From EBD to EBE, working at the interface of health design and innovation

Lessons from I-Corps @CCTSI

With my teammate Cody, I recently attended I-Corps at the University of Colorado Anschutz Medical Campus. Innovation Corps (I-Corps) is a National Science Foundation (NSF) initiative created to move research from benchtop to bedside. The National Institute of Health (NIH) collaborated with NSF to create an I-Corps program focused on biomedical and translational research, and selected Colorado Clinical & Translational Sciences Institute (CCTSI) as one of their pilot sites to train entrepreneurial teams in health innovation.

As an Evidence-Based Design (EBD) practitioner for healthcare facilities, I am familiar with evidence-based practices. Yet, Evidence-Based Entrepreneurship (EBE) was new to me. Like other evidence-based methodologies, EBE shares a lot in common with EBD. Reflecting on key distinctions between EBD and EBE, I discovered what EBD might learn from EBE.

Our charge.

Our cohort included teams of fellow academic researchers, largely from clinical research and practice, in early stages of developing a health innovation. We were guided by entrepreneurs during an immersive learning course grounded in Customer Discovery and tasked with completing 30 interviews by the end of three weeks to deliver a value proposition. My teammate, undergraduate Environmental Design student and fabrication technology genius, Cody Thurston, and I launched an idea for a nature-inspired haptic sensory surface, Biofeelia.

Lessons learned.

Customer discovery. In EBE, Customers are considered groups of people the innovation aims to reach and create value for. In EBD, we consider impacts of our designs on users– for example, workers, patients and visitors-- as well on clients. EBE embraces a process similar to a Lean Six Sigma Voice of the Customer approach where significant effort is devoted to organizing these groups of people – which include users and decision makers, but also influencers and even saboteurs-- into distinct categories, or customer segments, such that a dedicated value proposition can be constructed for each customer having identified the pains and gains (EBE), aka the critical to quality requirements (Lean Six Sigma).

This elucidated two key differences from my typical approach to starting a project: first, to cast a wider net and consider all the players reached by the innovation, not merely the users directly interfacing with it, and second to assign value to each potential stakeholder such that I could precisely answer the question, “What value, to whom?”.

Among the customer segments we considered for Biofeelia were users and also influencers such as interior designers, furniture manufacturers, policy makers and creative technologists. Having an understanding of our innovation’s impact across a networked ecosystem of customers, we were better poised to prioritize customers, and hypothesize key outcomes associated with each segment which our innovation would need to deliver.

Who is the primary customer? Potential Customer Segments for Biofeelia, adapted from Concannon, et al 2012[1]

Desirability. In practice, much of the evidence design researchers collect is aimed at validating that a need exists, and that the design solution has measurably improved quality associated with the specific need. In the customer discovery process of EBE, validating the functional performance need is also a critical goal. Gathering scholarly evidence to support my innovation felt familiar. But it didn’t answer the question: is this something people will want – not only want, but be willing to pay for? Equally important in EBE is the need to validate that the customer wants and desires a solution to a need, that there is an emotional appeal to having the need met by a particular innovation or design intervention. In human-centered design[2], this is represented as the sphere of desirability. In EBE, desirability is measured by sales. If an innovation is not profitable, and customers are not willing to pay for it, we might conclude it lacked desirability in some critical aspect. Startups often fail because the entrepreneurs are building something that no one wants. In facility design, desirability is often overlooked. There are many examples of designs which, though they are supported by evidence to improve a given performance need, are often retrofitted post-occupancy because the users don’t like the design. This phenomenon plagued the early years of the sustainability movement in cases where emphasis was placed exclusively on the building’s ability to perform, at the expense of a building’s desirability or ambient and physical appeal, with the result that highly performing buildings fell into disrepair and disuse when its users didn’t love or take care of the building. 

A cognitive model to validation. To validate our customer needs and desires, we conducted 30 customer interviews in three weeks. In design research, we strive for structuring experimental studies with as much rigor as possible. Thus, I assumed that the 30 interview sample size was targeted for approaching statistical significance, or the point of diminishing return in qualitative research. However, considering that 100 is the required number of interviews for the longer 7-week I-Corps course, I began to realize that: there is no point of saturation. Where scientific-method research embraces more of a sequential, linear waterfall-type process, innovation research works more in the Lean Six Sigma spirit of continuous improvement.

Value proposition exercise:

“For (users) that need ______, my innovation enables ______ better than ______ providing the benefits of: _______________, _____________, ________________”

While our first attempt at a value-proposition, utilized somewhat of a Mad Libs approach to filling in the blanks, after each data collection (i.e. each customer interview), we revisited our hypothesis (our value proposition statement) and updated it building on our accumulated learning. New information updated the development of our innovation, in contrast to staying a rigorous course bound by a linear scientific methodology. In this way, EBE benefits from a cognitive model of validation, that is, a model that adapts and improves as the system learns and extracts relevant and granular insights. The result: our value proposition was more refined at the end of 30 interviews, and would be even more developed after another 100. Or, we might at any point elect to pivot our solution, rather than persevere. An EBE approach offers the ability to change course at any point based on evidence.

The design process itself is an iterative spiral. It can be difficult for designers to shift their thinking to embrace research methods which utilizes a rigid, linear model of investigation. In this sense, EBE aligns well with the way design thinkers are trained, and more opportunities should be created for designers to engage in innovation research.

Putting the pieces together.

The value proposition tool we utilized is organized as a problem-solution fit diagram which clearly and precisely defines, in the circle, the customer’s job and their associated pains and gains. This information we distilled from evidence gleaned through our customer interviews. In the square, our innovation can be diagrammed- what Biofeelia is as a product and service- and its associated gain creators, and pain relievers. This resulted from iterative prototypes responding to our updated customer segment.

Value Proposition Canvas, Strategyzer[3]

This value proposition canvas is not merely a working tool, but rather a model of convergence. It effectively communicates innovation as a complement to research. In many ways, EBD maps to this EBE process: when we conduct EBD or design research we are practicing health innovation. That is, we devote significant effort to filling out the circle part of the canvas, the measurable user outcomes. The square (product and service) is filled with outcomes-based design strategies or, if a research study, might map to the experimental condition. That said, EBE offers lessons in customer discovery, desirability factors, and methodology which can strengthen this interface between the circle-square.

If I could redesign this diagram, I would show the interface of the circle and square interlocking as defined jigsaw puzzle pieces, whose fit is snug and intricately defined, rather than by a set of broken arrows which never quite meet, to suggest that design and research work best when integrated rather than as discrete systems. But I’d have to conduct at least 30 interviews before I’d have it developed enough to share with you.

[1] Concannon, T., Meissner, P., Grunbaum, J., McElwee, N., Guise, J., Santa, J., Conway, P., Daudelin, D., Morrato, E., and Leslie, L. (2012). A New Taxonomy for Stakeholder Engagement in Patient-Centered Outcomes Research. Journal of General Internal Medicine, 27(8):985–91 DOI: 10.1007/s11606-012-2037-1.

[2] IDEO, HCD Toolkit, www.designkit.org

[3] Strategyzer AG, Value Proposition Canvas. Strategyzer.com.