Click to learn more about author Robert Reynolds.
If 2020 has taught us anything, it’s the value of data. As the COVID-19 pandemic spread across the U.S. earlier this year, most states were unprepared to deal with it. Those states that have “followed the data” have more successfully managed the epidemic’s toll on public health and the local economy. But for many states, information sharing and data analysis is a perpetual challenge. To glean insights, data points must be integrated from various agencies, jurisdictions, healthcare, and social services entities, among others.
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Key to the success of any data-sharing initiative is the application of the National Information Exchange Model (NIEM) standard. NIEM is an XML-based framework that enables efficient information exchange across diverse public and private organizations. Although one of the communities served by NIEM provides data model content for the human services domain, the standard has not been applied to public health, human services, judicial, and public safety — all at the same time — until now.
Virginia Provides a Blueprint for Applying NIEM to Solving Complex Problems
What’s happening today in Virginia is an excellent example of how one state’s application of NIEM is helping data scientists and government leaders gather insights that are critical to combatting both the opioid epidemic and the COVID-19 pandemic.
In 2017, the Commonwealth piloted a data-sharing platform to track and proactively address the state’s growing opioid epidemic. That led to a 2019 launch of the Framework for Addiction Analysis and Community Transformation (FAACT) program. FAACT compiles data from various state and local agencies so that the government and community organizations have a detailed understanding of the crisis in order to better allocate resources and target treatment efforts.
The data includes tens of millions of records with information on drug use, drug-related incidents, types and length of treatment, mental health conditions, demographic information, hospital admissions, police data, and forensic analysis of compounds found at crime scenes.
The NIEM standard is key to FAACT’s success. Required by the Commonwealth of Virginia, NIEM is a common vocabulary that enables users from government, public safety, social services, and healthcare to look at data from other domains and gain insights that could help them better serve the citizens of the Commonwealth.
NIEM allows data to be categorized in the same manner, no matter how different the datasets are. For instance, FAACT uses the NIEM standard to bring together data across domains through the coding of location data. Location data runs the gamut from specific locations like geospatial latitude and longitude coordinates or addresses to more general areas like cities, counties, or regions. The NIEM standard has a defined type for any location data granularity, which can help users analyze data in terms of location, even when combining datasets using location data fields might not seem straightforward. The most common example is the field “County.” In other datasets, fields with this same granularity of location data may be labeled as “Locality,” “Incident Location,” “Facility Location,” or simply “Location.” FAACT uses the NIEM standard to code each of these fields with the same NIEM type. In this way, the NIEM standard clearly defines data elements and eliminates ambiguity as disparate users interact with the data.
Overcoming the Nuances of NIEM
Although easy to use, applying the NIEM standard to these datasets is a complex process. For example, depending on the opioid-related dataset, a “drug-related incident” could mean one of many things, including that police or an ambulance is called, a person is admitted to the hospital, a patient receives a diagnosis, or a patient is admitted to a treatment facility.
The categorization of overdoses across datasets can also be complex. Typically, they are logged by healthcare providers as either fatal or non-fatal, but certain datasets include greater granularity that must be accounted for by the FAACT platform. This creates questions, such as how do you log overdose victims that have multiple health conditions? How do you treat multiple admissions to a treatment facility? Is that considered one incident or many? And how do you log patients that have multiple compounds in their system — not just those of an opioid?
These are just some of the scenarios that must be considered when applying the NIEM standard to these various datasets. Because FAACT is a self-service platform, it’s important that users don’t waste time by needing to go back to the data steward to understand the data. In Virginia’s case, in addition to the NIEM standard, a data dictionary was created that defines each dataset and provides more granular information about how the data is structured. This ensures that the data is used correctly to make accurate decisions.
Helping States Respond to COVID-19
The Commonwealth of Virginia also applied the NIEM standard to the COVID-19 pandemic. Leveraging efforts from its opioid initiative, the Commonwealth used its investment in FAACT to unite information from sources across the agencies and entities to provide decision-makers with the insights needed to respond to the novel coronavirus pandemic.
With the technical, legal, and governance infrastructure in place through FAACT, the Commonwealth shaved weeks off the time needed to quickly identify hospitals in need of supplies and pharmaceuticals, healthcare facilities with the capacity to handle patient surges, supply chain difficulties, gaps in patient and lab testing, and areas with the largest occurrences of cases. This information is updated as frequently as every 15 minutes, providing a near real-time window into current COVID-19 metrics.
Preparedness Is Key
Following Virginia’s lead, it’s imperative that other states find ways to standardize the vast pools of data they already have. Doing so will enable states to effectively collaborate on data initiatives and apply NIEM standards so they can proactively step up and make smarter, faster decisions in times of need.