Continued improvements in
interoperability will expand data
sets for the “whole patient
approach” and increase AI-driven
predictive health, achieving greater
With the healthcare industry generating approximately 30% of
the world’s data volume, there is no shortage of data generated
by healthcare – from provider medical records to payer claims, to
patient-generated health data. The accessibility and usability of
this data, however, is another story. Interoperability – the ability
for multiple systems to utilize patient data – has long been a
healthcare industry goal, and new federal mandates, combined
with technological advances, only push us in that direction. As we continue towards a more connected and patient-centered
future, we will see expanded data sets and an increase in AI-driven
predictive health, resulting in more significant health outcomes.
Payers, providers, and patients face a multitude of blockers before
interoperability can be adequately achieved and leveraged,
primarily due to a lack of patient data standardization and data
stored in disparate and separate systems. The result is inconsistent
and siloed data that leaves providers and payers without a complete
picture and patients who cannot easily access their data. Layer on
privacy and security concerns, and interoperability becomes even
more of a challenge.
Payers may be well positioned to take the lead on reaching
interoperability as they collect, manage, and provide stewardship
of data throughout a patient’s life versus providers who may only
collect on an event-based basis. Payers should establish a common
patient marketplace, so they, patients, and providers would benefit
from a secure and interoperable repository. Payers would then
manage and provide access for authorized parties to obtain and
utilize the marketplace to support a holistic patient approach.
Shift the Marketplace
A key benefit to the marketplace would be the shift toward treating
the whole patient instead of treating a single issue. Healthcare
providers will be able to access data from different periods of a
patient’s life and act on that data – for example, predicting the
likelihood of disease or adverse health outcomes.
would also allow social determinants of health (SDOH) to play an
essential role in predicting health events. Layering in predictive
analytics would enable marketplace users to model outcomes and better understand what resources may be needed to promote
more equitable health outcomes.
Achieving data interoperability is not a one-size-fits-all solution.
Healthcare organizations must determine their goals for effective
data use for a whole-patient approach, which requires addressing
the challenges above and investing in technology infrastructure
and standardization initiatives. Healthcare organizations should
also make data more actionable through AI and ML, which can
help analyze and identify patterns, make predictions about future
health outcomes, and improve the patient journey.
Furthering access to patient data and using AI and ML has the
potential to improve the holistic patient journey. And increasing
data exchanges between patients, payers, and providers will
likely result in improved care delivery, better healthcare equity
among disparate populations, and improved patient health.
Now is the time to tackle the challenges due to the ever-increasing
volume of data, a growing aging population, and rising health
inequities, along with required federal regulations to make