Healthcare

Risk Scores for Patients

Hospitals can use data sources such as biometric data, claims data, lab testing, or patient-generated health data to create a risk score for patients. The risk score can identify patients who are at the highest risk of poor health outcomes. The risk score can be used to derive valuable insights such as which patients will benefit from improved care, health programs, and treatment plans.

Forecasting Appointments

SML’s predictive machine learning models can be used to forecast patterns of patient visits. The model can predict with high accuracy which patients are most likely to miss appointments. This can help hospitals and clinics improve their financial loss by offering additional reminders or steps to help the patient make their appointment, or by making room for new patients.

Clinic Operations Staff Utilization

Machine learning models can provide insights about how much staff is needed at different points of the day or month. For example, a model can predict that certain days may require additional staff in the morning time, while other days of the month may require additional staff in the evenings. This is particularly helpful for busy clinics without a fixed schedule such as urgent cares and emergency rooms.

Supply Chain Demand

Machine learning models can analyze patterns of supply utilization to predict demand of supplies needed. For example, a hospital may find that the quantity of medical supplies ordered have a low correlation with the number of medical supplies utilized. Insights such as these will help hospitals save costs by optimizing the ordering process.