Accelerating Candidate Matching with AI-Driven Workflow Automation

In healthcare staffing, matching the right physician or advanced practitioner to an open assignment is both urgent and detail-driven. Our client needed a way to streamline this process - turning what was once a manual, time-consuming workflow into a faster, more intelligent experience for their internal teams and their healthcare clients. This case study explores how we used AI and automation to reduce friction from the moment an order is received to the moment a candidate is presented, cutting turnaround time and unlocking greater speed, scale, and satisfaction for everyone involved.

Opportunity

In healthcare staffing, speed and precision are critical. When a hospital or clinic submits a request for a temporary physician or advanced practitioner, the ability to rapidly match and present a qualified candidate can mean the difference between uninterrupted patient care and costly gaps in coverage. Yet the internal workflow, moving from receiving a staffing request to presenting a vetted candidate, was filled with manual steps, fragmented systems, and time-consuming data entry. Staff often had to “swivel chair” between platforms, pulling data from emails, portals, and Salesforce to build a complete candidate profile.

Reducing friction in this process would empower recruiters to focus on building relationships instead of paperwork, improve response times for healthcare clients, and increase fill rates, ultimately helping more providers deliver care without delay.

Approach

We began by mapping the full lifecycle of the staffing process, from intake to candidate presentation, identifying bottlenecks and opportunities for automation. Rather than trying to automate everything at once, we selected one high-impact component of the workflow as a test bed: extracting and synthesizing staffing order details into a standardized, ready-to-act format for recruiters.

Using a low/no-code approach, we built an intelligent automation layer that integrated Salesforce with AI models from OpenAI and Anthropic. This allowed us to:

• Automatically parse incoming order data (often messy or inconsistent)

• Summarize and structure it into recruiter-ready briefs

• Push the data directly into existing systems without human re-entry

• Train the AI on real-world data with human-in-the-loop validation for accuracy

Throughout testing and training, we used recruiter feedback to fine-tune the model, continuously improving the system’s reliability and usability.

The recruiting team was able to reclaim time, reduce cognitive load, and trust that the AI was doing the tedious work right, freeing them to do what they do best: connect great clinicians with great organizations.

Outcomes

By the end of the pilot, the AI-driven solution achieved a 99% confidence rate as validated by human reviewers. What previously took multiple manual steps now runs automatically, in seconds. Recruiters no longer toggle between systems to piece together order data; they receive an accurate, structured summary that’s ready for action.

The client gained a proven blueprint for scaling automation across other parts of their workflow, backed by real ROI and recruiter adoption. The solution sped up time-to-present and unlocked a new level of efficiency, accuracy, and recruiter satisfaction, with measurable impact on client responsiveness and candidate engagement.