What role does data analytics play in population health management?

If you’re no stranger to the world of data analytics, you might already have stumbled across the concept of population health management. While it is a term that gets thrown around a lot in conversations around analytics in healthcare, if you’re not too familiar with ideas within this field, it might not immediately be clear what exactly it is. In this blog post, we’ll explore what population health management actually is, whilst unpacking the role data analytics plays in its execution and employment.

According to the AHA Center for Health Innovation, population health management is defined as “the process of improving clinical health outcomes of a defined group of individuals through improved care coordination and patient engagement supported by appropriate financial and care models”. Essentially, population health management aims to improve the health-related outcomes of a certain group by monitoring and evaluating information gathered from individuals within that group.

This information could be anything from prescription and sales data to patient response to a certain drug or therapy. There’s a wide range of potential data sources, resulting in huge amounts of information that can provide all the knowledge a physician would need to know about any indication in an individual patient.

Although this information can highlight specific aspects of an individual’s disease progression, without accurate and reliable evaluation, it’s not so clear how this knowledge can be of use in the grand scheme of things – and this is where data analytics steps in.

By creating a comprehensive picture of individual patients, healthcare providers are able to better understand how a particular condition or illness manifests itself within a certain population group or subgroup. This is key in development the treatments of tomorrow that are able to successfully tackle the nuances of a disease, rather than relying solely on blockbuster, one-size-fits-all drugs. Patients aren’t simply one big homogenous mass: they’re unique and separate entities with different thoughts, opinions, and preferences – something the healthcare sector is slowly starting to acknowledge.

Considering how different people respond to their care is the first step in breaking down the barriers to treatment and finding effective, long-term solutions that are tailored to the individual in question. Not everyone responds to treatment in the same way, and by using the valuable insights derived from analysis of patient data, pharmaceutical companies are able to really see what their consumers want, need, and expect from their care. 

The population of any given place is continually growing and changing, and recognizing the innate diversity within our society is crucial in ensuring healthcare equity. Using data analytics as a stepping stone to better understanding individual people can improve population health management dramatically by exploring and considering all aspects of patient care – with this approach backed up by concrete data that holds the potential to affect tangible change in the real world.

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