Healthcare is the most data intensive business in the economy, but is also the industry that uses its information the least.
This phenomenon, as set forth by David Cutler, Harvard Kennedy School economics professor, sheds light on how healthcare data complexities are stifling data sharing, utility and ultimately, progress. While health and human service organizations spend a lot of time with industry- and enterprise-wide data, much of the time allotted is dedicated to electronic health records (EHRs) and telehealth integration and management, rather than expending actionable data. Consider the unique data challenges of the health and human service field and how creating a data journey will help turn that data into knowledge.
Unique Healthcare Data Complexities
The primary data challenges for health and human service organizations stem from information overload bound by stringent requirements for management and reporting. Furthermore, the industry does not operate on standardized data models, but instead varying EHRs, making data sharing difficult. As such, additional healthcare data complexities include:
Despite these challenges however, data has helped springboard changes in healthcare by way of identifying and treating high-cost consumers, reducing admissions and enhancing care provider effectiveness. To further turn this data into knowledge, health and human service organizations must create a “data journey” – moving from simple data modeling, to big data for analytics.
Creating a Data Journey in 4 Steps