Inland Empire Health Plan in Rancho Cucamonga, California, is the largest not-for-profit Medi-Cal and Medicare health plan in the Inland Empire, a metropolitan area in Southern California. Comprehensive medication reviews, a significant utilizer of trained clinical pharmacist resources, were not focused on plan members most in need, and the review process was manual and time-consuming. Moreover, it was unclear if member outcomes were improving because of this program. Preveon is a specialty pharmacy focused on chronic disease management. Inland Empire Health Plan has outsourced its medication review process to Preveon for its MyMeds Program, and as such, purchased Surveyor Health licenses and allocated them to Preveon.
Handling and analyzing massive troves of unstructured data has become a strategic imperative for businesses in 2019, with the healthcare and sports industries being no exception. Emerging tech-enabled solutions can give fitness and other health-related companies a huge edge over competitors in terms of using Big Data analysis tools and introducing automated IoT devices across their employee and customer/patient base. New analysis from Accenture estimates that AI-driven applications can save up to $150 billion annually for the US healthcare industry by 2026. With these numbers, however, there exist some concerns among business owners and employees that can jeopardize the large-scale implementation and subsequent adoption of these new cognitive solutions. For instance, there are some groundless fears of massive job losses for people getting replaced by robots, a steep learning curve for both managers and customers, and suchlike.
X-rays of arms and legs are among the most frequent diagnosis processes used by NHS Scotland, with around 5,000 procedures annually. Although injuries in these areas are often categorised as minor, misdiagnosis and mismanagement can hamper recovery and lead to financial cost. However, the use of artificial intelligence (AI) and machine learning could help create systems that prevent misdiagnosis. Find out more about the SBRI and how it works. The competition will explore how AI and machine learning can be used to support limb radiographs in the diagnosis of fractures.
Israeli radiology startup Aidoc has received FDA clearance for its AI-based product meant to help identify potential cases of pulmonary embolism in chest CT scans. Pulmonary embolism (PE) – which occurs when a blood clot gets lodged in the lung – is considered a silent killer that causes up to 200,000 deaths a year in the United States. The condition often strikes with little to no warning and diagnosis of a case can be extremely time-sensitive. Aidoc's technology doesn't require dedicated hardware and runs continuously on hospital systems, automatically ingesting radiological images. The 70-person company focuses on workflow optimization in radiology to help triage high risk patients for additional and faster review.
One example is Steward Health Choice Network's (SHCN) Health Plan Division, a division of Steward Health Care Systems. During the past two years, using RPA has led to drastic improvements in productivity for SHCN. Within 30 days of implementing its first RPA script, SHCN began realizing an ROI. Since that time, the labor automation -- Foxtrot RPA has processed 4.5 million transactions, with a cost avoidance of $2.75 million for the $1.4 billion organization. SHCN's Health Plan Division's efficiency gains illustrate what RPA can deliver to the healthcare industry.
Artificial intelligence (AI) and its many related applications (ie, big data, deep analytics, machine learning) have entered medicine's "magic bullet" phase. Desperate for a solution for the never-ending challenges of cost, quality, equity, and access, a steady stream of books, articles, and corporate pronouncements makes it seem like health care is on the cusp of an "AI revolution," one that will finally result in high-value care. While AI has been responsible for some stunning advances, particularly in the area of visual pattern recognition,1-3 a major challenge will be in converting AI-derived predictions or recommendations into effective action. The most pressing problem with the US health care system is not a lack of data or analytics but changing the behavior of millions of patients and clinicians. Physician behaviors, including ordering tests, procedures, pharmaceuticals, and other treatments, are responsible for 80% of health care costs.
Artificial Intelligence (AI) software that forms an opinion of people by the way they speak - in much the same way people do - is being developed by a UK finance firm. The language and vocabulary used by customers who ring in seeking help with their mortgage or savings accounts will be used to create a profile. The system, which is being backed by the Nationwide Building Society, will then tailor a computer generated conversation with the customer defined by their'voice personality'. Artificial Intelligence (AI) software that forms an opinion of people by the way they speak - in much the same way people do - is being developed by a UK finance firm. The AI system, developed by the company Scaled Insights, is already being used in partnership with hospitals in Leeds to help communicate with patients in the treatment of obesity and other issues.
NHSX, in collaboration with the AHSN AI Initiative and other partners, and supported by experts across the system, are launching another State of the Nation Survey for Data-Driven Health and Care in 2019. We are aware that there are some truly remarkable data-driven innovations, apps, clinical decision support tools supported by intelligent algorithms being developed, and that electronic health systems are being widely adopted. In parallel, we are seeing advancements in technology and, in particular, artificial intelligence (AI) techniques. Combining these developments with data-sharing across the NHS has the potential to improve diagnosis, treatment, experience of care, efficiency of the system and overall outcomes for the people at the heart of the NHS, public health and the wider health and care system. Following on from the survey that was held last year to understand what AI technologies were being developed, we want to go one step further and understand where they are being developed, what problems they are solving and collect more tangible information on the data and regulatory landscape.
Artificial intelligence (AI) and analytics are providing clinicians and researchers with actionable insights, from early detection to end-of-life-care, and by changing the way research is done and diagnoses are made. However, unlocking the data treasure trove is not a simple exercise for any healthcare organisation. With Asia-Pacific (APAC) expected to become the global leader in IoT spending according to IDC1, healthcare is unsurprisingly becoming increasingly connected in the region. However, it is this connectivity that adds complexity to the data challenge. Healthcare data is now growing at a rate of 48 per cent every year.
At the same time, my relaxed post-vacation disposition was quickly rocked by the news of the day and recent discussions regarding the extent of AI bias within New York's financial system. These unrelated incidents are very much connected in representing the paradox of the acceleration of today's inventions. Last Friday, The University of Maryland Medical Center (UMMC) became the first hospital system to safely transport, via drone, a live organ to a waiting transplant patient with kidney failure. The demonstration illustrates the huge opportunity of Unmanned Aerial Vehicles (UAVs) to significantly reduce the time, costs, and outcome of organ transplants by removing human-piloted helicopters from the equation. As Dr. Joseph Scalea, UMMC project lead, explains "There remains a woeful disparity between the number of recipients on the organ transplant waiting list and the total number of transplantable organs. This new technology has the potential to help widen the donor organ pool and access to transplantation."