Artificial Intelligence Forecasting Census and Supporting Early Decisions. Issue 4 (October 2020)

• Record Type: Journal Article
• Authors: Griner, Todd E.; Thompson, Michael; High, Heidi; Buckles, Jenny
• Is Part Of: Nursing administration quarterly. Volume 44:Issue 4(2020)
• Publication Date: 2020-10
• Subjects: capacity management -- hospital flow -- predictive modeling -- staffing; Nursing services -- Administration -- Periodicals; 610.73068
• DOI: 10.1097/NAQ.0000000000000436
• Languages: English
• ISSNs: 0363-9568

Abstract

Abstract : Matching resources to demand is a daily challenge for hospital leadership. In interdisciplinary collaboration, nurse leaders and data scientists collaborated to develop advanced machine learning to support early proactive decisions to improve ability to accommodate demand. When hundreds or even thousands of forecasts are made, it becomes important to let machines do the hard work of mathematical pattern recognition, while efficiently using human feedback to address performance and accuracy problems. Nurse leaders and data scientists collaborated to create a usable, low-error predictive model to let machines do the hard work of pattern recognition and model evaluation, while efficiently using nurse leader domain expert feedback to address performance and accuracy problems. During the evaluation period, the overall census mean absolute percentage error was 3.7%. ALEx's predictions have become part of the team's operational norm, helping them anticipate and prepare for census fluctuations. This experience suggests that operational leaders empowered with effective predictive analytics can take decisive proactive staffing and capacity management choices. Predictive analytic information can also result in team learning and ensure safety and operational excellence is supported in all aspects of the organization.