Empowering Caregivers by Predicting Staffing Gaps with AI

NurseStaffing is a digital staffing platform that connects healthcare facilities with credentialed nurses and caregivers. The company’s network includes over 60,000 professionals across the United States. The company handles recruiting, scheduling, and payroll. Facilities get consistent coverage. Nurses get flexible schedules and daily pay.

01 The Challenge

Every unfilled shift is a cost to the business and a risk to patient care

The U.S. nursing workforce is short roughly 158,600 registered nurses. Projections point toward a gap of more than 250,000 by 2030. Turnover runs at 16.4% nationally. Nearly 40% of RNs report an intent to leave or retire within five years. For staffing marketplaces like NurseStaffing, those numbers define the operating environment.

NurseStaffing operates on the margin between what facilities pay for coverage and what nurses earn. The shift fill rate is the single most important metric in the business. A filled shift generates revenue. An unfilled shift generates nothing.

The team saw an opportunity to get smarter about which shifts needed attention. Operations managers were treating every open shift the same way. The same notifications went to the same pool of nurses, regardless of fill likelihood. Shifts already attracting interest got the same outreach as shifts at high risk of going empty.

NurseStaffing’s data science team began scoping a prediction model internally. They recognized they needed ML engineering to build it at production quality. Provectus, an AI-first systems integrator, joined through the AWS Fast Start Program. The objective: build a model accurate enough to change how operations managers prioritize their work.

02 The Approach

Prove the model on real shift data, then build the infrastructure to run it across the network

NurseStaffing’s internal data science team had done early exploration. They lacked the ML engineering capacity to move from analysis to a production-grade model. Provectus brought experience building prediction models on operational data.

The engagement followed three stages, each gated on the previous one’s output.

First, Provectus ran exploratory data analysis on NurseStaffing’s shift and staffing datasets. The team mapped data quality, identified gaps, and defined how the data needed to be structured. They evaluated modeling approaches and established a performance framework.

Second, Provectus built the training workflow. Data preparation, model training, and performance tuning in a repeatable pipeline. The model could be developed, tested, and iterated consistently.

Third, the team built a batch prediction pipeline. It runs the model against larger volumes of shift data and generates fill-rate predictions across the full network.

03 The Build

Prediction model, training pipeline, and batch scoring infrastructure

The core deliverable was an ML model that scores every open shift by its likelihood of being filled. Operations managers see which shifts are at risk. They focus outreach where it matters.

The build included:

• A training pipeline for the prediction model, running on managed cloud services
• Data preparation workflows that clean, structure, and feed shift data into the model
• A batch prediction pipeline that generates fill-rate scores across open shifts
• Performance monitoring to track accuracy over time and flag drift

Data security and privacy followed cloud best practices — required when handling healthcare workforce data. The model deployed directly into NurseStaffing’s environment. It was ready for integration with the Shift Management Platform.

Within six weeks, Provectus completed the exploratory analysis, built and validated the model, and hit target performance levels.

04 The Results

From treating every shift the same to knowing which ones need attention

The prediction model has driven a 7% increase in shift fill rates across the network. Operations managers now see which shifts are likely to fill and which ones need intervention.

7%

Increase in shift fill rate

Network-wide since launch

Outreach became more targeted. Nurses see shifts relevant to them instead of a flood of alerts for positions that would fill anyway. Response rates improved. Shifts that needed attention got it earlier, before they became gaps in facility coverage.

For the business, the impact is direct. Higher fill rates mean more revenue per shift cycle. Better prediction means fewer last-minute scrambles and more consistent coverage for facility partners.

05 What’s Next

A first AI deployment that proved the business case for broader adoption

The shift fill rate model was NurseStaffing’s first production AI deployment. It delivered measurable results in six weeks. Provectus works with NurseStaffing on AI strategy across scheduling, workforce planning, and facility operations.