Transitioning from older business models to a newer, more risk-based approach, many healthcare payers need stronger frameworks to help with improvements, enable better efficiencies, lower costs, and drive revenue. Without helpful data insights, maintaining excellence isn’t possible.
Healthcare payers are using data to modernize operations in these four ways:
- Enabling Wellness and Disease Management with Self-Service Analytics
Healthcare payers access to self-service analytics allow them to find insights from public health data sources such as Medicare and Medicaid. This is used to target and improve population health for their patients. By having this access allows case nurses or even patients understand the risks that could be associated with a portfolio of diseases. This same self-service data can also be used by the federal government to measure, analyze, and improve its health programs.
- Increasing Customer Service Efficiencies with Real-Time Analytics
Web and mobile portals, as well as customer call centers, are widely offered through most healthcare payers. Patients do not take time to become familiar with these portals and will call a customer service representative for even the most trivial question. This behavior places a higher demand on call centers and in return, increases the cost in operating those call centers. By using real-time analytics of how many patients use these self- service portals to the call centers, it will allow for payers to educate their patients on how to properly use these portals; lowering the cost of the call centers and improve customer service.
- Improving Healthcare Worker Productivity with Data Blending
Healthcare payers have large amounts of patient data across the entire initiative that is not being cross-connected. Understanding patients to gain proper access to care relies on the ability to blend this data. Allowing nurse case managers for instance, to such data about a patient, he/she can quickly decide what treatments can be done and whether or not the payer will cover it.
- Ad-hoc Analytics for Claims Management
Ad-hoc analytics is data from different disconnected systems, helping in normalizing that data, segmenting patient population based on risk. Every year, healthcare payers process millions of claims, at the same time screening for fraud, waste, and abuse. This process is often done manually and can be time consuming and tedious. The possibility of human error is inevitable. Ad-hoc analytics help claims manager answer specific questions on a case-by-case basis. By using ad-hoc analysis and some automation, monitoring claims takes a few hours instead of a few weeks.
Finding efficiencies in managing claims, monitoring, measuring, and analyzing healthcare will improve operations and secure new patients. Integrating data visualizations is easier than you think. Pairing that with your efficiencies will also help in improving patient outcomes.
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