To facilitate the delivery of world class advances in medical care through world class advances in hospital operations.
Improving the quality of patient care. Educating students, doctors, nurses, and hospital leaders. Sharing knowledge with the medical and academic communities.
We use machine learning, mathematical optimization, simulation, and a variety of statistical, probabilistic, and computational tools.
SURF student Isabel Alcivar presents on her research developing a system to measure patient acuity and nurse workload.
Hospital quality of care, nurse retention, and nurse satisfaction depend on the quality of the hospital’s decision-making processes. These decisions include: how to assign nurses to patients, which patients are at risk of decline, when to transfer patients between units, and how to bill for services provided. To assist with these decisions, hospitals currently rely on several closely related, but independent tools. These tools share several shortcomings: the data input is manual; the output requires manual manipulation; and only a small fraction of all relevant data are considered. We propose a single system to replace these tools. The central component of the system is a tool to estimate patient acuity and nurse workload from EMR data. In the proposed system the data input is automated; the output does not require further calculation; and all relevant data in the EMR are considered. Such a system can support the quality of care, nurse retention, and nurse satisfaction.