It’s fair to say data analytics is revolutionizing the healthcare industry by putting useful insights directly into the hands of decision-makers at every level. And there’s no one department in particular that stands to benefit the most from these advances in data analytics. Rather, there are use cases for nearly every type of healthcare organization and branch you can imagine.
Here’s how four types of professionals are harnessing medical data analytics today. By no means is this an exhaustive list, but it gives a glimpse into some of the ways healthcare teams are harnessing data to improve their best practices.
Healthcare administrators are using data analytics to optimize the allocation of resources. For instance, staffing used to depend primarily on rules of thumb: certain days of the week and hours of the day tended to be less busy than others. But this approach put hospitals at risk of being understaffed during critical times or overstaffed during slow ones. The former is detrimental to patient outcomes and employee experience; the latter eats into the bottom line.
For example, access to advanced predictive analytics is now helping administrators and coordinators use data to properly staff emergency departments— with the goal of finding the right balance between reducing operational costs while also optimizing patient outcomes.
Clinicians are increasingly able to incorporate data-driven insights into the decisions they make regarding patient care. What may not be evident on a case-by-case basis becomes clearer in the context of many data points gathered over time — essentially, data is helping doctors and nurses make decisions based on trends and relationships that may have been obscured before algorithms identified them and brought them to the attention of clinical teams. And, there are more data sources than ever before from which to draw: electronic health records, insurance claims, wearable devices, etc.
One particularly exciting area is the connection between individual patient outcomes and population health — mining for patterns within large datasets can even help clinicians detect, diagnose and monitor diseases earlier in individuals.
Pharmaceutical companies are using data to bring drugs to market faster. One Fortune 500 pharmaceutical firm rolled out the ThoughtSpot analytics platform to give scientists the ability to instantly search for drug trial results and identify side effects quickly by patient groups. What used to take three months for this org — relying on centralized data teams to create static reports containing drug trial results — now takes three minutes, which allows users to make decisions in a timelier manner.
Health insurance providers are using advanced medical data analytics to optimize a number of processes, like managing risk. Artificial intelligence (AI) and machine learning (ML) algorithms are helping insurers accurately predict risk among members and set their premiums accordingly.
As one industry expert notes for Healthcare Finance, a percentage point — or even a fraction of a percentage point — one way or another can translate to millions of dollars. Insurers have always faced the challenge of setting premiums correctly. Now they have advanced analytics tools to help them assess risk and help them allocate their resources accordingly.
There’s a lot at stake here: If insurers set premiums too low, their bottom line suffers. But if they set premiums too high, they could lose out on enrollees and employer contracts because of prohibitively high pricing. Data helps insurers walk this tightrope, so to speak.
There are opportunities for administrators, clinicians, scientists and insurers alike to drive better outcomes. The good news? These data-driven improvements stand to benefit both providers and recipients of healthcare services alike.