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3 Keys to Cost-Conscious Population Health Management


Taking a holistic view of a patient population can help deliver more personalized care and a preventive approach to reducing healthcare costs.

The US is facing a steep rise in healthcare costs. Despite having one of the highest per capita healthcare spending expenditure in the world, the quality of care is not on par with some other developed nations.

Rising healthcare costs have prompted the government to enforce policies that require healthcare providers to deliver value-based care, including personalized care that can help reduce readmission rates and improve overall health. However, to do that, providers need to better understand their patients and their conditions, as well as the likelihood of their contracting specific diseases and recommending preventive measures that would work for them.

Population health management (PHM) makes use of new technologies that enable practitioners to gather relevant data related to a patient in different care settings and helps them use data analytics to derive meaningful insights about the patient’s condition. This approach has been proven to help practitioners provide the right treatment to improve patient conditions in the most affordable way without compromising quality.

Below are 3 key components of a PHM approach to affordable and relevant patient care:

Gathering relevant data about patients

To know a patient population, the first step is to gather all relevant data related to them at all points to get a complete clinical picture. This data should include both clinical data and patient-generated health data (PGHD). Clinical data fall into 6 major categories:

  • Health surveys
  • Claims data
  • Administrative data
  • Clinical research data
  • Electronic health records (EHRs)
  • Patient/disease registries

PGHD, as the name suggests, is any health data generated by the patient. It may include biometric data, wearables data, treatment history, symptoms and health history. With the rise of digital apps, gathering of PGHD is becoming easier to accomplish.

According to a recent report published by IQVIA, 40 percent of the digital health apps downloaded today are focused on managing a health condition, and about 55 percent of these health apps use sensor data driven by wearables like Fitbit and Jawbone. All of this means that there will be more data to leverage, manage and analyze to inform a patient’s medical/health condition.

Enabling interoperability

Data sharing is important for the success of any PHM program since it allows providers to eliminate process silos and see data holistically, which can result in actionable insights.

Ensuring interoperability across systems enables data sharing with other stakeholders to deliver care across the continuum. For example, when caring for a patient in a home setting, the attending nurse should have access to the same actionable insights or comprehensive view of the patient data as the doctor sitting far away in a hospital.

A great example of what interoperability can achieve at a regional level is a major initiative by the Louisiana Public Health Institute (LPHI). The Institute conducts clinical research using patient data pulled from different health systems through REACHnet, a partnership between LPHI and other regional healthcare entities that provides access to data on more than 3 million patients across Louisiana and Texas. Through a unique infrastructure for engagement between clinicians, researchers, and patients, REACHnet allows partnering entities to conduct clinic-based research activities while keeping patients engaged in their treatment over a longer period of time. The results of these research projects, along with holistic patient health data, enable providers to deliver improved care to their patients.

The other important factor to consider when sharing data is the importance of data governance to ensure the quality of data gathered and to avoid duplicate, or even worse, incomplete information.

Applying the right analytical tools

For the success of PHM, applying predictive analytics enables providers to make the most of the data and better target patient populations likely to develop a serious medical condition. Predicting these conditions allows them to make timely interventions, and ultimately reduce healthcare costs.

In 2014, Humana started Bold Gold — an initiative to make communities they serve 20 percent healthier by 2020. Humana identified the populations living with food insecurity, social isolation, and physical inactivity. Working closely with physicians, 2 of the 4 Bold Gold communities witnessed a substantial decrease in "unhealthy days” in 2016, surpassing 2017 projections. Through a holistic approach to PHM, Humana has been able to improve living conditions of its members.

By aggregating patient data across multiple sources, analyzing that data and sharing it to make informed treatment decisions, providers in the U.S. are elevating the standards of care and improving financial outcomes.

Dr. Sawad Thotathil is a clinician and Senior Director of Healthcare at Persistent Systems. He directs product strategy and management for the company’s Digital Healthcare group, helping health systems achieve their digital transformation goals.

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