Due to shifting payment models and an increasing reliance on electronic health records, data analytics tools have become an important part of the medical industry. Although data analytics have historically been used by only the largest of payers and their medical management operations, even smaller payers are leveraging stratification and knowledge discovery tools to stay relevant. Payers of all sizes use these technologies to simultaneously drive improvements to the quality of care and reduce medical costs, either by engaging with targeted member populations or identifying providers that practice or bill outside the norm.
Common Mistakes Made With These Business Intelligence Tools
Unfortunately, many payers procure highly effective and often costly tools, but fail to execute them properly. The following mistakes can render even the powerful tools ineffective:
- Procuring the wrong tool for the desired goal
- Failing to define goals and execution plans that balance the needs of the entire organization
- Failing to properly define the data parameters that are required for clinical programs, quality programs, and reporting
- Allocating insufficient resources to engage the work identified by stratification tools (e.g., identifying 22,000 high risk members for outreach with only five case managers for engagement)
- Failing to develop outcomes and standards that can be used to identify the effectiveness of the analytics
- Failing to develop an ongoing continuous quality improvement (CQI) approach to refine the use of the tool
As payers consider procuring and implementing data tools, it is critical to consider what the end result should be. Assess any tool being considered against the needs of all groups involved, both across the organization and down through the workflow chain. Ask potential vendors for examples of best practices that they have seen among their client base. If the examples do not align with the needs of your organization, the tool may not be a good fit.
Once a tool is selected, vendor user groups are a good way to learn from the best practices and mistakes of others. Before defining data and reporting parameters, make sure to develop a solid understanding of work capacity and reporting needs. Use a basic gap analysis to identify the differences between current data sets and reports and the desired end goal. Finally, understand that while a “set it and forget it” approach is appealing, data parameters will need to be adjusted as regulatory and business operations change.
With the appropriate level of planning and discipline, data analytics tools can move an average payer organization to a highly effective and efficient one.