The Reducing Length of Stay module identifies and prioritizes actionable bottlenecks that may prevent patients from being discharged in a timely manner.
Problem of prioritization
Often, hospital staff are overwhelmed with requests and have difficulty prioritizing tasks. This lack of clear direction can cause delays with patient discharges which, in turn, impacts staffing needs, patient satisfaction, and insurance reimbursements.
Powered by Pieces’ advanced machine learning technology, the length of stay worklist reviews all clinical documentation and uses existing data from the Electronic Health Record (EHR) to surface potential barriers to discharge in real time and helps identify on whom to focus first.
Factors reLOS addresses
Better identify and prioritize patients closest to discharge.
Know the steps needed to enable a timely discharge.
Flag needed tests or procedures to avoid lapsing over into additional days admitted.
Be alerted to known factors that determine each patient?s risk of excess length of stay.
Risk can be mitigated in real time by coordinating and prioritizing the steps needed for a timely discharge.
reduction in average percentage of excess LOS
Positive predictive value (PPV) in identifying barriers to discharge
Reduction in mean LOS for sepsis patients
in nursing time saved annually
Nancy Temple, RN, MSN, CCM
Vice President of Comprehensive Care Management, North Texas Division Baylor Scott & White Health
“The challenge that we face executing length-of-stay reduction programs is to determine who can be discharged and how to prioritize the limited staff and resources that we have in order to help them prioritize and discharge patients.”
Ready to Learn More?
Our modular solution can be tailored to your needs. We’d love to schedule a demo to more fully walk you through how Pieces can help you achieve better outcomes for your patients and your bottom line.