Nursing Informatics Supports Emergency Department Nurse Staffing
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Suboptimal nurse staffing affects productivity throughout an entire healthcare organization. Traditionally, nurses’ work schedules have been based on schedulers’ best guess weeks in advance, and leaderships’ speculation during a shift. This approach makes it nearly impossible to know just how many nurses to have available for peak productivity and efficient healthcare delivery.
Bottlenecks throughout the hospital, from admission to discharge, contribute to patient overcrowding in the Emergency Department (ED) and impede patient throughput. The ED, the gateway to the hospital, is further impacted by an uncertain ebb and flow of patient arrival. Nursing informatics and prediction can play a crucial role in helping to improve ED throughput and staffing.
Prediction of how many ED nurses to staff for a given day is both necessary and beneficial to the organization from an efficiency, cost, and quality care perspective. ED demand prediction and subsequent actionable insights that facilitate appropriate staffing are essential to the delivery of safe, high-quality care in today’s competitive cost-containment environment.
To enhance nursing practice, KenSci has developed dynamic predictive Machine Learning models using advanced data analytics to improve staffing. The solutions predict patient volumes and acuities upon ED presentation within the next two to eight hours and within the next one to six months in advance. They also accurately forecast under- or over-staffing in real-time to advise staffing in the department.
KenSci ED predictive analytics Demand and Nurse Staffing Solutions:
- Apply Machine Learning methods to many different big data sources such as millions of electronic health record data points, historical department schedules, weather and local events
- Provide a snapshot of the current state of patient demand and appropriate recommended staffing for department managers and administrators in the department
- Reduce patient wait times to streamline patient triage and decrease the number of patients who leave without being evaluated by a healthcare provider
What do the KenSci ED Demand and Staffing Solutions mean for nursing and nursing informatics support? Actionable insights derived from KenSci’s predictive analytics tools enable:
- Nursing leadership and schedulers to schedule nurses according to appropriate nurse-patient ratios
- Care teams to better communicate and share information
- Nurses to spend more time with their patients and deliver better care
- Administrators and other leaders to substantially improve the ED environment, and decrease both burnout and turnover
Predictive analytics and Machine Learning optimize nurse staffing and healthcare delivery with the help of nursing informatics. KenSci’s ED Demand and Nurse Staffing Solutions promote a higher quality of care, improve patient outcomes and ultimately improve the patient and staff experience.
To learn more about other KenSci solutions, please visit appsource.microsoft.com.
