Big data analytics have ushered in the potential to dramatically improve health care costs, the care experience and patient outcomes. Masses of genomic data, clinical trial data, electronic health records (EHRs), claims data and research study data can be brought together to reveal important discoveries, support better operational and medical decisions and, ideally, to bring analytic insight right to the point of care.
As Director of Business Strategies for SAS Best Practices, I’ve seen a lot of change in the last 15 years in both the awareness of and execution of analytics programs to drive value. At the 11th annual SAS Health Analytics Executive Conference, I had the privilege of talking with some folks who are leading the analytics charge in pharmaceutical development and integrated health care delivery. In a panel discussion, they described some cases where their organizations are applying analytics to improve quality, close care gaps and accelerate scientific discovery.
- Quality. Preventable medical errors are a leading cause of death in the US – higher than diabetes. Imagine the opportunity to use data and analytics to reduce such events. Analysis of EHRs can be used to automatically detect misdiagnoses, monitor medication use and better assess risks to provide the optimal therapeutic interventions.
- Completeness of care. The health care industry lags financial services and retail in the ability create a 360-degree view of the customer/patient/member and deliver the right insights at the right moment. Imagine the potential to deliver analytic results into the exam room while the provider is with the patient, making care decisions. The potential exists to customize not only what, but how, information is communicated to a patient and adjust therapeutic approaches throughout the continuum of care based on a comprehensive understanding of not only a person’s medical history, but their personal preferences, family history and demonstrated behaviors (just to name a few).
- Speed of scientific discovery. Did you know that it takes 12 to 14 years and at least $1 billion to develop a drug? A staggering fact shared by one of our conference executives from a leading pharmaceutical company. But analytics can change that. Life sciences organizations are using analytics to design better clinical development programs that identify effective drugs more quickly, and also to identify subgroups of patients for whom a therapy will work particularly well.
- Real-world understanding. Pharmaceutical companies are working to integrate clinical trial data with real-world evidence. The scientific mandate: we need to do the correct science to create the correct drugs. But there’s also the practical mandate: how are we positively affecting or changing people’s lives?
The bottom line
Those in the health and life sciences professions have a mission to help those they serve. But whether your organization is categorized as for-profit or not-for-profit, if your bottom line isn’t black, you’re not going to be around to help anyone. Analytics can be used to identify waste and inefficiency as well as to optimize the allocation of limited resources – skilled providers, capital, facilities, etc. Smart, lean operational decisions become ever-more critical as we work to transform the health care system from a fee-for-service model to value-based care.
It’s easy to get people to agree in concept about the value of analytics, but it’s not always easy to move from vision to action, from high-level roadmap to implementation. In my next posting I’ll talk about why the industry isn’t exploiting analytics to the degree that it can – and should. Since I work at SAS, I can assure you it’s not a lack of data or analytical horsepower.
If you missed our Virtual Conference, I invite you to watch the session, An Analytic Prescription: Developing a Robust Strategy and Culture, as well as several others available on-demand.