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:
Much of the data are in multiple places
Data are structured and unstructured
Lack of universal definitions
The data is complex, not linear
Regulatory differences and changes
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
Data Modeling – Designed to represent reality and ensure exact communication, this step is accomplished by organizing data elements, including people, places, things and standardizing how they relate to one another. Healthcare organizations achieve this by deciding which data to track, report upon and base decisions off of.
Data Maturity – Helping organizations identify and quantify how sophisticated their data modeling is, data maturity is a four step framework that includes undisciplined, reactive, proactive and governed. View an infographic on this step for more detail here.
Data-Driven Decisions – Data-driven decision management (DDDM) is a business governance approach that values decisions that can be backed up with verified data. This approach begets key questions such as: how well do the sample data represent the population; does the data distribution include outliers, etc.
Big Data – Lastly, big data can be analyzed for insights that lead to better decisions and strategic business moves. It is often derived from structured and unstructured data that measures volume, variety, velocity, variability, and veracity.