In the Quest for Big Tech To Break Into Healthcare, Amazon Just Broke Away From Apple and Alphabet

Travis May

December 3, 2018

5 Minutes

Table of Contents

Last week, Amazon announced the launch of Comprehend Medical, a tool to mine unstructured health data from text.

The press around the announcement jumped to the long-term implications of AI mining medical records, describing use cases where Amazon could support clinical decision-making (aiding in diagnosis, finding mis-diagnosed patients, etc.) and help recruit for clinical trials; it also discussed the promise of direct-to-patient applications that allow patients to view their medical records and perform tasks like auto-scheduling appointments based on their medical history.

What was actually launched

While it’s likely Amazon eventually does break into these areas, the brilliance of Amazon’s launch was its simplicity: all Amazon actually launched was a tool to classify unstructured health data as part of AWS.

As an example of the product, below is a screenshot of how Amazon classifies the text “Mr. Smith is a 63-year old gentleman with coronary artery disease and hypertension. Current Medications: taking a dose of lipitor 20 mg once daily”:

That’s all the product does: classifies text. It doesn’t cure cancer. It doesn’t replace doctors. And it doesn’t help patients take control of their care.

But underlying this functionality, there is a major need: the vast majority of health data is unstructured, sitting in PDFs, hand-written text, audio recordings, pathology reports, and doctor’s notes. The problem of unstructured data (and previously, the problem of non-digitized data) has been one of the biggest bottlenecks for the emergence of health analytics tools.

Why the strategy is brilliant for Amazon

Big Tech (and Silicon Valley broadly) has a large graveyard of failed healthtech launches. While there are many reasons for this, Amazon’s approach solves for two of the big causes of tech’s poor track-record:

Avoiding Hype. Tech companies entering healthcare have repeatedly promised to solve problems that will take decades to solve, leading to skepticism by many and disappointment by early adopters (and often, shareholders and employees). Instead, Amazon has taken on a byte-sized problem; by looking at text classification problems, it is addressing a real need that it has the capacity to solve well. While not as exciting as a cure for cancer, this is an important step.
Building an ecosystem. Many big tech companies have focused on their relationship with consumers as the core of their strategy (this has been most notable with repeated attempts at building patient-centric medical record systems by Google, Apple, and Microsoft). This has not been a successful strategy in healthtech: too much power resides with healthcare institutions (payers, providers, and pharma) to build a strategy that ignores their incentives. Amazon has addressed this by building a product aimed directly at institutions that hold health data, not at patients. With Comprehend Medical, not only is Amazon building an ecosystem of hospitals, insurers, and pharma companies who send Amazon their health data, they are building a developer ecosystem; the thousands of health analytics companies building applications on top of health data all struggle with how to manage unstructured text.

Healthcare is an underserved market by big tech, and all of the major players are beginning to pay attention and more actively invest in their healthcare initiatives. Expect Amazon’s launch to be followed by several others trying to match Amazon’s approach, unleashing the most meaningful entry of tech into healthcare to date with a series of incremental, enterprise-focused products. These incremental steps are what are needed first to unlock the bigger visions of technology revolutionizing healthcare.

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.