Chronic Care Patient Monitoring using Artificial Intelligence
Over the last decade, online retail companies have successfully, metaphorically speaking, constructed the DNA of their customers. As a result, eCommerce companies can now precisely market specific products to customers based on their purchase history or browsing patterns. Companies delight their customers by enabling them to purchase products with a single click, providing both price and shipment tracking transparency. Contrarily, the healthcare industry significantly trails behind the retail industry in providing such an engaging experience to its consumers and thus giving rise to healthcare consumerism. Healthcare consumerism is designed to enable patients to become more fully involved in their healthcare decisions. It helps transform a patient’s health benefit plan, putting decision-making and economic purchasing power in the hands of plan participants.
Patients Concerns
Healthcare consumers are concerned about their long-term care expenses, chronic care, and end-of-life care. This need is driving customers to better understand their benefits, treatment coverage, and medical costs in an easily consumable manner. Furthermore, healthcare customers demand a better experience when shopping for a doctor, ordering refills, and monitoring their health metrics. Healthcare providers and payors can certainly learn a lot from the tactics employed by the retail industry to construct a needs-based profile of their healthcare consumers to effectively engage them. Constructing and analyzing the “customer journey map” of a healthcare consumer reveals both the potential opportunities and pitfalls of consumer touchpoints with the healthcare system (providers and payors). Broadly speaking, there are three phases of the healthcare consumer’s interaction with the healthcare system: 1) Before Treatment, 2) During Treatment, and 3) Post Treatment. The figure below shows the activities during each of these phases.
Monitoring Chronic Care Patients
A common observation across all of these phases is the use of wearables such as Fitbit and remote patient monitoring (RPM) devices such as pulse oximeters, glucometers, and blood pressure monitors. Although RPM devices have been around for the last 20 years, COVID-19 has accelerated their proliferation along with telemedicine, specifically for monitoring chronic care patients. These wearables and RPM devices can help providers and payors construct a profile of their healthcare consumers. The data transmitted from these RPM devices are monitored for any irregularities in patients’ biometrics by Care Managers, usually hired by providers to provide extended care. Should there be any fluctuations in a patient’s predetermined biometric threshold, Care Managers reach out to the chronic care patient advising them to adjust their medication level or schedule a TeleVisit with their primary care physician (PCP).
While this is an extremely useful service that meets the promise of value-based care, it is neither economically scalable nor can forecast fluctuations in patients’ biometrics. Moreover, Care Managers cannot discover patterns, such as causes of irregularities in patient biometrics, across the enormous amounts of the data collected from these RPM devices. This is where an artificial intelligence (AI)-based Care Manager (AICM) system can help.
AI-based Care Manager
An AICM is not intended to replace a Care Manager but help achieve economies of scale in chronic care patient monitoring and to predict irregular conditions before they occur. Along with making predictions about chronic care patients’ health and timely alerting the patient, the AICM can automate the scheduling of patients’ TeleVisits, order medication refills, answer patient’s dietary questions all based on analyzing patients’ real-time condition. As AICM continues to learn about the patient’s daily patterns from wearables and RPM device transmitted data, it will establish patient’s 360. Healthcare companies can depend on AICM to analyze patient habits and diets that influence their biometrics to better devise personalized treatment plans. On the other hand, AICM can be used by healthcare consumers to get recommendations, based on their health condition, the changes to their diet and lifestyle for their well-being.
Empowering patients, providing personal care, and leveraging data and intelligent technologies now serve as the backbone for optimizing outcomes. The use of artificial intelligence is just beginning to alter the way clinical providers make pertinent decisions regarding patient care and healthcare operations.
