AI for personnel management in the healthcare sector

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A massive influx of requests difficult to manage

The customer is a provider of temporary staffing services in the healthcare sector. The company was faced with a major personnel request management problem, receiving over 1000 emails per week from various healthcare institutions, including hospitals, clinics and correctional facilities. These emails contained information in disparate formats, including plain text, Word and Excel files, PDFs, as well as scanned images. The lack of standardization in communication made it difficult to extract and structure the information essential for assigning staff, resulting in delays and administrative overload.

An artificial intelligence solution to centralize and automate processing

Faced with this challenge, the customer collaborated with Explorai to implement an artificial intelligence-based solution capable of automatically processing incoming e-mails. Explorai developed an e-mail box monitoring and analysis tool, integrating Large Language Models (LLMs). This solution made it possible to extract, structure and analyze the key information needed to assign staff quickly and efficiently.

Faster document processing and recognition

To handle scanned or image-based documents, Azure's OCR technology was integrated to convert files into usable text. This automation significantly accelerated the processing of requests. The time required to process each email dropped from several minutes - or even hours in some cases - to an average of just 28 seconds.

Optimizing management and decision-making

The tool developed also enabled managers to view and organize requests more efficiently. However, the centralized tracking interface was not developed by Explorai, but by a third-party company. This platform provided managers with better visibility of incoming requests and facilitated the identification of necessary interventions.

Impact on efficiency and next steps

With this solution, the customer was able to reduce the administrative workload, freeing up time for managers to concentrate on more critical tasks. Planning optimization and emergency management were indirectly improved thanks to the automation of request processing, and the time saved was strategically redistributed within the organization.