Analytics
Organizations need tools that will seamlessly combine clinical, claims, PBM and lab data so providers can identify high-risk patients, pursue corrective action before an adverse event occurs and deliver trustworthy performance reports to physicians, all in real time.
CMS has signaled a renewed focus on interoperability, a welcome development for healthcare professionals anxious to more easily exchange insightful data. But there's still the matter of how well the people involved in collaborative initiatives operate together.
The healthcare industry is naturally rich with data. It's clear that analyzing this data collectively can improve patient care and outcomes, but how to actually collect, read, integrate, understand and leverage the data remains a broken process.
Population health, Big Data, predictive analytics and all that massive computing power help to improve health. But care still has to be delivered, and it will probably still be one patient and one caregiver at a time: a population of two.
Until recently, technology-enabled efforts to improve population health relied heavily on the use of claims data alone. While there is evidence this approach has merit, there is also a new opportunity to take these efforts to the next level.
Payers' involvement in patient care, and their access to clinical data, has remained limited. They have traditionally relied on claims data to build care management applications and cost reduction programs.
Tying reimbursement to outcomes can lead to better patient recoveries, more predictable costs for all parties, higher prescription adherence rates, fewer readmissions and fewer medical errors, among other benefits.
Like most technologies, big data can be used for good or it can be abused. But the good that big data brings far outweighs any accompanying risks.
Realizing the return on investment from implementations of population health management technologies can take years. But there are approaches to optimizing programs for an earlier ROI.
Recently, John Halamka, MD, has given many lectures about SMAC -- social media, mobile, analytics and cloud computing. He finds that the most popular analytics topics are business intelligence, big data, and novel data visualizations.
