Working diagnosis-related group (DRGs) are utilized for a variety of functions across healthcare organizations, including case management, finance, and care coordination. In particular, working DRGs contribute to many system-wide forecasts, so accuracy is vital. This necessity is increasingly apparent during times of high uncertainty when the margin for error is even more slim than usual.
The responsibility for generating working DRGs typically falls on CDI teams. This increases their already large workload and makes it difficult, if not impossible, for each patient to be assigned a working DRG and for these codes to be re-reviewed every day to incorporate new data. As a result, opportunity for DRG assignment and/or correction is often missed, decreasing the accuracy of both the overall clinical picture and DRG-based predictions.
Iodine built a fully-automated, clinically-driven module that increases the accuracy and efficiency of assigning working DRGs, supports clinical documentation integrity (CDI) workflow, and enables teams across the health system to make real-time, evidence-based predictions — without needing to rely on final coded data. The Iodine Forecast Module – part of Iodine’s AwareCDI suite – leverages Iodine’s CognitiveML™ engine to automatically and concurrently predict MS-DRG codes for all inpatients based on patient documents and data, even if no prior manual review has been performed.
In addition, geometric mean length of stay (GMLOS), relative weight, and other associated DRG information is included for each forecasted MS-DRG. This data can be leveraged for discharge planning, resource utilization, early identification of bundle DRG cases, revenue forecasting, and other relevant use cases. All Iodine Forecast data can be exported to and integrated with other systems, allowing it to be utilized by all relevant teams in their daily workflow and planning, and creating cohesiveness across the organization. All data is kept up-to-date from admission through discharge as new patient information is received.
Leveraging Forecast to help understand the reimbursement impact of COVID-19
Recently, Iodine applied its machine learning models to 60,000+ COVID-19 cases from more than 600 hospitals across the U.S. to better understand the reimbursement impact of COVID-19. In this analysis, Forecast enabled Iodine to consider all discharged patients without waiting weeks for final coded DRGs.
Timeliness of financial insights is critical–especially when navigating the challenges of a crisis such as COVID-19–and Iodine Forecast provides near-real-time access to the data that supports these insights. The Iodine Forecast Module enables hospitals to better understand trends such as the reimbursement impact of COVID-19 and develop strategies to get ahead of unforeseen challenges.
To learn how Forecast can empower your Case Management and Finance teams, reach out to email@example.com.