industryterm:health care management

  • Amazon, AI and Medical Records: Do the Benefits Outweigh the Risks? - Knowledge Wharton

    Last month, Amazon unveiled a service based on AI and machine-learning technology that could comb through patient medical records and extract valuable insights. It was seen as a game changer that could alleviate the administrative burden of doctors, introduce new treatments, empower patients and potentially lower health care costs. But it also carries risks to patient data privacy that calls for appropriate regulation, according to Wharton and other experts.

    Branded Comprehend Medical, the Amazon Web Services offering aims “to understand and analyze the information that is often trapped in free-form, unstructured medical text, such as hospital admission notes or patient medical histories.” Essentially, it is a natural language processing service that pores through medical text for insights into disease conditions, medications and treatment outcomes from patient notes and other electronic health records.

    The new service is Amazon’s latest foray into the health care sector. In June, the company paid $1 billion to buy online pharmacy PillPack, a Boston-based startup that specializes in packing monthly supplies of medicines to chronically ill patients. In January, Amazon teamed up with Berkshire Hathaway and JPMorgan Chase to form a health care alliance that aims to lower costs and improve the quality of medical care for their employees.

    “Health care, like everything else, is becoming more of an information-based industry, and data is the gold standard — and Amazon knows as well as anyone how to handle and analyze data,” said Robert Field, Wharton lecturer in health care management who is also professor of health management and policy at Drexel University. “It’s a $3.5 trillion industry and 18% of our economy, so who wouldn’t want a piece of that?”

    AI offers “enormous” promise when it comes to bringing in new and improved treatments for patient conditions, such as in the area of radiology, added Hempstead. Machine learning also potentially enables the continual improvement of treatment models, such as identifying people who could participate in clinical trials. Moreover, Amazon’s service could “empower a consumer to be more in charge of their own health and maybe be more active consumer of medical services that might be beneficial to their health,” she said.

    On the flip side, it also could enable insurers to refuse to enroll patients that they might see as too risky, Hempstead said. Insurers are already accessing medical data and using technology in pricing their products for specific markets, and the Amazon service might make it easier for them to have access to such data, she noted.

    #Santé_publique #Données_médicales #Amazon #Intelligence_artificielle

  • What the Reynolds-Lorillard Merger Means for the Tobacco Industry

    However, the standard measurement of what is good and bad in M&A becomes complicated when the product is tobacco. “If this were a classic monopoly/antitrust issue where the only consequence of a merger was a higher selling price, that would be good for public health because, at a higher price, fewer people would smoke,” says Pauly, a Wharton professor of health care management.

    The usual argument for antitrust is that low prices are good for consumers, but that is only true if the item being sold is beneficial to the buyer, Pauly notes. “While there would be financial harm to smokers from a merger that raised prices, they would be healthier if not wealthier.” If the combined entity achieves lower production costs or “engages in more effective selling,” it will increase not just profits but also tobacco use, which would be “bad for health.” In order to mitigate those effects, Pauly suggests that antitrust regulators could require Reynolds-Lorillard to undertake public health programs, such as anti-smoking campaigns for young people, as part of a deal to approve the merger.

    hum, vous ne trouvez pas que c’est un peu léger comme argument ?