When Sarah, a 45-year-old woman, visited her primary care physician with concerning symptoms, her family history of medical issues added to her anxiety. Her physician suspected an underlying condition and referred her to a specialist for further evaluation.
However, navigating the healthcare system proved to be anything but simple for Sarah. Healthcare fragmentation, caused by disconnected care systems, technologies, and specialists, resulted in crucial details getting lost in translation between her PCP and the specialist. This lack of coordination led to delays in receiving lab results and imaging reports, leaving the specialist without a complete understanding of Sarah’s health.
As Sarah’s diagnostic journey continued, each test and procedure generated its own set of results and reports, further adding to the scattered nature of her medical information and making it hard for her care team to access a comprehensive view of her health.
Sarah’s experience is not unique, as studies have shown that patients often see multiple providers across different practices each year, leading to challenges in care coordination. This fragmentation of healthcare data and delivery creates a scavenger hunt-like experience for both physicians and patients, where important information is scattered across various platforms and systems.
The Scavenger Hunt: A Symptom of Fragmentation
This fragmentation of healthcare data results in challenges for care delivery, as information is not seamlessly integrated across systems. To address this issue, the use of AI in healthcare has the potential to streamline communication, integrate data sources, and provide actionable insights at the point of care.
By leveraging clinical AI platforms, health systems can alleviate the burden of fragmented data and improve patient outcomes. These platforms serve as a one-stop shop for clinicians, empowering them with comprehensive patient information and facilitating more accurate treatments.
How Enterprise-Wide AI Can Alleviate Fragmentation
AI in healthcare is not just a single problem solution-solver but an architect that integrates disparate data sources to make data actionable. By adopting a platform approach, health systems can scale AI across the enterprise, enabling clinicians to access all the information needed to provide efficient and connected care.
For example, Yale New Haven Health saw a significant increase in the administration of advanced therapies for PE patients by utilizing an AI platform that streamlined communication between care teams and facilitated collaboration between hospitals.
The Health System Incentive
Clinical AI platforms offer health systems the ability to customize alerts, timing of alerts, risk stratification parameters, and interoperability within existing workflows. By harnessing the power of AI, health systems can act promptly on clinical signals and improve efficiency in care delivery.
Reducing Fragmentation
Healthcare’s fragmentation problem poses challenges for physicians and patients alike, leading to inefficiencies and potential diagnostic errors. By embracing enterprise-wide AI solutions, health systems can integrate data sources, streamline communication channels, and provide actionable insights for improved care delivery.
Imagine a future where all physicians, facilities, and systems are connected, exchanging crucial information seamlessly to ensure transparent and accessible care for patients like Sarah. With the help of AI, healthcare systems can work more efficiently and collaboratively to provide the best possible care for their patients.
