A robot has successfully performed "keyhole" intestinal surgery on pigs without any aid from humans, according to a study from John Hopkins University (published in Science Robotics). What's more, the Smart Tissue Autonomous Robot (STAR) handled the tricky procedure "significantly better" than human doctors. The breakthrough marks a significant step towards automated surgery that could one day help "democratize" patient care, the researchers said. Laparoscopic or keyhole surgery requires surgeons to manipulate and stitch intestines and other organs through tiny incisions, a technique that requires high levels of skill and has little margin for error. The team chose to do "intestinal anastomosis" (joining two ends of an intestine), a particularly challenging keyhole procedure.

As healthcare systems strive to realize IOM's vision for continuous improvement in care delivery, many are recognizing that they have outgrown their data management and reporting capacity. Those that have turned to new machine-learning approaches have found they can expand capacity and capabilities while reducing administrative burden on clinicians. Here's an example of how one health system used machine-learning tools to improve care delivery for intestinal surgery: Until recently, the health system's surgical services team used traditional methods of hospital data analysis to inform their creation of order sets, protocols, and provider and patient education materials spanning the pre-op, intraoperative and post-op phases of care. Then they applied a "machine intelligence" platform that pairs machine learning algorithms with topological data analysis (TDA)--a mathematical process that uses shape as an organizing principal for understanding complex data. By giving visible form to their data, the health system was able to replicate and validate years of analytical insights in a matter of days.