The State of Predictive Analytics in Healthcare in 2019 and Beyond

The past few years in healthcare have been symbolic of a shift towards consumerism as care continues to become more and more personalized. An increasing number of people want to become more active participants in the consumer-provider relationship and shop for their healthcare services similarly to how they shop for their streaming services—they want to be informed and seek out the lowest cost, the best value, and most convenient service as it relates to their specific needs. 

Technology and the myriad ways in which it’s being implemented has been one of the major catalysts propelling this dramatic shift as innovations like wearable devices and telemedicine have become more advanced and widely adopted. Additionally, big data and analytics have firmly established themselves as integral components of the modern healthcare system. Experts predict yet another trend emerging from ever-evolving technological applications: predictive analytics. With the potential to sharply increase the quality of patient outcomes, quality of care and lower costs, much is expected of the emerging technology—but there is much to be concerned about as well.

In short, predictive analytics is a segment of advanced analytics that exists to make predictions about unknown future events that can lead to actionable decisions. Far from restricted to just the world of healthcare, predictive analytics has already proven its use and value in numerous industries including manufacturing, law, marketing, fraud detection, and more. 

Below, we’ll share some expert insight on the risk and rewards of predictive analytics, and what it means for the future of healthcare. 

General Statistics

Before we touch one some of the benefits and risks, we’ll share some general statistics that reflect the latest predictive analytics trends in healthcare:

  • 60% of executives report they are using predictive analytics within their organization (and 20% reported they didn’t, but plan to within the next year)
    • This is a 13-point year-over-year increase from 2018
  • 60% of payors and providers expect to dedicate 15% or more of spending to predictive analytics throughout the year
    • 61% of executives forecast that predictive analytics will save their organization 15% or more over the next five years
  • 16% of providers cite “too much data” as the top barrier to implementing predictive analytics while 15% of payors cite “lack of skilled workers” as the largest obstacle
  • 23% of executives believe the future of predictive analytics lies in data visualization, while 16% believe it lies in machine learning
  • (per 2019 annual survey from Society of Actuaries)

Seeing the tangible potential of predictive analytics, there has been a noticeable uptick in its adoption, or plans for adoption, among healthcare executives over each of the past three years. Furthermore, for those who have yet to implement this branch of analytics, the majority (89%) of executives plan to use predictive analytics within the next five years—a 4% increase from 2018. More than just using it, though, nearly two-thirds of executives plan to increase their investments in the space by 15% or more over the next five years.

Benefits

There are plenty of good reasons for this increased adoption of and investment in predictive analytics. Executives report significant increases in patient satisfaction, as well as reduced cost and increased profitability. Utilizing numerous techniques like data mining, statistics, modeling, artificial intelligence, and machine learning, predictive analytics is able to evaluate historical and real-time data to make predictions about the future. 

These predictions allow for sizeable improvements in operational management, personal medicine to assist and enhance the accuracy of diagnosis and treatment, as well as potential risk factors for public health.

Potential Hazards

Because so much of predictive analytics is rooted in statistical modeling and predictions, there lies in an inherent risk as the precise timing at which the decision transitions from machine to a human is most often unclear and unregulated. The speed at which predictive analytics produces data far outpaces the capability of human decision-making processes, further stressing the relationship between the two. 

The algorithms behind the computer processes that make up predictive analytics have the potential to contain implicit biases from the people that coded them, which presents its own litany of potential moral and ethical risks. 

Additionally, the rapidly increasing amount of personal data being stored online presents significant risks as highly sensitive can be hijacked due to cyberattacks, resulting in a loss of trust and deteriorating relationship between patient and provider. 

Predictive analytics, like any major technological disruption, brings with it a certain level of risk as the technology goes through its initial stages of implementation, especially within a field as nuanced as healthcare. Going forward, to fully embrace and leverage the many benefits it can provide, considerable care and discretion will have to be used to ensure its ethical and nonbiased application. 

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