A recent HIMSS Market Insights study reveals that healthcare leaders understand the strategic need for patient identity matching, but struggles remain, particularly with data interoperability, duplicate records and inconsistent or missing data. They acknowledge that challenges must be overcome if they are to accomplish broader initiatives, especially around artificial intelligence (AI) implementation, workflow optimization and data exchange.
This briefing highlights how InterSystems is collaborating with diverse customer organizations to streamline the set-up of new data feeds to a high-performing longitudinal health record.
InterSystems technology enables you to optimize your supply chain performance by predicting disruptions before they occur, and optimally handling them when they do.
Dr. Alfredo Almerares, clinical executive manager at InterSystems, says the company's generative AI tools that summarize patient visits are helping Latin American clinicians to relieve burnout and build patient relationships.
Learn how access to data and data quality impact efforts to improve the supply chain and the primary causes of longer time to value and high implementation costs.
Compare RAG, fine-tuning, and prompt engineering to find the best AI approach for your needs. Includes practical examples and an interactive decision tool.
Developing solutions that deliver the promise of Internet of Healthcare Things (IoHT) can be difficult. To be successful, developers must capture vast amounts of medical device data in real time.
Next-Generation Enterprise Master Person Index for Identity Management Seamless patient, member, and beneficiary identification is essential for efficient operations across healthcare organisations and government agencies. Yet, fragmented systems, inconsistent identifiers, and data gaps continue to disrupt workflows, increase costs, and compromise care quality and service delivery. 35% of denied medical claims stem from inaccurate patient identification¹, while mergers and affiliations further complicate record consolidation. Even within a single information system, duplicate or overlaid records can introduce inefficiencies and risks; by creating costly administrative burdens and compromising the accuracy of AI workflows built on unreliable data. To maintain data integrity and prevent cascading errors, organisations must be able to detect problematic records in real time and trigger corrective actions.
