Τεχνητή Νοημοσύνη στη Διαχείριση Ροής Ασθενών και Κρεβατιών Νοσοκομείων

Patient flow is the movement of patients from arrival through admission, treatment, transfer and discharge. When any step stalls, the effects ripple: patients admitted from the emergency department wait on stretchers in hallways (called boarding), ambulances may be diverted, and elective surgeries get postponed. Operational AI targets these bottlenecks rather than making diagnoses. Three prediction tasks dominate. Demand forecasting estimates arrivals and admissions by hour or day, using history, seasonality, day of week, holidays and sometimes respiratory virus surveillance. Length-of-stay and discharge prediction estimates when each current inpatient is likely to leave, using diagnoses, procedures, lab trends, mobility and pending tasks such as imaging or placement in a rehabilitation facility. Capacity forecasting combines the two to project occupancy by unit, including specialized beds such as ICU or telemetry, which are not interchangeable with general ward beds. These predictions often feed command centers, where staff see hospital-wide status in one place. Johns Hopkins Hospital opened a capacity command center with GE Healthcare in 2016, and many systems have since adopted similar setups from vendors such as GE HealthCare, Qventus and LeanTaaS. Reported benefits usually involve less boarding or faster transfers, but results depend heavily on the process changes that accompany the software. A key misconception is that the model itself frees beds. A prediction helps only if someone acts on it: finishing a discharge summary, booking transport or reassigning nurses. Another is that capacity is just bed count. Staffing, room-cleaning turnaround and the availability of nursing-home or rehab placements often constrain flow more than physical beds. Fairness matters too: if discharge tools focus attention on patients who are easy to discharge, complex patients may wait longer unless the workflow compensates.