This document discusses population health and the need for healthcare providers to shift from a volume-based to a value-based reimbursement model. It defines population health as "the science and art of preventing disease, prolonging life, and promoting health through the organized efforts and informed choices of society, organizations, public and private communities, and individuals." The document states that leveraging automation, analytics, and historical data can help improve delivery of care to groups with similar needs and support the shift to more population-centric care. It also discusses using predictive analytics to identify high-risk patients, plan interventions, and ensure medication adherence to contain costs and mitigate risks.

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The science and art
behind Population Health
March 2017

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Introduction
T
he advent of value based reimbursement model has the payers
asking providers to shift from volume-based care to a value-based
reimbursement structure.
Population Health as defined by C.-E.A. Winslow, founder of Yale Department of
Public Health, as “the science and art of preventing disease, prolonging life, and
promoting health through the organized efforts and informed choices of society,
organizations, public and private communities, and individuals.”, can be im-
proved by leveraging automation and robust analytic capabilities that define
measurements for health outcomes and patterns from historical data. This not
only helps in improving delivery of care to a group of individuals with similar
needs, but also helps in meeting the paradigm shift from acute care to popula-
tion centric care.
Various community based health systems have practices using different EHR/
EMR and their clinical workflow varies significantly such that time and efforts
required to gather and interpret member information is overwhelming due to
diverse sources of data. Thus, there is a need to transform the traditional
“isolated" care model into a “network” care model, both for increased care coor-
dination and the ability to scale effective interventions with the Population
Health approach. Whether it’s about managing chronic conditions by effectively
identifying high risk patients or identification of care gaps against pre-defined
quality measures like HEDIS, there are growing needs to enhance clinical ana-
lytics for better care coordination and improved outcomes.
Care management, care coordination and lower aggregate costs can be
achieved at patient level by integrating and collating data from claim based,
PHR and EHR based data sources. This can help health insurers, community
based health systems and their affiliates play an active and increasingly person-
The science and art behind Population Health

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alized role to help patients’ engagement in their care and wellbeing.
Care Management is just not necessary to improve & track patient health but it’
s also a strategic and important tool for health plans as they find ways to con-
tain costs in modern data-centric era. Predictive analytics and modeling tech-
niques can be used to identify members with high risk of hospitalization, plan
necessary care interventions and ensure medication adherence to mitigate that
risk. This data-centric action-planning can provide meaningful insights into
quality, safety and cost of care available at contracting providers.
Given that population health management (PHM) and Care management are so
closely related in spirit, we have chosen to use those terms interchangeably in
this paper. The capabilities that one offers can be leveraged by the other to
offer meaningful capabilities to healthcare providers.
Healthcare payers
need to minimize
the likelihood of
patients going to
healthcare facilities
by identifying risks
and mitigating
them.
The science and art behind Population Health
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The science and art behind Population Health