Healthcare Data Analytics • 2 min read

The role of data analytics in population health management

The role of data analytics in population health management (1)

According to the AHA Center for Health Innovation, population health management (PHM) 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”. 

The importance of data insights 

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.

Data analytics has contributed significantly over the course of the past decade to improving patient outcomes, breaking down some of the barriers that exist in today’s healthcare system – but there’s still a lot of progress to be made. It’s been of particular use during the COVID-19 pandemic, allowing healthcare providers to prepare effective responses based on insights derived from the analysis of data across different spectrums of the scientific community.

Understanding patterns with a 360° patient view

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 developing 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. 

Tackling health inequity with real insights

Ensuring that the data of diverse populations is included throughout the analytics process is crucial – it’s hugely important to consider all the different types of data sources before conducting an in-depth analysis. By combining a variety of data sources, it’s far more feasible for analysts to guarantee and verify the accuracy of the information they’re dealing with, resulting in data that’s simply more reliable.

A solutions-oriented approach to medical equity is entirely possible, with patient-reported data playing a major role in its development. By listening to what patients have to say and considering their opinions, the healthcare sector is putting the person who matters most back into the center of it all. Highlighting the invaluable information derived from these types of data sources and applying these actionable insights at a population level will enable everyone from data analysts and medical researchers to healthcare providers to make real progress in closing the equity gap across the patient care spectrum.

Conclusion

The population of any given place is continually growing and changing, and recognising 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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