This case study describes how our team created and delivered a scalable, AI-driven solution for a top healthcare organization to extract, transform, and analyze high-volume healthcare data. The objective of the project was to enable people internal to the organization with actionable knowledge to improve patient outcomes, operational efficiencies, and strategic insights. The project aimed to address several key challenges, including unstructured data, data privacy regulations, and the need for analysis in real-time.
About Client
The client is a major healthcare service provider managing a large system of hospitals, clinics, and diagnostic centers. The client has a presence in multiple regions and connects with millions of patients a year. Their focus is on improving patient care, extending diagnostic care, and improving operational workflows through technology.
Client Requirement
The client is a major healthcare service provider managing a large system of hospitals, clinics, and diagnostic centers. The client has a presence in multiple regions and connects with millions of patients a year. Their focus is on improving patient care, extending diagnostic care, and improving operational workflows through technology.
Challenges
The project implementation faced multiple challenging situations:
Unstructured Medical Data
The data available for the project existed mostly as unstructured data (e.g., doctor’s notes, handwritten prescriptions, and images of reports).
Data Privacy & Data Compliance
The project task was to ensure the platform worked under data protection regulations and protected the sensitive information of patients.
Interoperability Problems
The data needed to be integrated from multiple hospital systems, departments, and external labs, which had different data formats and health data standards.
Real-Time Processing
The client required what was nearly real-time data processing to enable the necessary critical decision-making in clinical and administrative processes.
Volume and Complexity
The platform needed to store data sets which although limited to 5 hospitals spread over 7 years, needed to include historical and current medical records per patient visit and would therefore be relatively large data volumes and data complexity.
Solution
To solve these problems, we built a customized, AI-enabled data intelligence platform for the client’s healthcare ecosystem. Using next-generation natural language processing (NLP) and machine learning techniques, the solution extracted, processed, cleaned, and standardized the healthcare data from all disparate sources.
Core art inventory management
Created intelligent data extraction pipelines able to process scanned documents, EMRs, and patient files using OCR and NLP processes.
Core art inventory management
Created intelligent data extraction pipelines able to process scanned documents, EMRs, and patient files using OCR and NLP processes.
Core art inventory management
Created intelligent data extraction pipelines able to process scanned documents, EMRs, and patient files using OCR and NLP processes.
Core art inventory management
Created intelligent data extraction pipelines able to process scanned documents, EMRs, and patient files using OCR and NLP processes.
Core art inventory management
Created intelligent data extraction pipelines able to process scanned documents, EMRs, and patient files using OCR and NLP processes.
The Result
Starting from scratch we managed to build a modern innovative collection management software in a fairly conservative industry. It allowed our client to attract users, attention and investors over the first years and eventually have an exit with Christie’s. Team continued working on the fine art collection management integrating it into Christie’s enterprise ecosystem. We improved and introduced new functionality for general use and Chrisitie’s specifically, and developed multiple partner programs and features for collectors and art-professionals.
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