No items found.

Aneesh Kulkarni: Building the Team I Wanted to Be A Part Of

Author
Publish Date
Read Time
Datavant
August 23, 2022
 min
Datavant’s Head of Enginering, Aneesh Kulkarni

Head of Engineering Aneesh Kulkarni started at Datavant in his role as “the tech person” on a team of three. Now the Head of Engineering, Aneesh is focused on scaling the team while retaining its core culture of people who are smart, nice, and get things done. Below, Aneesh sits down to answer some questions about Datavant’s mission and the rapidly scaling engineering team.

Can you share your perspective of the Datavant mission as it relates to engineering.

Our mission is to connect the world’s health data. Two of the biggest ways I see this impact in my work are in technology and control.

First is technology. Much of healthcare data exchange today happens via postal mail, fax and flash drives, even as most other industries have moved on to modern communication technologies. We’re building the last-mile digital plumbing to enable the healthcare industry to make this leap.

Second is control. Much of healthcare data exchange happens with limited visibility and control for patients and providers. We’re helping patients, providers and other healthcare organizations gain and retain control of their data.

That is an inspirational goal. I assume that doesn’t come easily. What are some of the biggest technical challenges to realizing this mission?

There are three areas of technical challenge that I’d highlight: privacy, optimization, and scale.

The first is privacy. Our product needs to take advanced privacy-preservation techniques and present them with a simple user interface. Patients, providers and healthcare organizations trust us to keep their data private, and give them control over their data. Datavant is committed to remaining neutral with regard to the data itself. We are focused on building the conduit for exchange.

Our engineering teams are organized into “pods”: we have engineering pods focused on building de-identification and linking tools, machine learning algorithms, and UIs for surfacing all of this to patients and healthcare organizations in a simple way. We also have a large security team fully focused on protecting all of this data.

The second is optimization. We need to be able to locate records for hundreds of millions of patients across thousands of providers, applying complex authorization rules and matching algorithms while preventing false positive matches and unauthorized disclosures. Cross-disciplinary teams of data scientists, software engineers, and product managers are focused on solving these core matching and optimization problems.

And finally, we’re just starting to operate at significant scale. Our largest customers depend on Datavant to process billions of records. Our most-demanding customers want to be able to retrieve information from healthcare providers they work with across the country in seconds. Our engineers are building data pipelines and APIs to support these requests, and ensuring that our infrastructure will seamlessly scale to 100x the volume as we make healthcare data exchange much faster and cheaper in the coming months and years.

You are working on advancing the data connectivity for an entire industry, which requires a strong team of mission-driven people. What early-career experiences shaped your view of how engineering teams should function?

One of my primary goals at Datavant is to build the engineering team I wish I was a part of as a younger engineer.

Early in my career, one of my biggest frustrations was working in an organization that celebrated individual progress even at the expense of team achievements. I’ve always felt that a group of engineers succeeds or fails as a team, so I’ve tried to notice and celebrate people who accelerate the team, even if the work they’re doing is not “in their job description.”

The quirkiest manager I ever worked with espoused an extreme view of documentation and talent. “Documentation is irrelevant,” they claimed — “the people who wrote the code are the best documentation.” This person went on to build one of the strongest performing engineering teams I’ve ever seen in business. While I do think that writing things down is valuable, this experience helped shape my view that attracting and retaining great engineers is one of the biggest determinants of long-term success.

That is a helpful vision in shaping recognition and growth of a team. Tell me, how your role at Datavant has changed over the past five years?

Honestly, it has felt like a different job every year. Early on, we were focused on getting to product-market fit. My role was focused on deeply understanding customer concerns, and shaping our product to address those needs. Then, as the product and business gathered momentum, we went through a challenging scaling phase that saw customers putting increased demand on our technology just as we were growing the team and ramping up several new engineers. During this scaling phase, it often felt like everything was held together by duct tape, and we were a single incident away from massive pain. We were also supporting this rapidly growing, multi-million dollar business with just five or ten engineers. Thankfully, we came out of those experiences a stronger and more cohesive team. We strengthened our technical foundation, added helpful development and release processes, and built a playbook to onboard new engineers. My role started to shift away from technical fire-fighting towards mentoring our first few engineering managers and recruiting the next wave of engineers. Since our merger with Ciox a year ago, we’ve continued to scale the team to nearly 100 engineers across two product areas, spanning skill sets ranging from machine learning to infrastructure to front-end to security. Now, the most impactful things I spend my time on are stewarding our engineering culture and processes, figuring out roadmaps for our product and technology, and recruiting new engineers and leaders.

Before we wrap up, can you share what you are most proud of at Datavant?

Without a doubt, the team and culture. It might sound trite, but we truly have a team full of engineers who are nice, smart, and get things done. We operate as a team, helping each other achieve our big, shared goals. I’m proud that engineers who helped get the company off the ground three, four, five years ago have stayed with the company, taken on even more impactful roles, and are continuing to shape our product and culture today. I am also grateful to have had such a diverse set of learning experiences at Datavant.

Considering joining the team? Check out our careers page and our listing 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.

Many thanks to Aneesh for taking the time for this interview.
Interview compiled/edited by Nicholas DeMaison.

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.

Achieve your boldest ambitions

Explore how Datavant can be your health data logistics partner.

Contact us