by XSOLIS Insights, on May 19, 2021 6:00:00 AM
A hospital’s goal is not to minimize Length of Stay (LOS), but to optimize it at the intersection of quality and cost. By knowing the expected LOS up front, hospitals can better plancare, coordinate with partners, and line up discharge plans.
-Michelle Wyatt, Senior Director of Clinical Best Practice, XSOLIS
Diagnostic Related Groups (DRGs) are part of the financial fabric of hospital reimbursement. DRGs can determine how much hospitals will get paid for patient care and set expectations for the appropriate length of stay (LOS) for each patient.
Unfortunately, final DRGs are calculated after the patient has left the hospital, creating two fundamental issues:
- Geometric Mean Length of Stay (GMLOS) is not determined until after the patient is discharged .
- Missing data that validates the DRG or associated comorbidities may not be detected until the patient has left the hospital.
What would it be like if clinicians had access to information crucial to identifying that reimbursement framework earlier in the stay to help decrease and manage length of stay BEFORE the patient is discharged?
Predicting DRG
Predictive DRG (pDRG) is a machine learning model within XSOLIS’ CORTEX platform that allows hospitals to receive and utilize the most probable DRG, and the corresponding length of stay, for a patient within 24 hours of admission rather than waiting until post-discharge. The pDRG engine suggests the three most probable DRGs and associated GMLOS for each patient, updating continuously and in real-time.
Comparing and planning case expectations with DRG classifications allows clinicians and care teams to ensure patient care is optimal, necessary, and timely. With pDRG, XSOLIS offers a concurrent feedback loop that will align care determinations within the DRG reimbursement framework.
Prioritizing Near-Term Discharges
As a new and added feature to the pDRG model, XSOLIS has developed the Discharge Prioritization Scoring (DPS) Report as a method of prioritizing discharge planning by predicting the likelihood of near-term discharge. The DPS brings forth data and insights targeted toward helping prioritize and inform discharge planning workflows and managing length of stay.
The DPS Report identifies patients that are likely to discharge within 24 hours using the p24 model, which generates predictions about a patient’s likelihood of discharge and prioritizes the census based on that probability. Having this key information on a daily basis allows hospitals to prioritize discharge planning, focus on key data points to support decision making, and explore using new and unique ways to utilize clinical information in CORTEX to increase workflow efficiency.
To learn more about the pDRG model and the DPS Report available only through CORTEX, read the fact sheet.
To explore how these models can help transform utilization management at your organization, schedule a demo today.
