The potential benefits of AI in healthcare are enormous. However, we still face many challenges that must be addressed before AI can significantly impact.
Healthcare leaders must carefully select and design AI solutions to address specific needs and goals. They must also ensure that they have robust and compliant data-sharing policies.
AI is empowering patients.
Various subtypes of AI are already being deployed in the healthcare industry to streamline diagnostics, advance treatment options, boost patient adherence, and support administrative and operational efficiency. Natural language processing (NLP) and machine learning algorithms are key technology components that allow healthcare professionals to make data-driven decisions and automate processes that previously required human intervention.
One example is a predictive AI system that can identify potential patient risks, such as a missed dose or an upcoming appointment. This is designed to help doctors avoid medical errors, such as prescribing the wrong medication or mislocating a tumor in a radiology scan, that could lead to severe health-related consequences for their patients.
The use of AI in healthcare can also increase productivity and reduce costs. For instance, a hospital that uses an augmented intelligence platform from Healthcare to manage operational flow has been able to get patients admitted to beds 38 percent faster than it would have with traditional methods.
AI is empowering healthcare professionals.
Some AI solutions are helping improve operational workflows and staffing issues, while others aid healthcare practitioners in clinical decision support (CDS). Others are automating administrative tasks, freeing valuable time to focus on patient care.
AI has the potential to break down silos in healthcare data and connect information that might take years to process manually. It can also help detect fraud, which costs the industry a hundred billion annually. This is why many leading healthcare companies are deploying AI to identify and prevent claims fraud.
To be successful, however, the future of AI in healthcare must be designed to complement, rather than replace, medical professionals. This requires identifying the use cases that prioritize clinical work and reduce burdens on physicians and their teams. This could include prioritizing solutions that help with scheduling and logging medication or assisting with patient engagement through voice-to-text transcription. It also means ensuring that AI is used ethically and transparently, which will help physicians trust that it will not lead them astray.
AI is empowering health systems.
The emergence of AI-enabled devices that allow patients to monitor their health is helping to improve care outcomes. For example, the digital tool helps diagnose skin cancer, saving patients time and money by avoiding unnecessary visits to healthcare professionals. It also enables healthcare professionals to save time with administrative workflows such as dictation and documenting patient data.
Another key benefit of AI in healthcare is its ability to analyze large amounts of data, including medical images and patient records. This information can then be used to assist with diagnosis and treatment.
However, there are some challenges with using AI in healthcare. First, it is essential that data sets adequately represent the population and contain no biases. Second, it is necessary to have proper infrastructure for storing and organizing this data. Finally, it is essential to have robust cybersecurity measures in place to protect sensitive information from cyberattacks.
AI is empowering consumers.
Providing patients with seamless, personalized experiences is critical to improving healthcare outcomes. AI tools can help improve customer service by analyzing data to identify trends or predispositions to certain diseases and optimize operational processes like scheduling or bed management.
AI can also help physicians save time by automating repetitive, manual tasks, freeing more valuable human resources to focus on more complex patient care. This can lead to improved medical practices, better patient outcomes, and lower operating costs.
However, implementing quality AI for healthcare can be challenging. The need for healthcare practitioners to be open to new technologies and ensure that solutions can support and augment rather than replace their activities was a common theme in our interviews. This means identifying and prioritizing use cases that are safe, ethical, and effective, with the right level of transparency and minimizing risk for practitioners. This is a crucial challenge to adopt AI in healthcare successfully.