Analytics Staffing Models of Health Systems That Compete Well Using Data
Analytic leaders are facing unprecedented pressure as expectations from the digitization of health drives questions from every corner of the enterprise. Along with the operational and workflow changes that come with digital health, we are seeing greater demand for data to support care transformation, risk contracting and organizational performance.
The time is right to consider how analytics can support organizational strategies and how we can ensure alignment across the organization. As part of the strategic alignment exercise we often see organizations consider how to best deliver advanced analytic capabilities and the ask themselves the question “how should we organize our analytic teams?”
Often, an effective way to increase that efficiency, improve morale and achieve economy of scale is to consider changes to how analytics teams are organized. The most appropriate organizational structure will vary based on the health system size, culture, and analytics (and data) maturity. Should the analytics capabilities be centralized, decentralized, or should we consider an alternative, hybrid staffing model? Should analytics sit under IT or medical leadership?
In this talk, we will review the common models employed by leaders in healthcare, and describe how they align with business strategy. Further, we will outline common challenges as well as share success secrets via case studies from across the US healthcare landscape. The goal of this presentation is to provide the audience with a strong foundation for understanding the healthcare analytics staffing models used across the industry.