Helping patients following a stroke: In emergency rooms, when patients come in with a stroke called an intracerebral hemorrhage, they undergo a CT scan. That scan is examined by a computer trained to analyze CT data, cutting the time to diagnosis and limiting brain damage. Preventing heart problems: Applying AI to ECGs has resulted in a low-cost test that can be widely used to detect the presence of a weak heart pump, which can lead to heart failure if left untreated. Mayo Clinic is well situated to advance this use of AI because it has a database of more than 7 million ECGs. First, all identifying patient information is removed to protect privacy.

Shivrat Chhabra is CEO of Dosis, a company that offers an artificial intelligence-powered personalized dosing platform used by dialysis providers to manage chronic drug regimens. Dosis developed the Strategic Anemia Advisor, an AI-based dosing platform for patients with chronic anemia. Chhabra provides the following statistics for provider organizations using his company's anemia advisor: Healthcare IT News interviewed Chhabra to get his thoughts on whether AI precision dosing can be the standard of care for chronic disease management, why AI dosing is particularly impactful in the dosing of drugs used to manage chronic conditions, and the downsides of relying on one-size-fits-all drug dose recommendations from manufacturers and the FDA. Q: Why should AI-powered precision drug dosing be the standard of care for chronic disease management? A: Drug dosing driven by AI is becoming more prevalent in many areas of medicine, such as dialysis, cancer and transplant medicine.

Digital healthcare specialist Babylon has announced plans to invest $100 million to create a multi-disciplinary team dedicated to building next-generation, AI-powered healthcare technologies. The move forms part of a long-term product and service strategy to apply AI to chronic disease management. It builds on recent partnerships with the likes of Tencent, Samsung, Bupa, Prudential, The Gates Foundation, and TELUS. As Babylon scales its operations internationally, it is increasing its focus on chronic conditions – which affect half the US population. Twenty-five percent of the populace in developed countries suffer from mental health issues, while diabetes and anti-obesity treatments cost the UK's NHS an estimated £10 billion and £5 billion each year, respectively.

Healthcare organizations may be able to better identify variations in best practices for chronic disease management by utilizing EHR data and machine learning analytics to combine clinical and cost information, says a new article from Weill Cornell Medical School and Carnegie Mellon University. The study, published in the American Journal of Managed Care, details the process of creating clinical pathways for chronic disease care using statistical machine learning algorithms, which can divide patients into risk-based sub-groups based on spending patterns and the evolution of their clinical complexity. The resulting data may be able to foster patient engagement and care coordination by giving patients and providers more insight into how to best manage – and pay for – multiple chronic conditions. "With medical cost being such an opaque subject, providers may not have the best guidance strategy for the treatments that they offer to their patients," wrote authors Yiye Zhang, PhD, and Rema Padman, PhD. Value-based care and innovative payment models for chronic disease management are prompting providers to take a more patient-centered approach to treatment, Zhang and Padman said, and require more patient involvement in their own care.