Predictive Staffing: Healthcare’s Gateway to Labor and AI Success

Scheduling too many clinicians per shift can lead to financial strain. On the other hand, chronic understaffing can increase clinician burnout and worsen patient care. So how can healthcare providers correctly balance their staffing needs?

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In today’s newsletter, I explore how predictive staffing models, powered by artificial intelligence (AI), help healthcare providers make data-informed staffing decisions, which can address staffing challenges. I’ll also explain why AI-powered staffing models offer a great opportunity for healthcare providers to jumpstart their AI journey.

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Using AI-powered predictive staffing models offers key benefits

AI-powered predictive staffing models can help healthcare providers optimize staffing based on demand. These systems use 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.

Optimizing staffing with AI-powered models helps organizations manage operating expenses, reduce the need for costly contract labor, and enable leaders to identify the correct levels of float pool and per diem staff. Maintaining optimal staffing levels can also help prevent staff burnout or moral injury, reduce time spent on administrative tasks, and enable clinician staff to practice at the top of their licenses.

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While AI-powered predictive staffing models provide valuable insights, organization leadership should always make the final call on staffing decisions — especially in preparation for scenarios of extraordinary patient demand.

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Why begin your AI journey with predictive staffing models?

Many healthcare organizations are excited to explore how AI can support their patients and staff but want to reduce risk and complexity for their first foray. Implementing AI-powered predictive staffing models can be a lower-risk, higher-reward way to pilot AI implementation at your organization.

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Reason 1: Risk and reward

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For organizations that want to experiment with AI but aren’t ready to test it for clinical support 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.

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Should the implementation of an AI-powered predictive staffing model generate cost savings or reduce overall staffing challenges, it may help spur organization-wide support for more AI projects in the future. ???

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Reason 2: Fostering a friendly AI ecosystem?

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Building a predictive staffing model can help organizations learn how to tackle the data collection, training, and infrastructure needs of an AI-powered tool.

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An AI is only as good as the information on which it is trained. Organizations must develop processes to continuously train AI models, feed them new, high-quality data, and regularly validate them. Unlike data for clinical AI use cases, the data used by predictive staffing models may be easier to collect and organize.

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Reason 3: Offering a learning opportunity

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Finally, launching a predictive staffing model can become a learning opportunity for both leadership and staff. For clinician leadership, an AI-powered predictive staffing model can help them understand how AI can drive improvements across different functional areas, not just for higher risk use cases, like an AI-powered clinical decision support system. For staff, treating predictive staffing as an AI use case can help them understand how AI can support them and improve their working conditions.

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Ready to transform staffing decision making at your organization? Check out our guide to predictive AI staffing, which offers a checklist to help you get started.

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Which other improvements could AI deliver at your healthcare organization? Let me know in the comments below.