Health Data Analytics Maturity – Path to a Data Driven Culture
- Posted by Joe Crandall
- On August 29, 2013
Is your organization data adverse or data driven?
To have a data driven organization, it must embrace the gathering and sharing of data. Organizations can purchase the latest business intelligence tools, but not be optimized to leverage the power of their data. This is because the organization is still data adverse with a fancy tool. Healthcare organization need to grow the skills to be able to take advantage of the data they currently collect.
Maturity is a natural occurrence or evolutionary path for growth. As in nature, maturity will not fully evolve without the conditions that provide a conducive and nurturing environment. A planted seed will not germinate without water and the right temperature. Additionally, a properly germinated seed will not grow without the addition of sunlight and nutrients. Just as a seed follows the evolutionary steps needed to produce fruit, the hospitals development of a well thought out health data analytics (HDA) maturity model is a necessity to allow the HDA capability to flourish. A conditionally based HDA maturity model helps an organization build systematically on previous successes to meet and exceed their internal and external requirements.
A well-defined maturity model provides the HDA roadmap and milestones needed to measure organizational progress towards a HDA enabled capability. In addition, it provides a recognized standard to benchmark the current process, enablers and outcomes.
The HDA benefits chart illustrates the foundational shift in reducing the relative time spent on data collection/validation and transferring it to increasing the relative time and effort for analysis/decision making.
Organizational HDA maturity typically has one of three the following characteristics:
Low Maturity (80% to 85% of healthcare organizations): Islands of data with a high degree of data pollution with limited access requires the majority of time spent to be spent on data collection and cleansing. The analytic process is focused on the retrospective questions; where some of these questions are answered only some of the time. There might be a successful use of data in isolated pockets within the organization, but overall, the organization is fragmented.
Medium Maturity (10% of healthcare organizations) : Data sharing problems between departments begin to fade and the availability of curated data increases. Developing standards and common tools increases the data quality allowing the total effort to focus on analyzing data. Within this stage of maturity, most of the questions are answered in near real-time and can impact the current situation.
High Maturity (Approximately 5% of healthcare organizations) : Very few healthcare organizations have a high degree of maturity. They are usually organizations that are steeped in academics and research. At this stage, efforts are focused on predictive analytics and population health. Real-time decisions are more widespread due to the high trust and confidence with the data being used.
The goal of increasing maturity is to reduce time collecting/validating data and spend that time instead on analyzing the data and using the information to make decisions to drive better outcomes. As seen on the HDA benefits chart, organizations that are mature spend more time on decisions that drive action.