The care pathway of every patient is unique, but for persons living with diabetes, it is a long and complex intervention of care processes spread across the care continuum. Luckily, AI is transforming how diabetes is detected, managed, and prevented while improving patient outcomes.
Today, about 463 million people are living with diabetes all over the world, with one in two persons undiagnosed and untreated. In the United States, diabetes affects 34.2 million people of all ages and is the seventh leading cause of death, as stated by the American Diabetes Association. Caused by the body’s inability to take up glucose from food and use it as fuel – diabetes is characterized by elevated blood glucose levels. And presently, it is a global concern for healthcare providers due to its skyrocketing incidence rate that has already surpassed future projections.
A poorly controlled diabetes can lead to serious health consequences because it predisposes the body to long-term damage and no known cure currently exists. Hence, choosing which interventions to prioritize for a patient living with diabetes can be difficult for healthcare practitioners. It can even be more challenging to ensure that the right interventions are delivered appropriately.
As a solution, health systems are implementing an evidence-based care-pathways approach to map out the steps in a patient’s journey throughout the entire health system. With AI in the diabetes care pathway, clinicians have a well-defined roadmap to diagnose, treat, and manage patients living with diabetes for improved health outcomes. The healthcare team can make well-informed decisions so that each patient gets the best evidence-based treatment. Patients are also equipped with AI-enabled self-care tools and are participants in their health management process. Here are five ways artificial intelligence is advancing the diabetes care pathway.
Automated Screening for Early Detection of Diabetic Retinopathy
A diabetic retinopathy diagnosis signifies the progression of diabetes, and persons living with diabetes are highly susceptible to it. According to the American Academy of Ophthalmology, diabetic retinopathy occurs when high blood sugar levels damage the blood vessels in the retina – the light-sensitive tissue at the back of the eyes, thereby affecting vision. Apart from being a symptom of diabetes, it is a tricky condition because it has a long asymptomatic early stage that is usually unnoticed until vision gets impaired.
Many eye specialists are turning to AI-powered devices like the FDA-approved IDX-DR to detect and observe the incidence of the disease early. The automated software uses artificial intelligence to scan and analyze the retina for any anomaly. AI arrests resultant comorbidities and ophthalmic complications that may arise by fast-tracking the early detection of the disease. Furthermore, it eliminates the need for expensive and invasive surgical procedures.
Predictive Diabetes Risk Modelling
Diabetes risk modeling utilizes machine learning and patients’ health data to draw insights on the incidence, risks, and potential severity of diabetes. As far as the diabetes care pathway is concerned, risk modeling is important because it helps researchers and health practitioners mitigate the risks of target populations or patient subgroups to diabetes for a better health intervention.
Machine learning marks the patient’s physical health, mental health, lifestyle, and social activities to predict diabetes accurately. Beyond the accurate prediction of the incidence of diabetes, such predictive models can spot behavioral patterns that caused high blood sugar levels. With this personalized analytics, it is easy to make conclusions on the likelihood of a patient developing either short or long-term diabetic complications following a diagnosis.
Clear-cut Diabetes Genomics
The integration of AI, genomic data, and health data is advancing precision in diabetes care. Genomics help clinicians understand from a genetic standpoint – the link between genetics and diabetes. And by leveraging AI to study and analyze the marker genes of a patient, it is easy to uncover unusual patterns of the disease to understand it better. It is then easier for health practitioners to map out prevention plans, create bespoke treatment protocols, and create a care pathway to improve health outcomes for each patient or subgroup.
Studies have identified over 400 diagnostic biomarkers that give pointers to a person’s risk of diabetes. Deep learning is gaining widespread approval in extracting deep phenotypic information from genetic databases and processing complex genomic datasets of patients – to help researchers identify more diabetic biomarkers. Clinicians can draw insights from these data to create a simplified care pathway for various patient subgroups and match them with the best possible treatment plan.
All-inclusive Clinical Care
The clinical care phase of the diabetes care pathway guides clinicians to select and optimize the best possible treatment plan for all patients living with diabetes in need of clinical intervention. A standardized clinical care pathway is: all-inclusive for all patients and members of the healthcare team, provides a detailed structure of choosing a treatment plan for each patient, and must consider every vital detail of their health. It is not unusual for persons living with diabetes to have other underlying health conditions that may impact their treatment plan. For instance, the treatment plan of a person living with diabetes and heart disease will be different from one with the same condition but presenting with rheumatoid arthritis. Yet, it is easy to miss out on little details that can impact their health.
Machine learning algorithms can help clinicians – spot underlying medical conditions and map out an all-encompassing treatment plan so that patients get the needed intervention before their condition progresses. AI-powered systems also help clinicians identify persons living with diabetes at risk of developing other chronic diseases like kidney failure, liver cancer, or stroke – so that clinicians can customize and monitor their interventions for optimized health outcomes.
AI-Powered Diabetes Self-Management Devices
In the treatment of diabetes, self-management is vital. Hence, following a diabetes diagnosis or clinical intervention, the patient gets a self-monitoring device. From decision support aids to glucose sensors and smartphone apps, the diabetes care market has many AI-powered self-management devices that empower patients to monitor and take charge of their health. These devices and monitoring software are easy to use and offer accuracy and efficiency in getting personalized data-driven insights. A handful of diabetes care start-ups are using personalized analytics to help patients manage their health effectively.
Leading the pack in the metabolic health space, January AI uses personalized machine learning and real-time glucose monitoring to predict the effect of diet on health. January AI’s program “Season of Me” analyzes a patient’s blood sugar and comes up with a list of foods to eat and avoid to help build healthy habits in 90 days. “We believe that every day can feel like January 1st – a day to take a fresh start towards better health – and that self-improvement is a team sport. Additionally, we’re excited to expand our footprint into the enterprise, including pharma, where we already have a paying customer. With these partners and our growing team of advisors, we can unlock the power of personalized self-care for consumers wanting to take control of their health,” said Noosheen Hashemi, founder, and CEO of January AI, during a press release. With novel AI advances in the diabetes care pathway, ‘diabetes no longer has to be passed that way over and down, like a knight in chess.
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