Powering Predictive Staffing in Healthcare with AI

Predictive staffing helps ensure the right people are available at the right time to provide the right care.

The healthcare labor shortage is still affecting staffing levels for provider organizations — and creating financial strain. Predictive staffing optimizes staffing levels and may help organizations better provide timely and safe care while saving money.

This methodology uses historical data, such as specific dates, the season or time of year, weather, and other factors to help determine the number and types of clinicians and other workers a healthcare organization needs to staff on certain shifts.

Nursing, medical technologist, and medical assistant roles tend to be the most conducive to predictive staffing, but it can also be used with some physician roles. While predictive staffing itself is not a new concept, artificial intelligence (AI) presents opportunities for healthcare providers to use the methodology more efficiently — and to begin exploring other ways to implement AI technology throughout their organizations.

Benefits of Leveraging AI for Predictive Staffing Models

Predictive staffing models play a crucial role in helping providers schedule the optimal number of clinicians for each shift. Right now, most predictive staffing models require someone like a charge nurse to manually examine prior data, create a model, and use the model to determine how many people are needed for a given shift. 

AI presents an opportunity to streamline the process by creating data-based models that humans then use to make final staffing decisions. Using AI for predictive staffing can also help optimize staffing based on demand, potentially creating a positive impact on operating margins. 

The benefits are not limited to planning schedules. They can also help manage operating expenses by optimizing staffing levels on a given shift, decreasing the need for costly contract labor, and identifying the correct levels of float pool and per diem staff. This not only benefits the finance function but also enables the organization to retain the right number of full-time employees to appropriately staff each shift. Maintaining optimal staffing levels may also help prevent staff burnout or moral injury, create a better overall work environment, and reduce time spent on administrative tasks, all of which enables clinical staff to practice at the top of their licenses. 

While using AI for predictive staffing is still in its infancy, there are several other ways AI is poised to transform business processes. Specifically, healthcare may leverage AI to transform staffing and scheduling processes by: 

• Integrating AI-powered predictive staffing models with scheduling technology that identifies open shifts and enables nurses or other clinical staff to claim available shifts via a mobile app. This can be particularly attractive to millennials and Gen Z because it mirrors the “gig economy” approach typically associated with rideshare and hospitality work. It can also help support staffing needs for a variety of specialties such as surgical technicians, respiratory technicians, pharmacy technicians, radiology technicians, environmental services (EVS) workers, and more. Physician staffing agencies could also leverage these scheduling tools. 
• Meeting growing staffing demands at retail or urgent care clinics as consumer site-of-care demands shift. These clinics are primarily staffed with advanced practice providers (APP), such as nurse practitioners (NPs), physician assistants (PAs), and advanced practice registered nurses (APRNs) during certain times, and with physicians during others. If a clinic is experiencing unusually high demand, an AI-powered model could identify real-time or upcoming staffing needs, send push notifications to available staff to ask if they’re interested in picking up shifts, and enable clinicians to add themselves to the schedule.

Why Now is the Time to Adopt AI 

The healthcare industry tends to be slow to adopt new technologies. Leaders that hesitate to adopt AI may face financial and cultural risks such as: 

• Continued excessive spending on unnecessary staffing and contract labor costs 
• Persistent labor shortages
• Higher rates of clinician burnout or moral injury

For organizations that want to experiment with AI, but don’t have the risk tolerance to test it for patients or diagnoses yet, predictive staffing offers an excellent first use case. Because predictive staffing models do not require sensitive patient information, integrating AI in this area presents a lower risk from both a data privacy and ethical standpoint. 

As your organization prepares to implement AI for predictive staffing, remember that the model is only as good as the information on which it is trained. Organizations ready to take this first step should recognize the commitment involved in continuously training the model, feeding it new data, and regularly validating it. This may require some additional process improvements to other technology infrastructure and systems to simplify data entry. 

AI-powered predictive staffing is an excellent opportunity for organizations with a lower risk tolerance to integrate AI into their operations, but there are still many important steps and considerations that we recommend taking. It’s essential to make sure leadership understands the capabilities and limitations of the AI, that you have considered the necessary IT infrastructure and types of data needed, and that your employees understand the model isn’t there to replace people or jobs.