Los Angeles County Works to Modernize Its Public Health Data Infrastructure

So I am a data engineer, and my role has been focusing on modernizing how public health data is received, secured, and shared.

To give an example, currently we’re building ways to efficiently receive lab reports from all across L.A. County and collate them for the CDC so that analysis and outbreak tracking can be done in real time. That’s the goal, anyway.

The goal of the Workforce Acceleration Initiative—or WAI—is to place experienced technologists into public health agencies to help modernize our data systems and improve interoperability, the seamless exchange of electronic data among different systems.

Lots of disease surges, like COVID or influenza, have exposed long-standing structural challenges where we have labs reporting from fragmented systems, inconsistent data standards, and inadvertent barriers that have made sharing information across health and other governmental departments really difficult.

So, I joined the initiative because my prior work focused on interoperability and modernized data systems that allow vast amounts of data to be shared quickly and securely. This was a chance to apply my background and experience to the needs of L.A. County and help to improve their data systems and support the decision-making by public health officials.

Data modernization is really about moving away from one-off, hand-built solutions and toward systems that are designed to be shared, reusable, and potentially more forgiving over time. We saw that these bespoke systems didn’t work very well for sharing data between departments.

For example, in L.A. County, we have a custom system that was built for epidemiologists—the disease detectives who work to figure out how diseases spread and can be prevented—to be able to receive and query lab reports for specific information for case investigations. We’re currently working to update the system by shifting to FHIR (Fast Healthcare Interoperability Resources), a more modern standard for capturing and exchanging health information.

Once FHIR is adopted, the epidemiologists will be able to more easily access and collate these lab reports for analysis. And the system will also be able to seamlessly evolve over time to integrate data and allow for more connections between different data sources without needing to find a specialized developer or a custom solution. Long term, we’re talking years of time saved with this shift to a standardized model.

Public health is local. Counties, like L.A., are where most public health data is first generated. And the way the data is structured at the county level has an outsized impact on everything that follows in terms of how that data can be shared and analyzed.

When each county reports data in a slightly different format, the state spends a lot of time translating it before that data can be used. So, by standardizing the county data, we can aggregate this information from across regions much more quickly. And many benefits follow: Trends can be identified more readily, data gaps become easier to spot, and responses can be coordinated faster.

At the everyday level, data modernization shows up in how quickly information moves from the lab to the public health officials who need to act on this information. So, when lab results are received by public health agencies automatically, public health teams can spot patterns sooner, whether that’s an emerging outbreak, a shift in the number of people being tested for a specific condition, or maybe changes in who’s being affected by a pathogen or a disease.

Currently, a lot of systems out there only work because individual people are tracking down information, making phone calls, and reconciling and removing duplicates in spreadsheets from different departments. People are manually doing this work so that they can have structured data to base decisions on. And you can imagine the workload that creates.

When we automate lab reporting, that manual process and the time it takes is eliminated so that public health teams can act and we can make more informed decisions faster.

There are a lot of challenges. A few of the biggest ones I’d highlight are time-intensive processes, data security, and insufficient funding.

I’ve already talked about the long, exhausting, difficult process typically required to move from bespoke, one-off data systems to standardized solutions. It’s a lot of time, energy, and expense, and that understandably is a barrier.   

Similarly, there is risk in making changes to systems that house personally identifiable information. Security considerations are paramount—really, a constant conversation. And while data security is an absolute necessity and priority, security requirements and concerns do also make system changes challenging.

In terms of funding, data modernization is a long-term effort that will pay long-term dividends, but that unfortunately often does not have long-term funding commitments. During my time in L.A. County, we’ve lost contractors due to funding cuts, and that hampered our efforts. I was working closely with an epidemiologist who was just great and super helpful in contextualizing the various types of genetic information and other data we’ve been working to standardize. Unfortunately, the money for her role ran out, and we lost her from the project. That sudden loss of expertise probably cost us months.

Through WAI, we are building some of the first cloud-based FHIR workflows within our public health infrastructure. We’re at an exciting time, since there is a strong federal push toward FHIR as a common interoperability standard.

Once we build those workflows, outbreak tracking and lab reporting can be deployed much faster. Developers will not be starting from scratch each time, and epidemiologists will receive data in a consistent format that is immediately usable.

WAI’s value is in accelerating that transition. FHIR adoption requires aligning security and programmatic needs across teams with very different responsibilities. IT groups are rightly focused on security, compliance, and operational stability. Epidemiologists are focused on surveillance, investigation, and protecting community health. Both are strong in their domains, but their priorities and constraints do not always naturally intersect.

WAI helps to translate between those domains. Without that embedded support, FHIR adoption would likely move slowly, and data would remain fragmented.

L.A. County operates at a scale where data modernization solutions get battle tested very quickly. This hopefully means that when we build a system that works for L.A. County, we’re building a system that can be effectively applied in other counties as well. So, if we can get it right here, we’ve created a reusable model that could be adopted quickly across California without starting from scratch.