Why We Need Artificial Intelligence and Machine Learning in Healthcare
Wearables have gained massive popularity over the past few years, and the global wearable tech market is forecasted to exceed 265 billion by 2026. This boom effectively bolsters the Big Data paradigm shift, with wearables becoming an increasingly essential source of personal data to leverage for a range of purposes. Wearables are bound to have a significant impact on healthcare, in which aggregated data can drastically reform how patients and care providers interact.
Smartwatches and smartbands help people stay fit by tracking their activity levels, sleep cycles, nutrient intake, and step and calorie count. When an illness arises, patient interactions with physicians traditionally involve on-demand curing, with patients visiting clinics or meeting their doctor face-to-face. While patients receive care for whatever ailment they may be suffering at the given moment, they maintain little to no relationship with their caregiver in-between visits.
This dynamic is already changing and is set to evolve to a 24/7 doctor-patient connection in the imminent future. Doctors and caregivers — continuously “fed” medical parameters on their patient(s) — will be able to view and validate general wellbeing and determine illnesses and other irregularities even before those under their care become aware of them.
The switch from curing to caring will significantly enhance the quality of healthcare. It will also produce massive healthcare-related cost savings — an objective well acknowledged by regulators. In recent years, regulators have increasingly invested in improved reimbursements that incentivize remote patient monitoring and new telemedicine codes. Just this past year, for example, the Centers for Medicare and Medicaid Services added new codes to its 2022 Physician Fee Schedule that expand coverage for remote patient monitoring services.
While wearable-assisted monitoring will help transform patient-caregiver relationships, it won’t necessarily be sufficient on its own to revolutionize diagnosis and healthcare. Data by itself is, after all, useless. Not only does data need to be collected, but must also be analyzed, interpreted, and ultimately acted on to be valuable. That’s exactly where machine learning and its advanced algorithms come into play.
In general, medicine is largely a statistics-based discipline. The more information collected and analyzed, the more accurate the diagnosis. However, as the amount of information collected grows exponentially with the proliferation of wearables, the task of processing it can become insurmountable.
Machine learning can take over much of this effort and complexity. Machine learning can help filter out “noise” and extract non-explicit information, discover hidden connections, determine trends, and even forecast emerging patterns — all critical to effective diagnosis based on vast amounts of data.
The shift from curing to caring — or reactive to proactive medicine — is inevitable for better, more impactful diagnosis and treatment to take place. Al and machine learning are crucial for this healthcare revolution to succeed.
Advances in machine learning well underway will enable the technology to approach wearable-generated data, learning and deriving rules as it goes. Wearable algorithms start by making patient-level observations, then sift through staggering numbers of potential variables and predictors that no human doctor or caregiver could ever realistically handle, and ultimately uncover combinations and patterns that contribute to reliable outcome prediction.
Most importantly, machine learning technology will support experience-based improvement. With each subsequent diagnosis, the medical analysis and prognosis increase in efficiency and accuracy.
