Sarah, a 45-year-old woman suffering from troubling symptoms, represents the difficulties faced in a fragmented healthcare system. With a family history of severe medical issues, she’s referred by her primary care physician to a variety of specialists. However, her records frequently get lost or misinterpreted in the process, leading to incomplete data about her overall health for each healthcare provider.
Healthcare fragmentation results in care delivery across disconnected systems and technologies, diminishing the cohesion and effectiveness of treatment. This is not a rare situation; a study indicates that Medicare patients usually consult around seven providers annually, over several practices, which their primary care physicians need to coordinate with.
The fragmentation of healthcare data is not just due to disparate sources, but also from disparate platforms that don’t interact efficiently. The situation results in a “healthcare scavenger hunt,” where providers, faced with increasing patient volumes and administrative burdens, are forced to sift through extensive notes and reports. This leads to over 40% of follow-up recommendations being ignored and an alarming $4 trillion in annual spending on care, with only around 10-20% leading to patient outcomes.
Application of AI in healthcare indicates potential solutions for these issues. Clinical AI technologies can weave disparate threads to form an actionable data set that can aid in improving patient outcomes. AI can even collate and analyze data from different sources, streamline communication, and provide actionable insights.
An example of the effective implementation of AI comes from Yale New Haven Health, wherein it resulted in a 40% increase in delivering advanced therapies. This included notifying team members about high-risk patients, secure text communications for care teams with real time scan data, and streamlining communication channels.
With a clinical AI platform, health systems can dictate how these problems are navigated, empowering physicians to work smarter and faster. Such platforms can ease and take over the responsibility of data aggregation, analysis, identification of clinical signals, and activating necessary interfaces.
Addressing the issue of healthcare’s fragmentation can enhance the efficiency of care delivery and improve patient outcomes. The application of an enterprise-wide clinical AI could potentially resolve these issues by integrating diverse data sources, improving communication, and providing critical insights at point of care.
By adopting this technologically advanced approach, envisage a streamlined coordination of healthcare providers like in Sarah’s case, where each provider has access to up-to-date, comprehensive views of her health, leading to more informed decisions about her care. Thus, the age-old fragmentation problem may finally be addressed, leading to a more efficient healthcare system.
This critical endeavor, referred to as “stopping the healthcare scavenger hunt,” puts the power and potential of AI front and center in healthcare, significantly enhancing the industry’s capacity to improve patient care and outcomes.
