Overview
Data exchange between diverse systems presents significant challenges due to varying formats, inconsistent standards, and incompatible technologies. This fragmentation hinders communication and integration, especially in healthcare, complicating patient care coordination and delaying access to critical information. To address this issue, Sirma has developed a solution that consolidates fragmented medical records, enabling the structured exchange of data across health systems. It converts unstructured data inputs—such as screenshots, voice notes, PDFs, and faxes — into standardised formats, including JSON, XML, and spreadsheets. This innovation aids the transition from traditional fee-for-service models to value-based care by organizing detailed patient information, including observations, diagnoses, and treatment plans.
The Challenge
Healthcare data is generated from various sources and stored across highly diverse systems, making data sharing and integration extremely challenging. Unstructured data formats, outdated infrastructure, and a lack of interoperability standards hinder meaningful clinical collaboration and efficient care coordination. Moreover, the shift towards value-based care necessitates precise and detailed data capture and exchange to evaluate patient outcomes and enhance care quality—something that legacy systems and manual data processes struggle to support effectively. Addressing data interoperability challenges is essential to enable seamless, accurate, and timely data flow across different platforms, which in turn fosters collaboration and supports informed, data-driven decision-making.
The Project Scope
Sirma was engaged to deliver a comprehensive AI-driven interoperability platform capable of:
- Extracting and structuring data from multiple unstructured formats standard in healthcare communications (SMS, PDFs, faxes, voice);
- Supporting standard data exchange formats such as JSON, XML, and spreadsheets for downstream integration;
- Enabling the structuring of detailed encounter-level healthcare data (clinical notes, diagnostics, treatment plans) to facilitate value-based care metrics and reporting;
- Enhancing data portability for patient-centred care and ensuring insurer access for population-level health analysis;
- Ensuring compliance with healthcare data standards and security regulations.
The Solution
Sirma has developed an AI-powered data transformation platform that combines Optical Character Recognition (OCR) with advanced Large Language Models (LLMs) to accurately extract, interpret, and structure healthcare data. The platform leverages:
- OCR to digitize scanned documents, faxes, and images into machine-readable text;
- Natural Language Processing with LLMs to understand and structure complex clinical narratives and voice inputs into standardized medical terminologies;
- Automated mapping and normalization algorithms to convert diverse data into interoperable formats like JSON and XML;
- Secure APIs and data pipelines to facilitate integration with electronic health records (EHRs), insurer systems, and analytics platforms;
- Continuous learning mechanisms to improve extraction accuracy and adaptability to evolving healthcare documentation styles.
Results
- Enabled patient-centred data portability, empowering individuals to carry accurate, structured health records across care providers;
- Supported insurers’ requirements for comprehensive, population-level health data analysis, enabling better risk stratification and outcome-based reimbursements;
- Streamlined clinical workflows by automating manual data entry and reconciliation across fragmented systems, improving data quality and accessibility;
- Advanced the provider ecosystem’s transition towards value-based care, focusing on health outcomes and cost-efficiency rather than volume of services.
Technologies
- Optical Character Recognition (OCR) engines tailored for diverse healthcare document types;
- Large Language Models (LLMs) fine-tuned for clinical language understanding and data structuring;
- Data mapping and normalization frameworks supporting standards like HL7, FHIR, JSON, and XML;
- Secure RESTful APIs for real-time data integration and interoperability;
- Machine learning pipelines enabling continuous model retraining and improvement on healthcare datasets;
- Cloud-native infrastructure ensuring scalability, security, and compliance with regulations like HIPAA.
Sirma’s Partnership with the client
Sirma’s expertise in advanced AI algorithms, language models, and interoperability frameworks is crucial for enabling the structured exchange of data and transforming healthcare to focus on value and outcomes. Technological advancements help address hospital data interoperability challenges, bridging legacy systems with modern platforms. These innovations improve the transformation and exchange of diverse health data, enhancing accuracy and speed. Embracing these interoperability solutions is essential for healthcare organizations to improve clinical outcomes, boost operational efficiencies, and support value-based care in today’s digital health ecosystem.
