Device makers have been scrambling for space in value-based care arrangements even though the pace of adoption of those arrangements has been somewhat tepid. While device makers are not explicitly included in a proposed overhaul of the Stark and Anti-Kickback Statute (AKS) regulations, providers may soon be more engaged in these arrangements, thus providing device makers with more opportunities even if they are not included in the rewrite of the related regulatory provisions.

Deciding which patients should go into the intensive care unit (ICU) after surgery is a difficult call and typically made entirely at the surgeon's discretion. The result is that surgeons typically err on the side of caution by putting more post-operative patients in the ICU than necessary. To aid in better ICU decision-making, physicians at New York University Langone Hospital System (NYU Langone) developed a machine learning algorithm that combs through a patient's electronic medical record to identify relevant factors to determine if they needed the ICU after surgery.

Information technology (IT) has been promising for decades, largely since the advent of electronic medical records (EMR), to improve and streamline health care as it has multiplied productivity in countless other industries. In addition to the long-standing problems with EMRs, more recently there have been early disappointments with the latest iteration of IT focused on artificial intelligence (AI) and machine learning (ML), as big players like IBM Watson and Google have tended to over-promise and under-deliver with algorithms that are poorly matched to the data or the patient need.

Information technology and connectivity have transformed productivity and costs in nearly every industry. Health care, however, has remained persistently immune to this transmogrification. Electronic health records (EHRs) have been particularly disappointing on this front, with time-consuming and inconsistent physician data entry as well as poor integration across complex and emerging data sources from medical devices, imaging, genomics and wearables and, as a consequence, a lack of usefulness in improving population health analytics or personalized care.

Getting on top of the persistent HIV epidemic requires getting ahead of new cases, but only about 7% of at-risk patients have been advised of a prophylactic drug regime approved by the FDA seven years ago. Two new studies appearing in The Lancet HIV suggest that an algorithm that uses electronic health record (EHR) data can help physicians identify their at-risk patients who are good candidates for pre-exposure prophylaxis (PrEP), thus improving the odds that modern medicine might finally put an end to the scourge of acquired immunodeficiency syndrome.

Xealth Inc., of Seattle, closed a series A financing with an additional $3 million from new investors Atrium Health, Cleveland clinic and Memorialcare Innovation Fund. The proceeds, which now total $14 million, will go toward further developing and deploying the company's digital prescribing and analytics platform. The company focuses on helping health systems organize and utilize digital health tools to optimize workflow, patient engagement and financial results.