The promise of Big Data and analytics in managing population health is one of the most hyped, yet least understood opportunities in health care today. The Center for Health Analytics and Insights at SAS hopes to change that by offering you practical advice to help you create a winning strategy for your organization. In this ten-part blog series, we’ll describe the technological capabilities you’ll need, anticipate some of the challenges you’ll overcome, and outline the many benefits you’ll receive from adopting population health analytics (PHA).
The combination of government reform and consumerism is driving the health care industry shift from fee-for-service (FFS) to fee-for-value (FFV) – sometimes characterized as a move toward more “accountable care.” Proponents believe that economic opportunities – in the form of shared savings or value-based payments – will encourage health care providers to accept risk for delivering improved outcomes at lower-than-expected cost. This opportunity is already motivating a significant number of entrepreneurial health care providers to begin transforming their traditional care delivery process from responsive episodic care into cross-continuum coordinated care, for defined groups of patients. This emerging delivery model is often referred to as “population health management”.
We know that managing population health requires providers to blur the boundaries between public health and the medical treatment of individuals. As health care providers accept financial responsibility for the health care costs of certain groups of people, the need for – and value proposition of – investment in longitudinal cross-continuum care becomes evident. Scarce clinical resources, thin margins and competing priorities require efficient deployment and judicious management of those resources, at scale. Thus, providers on this journey will need health information technologies (HIT) that enable automation, ensure accuracy and integrate seamlessly into clinical workflows.
The strategy in a nut shell
The Center for Health Analytics and Insights at SAS collated best practices and advice from experts across the US into a strategy we call population health analytics, or PHA. As shown at the top of the graphic of the PHA wheel, the strategy begins with integrating data from diverse sources and preparing it for analysis. This leads directly into assessing performance across the continuum of care. System-level performance reporting inevitably surfaces opportunities for improvement that will require providers to “peel back successive layers of the onion” and define increasingly granular cohorts of patients, ultimately ending with a “population of one.” Then, by understanding the needs and risks of each unique individual, providers can design interventions and tailor programs to engage each patient in a personalized care plan. Automation and workflow integration support the delivery of interventions that were strategically designed to improve care coordination and performance. Lastly, by measuring the impact (success or failure) of each intervention, experimenting with new methods, and testing incremental quality improvements along the way, providers can learn and adapt to optimize the entire process.
There are some important considerations for each of the eight analytical competencies, which we will cover in parts 2 through 9 of this series. In subsequent entries, we’ll elaborate on the business rationale, the desired output and the enabling technology that powers each capability. Check back for part 2 of the PHA Strategy series. In the meantime, please tell me your thoughts and ask us your questions in the comments section below.