Identified Discharge Barriers

Manage clinical and non-clinical barriers to discharge earlier in the patient journey to reduce hospital length of stay.

Problem of prioritization increases hospital length of stay

Often, hospital staff are overwhelmed with requests and have difficulty prioritizing tasks which then increases hospital length of stay. This lack of clear direction can cause delays with patient discharges which, in turn, impacts staffing needs, patient satisfaction, and insurance reimbursements.

Machine Learning technology surfaces barriers

Powered by Pieces’ advanced machine learning technology, Predict reviews all clinical documentation and uses existing data from the Electronic Health Record (EHR) to surface potential discharge barriers in real time and helps identify on whom to focus first.

14% of all hospital stays are longer than they need to be. Pieces can help make the hospital discharge process more efficient, reducing the hospital length of stay.

Identification

Better identify and prioritize patients closest to discharge.

Readiness

Know the steps needed to enable a timely discharge.

Barriers

Flag needed tests or procedures to avoid lapsing over into additional days admitted.

Risk

Be alerted to known factors that determine each patient’s risk of excess hospital length of stay.

Real-Time

Risk can be mitigated in real time by coordinating and prioritizing the steps needed for a timely discharge.

Give your team visibility into discharge barriers up to 20 days prior to the projected discharge date.

65% Reduction of Hospital Length of Stay

Reduction in average percentage of excess length of stay (LOS)

3.428 days

saved per bed, per year at a 350 bed hospital customer.

2-days Less

Reduction in mean length of stay for sepsis patients

850 Hours

in nursing time saved annually

Pete Perialas, Jr.

Chief Operating Officer, Children’s Health

“EHRs do a good job of accumulating information but not a very good job of helping you decide what to do next. Pieces complements the electronic health record by doing the things that an electronic health record was never designed to do.”

Ready to learn more?

Pieces Predict delivers actionable insights directly to your EHR patient list. We’d love to schedule a meeting.