How data, analytics and AI power public health
Advances in immunization information systems and contact tracing are a few example of progress made during the pandemic that will set the tone for years to come.
The pandemic has put a spotlight on how big data and analytics technologies are being used in the public health sector.
A prime example of this? Contact tracing, where phone numbers and location data from mobile devices were combined with lab results in public health systems to issue alerts when an individual came in contact with a confirmed COVID patient. This information empowered people to preemptively self-isolate and/or head for rapid testing. Google and Apple, meanwhile, developed some groundbreaking application programming applications (APIs) for contact tracing that protected anonymity, while allowing their devices to receive updates from state disease surveillance systems and send out alerts.
The use of big data during the pandemic is certainly a harbinger of things to come, and public health agencies must understand how such data is being used. They should start working on plans to protect the privacy of the end user and comply with the evolving laws around personal data privacy.
Additionally, organizations should determine what they’ll do with the data they’re gathering. Of course, all the data in the world is worthless without the right tools to read and interpret it. Artificial intelligence is vital for processing the vast amounts of data collected by today’s technology. It has powered everything from tracking the initial spread of the outbreak to helping researchers quickly analyze and interpret huge amounts of data to come up with a vaccine. Going forward, AI and big data will be vital to analyzing vaccine efficacy, identifying breakthrough case trends and more.
Targeted outreach and prevention
Big data and AI have been foundational technologies for other programs. During the pandemic, data has been used for targeted outreach and prevention efforts, especially during the vaccine rollout. The ability to recognize trends in a cohort or region allowed for more effective risk mitigation. For immunization information systems (IIS), this meant parsing data to identify and prioritize groups who are at the most risk from a lack of vaccination. The age-bracketed rollout of the vaccine is a good example of this. Yet, new insights from ongoing data analytics efforts will help micro-target more at-risk groups as time goes on.
Identifying at-risk groups is just one part of the pandemic response process. From there, it involves what are essentially next-generation logistics efforts: monitoring vaccine distribution, managing vaccine appointments and tracking the growing numbers of vaccinated individuals. This efforts feature an incredible number of moving parts: government and public health offices, vaccine providers, health care workers and more. Marketing and education components will also see data analytics play an important role in their efforts.
All of which places a high data load on systems -- a load which requires modern architectures and flexibility to manage. Unfortunately, most public health offices today are using outdated systems that were designed for managing a load about 10 times smaller than what they're forced to deal with now.
Cloud computing can help public health agencies scale up to accommodate the new data load, with architectures that auto-scale and adapt to changing flows. But the systems themselves must also be architected to support the horizontal scaling enabled by cloud computing.
Stateless architectures and BPMN 2.0
Newer architectures are designed for this sort of flexibility. Called "stateless applications," these architectures don’t store their state on the server and don’t need to know the history of what was happening on the system, allowing organizations to add more servers to scale up and meet demand. The pandemic served as a powerful reminder of just how fast things can change. Stateless applications are the ideal way to keep up with evolving requirements and mandates, allowing agencies to implement new functional changes quickly and easily.
Along with the versatility of stateless architectures, public health organizations should be leveraging Business Process Management Notation 2.0. This notation method allows systems to take in, change and adapt to new requirements with ease. One of the major stresses placed on health systems during the pandemic was that new requirements from the Centers for Disease Control and Prevention didn’t necessarily fit into the outdated systems public health offices used. Agencies then had to manually solve for many new processes. BPMN 2.0 avoids that problem altogether, saving countless hours of work and ensuring a higher level of compliance.
Sharing data among multiple entities
Science depends on reliable data, but it has traditionally been a rare occurrence for health care data to be shared among multiple entities. Data privacy concerns are one of the main reasons for this siloed approach. However, those silos started coming down as health care researchers and public health agencies around the world started collaborating during the pandemic.
REST-based APIs can serve an important function here. Public health agencies have relied on the HL7 industry standard for sharing health information via APIs, but before the COVID outbreak many of these agencies were transitioning to the updated FHIR standard, which offers more functionality and flexibility than HL7. During the pandemic, many of these transition projects went on hold. It’s now time to get back to implementing FHIR in order to meet public certification requirements.
Sharing health care data is a new trend, and an exciting one. As AI makes it easier to provide meaningful data ownership and protect personal data privacy, it facilitates collaboration by multiple entities on shared data. This in turn spurs innovation, allowing the best minds in science to work together toward a better future. For IIS, this means sharing ideas about “what works” that will lead to new best practices across organizations.
Big data, analytics and AI allow public health organizations to respond rapidly to public health emergencies, which potentially translates into lives saved.
And that’s the big goal for all of these innovative new approaches for IIS: save lives and improve public health. Today’s developing trends will help mitigate the effects of the next pandemic and result in a safer future for all.