Today, approximately 30% of the world’s data volume is being generated by the healthcare industry. According to the World Economic Forum, less than 3% of that data gets used.¹ By 2025, healthcare will produce more data than any other market segment, and is the least poised to put that data to work.
Our aspiration is simple: every decision in healthcare should be made through the lens of the full patient life story and in the context of all patient’s full life stories.
Examples of the many decisions in healthcare that need more complete data:
- A pharmaceutical sponsor deciding on a trial recruitment strategy to ensure a diverse and representative population into a pivotal clinical trial.
- A regulator determining whether an intervention that came to market under an accelerated approval pathway continues to provide meaningful benefit in a larger patient population.
- A physician making quick decisions in an emergency care setting.
- Public health authorities who need real-time data to act quickly and decisively to avoid or contain disease outbreaks, using data to track disease patterns and detect emerging threats.
and so many more…
It isn’t a far-fetched, utopian dream. The data exists. The computational power exists. Analytical capabilities increase everyday. We have a compelling need, in human health. So what is stopping us?
Let’s start by reframing healthcare’s key challenge: a broken data supply chain
We and others in the industry often report that data fragmentation is holding back healthcare. But the underlying issue is bigger than data fragmentation. Fundamentally, the problem is a broken data supply chain.
A functioning data supply chain is one where health data movement can overcome systemic isolation while complying with critical privacy regulations. In healthcare, it would mean data moves and connects securely, compliantly, and frictionlessly.
We benefit from high functioning supply chains every day. Building a car takes 30,000 parts across 4,000 suppliers, from 20 different countries with hundreds of different regulations. That’s fragmentation on steroids. But it comes together like a symphony, and we trust the output of that supply chain with our own safety and the safety of our loved ones.
Assembling a patient’s full, longitudinal health story is similar. It is made up of all the different doctor’s visits, all the medications, treatment history, family history, and social determinants. Doing so should be as efficient as an automaker assembles a car or the way an internet search engine or an online retailer makes information and goods available and useful at the click of a button.
Data logistics is the connective tissue required for a supply chain to operate effectively. The challenges that data logistics help overcome are two-fold:
- 1. Organizations are reluctant to share health data. Health data is the world’s most personal and precious data — regulated, sensitive, and rightfully, nearly impossible to move. Healthcare is made up of an ecosystem of organizations that need to exchange data, but organizations are reluctant to do so given the strict compliance regulations.
- 2. Data is not designed to be shared and connected. It is in many formats, much of it paper, and collected across fragmented systems that don’t speak to each other. That requires significant technology and healthcare coding effort to ensure information can be “connected” in a way to create an actual patient journey across settings.
So how can we get an effective supply chain? In the world of physical goods, “multimodal” logistics are the thread that stitch together a supply chain — ocean, air, rail, and trucking. Healthcare data, too, necessitates different “modalities” to facilitate its safe and secure journey.
To assemble the full patient story, logistics capabilities are required across a variety of factors:
- authorized consent (e.g. de-identified and identified)
- source (e.g. hospital EHR, imaging, genomic data, doctor visits, wearables)
- format (e.g. structured, semi-structured and unstructured data)
- use case (e.g. clinical decisions, research purposes, health plan optimizations)
From request, to fulfillment, to quality check, to delivery – we need a system agile enough to support each piece of data and the unique attention required to move it in a secure and compliant manner. Properly moving data not only requires an understanding of the healthcare ecosystem, but infrastructure, technology, and massive human coordination.
Data logistics is the critical missing piece to bring the data revolution to healthcare
Datavant is the leading company to bring data logistics to healthcare, so organizations can securely and compliantly move health data from where it sits to where it needs to be.
Datavant brings together all the necessary modalities to ensure that health data moves from where it sits to where it needs to be. Through a series of strategic mergers, Datavant created the biggest ecosystem of deidentified data, the largest footprint of identified data exchange, the industry leading experts in patient privacy, and seamless tools for patients.
For decades, we’ve put in the time to partner with and truly understand the different players across the industry, to discover the best ways of connecting and sharing necessary data, and to build trust that the infrastructure for the compliant exchange of health data is not only now possible, but essential.
Data logistics will change the face of the healthcare industry. Providers can better serve their patients. Health plans can fund high quality care of more patients. Life Sciences companies can develop more breakthrough treatments and preventative health approaches. The public sector can serve the needs of diverse populations proactively and equitably. Private sector companies can make informed and timely decisions. And most importantly, patients — all of us — will live longer, better, healthier lives.
In order to provide data logistics for healthcare, Datavant has built three key pillars of excellence: we protect, connect, and deliver the world’s most precious data.
Protect
We designed Datavant’s solutions to optimally address privacy, compliance, and security. We protect health data, while always ensuring every organization has complete control over how their data is accessed and used.
Connect
We enable data to move from the very beginning with the broadest footprint of providers in the U.S. to reach every patient record. We empower organizations by creating access to relevant data sources, so they can put together all the pieces needed for a complete view of the patient.
Deliver
We deliver data that is relevant and timely. Through technology and our value-added services, we power countless decisions that support clinical, operational, and research questions with the most relevant, usable data.
Data logistics: a tide that will raise all boats
Datavant built a business record by record, request by request. We started at a time when fax machines were the innovation, and are now focused on interfaces such as APIs and database connections and a frictionless user experience. Throughout our journey as a company, we’ve focused tirelessly on the need for a privacy-centric health data network. This has been – and will only be – possible due to the incredible partner organizations across healthcare striving toward better patient outcomes.
Our network-of-networks includes over 70,000 hospitals and clinics, and we are a trusted partner to hundreds of real-world data companies, and many others working daily to improve healthcare.
Because health data is precious. And healthcare doesn’t work without it. We believe data logistics is the missing layer that will allow healthcare to do what it does best: take care of people.
"It’s hard to be in the trenches, especially for a company that’s building core infrastructure. But through the challenge, I hope everybody recognizes the impact that data logistics is having and will have, because it is truly transformative."
– Amy Abernethy, President of Product Development & Chief Medical Officer at Verily
Contact us to learn more about how data logistics can help solve your organization’s data needs.
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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