.” The solution was to separate information into layered views, allowing users to move between highlevel summaries and more detailed breakdowns depending on the question at hand.
Over time, the dashboard began to reveal its greatest potential, not as a reporting tool, but as a platform for exploration. With the ability to “slice and dice” data across projects, room types, and functions, teams could begin testing scenarios and understanding
how design decisions affected space efficiency, staffing, and cost. Beatty described this as the moment when the work started to flourish, enabling teams to predict how future projects might perform rather than relying solely on precedent or intuition.
Looking ahead, the team sees broad applications for this framework beyond healthcare. Beatty has already begun conversations with higher education and K-12 teams, noting that while buildings in those sectors tend to be less complex than, say, an inpatient facility, the underlying structure remains relevant. The long-term vision is a set of adaptable templates that draw from a shared data foundation, integrated with firmwide systems and capable of supporting emerging tools such as generative planning and scenario modeling. As Beatty observed, starting with healthcare made sense, “because it’s a lot easier to get more simple than more complex.”
Building on this foundation, the dashboard is evolving beyond a repository of project information into a more dynamic decision-making tool. It enables teams to compare project types, filter data across key variables, and establish a feedback loop between past and future work. Using factors such as square footage, location, and year, the dashboard can be used to inform cost projections or, conversely, to estimate project scale. As additional
datasets and market inputs are integrated, the framework becomes a bridge between raw data and actionable insight, with the potential to inform early planning decisions and shape design development in the Programming and Schematic Design phases.
Ultimately, this dashboard represents a shift in mindset as much as a technological achievement. By embedding data directly into the design workflow, Beatty and Harper have demonstrated how design technology can support smarter conversations, clearer decisions, and more resilient outcomes. The real value lies not in the dashboard itself, but in the iterative, collaborative process that shaped it, and in its potential to help design teams see their work, and their impact, more clearly.
