Defining Underserved Communities
Identifying the factors that make a community underserved requires having a baseline of what constitutes ‘typical’ or ‘ideal’ service. This allows communities to be characterized and grouped relative to one another, making it easier to identify underserved community outliers. In addition to identifying a true baseline, it’s important to look at the context surrounding the data to gain insight about what might be correlated with community service and how those factors might change over time. Building and maintaining a rich data set to help inform this context is a critical component of analyzing data and gaining actionable insights.
“Models are often focused on snapshots of data and assumptions of how the future is similar to the past,” says Brant Horio, a Technical Fellow at LMI. “But communities, even well-off ones, are never static—people and social determinants of health are constantly evolving over time. Gentrification and other migration patterns, for example, can drive food and housing insecurity that weren’t previously part of a neighborhood. If you analyze insufficient or outdated data, you run the risk of coming away with insights that aren't representative or useful.”
Identifying What You Are Trying to Solve For
Using data analytics to gain insights and model solutions works best when agencies take the time to first understand what they are trying to solve for. When it comes to a topic as broad as understanding social determinants of health, each agency could potentially bring a unique lens to that topic. Beginning with a collaborative discovery session and data audit that looks at how a given agency can help and what data they have available can save a lot of time upfront.
“One of the challenges that agencies run into when they begin using data analytics is that they don’t ask the right questions or take the time to identify what data they have, where the gaps are, and what data might be needed,” explains Horio. “These can seem like obvious things to identify, but it’s not always straightforward. It should require robust discovery sessions with stakeholders as well, rather than relying on only the data scientists in the room.”
Underserved communities are still connected and complex. Simply put, everything influences everything else. Therefore, effectively addressing underserved communities requires more than a singular healthcare solution. A comprehensive approach isn’t only about healthcare, but addresses healthcare, food security, housing, and potentially many other factors all working together.
“If you’re looking at social determinants of health, for example, just using patient data alone to provide generic solutions isn’t going to be enough. It is not uncommon to see patients who can’t get to their recommended appointments, for example, because there isn’t a doctor nearby. Or maybe, they can’t improve their diet because they live in a food desert,” Horio says. “Beginning with a data audit as part of a stakeholder discovery session to fully understand the question and the complex factors that make the problem so challenging can help identify what other data sets and agencies you need to bring in to get the most meaningful insights.”
Bringing in a variety of respondents can improve the data available for analysis and get a response group together early to engender buy-in and vested interest so that they can start thinking through how best to craft, adopt, and coordinate data-driven responses to the problem.
Creating Meaningful Improvements For Underserved Communities
There are many practical applications where data can extend and improve access to benefits and care.
For example: If an individual qualifies for a service like Supplemental Nutrition Assistance Program (SNAP) or Special Supplemental Nutrition Program for Women, Infants, and Children (WIC), they may also qualify for other services but may not know it. LMI’s work with the WIC program involved building a referral engine designed to help citizens and program coordinators know if applicants qualified for other services. Gathering this data from applicants allows agencies to better understand and serve these populations.
“Siloing data and information from agencies and individuals can make it difficult to respond effectively to complex problems,” explains Derrick Spain, a Solutions Architect at LMI. “There are a lot of legitimate privacy concerns, but we have the technology now that allows us to look at common answers on application forms and make suggestions about other services or at least flag to an applicant that they should ask about other services without sacrificing privacy. It’s a small start, but building in suggestions like this into other services could be one way that agencies can start reaching underserved individuals.”
Les Milner, a Program Manager and IT Consultant with LMI, adds that there are also natural points within programs like WIC where the government could work with community organizations or the private sector to improve access to services. “In many states, SNAP and WIC benefits can be used at farmers markets, which can bridge the gap in areas that would otherwise be food deserts. Working with the community organizations involved in these markets or in the localities to share information about accessing WIC and SNAP benefits can help get information to people who qualify,” he says. He adds that when government agencies take the time to work with local community organizations, they can also get feedback and information that can improve data analysis.
“If you want outreach to be effective, it has to be comprehensive. At the agency level, relying on data analysis is an important tool because it can help policymakers identify gaps and model solutions, but it’s only part of the picture,” Milner explains. “Sharing insights with community organizations and getting information from them can provide new layers of understanding that help us arrive at more comprehensive solutions for how to improve access to services.”
LMI has a 60-year history of providing innovative, analytically based solutions to government logistics challenges. We work at the intersection of science, policy, logistics, and analytics to facilitate innovation in healthcare provision and payment, implement federal healthcare priorities, advance health security, and optimize service delivery and program effectiveness. If you’re interested in learning more about how we can aid in using advanced data analytics to better reach and serve unserved communities, reach out to Janet Webb at email@example.com.