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Datavant Future of Healthcare Hackathon Runner-Up: HomeClinic: Closing the Testing Gap

Datavant

September 22, 2022

From September 8–11, Datavant hosted its first annual Future of Healthcare Hackathon. Over 200 attendees spent the weekend developing innovative solutions to improve the future of healthcare. This week, we are excited to announce the various winners of the Hackathon.

The Runner-Up Project is HomeClinic: Closing the Testing Gap. Read on to learn more about this fantastic project.

HomeClinic Team Members

Lukas Haffer — Stanford MBA student (class of 2023) & Venture Capital Investor at Bee Partners LLC
Patrick Dideum — MD, physician and medical consultant, Board certified Pediatrician and Preventive Medicine resident at Johns Hopkins (Class of 2024)
Mingoo Kim — Stanford MBA student (class of 2023)
Gashon Hussein — Second year Stanford undergraduate student

Project Summary: HomeClinic: Closing the Testing Gap

Virtual healthcare has the potential to improve the lives of people around the globe. The HomeClinic team points out that 80% of doctors have integrated virtual care into their practice to provide timely and complete care. However, without integrated and affordable at-home testing, services can be limited. At present, testing for virtual care is done through prescribed lab testing, which can take 1–2 weeks to complete.

HomeClinic’s solution is a virtual healthcare platform, powered by an at-home testing device. During the Hackathon, HomeClinic built a mobile application that allows patients to connect with an AI nurse and on-call doctors in real-time to perform reimbursable, live blood tests and receive medical advice.

A mockup of the HomeClinic mobile app

An accompanying web application provides real-time patient data to the on-call doctor in order to address ongoing patient concerns.

Project Inspiration

Lukas Haffer, one of the HomeClinic team members, has a thyroid disorder and has experienced firsthand the difficulty of managing his medications, waiting weeks for appointments and lab results. The team envisioned a future that would empower Lukas and 150 million other Americans with chronic conditions to take control of managing their conditions from the comfort of their own home: “Feeling symptoms? Call our AI nurse, get connected to an on-call doctor, and test at home for a quarter of the cost!”

The team was also inspired by the testing gap that exists in the U.S. They found that millions of people are unaware of their chronic conditions due to infrequent testing. According to the CDC, an estimated 96 million US adults (38%) are pre-diabetic and less than 20% have been notified by a doctor of their condition. It is estimated that more than 12% of the U.S. population will develop thyroid disease within their life, on top of the estimated 20 million living with disease currently. Up to 60% of people with thyroid disease are unaware of their current condition.

In the data set provided by Datavant, the HomeClinic team found the percentage of diabetic Medicare enrollees receiving lipid testing ranged from 51.8% — 89.4%, depending on the region in which they live. Similarly, the percentage of diabetic Medicare enrollees receiving HbA1c testing ranged from 69.6% ~ 94.6% depending on the region in which they lived.

HomeClinic would significantly lower geographical barriers that prevent patients from testing and consulting doctors, particularly in rural areas where healthcare can be considerably less accessible. The app is able to screen and detect chronic conditions early, thereby decreasing wait times, costs, and access to a provider, which in turn minimizes disease impact and has the potential to increase longevity.

Approaching the Problem

The HomeClinic team completed market research by interviewing 10 people, including doctors, patients, and payers. They learned that while a virtual care platform may not replace a doctor’s electronic health record (EHR) system, integrating key patient information to a dashboard is crucial. They designed a platform with integrated vital signs, notes from the AI nurse, past testing results, and current medications.

The HomeClinic dashboard

Bootstrapping was at top of mind for the team. Creating a web and mobile app within 3 days is major challenge, so the team worked to speed up formulaic processes in order to create a project in line with their vision. Accordingly, there were trade-offs for valuing product development speed over scalability.

A main challenge for the group was determining the appropriate uses for the device within the home under the direction of a physician. The team worked from the premise that safe and reliable care is always the primary goal in developing a new medical device. They wanted to be sure the device would be used in a way that would not do any harm to the patient, and they therefore needed to think about what types of testing would be useful for the patient and the doctor, and also relevant to their care. They noted that testing for the sake of testing can often lead to more questions than answers.

Implications

At-home medical testing that goes beyond the devices currently on the market, such as blood glucose monitors, would allow better monitoring of patients with chronic diseases. The HomeClinic team notes that such patients are often taking multiple medications, which may demand frequent laboratory tests, especially when needing dosing adjustments.

Allowing this testing to be completed within the home would help decrease barriers for patients who may need to take unpaid leave from work, find childcare, ask a family member for transportation, or travel long distances otherwise. The team has seen patients skip check-ups when faced with these obstacles. HomeClinic can be a solution to reducing the resources needed to engage in recommended care and empower the patient to feel more in control of their disease rather than controlled by it.

Future Steps

The HomeClinic team acknowledges that there will be many hurdles to overcome in building a market-ready at-home testing device, including issues of regulatory approval and reimbursement.

The team intends to continue building this platform past the completion of the Future of Healtchcare Hackathon, with an eye toward incorporating more key features essential for home testing to be successfully life changing. For example, because of timing constraints within the Hackathon, the team did not include historical physician notes in the app, but would include that feature in a future iteration of it. They also aim to include clinically proven models as well as their own machine learning models to further implement a value-based care model benefiting both patients and payers. Finally, they plan to continue their market research to develop more potential uses for a product that is both capable and user friendly.

Lukas Haffer and Mingoo Kim reacting to the Hackathon award announcement

The HomeClinic Team

Left to right: Lukas Haffer, Patrick Dideum, Gashon Hussein, Mingoo Kim

Congratulations to the HomeClinic Team for developing this project!

Considering joining the Datavant 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 Datavanters who are nice, smart, and get things done and want to build the future tools for securely connecting health data and improving patient outcomes.

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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