Accurate service line reporting is necessary for a healthcare organization to understand its total cost of care. Organizations that do not understand the total cost of care cannot be successful in risk-sharing and other forms of value-based payment, resulting in a loss of reimbursement.
In an effort to reduce costs, MultiCare Health System, an integrated delivery system serving Washington, decided to outsource all encounter coding, which eliminated the coding of outpatient encounters, negatively impacting service line reporting. To ensure accurate reporting, MultiCare asked its coders to assign an MS DRG code to all hospital-based outpatient encounters, which brought significant additional costs. To mitigate this, MultiCare utilized data analytics and machine learning to develop an algorithm that predicts the MS DRG code for hospital-based outpatient encounters.
By employing machine learning, MultiCare has achieved impressive results, including:
Successfully restoring service line reporting, enabling the organization to better understand the total cost of care, and supporting future participation in value-based care and risk-sharing agreements.
Ability to avoid additional labor costs that would be required to perform dual coding, saving more than $1M annually.