If health plans and providers had any doubts that they live and die on the strength of their data management tools, they’re fading fast. A new survey has found that payers and providers are fully on board with investing in predictive analytics tools, and what’s more, expect them to yield great results.
The survey, which was conducted by the Society of Actuaries, found that 92% of payers and 93% of providers agreed that predictive analytics would be important to the future of their business.
Sixty percent of executives polled were using predictive analytics in their organizations, a 13% jump from last year’s results. Meanwhile, 89% of healthcare execs reported that they either used or planned to use predictive analytics within the next five years, which represents a 4% year-over-year increase from 2018.
What’s more, 60% of payers and providers expect to dedicate 15% or more of their tech spending to predictive analytics this year, and almost two-thirds of them think that predictive analytics will save their organizations 15% or more over the next five years.
The top preferred outcomes health leaders cited as reasons for using predictive analytics include “reduced cost” (54%) and “patient satisfaction” (45%). This is logical given their previous year’s results, in which 42% saw “improved patient satisfaction,” 39% saw “reduced cost” and 33% enjoyed “increased profitability.”
To move forward with their plans, however, they’ve got some big challenges to tackle. For example, 16% of providers said that they saw “too much data” as a top obstacle to implementing predictive analytics, while payers identified “lack of skilled workers.”
When asked to predict which technology capabilities will be important going forward, 23% of respondents said that data visualization was the most promising tool for predictive analytics efforts, 18% cited refining data collection methods to increase security and 16% named machine learning.
What makes all of this even more interesting is that in truth, we’re just getting started in defining use cases for predictive analytics technologies.
Healthcare organizations are already seeing success in such areas as optimizing OR utilization and streamlining ED workflow. Other emerging uses for predictive analytics include identifying patients at increased risk for developing sepsis, as well as patients at risk for hospital readmissions within 30 days.
But there are other potential ways to leverage predictive analytics which are just on the horizon. For example, in the future, it may be possible to use genomic information to predict which patients are at greater risk of developing costly illnesses such as cancer, heart disease and diabetes.
As the survey underscores, at present providers are having a hard time getting their arms around their data and creating an analytical framework, but it seems likely that they’ll figure things out relatively soon.
We could be on the brink of some amazing shifts in our ability to rethink many critical processes an avoid many adverse outcomes. Let’s hope predictive analytics approaches live up to their reputation.