How a Data Sharing Framework Can Help Tackle Public Health Crises

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Read more about author Robert Reynolds.

Our modern world, with its complex social structures, geopolitical risks, and increasingly mobile populations, presents more and more formidable public health challenges. From COVID-19 to the opioid epidemic to human trafficking, crises can emerge unexpectedly and seem intractable.

But organizations are increasingly aware of a powerful antidote: data. With reliable, up-to-date data, they can gain new insights that enable them to identify and build solutions to improve community well-being.

The difficulty is getting the right data to the right people at the right time. Data that’s hoarded can’t benefit constituencies. Data in one organization might not point to a solution till it’s combined with data in another. To be truly valuable, data needs to be shared quickly and securely among public and private organizations that can effect positive change.

The solution is an innovative data sharing framework that enables organizations to capture, exchange, understand, and act on relevant data. Here’s how to achieve it.

Components of Data Sharing

A data sharing framework involves two primary components: a data trust and a data sharing platform.

Sharing data among organizations calls for mutual trust. The best way to achieve that trust is by establishing a legal and technical agreement, as well as clear Data Governance processes, that stakeholders adhere to.

The data trust legally formalizes the relationship among participants, sets rules for data security and privacy, and provides a mechanism for participants to connect their data sources to create a shared repository.

Both the trustee and the trust members have roles to play. The trust members should sign the agreement, deliver the agreed-upon data, and then gain secure access to data outputs. The trustee must provide the technical infrastructure, maintain the terms of data usage, and keep member data secure.

That technical infrastructure is the data sharing platform. That includes the necessary hardware and software to physically store the data, enable secure access, scale to accommodate all users, and maintain compliance with privacy regulations and policies. The platform should also provide self-service analytics, predictive models, and visualizations that empower users with new insights.

VAST Impacts

The Commonwealth of Virginia followed this approached to combat human trafficking in the state. Virginia averages more than 150 reported cases of labor or sex trafficking each year, higher than about half of states – though observers agree trafficking is seriously undercounted throughout the country.

To address this scourge, the state created Virginia Analysis System for Trafficking (VAST). VAST provides the state’s Department of Criminal Justice Services and HHS organizations with insights into trafficking victims, perpetrators, and factors that contribute to increases and decreases in cases. Collection and correlation of previously siloed data allows organizations to see trends in offenses, place risk factors in context, more effectively prosecute criminals, and better target services to victims.

Authorized users can quickly review data on incidents, victims, offenders, and arrests. They can see color-coded maps by county and breakdowns of incidents and arrests by type of trafficking. Charts and graphs show victim and offender demographics, relationship between victim and offender, offender drug possession, location of incidents, and associated events such as conventions.

The data sharing can reveal otherwise hidden correlations. Users can quickly determine whether certain events resulted in a spike in trafficking, for example, or whether an increase in drug crimes corresponds to an increase in trafficking. As a result, agencies can target interventions and measure impacts.

Best Practices to Build Data Sharing

Virginia built its data sharing framework following best practices and proven strategies. To have the greatest chance of achieving similar results, public- and private-sector organizations alike should follow these steps:

1. Set goals and metrics. Start by identifying a compelling and unifying issue to address. That can help counter any cultural resistance. It will also guide the rest of your efforts and enable you to measure whether you’re actually achieving your goals.

2. Conduct a pilot program. Establishing the legal structure of a data trust and building an effective data platform aren’t necessarily trivial undertakings. You can reduce complexity by starting with a pilot program as a proof of concept and to gain quick wins – often in less than six months.

3. Evaluate existing and required data storage, sharing, and analytics tools and capabilities. Inventory the data management capabilities you already have in place to identify where you need to fill gaps. You’ll require technology for securely storing and sharing data, as well as solutions for data analytics and visualization. You might also require technical expertise you don’t currently have in place.

4. Assess, document, and standardize available data. Identify where relevant data resides across agencies and who currently “owns” that data. Also look for data sources outside government, such as health care systems and nonprofit providers of HHS-related services. Eliminate data you don’t need. Once you have a data inventory, you’ll need to normalize the data in a standard format that can be securely combined and shared.

5. Implement an agile and iterative approach for continuous improvement. The goals for your data sharing framework might change over time. New organizations might want to participate. New data sources might become available. You’ll need processes for continual assessment and improvement, including feedback loops from all stakeholders.

Similarly, look for ways you can extend the framework to additional use cases. You’ll increase your return on investment (ROI) while driving positive outcomes for a larger number of constituents.

Extending Your Data Sharing ROI

Virginia’s experience with a data sharing framework didn’t begin with human trafficking. The state’s initial data-sharing initiative was the Framework for Addiction Analysis and Community Transformation (FAACT), an effort to reduce incidence of opioid addiction. FAACT shares data among multiple state and local agencies, health care and community organizations, and law enforcement to track opioid use, target interventions, and deliver HHS and other relevant services.

Based on that program’s effectiveness, Virginia rapidly repurposed the platform to track COVID-19 in the state and coordinate response measures, before extending the framework yet again to create VAST. Virginia’s success clearly illustrates that with the right strategy, a data sharing framework can help solve problems and deliver continued and expansive positive community outcomes.

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