Sepsis is a is a life-threatening condition that arises when the body's response to an infection injures its own tissues and organs. Sepsis is a complex syndrome that is difficult to identify early, as its symptoms, such as fever and low blood pressure, overlap with those of other common illnesses. Without timely treatment, sepsis can progress to septic shock, which has a hospital mortality rate greater than 40%. Understanding which sepsis patients are at the highest risk for death could be useful for clinicians in prioritizing care. Our team partnered with researchers from Geisinger Healthcare System to build a model to predict in-hospital or 90-day post-discharge all-cause mortality among hospitalized sepsis patients using historic electronic healthcare record (EHR) data.
Major industries from retail to aeronautics are leveraging big data. But despite the abundance of data in healthcare, and the clear promise of big-data analytics, the sector has been slow to put it to work. Among the obstacles to adoption are laws aimed at protecting patient information, and a shortage of technical talent; hospitals and clinics compete for big data engineers whose technical skills can be agnostically applied across industries. Nonetheless, in 2015 Geisinger Health System implemented an IT system called a Unified Data Architecture (UDA) which allowed us to integrate big data into our existing data analytics and management systems. We use the UDA's big data capabilities to track and analyze patient outcomes, to correlate their genomic sequences with clinical care, and to visualize healthcare data across cohorts of patients and networks of providers.
Sepsis is a major medical issue. In the next week, an estimated 5,000 people will die from sepsis in the U.S. alone, and one third of all hospital deaths are related to sepsis (according to U.S. Centers for Disease Control and Prevention figures). These deaths are preventable, but by the time sepsis is detected, it's often already too late. One way to reduce incidences of sepsis is with the application of artificial intelligence. The staff at Sentara Healthcare are using an AI-enabled prescriptive analytic tool developed by Jvion, which identifies who is at risk of sepsis, alerts clinicians and suggests interventions tailored to each patient's needs.
Israel-based Medial EarlySign and Geisinger Health System have partnered to apply advanced artificial intelligence and machine learning algorithms to Medicare claims data to predict and improve patient outcomes. An EarlySign-Geisinger proposal has been selected as one of 25 participants to advance to Stage 1 of a technology challenge from the Centers for Medicare and Medicaid Services to accelerate the development of AI and machine learning solutions for healthcare. "Approximately 4.3 million hospital readmissions occur each year in the U.S., costing more than $60 billion, with preventable adverse patient events creating additional clinical and financial burdens for both patients and healthcare systems," says David Vawdrey, Geisinger's chief data informatics officer. "Together with our partner EarlySign, we have forged a dynamic team that is rapidly developing novel solutions to achieve the Quadruple Aim of improving the patient experience of care, improving the health of populations, reducing cost and improving clinical care provider satisfaction," adds Vawdrey. The AI vendor and Danville, Penn.-based regional healthcare provider intend to develop models that predict unplanned hospital and skilled nursing facility admissions within 30 days of discharge and adverse events such as respiratory failure, postoperative pulmonary embolism or deep vein thrombosis, as well as postoperative sepsis before they occur.
In 2016, venture capitalists invested $5 billion in startups involving artificial intelligence, representing a 40 percent increase from 2012. With hopes of securing a foothold in what promises to be a multi-billion dollar industry, some of the most influential companies in the world--including IBM, Apple, and Google--are pouring hundreds of millions of dollars into their AI research-and-development labs. Health care in particular has been a favourite target for these investments. Google's research website states that "machine learning has dozens of possible application areas, but healthcare stands out as a remarkable opportunity to benefit people."