In the dynamic world of healthcare staffing, timely and accurate placement of clinicians is vital. AMN Healthcare, a leading provider of healthcare workforce solutions, faced the complex challenge of efficiently matching a large volume of candidate profiles to thousands of open positions—quickly and accurately.
To address this, AMN partnered with Blackstraw to build an AI-powered automated matching system. Designed to integrate seamlessly with AMN’s data ecosystem, the solution uses Azure Machine Learning to process and analyze vast amounts of structured and unstructured data in real time.
The solution is built on four core components:
Advanced Multi-Model Matching Engine: Uses classification and regression models to evaluate candidate eligibility, likelihood of assignment completion, and relevance to previous work experience.
Multi-Phase Machine Learning Pipeline: Predicts outcomes such as credentialing success and interview clearance, combining weighted probabilities into a single match score.
Bias-Reducing Sourcing Tools: Applies natural language processing to objectively analyze resumes and cover letters, reducing unconscious bias in candidate selection.
Match Explainability Dashboard: Offers recruiters a transparent view into how match decisions are made, improving trust and accountability.
The results were transformative. Candidate matches could be identified in as little as one minute after new job orders were submitted. Average processing times dropped to under six minutes—down from several days—dramatically improving speed and operational efficiency.
Mark Hagan, CIO of AMN Healthcare, underscored the strategic value of the AI solution, stating that AI is enabling the company to manage the entire recruitment process more effectively. He emphasized that in a digital-first era, the staffing industry must continually evolve with cutting-edge technologies like AI to stay competitive and responsive to market needs.
In addition to streamlining talent matching, the system provided forecasting capabilities, offering insights into future order volumes and rate trends. This improved workforce planning and helped ensure clinicians could be deployed promptly where they are needed most.
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