Crisis in the U.S. Healthcare Workforce: Why It's Time to introduce AI

Aside from staffing shortages among the general U.S. healthcare workforce, the challenges faced by rural hospitals are multi-pronged.

This has impacted the well-being of the remaining healthcare practitioners 5 as well as their patients – many of whom are deferring care that is vital to disease management and overall maintenance of health,6,7 or who do not have access to timely emergency care.1 Though such challenges may be perceived as insurmountable in the face of overcrowded emergency rooms, burnout of healthcare personnel, and patients who are reluctant to seek care, the incorporation of artificial intelligence (AI) and machine learning (ML) into healthcare systems may alleviate some of these burdens. By incorporating AI and ML into electronic healthcare record (EHR) systems, hospitals with limited resources – whether it be due to geographic isolation or staffing shortages –may improve patient care and outcomes. In the context of the ongoing global health emergency, AI and ML-based CDS tools have the potential to emerge as an invaluable resource to overburdened care teams by facilitating prioritization of medical care for patients based on individual risk levels. In hospitals that are overcrowded or have inadequate staffing, ML-based risk assessments identify patients who are in more immediate need of care and guide the allocation of personnel and health-related resources. By identifying which patients are in the greatest need of acute care, healthcare providers will be better enabled to focus limited tangible and personnel resources on high-risk patients or request the services of a specialist who may not be immediately available on-site in a timely manner.