"Orchestrating Patient Selection for Hospital-at-Home with EdgeAi:
A Success Story in Reducing Costs
A Midwest health system implemented a hospital-at-home program to reduce costs and improve patient outcomes. Selecting suitable patients for the program was expensive, tedious, and labor-intensive. This system partnered with Edgility and leveraged EdgeAi to identify patients rapidly, continually, and in real-time, reducing labor costs and significantly scaling the hospital at-home census.
Identifying suitable patients for at-home care is a considerable challenge. One of the primary obstacles is the lack of standardized criteria for patient selection. Without algorithmic precision, there is a risk of admitting patients who are not appropriate, leading to overly expensive care at home and a likely inpatient transfer.
A second challenge is the patient identification process. Staff is asked to review countless patient records for suitability. Reviews require combing through innumerable charts, ED visits, and consulting with multiple providers repeatedly. These activities are time-consuming, require dedicated nursing labor, and often result in negative ROI.
Edgility's Hospital at Home Patient Identification lens solved both challenges. Clinical staff defined the criteria for identifying and selecting suitable patients for at-home care. EdgeAI expanded the integrity of these criteria by algorithmically validating the decision inputs and continually testing for suitability.
The cost of patient identification plummeted. The hospital reduced labor costs by automating the patient identification process. Nurses who previously combed through records are now providing direct patient care.
In addition to reducing labor costs, the system expanded the program to additional hospitals with little to no extra cost. Ultimately, Edgility's patient identification lens increased the number of selected patients, eliminating high inpatient costs and freeing valuable space for patients requiring higher levels of care.
Results after Implementation
Patient Census in 15 Months