Today’s hospitals and health systems are tormented by a conundrum: providers have an excessive amount of data, but not enough data .
Healthcare providers and administrative staff are sometimes burdened by the sheer amount of data they need to manage. A 2022 survey of three,000 practicing nurses and doctors found that 69% were overwhelmed by the amount of patient data. Nonetheless, an estimated 97% of this data goes unused as a result of difficulties with extraction and contextualization. Despite the potential for improved diagnosis and treatment, these obstacles, together with clinicians’ limited time, create barriers to efficient utilization.
With continued innovation within the industry, more organizations are implementing advanced technological solutions to deal with this ongoing challenge. Today, some hospitals and health systems are leveraging AI to enhance patient safety event evaluation by streamlining incident reporting and automating data extraction. This automation is only one example of how providers are maximizing patient data to boost care quality, turning previously neglected information into actionable insights.
Beyond this instance, AI technology can be being increasingly applied to distant patient monitoring (RPM) tools and wearables. Â It enables quick processing and integration of information emitted from these devices that, previously, was often underutilized as a result of an absence of context and difficulty incorporating it into the care workflow. Looking forward, AI in healthcare has the potential to unify and interpret data across care settings to unlock deeper insights and enable preventative patient care.
The Problem with Disjointed Care Settings
Everyone who has seen a brand new provider is aware of the tedious means of having to relay their medical history all once again. Lack of information sharing between care settings can have a major impact on care quality. It might probably result in delays, disruption in care and increased probabilities of misdiagnosis and medicine errors. These issues also pile on to providers’ administrative burden and might negatively impact how the hospital or health system performs.
In accordance with the American College of Physicians, effective data sharing is certainly one of 4 key principles to improving care coordination and reducing error. Decreasing system limitations to share patient data in a timely and actionable manner allows healthcare providers to construct a comprehensive and proactive care plan that improves health outcomes. Prioritizing interoperability between care settings is vital to enhancing workforce efficiency and providing quality care.
Enhancing the Role of Distant Monitoring Tools
When patients have vitals taken at an appointment, the provider is simply getting a small glimpse into the larger picture. They capture this information in a single moment versus monitoring over time. Metrics like heart rate, blood oxygen saturation or blood pressure might be higher or lower than normal on the time it’s taken. Without knowledge of how these metrics change throughout the day, it’s difficult for a provider to contextualize the readings. But what if doctors could access at-home vitals through data collected from wearable devices like a fitness tracker or distant monitoring device? What if that data might be robotically uploaded and mapped to a patient data record and analyzed with the assistance of AI?
As at-home care programs and RPM usage turn into more common, AI has the potential to help with the connection and interpretation of information from non-acute and acute care settings, providing insights into key trends. By repeatedly analyzing and integrating data from multiple sources, AI can detect and alert clinicians to critical updates in a patient’s condition. This provides timely perspective that – when paired with interoperability and open data exchange – can ensure alerts reach the suitable person for swift and informed motion.
The implications of this technology are far-reaching, with the potential to affect every area of our lives and completely alter the way in which patient care is managed. This continuous, AI-supported data exchange couldn’t only minimize administrative burden but additionally foster a more proactive approach to care designed to anticipate patient needs and coverings before conditions worsen.
Moving From Reactive to Preventative Care
As AI tools and their use cases in healthcare proceed to expand, hospitals and health systems might want to explore the worth of constructing strategic decisions to implement promising solutions that may reduce administrative burden while also making a meaningful and positive impact on patient care.
Many RPM and AI tools are still within the early stages of development and research continues to analyze the outcomes of implementation. There’s a protracted road ahead before leveraging AI to attach data across care settings becomes a full reality for the healthcare industry. Nonetheless, the long run looks promising. AI has the potential to facilitate the shift for all providers to rework care delivery from reactive to a preventative and proactive approach. By converging patient data from across care settings, AI could make it easier for providers to treat the entire person fairly than the symptom, ultimately enabling safer take care of all.
