Driven by the shift to value-based health care, advanced predictive analytics are gaining a larger footprint as providers search for analytic capabilities that will help them prevent patient illness, avoid penalties, and reduce the cost of care. This new landscape requires a different analytic approach that can identify at-risk patients before they get sick so that interventions can be applied and penalties avoided.
Despite the growing need, only 15 percent of providers are currently using advanced predictive analytics, according to a recent survey by Jvion. Conducted by analytics provider, Jvion and HIMSS in March, the study asked the hospital community if and how they are using advanced predictive modeling to support their clinical and operational goals. For the study, advanced predictive analytics/ modeling was defined as the application of machine learning algorithms to find patterns within data to predict patient-level risk.
Key Findings
Of the 15 percent who do use some kind of advanced predictive analytics solution, 92 percent of these providers are utilizing predictive analytics solutions to predict patient risk or illness, while the other 8 percent are using solutions to support other organizational goals.
The study also finds that providers prefer vendor solution (82%) for their predictive analytics over developing their own home-grown solution (18%). Of those providers who indicated that they are not currently using advanced predictive analytics solutions within their facilities, 96% indicated that they would or may consider an advanced predictive modeling solution in the future.
Clinical Challenges
Providers who use advanced predictive analytics are tackling the following clinical challenges:
– 27% are predicting readmissions
– 18% are predicting patient deterioration
– 27% are predicting sepsis
– 10% are predicting general patient health
– 18% are finalizing decisions
Operational Challenges
The operational goals identified by respondents were more varied and evenly distributed across the surveys submitted. Operational goals included:
– Targeting length of stay expectations
– Project reimbursements
– Target intervention activities
– Improve patient safety outcomes
– Meet nurse staffing goals
– Reduce mortality
– Reduce readmissions
– Currently defining/in process
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