Machine learning and other artificial intelligence approaches are being investigated by a number of healthcare companies for the role these technologies play in data management. Thanks to the potential that AI and machine learning has to streamline data capture, data management, and data leveraging regardless of how it is structured or how it is presented, new and innovative ways to glean insights from this data, both for coordinated care and research collaboration, may make it possible to rewrite the book on healthcare data as a result.
There are a number of major players in the AI and machine learning game. Combined with ease-of-access and distributed data technologies such as cloud computing, these approaches allow healthcare providers to scale with demand in order to facilitate data gathering processes. Whether it’s through a research pipeline or it’s through the journey a patient undergoes for care, the relevant data can be processed using AI tools and then presented through data visualization to reveal insights that might not have been visible otherwise.
Like with many other business sectors, Amazon is an early adopter of both cloud-based computing and providing commercial or institutional access to machine learning. Cloud-based Amazon Web Services partners with payment providers such as Orion Health to aid in managing a 50-million-user-strong data network, making patient care and clinical data accessible alongside reimbursement and claims information. Care providers can, likewise, use their access to help identify more personalized strategies for patient treatment and prevention, making their ability to make clinical decisions in an optimal environment.
The potentially positive impact that AI and machine learning may have on healthcare data has led to a renewed interest in the industry sector on the part of developers and engineers. With care provider networks generating ever-increasing amounts of both structured and unstructured data, there has been a concerted push to provide storage, management, and analysis options for electronic health records and other types of doctor-patient communications.
One of these new approaches is Amazon Comprehend Medical, an AWS service that uses machine learning to aid in the processing of unstructured data. Whether it’s radiology reports, transcripts of audio interviews, prescriptions, or medical notes, Amazon Comprehend Medical helps to identify this information to help categorize diagnoses, signs and symptoms, treatments, and medication dosage amounts.
Yet the benefits of AI and machine learning aren’t limited to better organization and analysis of patient data. Medical imaging is another facet of healthcare that can make use of artificial intelligence to improve patient health, both on an individual and a community level, by identifying and closing care gaps due to poor detection. The key is in using tools such as risk identification, pattern recognition, and natural language processing, all of which are becoming more refined and can now provide practical solutions.
One industry leader is the HealthSuite platform by Philips. Again leveraging the power of cloud computing, HealthSuite has access to 390 million data points, ranging from patient inputs, medical records, and medical imaging. All of this adds up to 21 petabytes of data, and the end result is that care experiences can be personalized by applying analytical approaches to this data. This offers software developers, data scientists, clinicians, and providers a wealth of tools to accomplish these goals.
With increased pressure being put on healthcare providers to use data in more focused ways, AI and machine learning provide a number of advantages. Better patient experience, reduced costs, and improvements to the quality of care are all possible through the application of artificial intelligence technologies.