The abundance of Internet of Things (IoT) devices and connected technologies being introduced daily are pushing the limits of innovation and raising expectations with every passing day. Thanks to these developments, particularly in machine learning and artificial intelligence (AI), we have already seen some successes in autonomous cars, smart cities and manufacturing that we used to believe could only happen in our Jetsons fantasies.
These innovations are also impacting the healthcare industry. While nurses and physicians will never be replaced by “Rosie” in the patient exam room, machine learning and AI are poised to transform the healthcare industry in ways that positively impact physicians and their patients.
The Impact of Machine Learning and AI on Patients and Physicians
Increased machine learning and AI will help solve two key issues in healthcare:
1. Overcoming the huge documentation burden faced by physicians.
2. Providing patients with more personalized care.
It’s been well-established that physicians today spend too much time on administrative tasks, particularly documentation, even with the advent of electronic health records (EHRs). For example, emergency department physicians spend 43 percent of their time entering data into a computer, according to The American Journal of Emergency Medicine, which translates to about 4,000 clicks in a 10-hour shift. As AI technologies evolve and take hold in the exam room setting, physicians will no longer have to spend the majority of their time documenting every detail of each patient visit. It will allow physicians to spend more time face to face with patients while technologies like ambient speech and applications powered by deep learning operate in the background, collecting and sifting through information to make it actionable.
"As AI technologies evolve and take hold in the exam room will no longer have to spend the majority of their time documenting every detail of each patient visit "
This will revolutionize healthcare by bringing a conversational and intelligent experience amplified by technology to the physician-patient interaction, eliminating mundane tasks related to hundreds to thousands of data points each day that distract physicians and consume much of their time today. This leads to the second benefit, because as the physician is more focused on the patient versus a computer screen, patients feel like they are receiving more authentic and personalized care.
In addition to helping physicians provide patients with greater attention, AI and machine learning will introduce and correlate information that may not be visible like drug interactions or a past medical history found in the record or elsewhere related to the patient. This gives health systems the ability to interpret the patient’s profile and health information from a holistic standpoint more readily than a physician. For example, a patient’s recent test results could reveal that her glucose levels are high. The AI-based system can quickly remind the physician of the patient’s family history, noting that the patient’s grandparents had diabetes, meaning that the patient is at increased risk for the disease and these elevated glucose levels require attention.
This is a critical benefit because there is no guarantee that the physician would have put two and two together, or gone back later to fully analyze the patient’s profile in today’s busy environment. Overall intelligence supported by machine learning and AI at the point of care provides great value to physicians in managing quality, compliance and risks.
The Role of Interoperability in Machine Learning
Today, transparency of care ends when a patient leaves the clinic or hospital, making it difficult to keep track of what is happening outside of a care setting. AI and machine learning will help connect the dots between various settings in the future. Similar to advances in customer service that leverage technology to sift through data to create a more meaningful picture of a person to provide better service, this same opportunity exists in healthcare for patient engagement. There is much untapped potential in home care settings—how patients engage in healthcare will likely become more connected and supportive of the physician-patient relationship and overall wellness.
Patients are already allowing for some health-related data sharing on a daily basis–whether it’s through allowing Fitbit to track steps and heart rate, or using Bluetooth pill boxes that monitor medication adherence and remind you when it is time to take your pill. Taking the data collected from these devices one step further and sharing it with physicians could lead to a multitude of new benefits.
Full implementation of AI solutions in the healthcare sector will not only require data sharing between physicians and patients, but also interoperability among EHR vendors—a crucial aspect to the success of AI. However, for this to work and achieve true interoperability there needs to be a way for the systems to link together at the patient level. The US health industry needs to adopt a national patient ID—a rememberable number unique to each person that allows the systems to communicate with each other using the patient’s indicator. This has been part of the interoperability discussion for years, and now is the time for it to become a reality.
Where Do We Go from Here?
Getting patients to stay healthy or follow at-home regimens is a huge struggle for most physicians. If a patient does have to return to a care setting, they then have to update the physician on their overall health or issues they have experienced since their last visit. Automated data sharing can resolve this issue and also eliminate inconsistencies, as it is hard for patients to remember all of their symptoms, or know what is going on inside their own bodies. AI and data sharing from devices like a Fitbit or a pill box could keep track of that data and monitor their health in an automated fashion until the next visit.
This kind of tracking will require patient buy-in, but with notable ROI and tools that are easy to use, patients will engage. An example is TSA Pre. Consumers are now voluntarily submitting themselves for background checks and finger printing, and are willing to pay a fee, to avoid longer lines at airport security check points, which would have been unthinkable a few years ago. It proved its value and time-saving capabilities, so participation increased.
The potential for machine learning and AI in healthcare is limitless. Integration of these technologies will continue to automate certain aspects of healthcare visits, making them more seamless and enjoyable for both patients and physicians. From no longer having to fill out repetitive paper work and stare at computer screens, to automatically checking patients in upon arrival, to making medical histories immediately available regardless of location, these technologies will make positive impacts throughout the industry. They will allow physicians to put the focus back on the patient by improving physician-to-patient communication and providing the focused, quality care patients expect.