The Future of AI in Healthcare: Connecting patient data in nursing settings to improve preventive care

Today’s hospitals and health systems are plaguing a problem: too much data from providers, but insufficient data opinion.

Healthcare providers and administrators are often burdened with a large amount of information that must be managed. A 2022 survey of 3,000 nurses and doctors found that 69% of patients had overwhelmed the data. However, due to the difficulties in extraction and context, an estimated 97% of the data are not used. Although it is possible to improve diagnosis and treatment, these disorders, as well as the limited time of clinicians, create obstacles for effective utilization.

As the industry continues to innovate, more and more organizations are implementing advanced technology solutions to address this ongoing challenge. Today, some hospitals and health systems use AI to increase patient safety incident analysis by simplifying incident reporting and automated data extraction. This automation is just one example of how providers can maximize patient data to improve quality of care, thus turning previously ignored information into actionable insights.

In addition to this example, AI technology is increasingly used in remote patient monitoring (RPM) tools and wearable devices. It can quickly process and integrate data emitted by these devices, which have been often underutilized in the past due to lack of context and incorporation into care workflows. Going forward, AI in healthcare has the potential to unify and interpret data across care settings to unlock deeper insights and enable preventive patient care.

Problems with disconnected care settings

Everyone who has seen a new provider is familiar with the tedious process of having to communicate the medical history again. Lack of data sharing among nursing environments can have a significant impact on the quality of care. It can lead to delays, interruptions in care and increased chances of misdiagnosis and medication errors. These issues also accumulate administrative burdens for providers and may negatively impact the performance of hospitals or health systems.

According to the American College of Physicians, effective data sharing is one of four key principles for improving care coordination and reducing errors. Reducing system restrictions to share patient data in a timely and feasible way, enabling healthcare providers to develop a comprehensive and proactive care plan to improve health outcomes. Prioritizing interoperability among nursing environments is key to improving employee efficiency and delivering quality care.

Enhance the role of remote monitoring tools

When patients have health points at appointments, the provider only gets glimpses of larger images. They capture this information in a moment and monitor it over time. When taken, indicators such as heart rate, blood oxygen saturation, or blood pressure may be higher or lower than normal. Without understanding how these metrics change throughout the day, it will be difficult for providers to contextualize readings. But what if doctors can get home vitality through data collected from fitness devices, such as fitness trackers or remote monitoring devices? What if this data can be automatically uploaded and mapped to patient data records and analyzed with the help of AI?

As home care planning and RPM use become more common, AI has the potential to assist in the connectivity and interpretation of data in non-acute and acute care settings, thus providing insights into key trends. By continuously analyzing and integrating data from multiple sources, AI can detect and alert clinicians to critical updates in patient conditions. This provides a timely view that when paired with interoperability and open data exchanges, it is possible to ensure alerts touch the right people and act quickly and wisely.

The implications of this technology are profound and have the potential to impact every area of ​​our lives and completely change the way patient care is managed. This continuous, AI-powered exchange of data not only minimizes administrative burdens, but also promotes a more proactive approach to care designed to predict patient needs and treatments before the situation worsens.

Switching from reactive to preventive care

As AI tools and their use cases in healthcare continue to expand, hospitals and health systems will need to explore the value of making strategic decisions to implement promising solutions to reduce administrative burdens while also having a meaningful and positive impact on patient care.

Many RPM and AI tools are still in the early stages of development, and the research continues to study the results of implementation. There is a long way to go before leveraging AI to connect data across care environments becomes a comprehensive reality in the healthcare industry. However, the future looks promising. AI has the potential to facilitate the transition of all providers to shift care delivery from responsiveness to preventive and proactive approaches. By collecting patient data from various care settings, AI can make it easier for providers to treat the entire person rather than the symptoms, ultimately making it safer for everyone.