Predicting if a patient will be readmitted to a hospital or develop a life-threatening infection is now within reach thanks to predictive analytics. But its real power lies in being able to act when something is predicted.
Not too long ago, skeptics claimed predictive analytics was more buzzword du jour than a powerful strategy that could harness learnings for the advancement of clinical decision making. The problem, many argued, was that the industry lacked the proper infrastructure, staffing, and resources to act when something was predicted to happen with a high degree of certainty. Further, critics believed that although the data served a purpose and was often directionally accurate, it was not precise enough to reflect the nuances of patient care.
As payers and providers increasingly unlock their data and marshal the resources to understand its patterns and trends, there is an opportunity to apply analytic techniques with advanced clinical decision support (CDS) and real-time clinical surveillance, significantly affecting mortality rates and influencing outcomes through early detection and by directing the most appropriate care. The power of actionable clinical knowledge is playing out as providers focus on some of the most prevalent, undertreated diseases in healthcare today: conditions that are among the costliest to treat.
The power of predictive analytics innovation
Real-world situations are in fact the only ways progress will be made in advancing analytics and affecting outcomes. Providers, payers, and vendors must collaborate so they can better identify ways to aggregate data that is scattered across multiple clinical systems – before the data can be analyzed – so the results can be communicated to the point of care in a meaningful way.
Given that delivery of the information is just as vital as the information itself, the use of analytics is getting an added boost from more-intelligent workflow systems that can accelerate the diagnosis of a condition by way of the timely delivery of patient-specific advice to nurses and other clinical staff at the point of care. Such cutting-edge systems can leverage hundreds of rules accounting for possible comorbidities and medication abnormalities, putting within reach higher-quality treatment and improved outcomes.
Holistic analytic approach relies on behavior change and systems
- Change Management
- Deep, up-to-date medical content
- Analytics
- Decision support systems
