Build, Seed, Then Pave: Creating data-driven solutions for healthcare customers

Sometimes, truly innovative solutions break the rules.

Project managers sometimes tell a story about how smart architects rely on an approach known as ‚ÄúBuild, Seed, Then Pave¬≠‚ÄĚ so that the completed project meets the objectives of the client.

Take the design of a college or professional campus, for example. The traditional (and less effective) method is to build everything more or less at once‚ÄĒbuildings, sidewalks, and grassy areas. It’s a method that allows projects to be completed more quickly when deadlines and budgets are critical factors to the client.

But the problem with this method often arises six months after the campus opens. Pedestrians have killed off large swaths of planted grass because‚ÄĒrather than using sidewalks‚ÄĒthey prefer different paths between buildings.

A different approach

Employing the Build, Seed, Then Pave approach, savvy architects can avoid such problems by relying on the importance of user data. Instead of designing and building everything at once, architects construct the buildings and plant grass seed in all open areas first, but delay putting in sidewalks. Then, they wait a few months to see how pedestrians actually navigate the areas between buildings. The places where the grass has been worn down by constant pedestrian traffic clearly indicate the best location for sidewalks.

The genius of the approach is that even the most experienced architects face challenges in knowing exactly how the systems they build will be used in practice. Even with thorough analysis, it is nearly impossible to find all the potential efficiencies within a system until people are actually using it.

Whether or not any actual architects use this method, the idea behind it is still helpful to improve health IT projects and complex business processes. When we standardize on emergent efficiencies, the benefits multiply.

Your request can encourage innovation

At Cognosante, we aim to adopt the Build, Seed, and Pave approach so that the health IT solutions we design for our customers work in the real world, not just on paper. You may not be aware that how you request a solution may affect the flexibility we have in creating a unique plan that effectively fits all your needs.

Most proposal requests use a Statement of Work (SOW) that describes, with varying degrees of specificity, exactly what the customer wants us to do. The more specific that SOW is, the less opportunity there is for the client to obtain a solution that is truly innovative and effectively solves their problem.

On the other hand, some requests instead provide what is known as a Statement of Objectives (SOO). The SOO outlines what outcomes the organization is looking to achieve rather than detailing exactly how it wants a service provider to help it achieve those outcomes. In a well-constructed SOO, the customer may say it wants increased customer satisfaction or faster throughput times. Or, it wants to detect and eliminate waste, fraud, and abuse. This type of scenario is where Cognosante has the freedom and latitude to bring innovation into a health IT solution to achieve effectiveness.

At Cognosante, we try to treat every Statement of Work as a Statement of Objectives. Through ongoing relationships with our customers, we determine exactly what they’re trying to accomplish. This is the underlying principle that guides all of our solution design.

Our Solutions Architects are specifically focused on developing and identifying business-centric technical solutions to meet some of the most challenging healthcare issues. They each have a broad technical background and deep understanding in designing solutions that fit the way people will actually use a system.

Sometimes, the solutions we come up with are a bit unexpected. But then, that is all part of innovation. You wouldn‚Äôt necessarily think that planting grass and delaying the construction of sidewalks for a building project is a good idea, but in most instances, a flexible design that can incorporate additional data‚ÄĒplanning for the unexpected‚ÄĒresults in the best possible solution.



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