Join us to use cutting edge machine learning to unbreak healthcare in the US. In the US, physicians face huge informational challenges – from dealing with mountains of formulaic email to wrestling with arcane insurance rules to finding at-risk patients in their large client pools. Athenahealth's Data Science group is using advanced machine learning and AI to develop a new generation of smart tools that can help physicians by reducing their paperwork, finding at-risk patients, providing key information at the right time, and overall allowing physicians to focus on what's important: spending time with patients. We're seeking experienced data scientists who love machine learning and complex data and who care about making a positive impact on the world by fielding real ML-driven systems. Positions are available at multiple levels of seniority.

Health care executives from IBM Watson and Athenahealth athn debated that question onstage at Fortune's inaugural Brainstorm Health conference Tuesday. In addition to partnering with Celgene celg to better track negative drug side effects, IBM ibm is applying its cognitive computing AI technology to recommend cancer treatment in rural areas in the U.S., India, and China, where there is a dearth of oncologists, said Deborah DiSanzo, general manager for IBM Watson Health. For example, IBM Watson could read a patient's electronic medical record, analyze imagery of the cancer, and even look at gene sequencing of the tumor to figure out the optimal treatment plan for a particular person, she said. "That is the promise of AI--not that we are going to replace people, not that we're going to replace doctors, but that we really augment the intelligence and help," DiSanzo said. Athenahealth CEO Jonathan Bush, however, disagreed.

If this were Jeopardy, the correct answer would be, What are some indicators that a patient will cancel their doctor's appointment? The answer to that question is the kind of information for which physicians and hospitals are prepared to pay. The machine learning engine that helps generate these answers propelled Arsenal Health's acquisition by athenahealth last week. In an interview with Arsenal Health CEO Chris Moses, he talked about how its business and product development plans fit into athenahealth's machine learning aspirations. Although the company started life in 2012 as Smart Scheduling, it recently changed its name to allow for wider applications of its approach. Moses co-founded the company with Joel Sutherland.

Cloud-based athenahealth is expanding its portfolio to include machine learning and artificial intelligence with its acquisition of analytics startup Arsenal Health. Arsenal's Smart Scheduling tool has already been effective with athenahealth's providers, officials said. The acquisition, terms of which were not disclosed, will move Arsenal from a third-party vendor to a native capability available for all athenahealth's customers through its athenaCoordinator network. In the future, athenahealth's officials say they hope the acquisition will accelerate the company's analytics and AI capabilities, broadening insights and enhancing offerings for its 74 million patient records. "The prospect of building on Arsenal Health's technology and combining it with our own valuable data to positively impact care and expand the power of our network is extremely compelling," said Robinson.

Introduction Influenza is a leading cause of death in the United States (US), where up to 50,000 are killed each year by influenza- ‐like illnesses (ILI) [1]. Therefore, monitoring, early detection, and prediction of influenza outbreaks are crucial to public health. Disease detection and surveillance systems provide epidemiologic intelligence that allows health officials to deploy preventive measures and help clinic and hospital administrators make optimal staffing and stocking decisions [2]. The US Centers for Disease Control and Prevention (CDC) monitors ILI in the US by gathering information from physicians' reports about patients with ILI seeking medical attention [3]. CDC's ILI data provides useful estimates of influenza activity; however, its availability has a known time lag of one to two weeks. This time lag is far from optimal since public health decisions need to be made based on information that is two weeks old. Systems capable of providing real- ‐time estimates of influenza activity are, thus, critical. Many attempts have been made to design methods capable of providing real- ‐time estimates of ILI activity in the US by leveraging Internet- ‐based data sources that could potentially measure ILI in an indirect manner [4, 5, 6, 7, 8, 9, 10, 11].