The healthcare system generates
approximately a zettabyte (a trillion gigabytes)
of data each year, which includes both classic data from sources
such as EHRs, diagnostics and genetics, as well as newer data sources such as
gut biome sensors, wearable devices and environmental monitors, and social
media. Consequently, it’s now possible to quantify a person across three
dimensions of human existence: biological, environmental and digital/social.
Big tech is leading the way in
quantifying our existence, creating tools and technology to track and measure
consumers’ weight, heart rate and other traditional health signifiers, as well
as their social determinants of health—information on where and how people
live, such as what (or if) they eat, their access to travel, how much they
exercise and how they socialize. Social determinants account for roughly 60% of human health and
well-being, while healthcare accounts for only 10%. In other words,
having the data on where and how people live provides the power to influence
people’s health status, so as tech companies race to collect and link this
information, they’re reshaping how we view and deliver healthcare. Pharma companies—and
healthcare stakeholders more broadly—should take note, paying attention to
what’s coming, how healthcare delivery could change, and what it means for
their vertical.
Data Advances Abound—and
Challenges Too
As most anyone following
business news these days knows, all of the biggest tech players have announced efforts
or products to claim a piece of the exploding healthcare data pie, and they’re
partnering with traditional stakeholders across the industry to plan their
entry. For example, Apple has its FDA-cleared electrocardiogram feature on its
Apple Watch; ResearchKit, a software framework for clinical
trial apps; Health Records, an app
that aggregates existing patient-entered data from its Health app with a user’s electronic
medical record data; and a collaboration with Aetna on Attain, an iPhone/Apple
Watch app that tracks and rewards users for healthy behaviors. Apple is using
its iPhone and other technologies—and even a new Apple credit card—to
systematically build a platform to quantify many aspects of human existence,
and has implemented methods to assure that the information can be readily
shared and accessed if permission is granted.
Other companies such as Amazon
and Google have created an integrated suite of offerings (with Amazon acquiring Whole Foods and
Google offering hardware like Nest, Google Home and
Pixel). These tech companies will have enough data on consumers to gain a
holistic view into their lives and to offer targeted solutions to their
healthcare needs.
The healthcare industry has
already found itself behind the eight ball, and it’s now grappling with the
larger question about how data structure, ownership and access will work in the
fully integrated healthcare data landscape of the future. Moreover, there are
still considerable challenges to using the emerging healthcare data
effectively. Some data is messy or is missing
altogether, and AI and machine learning systems often lack the
necessary training data sets. And, of course, interoperability issues abound.
The fragmented nature of healthcare data has led to the formation of data
aggregators that sell data to other stakeholders in data marketplaces, and
these data marketplaces either aggregate data across different data types or
focus almost exclusively on one data type.
For example, the HealthVerity Marketplace contains
HIPAA-compliant, de-identified data on more than 300 million U.S. consumers,
pulling together medical and prescription claims, lab results, EMRs and other
data types from more than 30 data suppliers across the country.
Meanwhile, Nebula Genomics offers
a blockchain-based network that houses users’ genetic information.
Some of these data marketplaces
are starting to partner with each other and form more comprehensive
marketplaces, which is a step in the right direction, and other solutions are
emerging. For example, Fast Healthcare Interoperability
Resources (FHIR), a new web standard that enables healthcare
information to be shared electronically, and SMART
on FHIR, which are open specifications that can be used to integrate
health-related apps with EHRs and other healthcare IT systems, are burning down interoperability
barriers.
Big players like Intermountain Healthcare and
Partners Healthcare are using SMART on FHIR to build and
utilize apps that work seamlessly with their EHR systems—providing better
access to data for them and their patients while expanding data collection.
Regulators are on board: New rules from CMS and
ONC require healthcare providers and insurers to implement open data-sharing
technology that will ensure data movement across plans and expand patients’
access to data.
Furthermore, there have been
advancements in structuring some of the unstructured data that better describe
the “whole person.” For example, the American Medical Association has
partnered with UnitedHealthcare to create 22 new ICD-10 codes
for social determinants of health (such as food insecurity, access to
transportation, and social connectedness) so that researchers can structure
information about wellness.
Pharma Needs to Track—and Adapt
to—Four Major Data Trends
To capitalize on healthcare’s
data-driven evolution—and to keep pace with the change—pharma needs to keep an
eye on four major trends.
1. New patient segmentation: Payers have a strong
incentive to harness all of the data that they can get to ensure that their
members are as healthy as possible. As a result, they lead the pack in applying
machine learning, big data analytics and even natural language processing (from
phone conversations) to segment people by risk.
For example, Anthem has created
an integrated data warehouse that holds its claims data along with EHRs, lab
results and other necessary data sets—allowing analysts to investigate members’
specific characteristics and determine their risks for emergency medical treatment or
unstable health conditions, and creating the ability to segment and target
members with offers of health coaching or additional services.
What will this new approach to
patient segmentation mean for pharma’s clinical trial design and recruitment? How
will it change the way that payers evaluate patient populations for access to
drugs? Pharma companies are going to have to modify their clinical trial
designs to match new evidence standards that link social determinants of health
to outcomes. They’ll also need to consider new dimensions of patient
segmentation along the lines of access to travel, food security, social
engagement and the like. This will be necessary as payers consider this
information in their population health analyses.
2. Care model change: Fueled by data, the
healthcare delivery model is beginning to change, shifting the focus from
treating sickness to maintaining and encouraging wellness. While there will
always be specialty care, the majority of healthcare is moving towards localized
health hubs offering social program-like services related to education,
prevention and treatment in a retail setting.
For example, Cityblock,
a company under the Alphabet umbrella, is partnering with health plans to reach
people in neighborhoods with high poverty rates and other social challenges.
They collect structured and unstructured data, and synthesize it into
dashboards to enable community health practitioners to create personalized overall
life wellness plans that address personal habits and social behaviors—ideally
heading off health conditions before they start.
For pharma companies, the
increasingly local and down-skilled care model indicates that it’s time to
assess the new influence points for care decisions and to optimize their
commercial models appropriately. This tech-enabled care delivery will rely on
pathways and rules-based decisions. Pharma will need to identify where
and how to influence these predetermined care decisions in a more
business-to-business manner. For example, as payers move patients to post-acute
sites of care, providers here are gaining the power to switch treatments to
generics or drive biosimilar adoption.
3. Evolving engagement
dynamics: In the race for access to healthcare data, many former
enemies are “frenemies.” Everyone is a potential partner. For example, Pfizer buys cancer data from
Flatiron, which is owned by Roche. Roche and Pfizer are competitors.
Everyone is cool with this. Meanwhile, the Yoda Project at Yale has
direct competitors contributing their data to
an open-access platform.
Data-oriented partnerships and
alliances are becoming the norm, so pharma companies should consider adding
more data-focused roles to their roster. In addition to the chief data officers
and chief digital officers being recruited now, they’ll need data
alliance/venture teams and data stewards, with savvy data monitoring and
licensing experts leading the way on finding and securing valuable data
relationships.
4. Using data as an R&D
value generator: Increasingly, pharma companies are using new forms of data
in new ways, in all parts of their business and throughout their product
development cycle. For example, Daphne Koller, a computer science professor at
Stanford University, founded Insitro, which is focused on reversing the
death spiral of R&D productivity by leveraging machine learning, CRISPR and
other techniques to make drug discovery more efficient. Leveraging technologies
such as organs on a chip and stem cells, mutating these cells, and phenotyping,
Insitro has created a bio-data factory to explicitly enable machine learning.
The results have been promising—leading to a partnership with Gilead.
Data is now a strategic asset
for pharma companies. As data sources paint a more complete picture of
biological function, pharma companies can examine their small molecule
libraries for new applications. In addition, they can design new
biomarkers (including digital) to create product differentiation and, ideally,
to enable earlier read-outs on clinical trials, which will bring products to
market sooner and cut development costs. More rich and complete data handled in
the right way will enable better and more effective decisions from R&D all
the way to commercial.
The race is on to acquire and
aggregate data, clean data, structure unstructured data, find new forms of data
and generate missing data. In the next decade, navigating the data marketplace
will be as critical as navigating the reimbursement landscape was in the past
decade. Commercial viability will depend on it.
No comments:
Post a Comment