Long Inpatient Care Stays Drive High Costs and Poor Patient Outcomes
In the U.S., hospital inpatient care is one-third of all healthcare expenditures, accounting for 35 million stays a year with an average length of stay (LOS) of 4.5 days and a cost $13,600 per day. Consequently, even a small reduction in LOS can account for millions of dollars in annual savings across a health system. Shorter lengths of stay can also benefit patients, decreasing the risk of hospital-acquired conditions, such as pressure injuries and infections.
Solution
Aloha is a modern data science tool that helps clinicians efficiently allocate hospital beds to those who need them most. Aloha flags a patient if they are anticipated to experience an avoidable hospital day during their inpatient encounter. Flagging an avoidable day allows healthcare providers to transfer the patient to a new facility for the same or better care, send the patient home in a more timely manner, and open a bed for someone else.
Aloha works by gathering clinical and demographic data and then training an ML model to work on data hosted in a Python/ Snowpark/ Snowflake system. This ML model is implemented in real-time systems, allowing healthcare providers to better manage patients that are already in the hospital and provide more individualized care.
The final information lives in an Electronic Medical Records (EMR) system, with the ability to:
- Create real-time flagging that complements a patient’s care processes
- Ensure operational excellence by improving patient outcomes and patient satisfaction
- Improve the quality of your care through deliberate bed allocation, managed weekend discharges, and insight into unwarranted variation by department
- Increase revenue and optimize the cost of patient care
- Finding key indicators for the causes of longer lengths of stays, and identifying unwarranted variation within and between hospitals
- Help healthcare institutions allocate their resources (from nurses and healthcare personnel to medication and hospital beds)