When the California HealthCare Foundation launched the California Health Data Project at the Health and Human Services DataFest in March this year, my fellow Sacramento Health Data Ambassador, Joel Riphagen, and I were still in the early stages of brainstorming ways to connect our local health community with the California Health and Human Services Agency’s open data efforts.
At the DataFest, we heard from many open data evangelists who spoke of efforts to free health data across the globe. One of those speakers was Damon Davis, director of the federal Health and Human Services Agency’s (HHS’) Health Data Initiative. Davis works in the HHS IDEA Lab, an incubator for innovation at HHS, and shared with us a project that one of their entrepreneurs-in-residence was working on: Demand-Driven Open Data.
Project lead David Portnoy describes the approach:
Demand-Driven Open Data (DDOD) is a framework of tools and methodologies to provide a systematic, ongoing and transparent mechanism for you to tell public data owners what’s most valuable.
This elegant concept encapsulates our role as health data ambassadors: We’re connecting data users with data publishers to help those publishers strategically prioritize data that’s most important to users. And DDOD shows us how to more transparently operationalize an open data feedback loop.
How It Works
Like any user-centric approach, DDOD starts with user needs. Requests for data are not framed in terms of specific data sets, but rather as use cases that articulate the value of a data set. Because DDOD is a highly structured process, Portnoy developed a template to delineate use cases in the following format:
- Use case summary
- Current data and limitation
- Short-term workaround
- Long-term implementation
Those use cases are then tracked through a set of milestones that move the use case from a posted need to an implemented (and documented) solution.
Each milestone belongs to one of three roles: the data user, the DDOD administrator and the data owner. The DDOD administrator plays the role of mediator between the data user and the data owner in the DDOD framework.
Portnoy deployed free and open source tools to pilot the DDOD process, including the use of a wiki, Google Docs and GitHub Issues. These off-the-shelf technologies can empower innovators to quickly prototype their ideas, test them with real users, and iterate in an agile way. This process of rapid learning helps validate assumptions about the value of a product or process prior to investing in custom software development. Ultimately, however, DDOD will need a more integrated, user-friendly approach in order to scale.
Adopting DDOD in California
One of the more appealing parts of DDOD is that it moves us closer to a world in which interfaces to government can be agnostic as to which agency, department or level of government is responsible for a given program (or data set, in this case). That is, we can easily imagine a DDOD software solution that allows users to enter their use cases on the front end, and those requests are then routed to the responsible agency on the back end, regardless of the level of government. Local health data stakeholders have consistently expressed that the current reality of health data being warehoused in myriad locations on the Internet as a source of friction that interferes with putting data to good use.
In fact, Portnoy has posed the question, "What improvements in population health or acceleration of medical discoveries are possible with &hellip federal, state and local data?" as a challenge to spur more interagency collaboration around open health data. In response, we asked a local health data stakeholder, Valley Vision, to submit a use case, the publication of community health needs assessments in the Sacramento region. And our colleagues at CHHS have expressed interest in piloting DDOD at the state level.
As local health data ambassadors, we stand ready to not only help move these conversations forward, but also provide the technical assistance needed to stand up the technology platforms to make DDOD frictionless. And who knows? This could open the doors to something even more ambitious, such as two-way open data publishing (state to local, local to state) on a single portal. What we do know is that a demand-driven approach will be centered around user needs, and that’s something to celebrate in and of itself in a world of increasingly abundant data.
This commentary originally was published on Medium.