Health care organizations around the world face hard challenges in managing health information and using data to plan ahead. Those challenges have grown, opening new possibilities for population health management (PHM), especially in countries such as the U.S. and the U.K., where health care costs are high. This article looks at how data analytics in population health management can help, and what the COVID-19 pandemic showed about its future.

What is data analytics in health care?

Data analytics is widely used by growing businesses. It models and transforms data to draw out useful information that supports decisions about the future.

Can the same approach work in health care management? And can it keep up with the growing demands of population health management? The sections below look at both questions.

Why move data analysis from manual to automated?

The COVID-19 pandemic put enormous strain on health care systems. Analyzing data from large groups of patients, gathering information and drawing conclusions became a complex process in its own right.

As many services moved online to support no-touch care and social distancing, the need to shift data analysis from manual work to automation became clear. Automation makes the process faster and less error-prone, and it can reduce an organization’s staffing costs.

How do electronic medical records help analyze patient data?

It is easy to assume paper records make analysis simple. That is not the case when you are analyzing grouped data covering months of patient history.

Electronic medical records (EMRs) are a better option than tracking a patient’s history on paper. They keep a patient’s history in one place, make trends across many visits easier to spot, and can be transferred from one hospital to another within seconds in an emergency. EMRs also help when a patient’s prescription is needed urgently, and fast access to electronic prescriptions can save lives.

How can health IT improve population health management?

Better data collection and health IT can strengthen PHM by providing data on groups of patients sorted by social determinants, location and age. Analytics on this grouped data can help PHM reach broader goals faster, such as spotting at-risk groups early and planning care where it is needed most.

Modern health IT tools can track and monitor a patient’s health and keep a record of their medical history. They also let patients contact their doctors quickly, book appointments and get test results on time. This makes it easier for patients to keep up with checkups and look after their health with very little effort.

Why shift from fee-for-service to value-based care?

Better data analysis on grouped patient data is one way to improve health care. Other factors matter just as much to how health care is managed and improved in the U.S.

The pressure of the pandemic added urgency to one change in particular: moving from fee-for-service payment models to value-based care. Value-based models look at overall cost and balance the risks within a health system. But a system that adopts value-based care without the payment infrastructure to support it will struggle to deliver PHM, whether it relies on individual or population-level data analysis.

Key takeaways

The pandemic pushed population health management to take new initiatives and pursue reforms across health care. Data analytics plays a large part in analyzing health data, but it is not the only tool for transforming health care management.

It is best seen as support that helps health systems handle complex analysis of patient data and meet broader goals. In the end, the choices we make shape both our own health and the health care system we want.

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