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Leon Ho: Iterating the Growth Process

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Datavant
September 14, 2022

Leon Ho joined Datavant in 2019 and has been twice promoted from Individual Contributor to Engineering Manager. Below, Leon reflects on the constantly evolving nature of the Engineering team at Datavant, and how this dynamism has directly supported his goals for personal and professional growth.

Engineering Manager, Leon Ho

Datavant’s Engineering team is highly dynamic by design in response to our ever-evolving product development needs. Tell us a little about your path with the team.

When I joined, Datavant engineering had two main focus areas: connecting data and maintaining privacy and security. I came in as part of the Connect Team, focused on connecting data. The team has undergone several reorganizations since then, the most significant one following our merger with Ciox in 2021, which resulted in the formation of the largest patient health data ecosystem in the US under the name Datavant.

Explaining the details of my movements around the Engineering team would involve a lot of very Datavant-specific language. We could visualize my movement within the team like this:

Step 1:

Step 2:

Step 3:

Step 4:

The structure is intentionally very flexible and responsive to our product development needs (ie: responsive to our customer needs). There are many opportunities to develop skills here, whether that means becoming a better programmer or a better communicator. I decided my own roadmap for growth very early on and feel that I’m constantly being challenged in a good way. I continue to learn a lot.

Along the way, you must have worked on some interesting projects. Can you tell us about a couple projects?

Yes. Datavant puts a lot of responsibility on each member of the team, which allowed me to have a significant impact immediately from the time I joined. My first project involved building out two-factor authentication for our web application, a core feature still in use. Even though I was new to the company, I was empowered to make impactful design choices.

One of the most memorable projects that I worked on involved combining two legacy configuration tools. One was oriented toward command line applications and the other was for our ingestion pipeline. At a particular point, we began to see people using both of these applications in tandem, configuring the command line application and then turning around and immediately using the ingestion pipeline. We wanted to streamline that process for our customers so they wouldn’t need to set up one application and then go back and also configure the other. So we built a single tool that could configure both applications at the same time, knowing that users were likely to put data through both. This not only saves time, but reduces the possibility of error while configuring two applications. Datavant’s goal, after all, isn’t just to de-identify data, it’s to connect that data after it has been de-identified. I was the lead engineer on the migration from the two applications to one. This entailed determining the interface between the two systems and merging the two older configuration systems into a newer, scalable configuration system.

Another project that I’m proud of working on involved building out a brand new web application that served as the first UI for our clients to interact with the Identified Switchboard platform. This project allowed me to design and build an entire web application from scratch along with several supporting backend services. I was forced to consider a lot of trade-offs beyond the scope of a single webpage, which I enjoyed.

You’re now the Manager of the Expansion Pod. What does that Pod do?

The Expansion Pod does full stack work. Our goal is to expand the number of use cases that we can digitally retrieve medical records for, moving beyond payer requests to patient or attorney requests, for example. For our digital record retrieval process, we’re currently only retrieving data for a single use case. If payers want data, we can go through our digital data retrieval process. For other use cases we’re still in an era of manual retrieval. In those cases, a Ciox team member at a health system pulls data manually and compiles it before sending it back to the requester. By unlocking new use cases, the Expansion Pod is trying to fully digitize and significantly accelerate that process. We also build tools and features that enhance the provider experience within our Identified Switchboard platform. (You can read about some of the other work Datavant is doing on the incredibly challenging project of digitizing health record exchange here.)

What does your tech stack look like?

On the frontend, we use Typescript with React for the UI design, and on the backend we use a combination of Java and Python.

Connecting the world’s health data sounds like a cool slogan to have, but it never seemed real to me until I started working at Datavant.

What considerations did you make when you accepted the job with Datavant?

When I accepted the job with Datavant in 2019, I was choosing between this position and one with a larger, more established company. That other company seemed like a more stable bet. What convinced me to accept Datavant’s offer was hearing Datavant engineers talk about the opportunities they had to work on a variety of different projects, and develop a lot of different skills. The mindset seemed more scrappy, and I felt like my individual contribution would have a greater impact here.

My previous job was also with a larger, more established company, but was lacking in that kind of opportunity for growth. It felt like I was performing the same job over and over again, and that I never had the chance to develop new skills. The company making the competing offer I received at the time I joined Datavant obviously didn’t tell me that I would be doing a high degree of repetitive work, but because they couldn’t provide details about different opportunities, it was clear that Datavant was offering more opportunities for different kinds of work. The chance for growth offered by the flexible engineering structure outlined above really spoke to me.

It sounds like you’re also very drawn to Datavant’s Mission. Can you tell us why?

Connecting the world’s health data sounds like a cool slogan to have, but it never seemed real to me until I started working at Datavant. I can, now, see an actual path towards a future where health data is no longer siloed, and can be used to benefit patients through lower treatment costs and more targeted treatments. It’s also incredible to work with a team of people who believe in this mission and are so dedicated to making it a reality. Connecting the world’s health data is an ambitious goal, but is one that will have an impact for generations to come.

I’m proud of the positive impact that I have here. I feel like my work has a direct contribution to public health, and that people will have more access to data more easily, and because of that have better medical outcomes.

Leon Ho has a background in electrical and electronic engineering and has been with Datavant since 2019. Connect with Leon via Linkedin.
Many thanks to Leon for taking the time for this interview.

Interview compiled/edited by Nicholas DeMaison.

Considering joining the team? Check out our careers page and see us listed on the 2022 Forbes top startup employers in America. We’re currently hiring remotely across teams and would love to speak with any new potential Datvanters who are nice, smart, and get things done.

Spotlight on AnalyticsIQ: Privacy Leadership in State De-Identification

AnalyticsIQ, a marketing data and analytics company, recently adopted Datavant’s state de-identification process to enhance the privacy of its SDOH datasets. By undergoing this privacy analysis prior to linking its data with other datasets, AnalyticsIQ has taken an extra step that could contribute to a more efficient Expert Determination (which is required when its data is linked with others in Datavant’s ecosystem).

AnalyticsIQ’s decision to adopt state de-identification standards underscores the importance of privacy in the data ecosystem. By addressing privacy challenges head-on, AnalyticsIQ and similar partners are poised to lead clinical research forward, providing datasets that are not only compliant with privacy requirements, but also ready for seamless integration into larger datasets.

"Stakeholders across the industry are seeking swift, secure access to high-quality, privacy-compliant SDOH data to drive efficiencies and improve patient outcomes,” says Christine Lee, head of health strategy and partnerships at AnalyticsIQ. 

“By collaborating with Datavant to proactively perform state de-identification and Expert Determination on our consumer dataset, we help minimize potentially time-consuming steps upfront and enable partners to leverage actionable insights when they need them most. This approach underscores our commitment to supporting healthcare innovation while upholding the highest standards of privacy and compliance."

Building Trust in Privacy-Preserving Data Ecosystems

As the regulatory landscape continues to evolve, Datavant’s state de-identification product offers an innovative tool for privacy officers and data custodians alike. By addressing both state-specific and HIPAA requirements, companies can stay ahead of regulatory demands and build trust across data partners and end-users. For life sciences organizations, this can lead to faster, more reliable access to the datasets they need to drive research and innovation while supporting high privacy standards.

As life sciences companies increasingly rely on SDOH data to drive insights, the need for privacy-preserving solutions grows. Data ecosystems like Datavant’s, which link real-world datasets while safeguarding privacy, are critical to driving innovation in healthcare. By integrating state de-identified SDOH data, life sciences can gain a more comprehensive view of patient populations, uncover social factors that impact health outcomes, and ultimately guide clinical research that improves health. 

The Power of SDOH Data with Providers and Payers to Close Gaps in Care

Both payers and providers are increasingly utilizing SDOH data to enhance care delivery and improve health equity. By incorporating SDOH data into their strategies, both groups aim to deliver more personalized care, address disparities, and better understand the social factors affecting patient outcomes.

Payers Deploy Targeted Care Using SDOH Data

Payers increasingly leverage SDOH data to meet health equity requirements and enhance care delivery:

  • Tailored Member Programs: Payers develop specialized initiatives like nutrition delivery services and transportation to and from medical appointments.
  • Identifying Care Gaps: SDOH data helps payers identify gaps in care for underserved communities, enabling strategic in-home assessments and interventions.
  • Future Risk Adjustment Models: The Centers for Medicare & Medicaid Services (CMS) plans to incorporate SDOH-related Z codes into risk adjustment models, recognizing the significance of SDOH data in assessing healthcare needs.

Payers’ consideration of SDOH underscores their commitment to improving health equity, delivering targeted care, and addressing disparities for vulnerable populations.

Example: CDPHP supports physical and mental wellbeing with non-medical assistance

Capital District Physicians’ Health Plan (CDPHP) incorporated SDOH, partnering with Papa, to combat loneliness and isolation in older adults, families, and other vulnerable populations. CDPHP aimed to address:

  • Social isolation
  • Loneliness
  • Transportation barriers
  • Gaps in care

By integrating SDOH data, CDPHP enhanced their services to deliver comprehensive care for its Medicare Advantage members.

Providers Optimize Value-Based Care Using SDOH Data

Value-based care organizations face challenges in fully understanding their patient panels. SDOH data significantly assists providers to address these challenges and improve patient care. Here are some examples of how:

  • Onboard Patients Into Care Programs: Providers use SDOH data to identify patients who require additional support and connect them with appropriate resources.
  • Stratify Patients by Risk: SDOH data combined with clinical information identifies high-risk patients, enabling targeted interventions and resource allocation.
  • Manage Transition of Care: SDOH data informs post-discharge plans, considering social factors to support smoother transitions and reduce readmissions.

By leveraging SDOH data, providers gain a more comprehensive understanding of their patient population, leading to more targeted and personalized care interventions.

While accessing SDOH data offers significant advantages, challenges can arise from:

  • Lack of Interoperability and Uniformity: Data exists in fragmented sources like electronic health records (EHRs), public health databases, social service systems, and proprietary databases. Integrating and securing data while ensuring data integrity and confidentiality can be complex, resource-intensive and risky.
  • Lag in Payer Claims Data: Payers can take weeks or months to release claims data. This delays informed decision-making, care improvement, analysis, and performance evaluation.
  • Incomplete Data Sets in Health Information Exchanges (HIEs): Not all healthcare providers or organizations participate in HIEs. This reduces the available data pool. Moreover, varying data sharing policies result in data gaps or inconsistencies.

To overcome these challenges, providers must have robust data integration strategies, standardization efforts, and access to health data ecosystems to ensure comprehensive and timely access to SDOH data.

SDOH data holds immense potential in transforming healthcare and addressing health disparities. 

With Datavant, healthcare organizations are securely accessing SDOH data, and further enhancing the efficiency of their datasets through state de-identification capabilities - empowering stakeholders across the industry to make data-driven decisions that drive care forward.

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