Insights gleaned though data-mining and machine-learning give hospitals the ability to draw upon exponentially greater information points that feed data-driven predictions of charging anomalies.
Stacy State, director of enterprise marketing for ZirMed in Louisville, Ky.: These identifications can then be intelligently and automatically routed to the right person at the right time — allowing issues to be prioritized and corrected in a manner aligned with a hospital or health system's unique strategies. If the technology incorporates the charge description master as well as the commercial contracts and applicable government rates, all charging anomalies can be assigned a net revenue impact specific to your health system. Further, machine-learning algorithms underlying these identifications can continue to adapt to changes in a hospital or health system's charging data, clinical practices, payer contracts and healthcare information systems configuration as these elements evolve over time. This is not possible with manual intervention and rules-based logic alone due to the volume, variety, and non-uniform forces that drive changes across healthcare data-sets.