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A leading academic medical center sought to implement an AI-driven image analysis solution to validate and categorize photos of employees displayed in the corporate EHR system. Utilizing Azure's AI image recognition capabilities, we developed the desired solution which combined with a user-friendly interface, allows an administrator to review, easily identify, and tag images that are not in compliance with established policies.
Our client is a leading academic medical center in the Eastern US.
Our client aimed to identify photos of employees within its EHR system that don’t comply with organizational standards. The need for action arose from the accumulation of non-compliant images uploaded by employees, prompting a push for improved visual consistency.
Our team consisted of a full-stack developer and a tester who collaborated over six months to develop, test, and validate the solution.
The Clinovera team has implemented a background process that analyzes images provided by our client, and adds necessary metadata generated by Azure AI services in the Database. The team has further implemented a web application that allows administrators to search, view, and visually confirm image processing results. The architecture of the solutions utilized .NET and React technologies.
Key features included:
The implemented solution significantly improved the quality of personnel images. By leveraging AI image analysis, we enabled the institution to sort through high-quality and low-quality images efficiently, enhancing overall operational efficiency. This tool not only streamlined the validation process but ensured that only suitable images were retained for corporate use, fostering a more professional appearance across the organization.
Through this project, we demonstrated how AI image interpretation can transform internal processes and elevate standards within healthcare institutions.