Predictive Analytics Help Improve Outcomes and Lower Costs

The shift in healthcare from paper-based to digital workflows is giving hospitals access to a wealth of digital data. At the core of this transition are the adoption of Electronic Health Records (EHRs) and the digital transformation of the healthcare industry at large. This transformation is introducing new opportunities for healthcare organizations to fundamentally re-architect workflows to be more data-driven, dynamic, and patient-centered. Predictive analytics can play a key part in this process—helping healthcare organizations thrive through the digital transition, and establish more efficient workflows and higher quality operating models. Healthcare experts agree on the enormous potential of predictive analytics to help identify patients at risk for chronic conditions, develop new protocols of care, forecast readmissions, and proactively identify potential obstacles to care plan adherence. It is also widely recognized that, when applied to intelligent staffing, billing optimization, and resource allocation, predictive analytics can improve operational efficiency. Given the relatively recent introduction of predictive analytics in healthcare, it is likely that some of the most exciting use cases have yet to emerge. Implementing predictive analytics requires overcoming some common hurdles. To begin with, target data is often contained in different formats across segregated silos. That data, like external data such as socioeconomic information, is typically ignored by traditional business intelligence (BI) tools. In addition, predictive analytics favor real-time systems, which requires evaluating data center, network and application architectures to ensure that system performance matches computational requirements. Finally, acquiring the expertise needed to not only develop predictive models but to integrate results into clinical workflows can be a barrier. Despite these challenges, leading organizations are finding ways to implement predictive analytics to optimize care delivery and boost the bottom line. As the aging global population continues to strain access to care and healthcare systems shift from volume to value, the demand for data-driven decision-making tools is high—and the opportunity for predictive analytics in healthcare has never been greater.