Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An undercover child sex sting in Florida led to the arrests of over a dozen predators, including three Disney World employees and a registered nurse, investigators said Tuesday. In total, 17 people were taken into custody in the operation dubbed "Operation Child Protector" and face a total of 49 felony and two misdemeanor charges, Polk County Sheriff Grady Judd announced during a press conference. Undercover detectives posed as children between the ages of 13 and 14 on social media platforms, mobile apps and online dating sites to investigate child predators from July 27 through Aug. 1.
As populations age, more and more people are asking how best to organise care for the elderly in future. Trained staff will be important, as will technology and innovation – which may include artificial intelligence (AI). Some people get fearful when talk turns to AI, a topic riddled with misconceptions. At the end of the day, what it means is the attempt to map human decisions using computers, says Andreas Hein, an expert in assistance systems at Oldenburg University in Germany. In a medicine or health care setting, that means providing doctors and nurses suggestions that a computer has created based on data.
It would be difficult to overestimate the impact COVID-19 appears to be having on the automation sector. No where will the change be more apparent than in healthcare, where a major transition to automation has long been in the offing. What would have been a slower easing in has, in light of overstressed capacity in some areas of healthcare (and an eerie diminishment of demand in others), as well as a complete reorientation of consumer expectations in the pandemic era, set the stage for a jarring transformation. Healthline cuts through the confusion with straightforward, expert-reviewed, person-first experiences -- all designed to help you make the best decisions. Major hospitals have deployed specialized robot nurses with remote patient monitoring tech so that doctors can keep an eye on people from afar.
Robbie Freeman, vice president of clinical innovation at New York's Mount Sinai Health System began his career working at the bedside, so he has an intimate appreciation of the real-world value of patient safety projects – and importance of ensuring key data is gathered and made actionable with optimal workflows. "I'm a registered nurse, and I think working with patients and spending a lot of time on data entry is what kind of led us to this real focus on clinical workflows and delivering additional value," said Freeman, speaking Wednesday at the HIMSS Machine Learning & AI for Healthcare Digital Summit about some of Mount Sinai's recent automation initiatives. In an earlier, pre-digital age, many of the flow sheets and assessments collected during a nursing assessment, or other clinical information entered into the chart, might not have been "used or even necessarily looked at," he said. But in recent years, "they've become very valuable in the world of predictive analytics. There's a lot of information in those flow sheets that we can tap into for these models."
Journal of Scheduling manuscript No. (will be inserted by the editor) Abstract The personnel rostering problem is the problem on which branch to choose next and to prune the search of finding an optimal way to assign employees to tree. The problem has received significant attention in the literature and is addressed by a large number of exact and 1 Introduction metaheuristic methods. In order to make the complex and costly design of heuristics for the personnel rostering In various occupations and work scenarios, arranging problem automatic, we propose a new method employees to different shifts is a difficult job. The difficulty combined Deep Neural Network and Tree Search. By is that different employees have different requirements treating schedules as matrices, the neural network can for life and work, which leads to preference of predict the distance between the current solution and each employee. And there are also requirements of the the optimal solution. It can select solution strategies by law that must be followed or diverse properties of different analyzing existing (near-)optimal solutions to personnel occupations. These regulations are what we call soft rostering problem instances.
WTWH Media, which produces The Robot Report, has announced that complimentary registration for the Healthcare Robotics Engineering Forum is open to members of the news media, as well as business and technology analysts. This inaugural conference and exposition will focus on the design, development, and production of healthcare and medical robotics. Qualifying analyst and media attendees can get free registration, which includes two full days of keynotes and technical sessions. The Healthcare Robotics Engineering Forum will be at the Santa Clara Convention Center on Dec. 9-10, 2019. Complimentary registration is available for approved members of the press and analyst communities.
Tokyo AI company Exawizards is using deep learning to analyze unstructured nursing care data such as audio and video recordings. Faced with a rapidly aging population, Japan is turning to new solutions for a many-faceted problem. Amid a low birthrate, more than one-fifth of Japanese are now 70 or older, according to government data. Meanwhile, there are not enough people to care for this cohort. By 2025, there will be a shortfall of 340,000 nursing care workers.
Scheduling of personnel in a hospital environment is vital to improving the service provided to patients and balancing the workload assigned to clinicians. Many approaches have been tried and successfully applied to generate efficient schedules in such settings. However, due to the computational complexity of the scheduling problem in general, most approaches resort to heuristics to find a non-optimal solution in a reasonable amount of time. We designed an integer linear programming formulation to find an optimal schedule in a clinical division of a hospital. Our formulation mitigates issues related to computational complexity by minimizing the set of constraints, yet retains sufficient flexibility so that it can be adapted to a variety of clinical divisions. We then conducted a case study for our approach using data from the Infectious Diseases division at St. Michael's Hospital in Toronto, Canada. We analyzed and compared the results of our approach to manually-created schedules at the hospital, and found improved adherence to departmental constraints and clinician preferences. We used simulated data to examine the sensitivity of the runtime of our linear program for various parameters and observed reassuring results, signifying the practicality and generalizability of our approach in different real-world scenarios.
In June 2018, Babylon Health hosted an event in London at which it showed off its latest digital healthcare development. It had developed artificial intelligence (AI) that it claimed was "better than a doctor". Considered a world-first, the AI proved it was on par with practising clinicians by taking tests, including a set of questions from the MRCGP exam – a test that has to be taken by every GP in the UK. Scoring higher than the average score over a period of five years, the AI achieved 81% during its first sitting. The industry and media alike were abuzz with excitement.