One of the country's biggest hospitals has unveiled sweeping plans to use artificial intelligence to carry out tasks traditionally performed by doctors and nurses, from diagnosing cancer on CT scans to deciding which A&E patients are seen first. The three-year partnership between University College London Hospitals (UCLH) and the Alan Turing Institute aims to bring the benefits of the machine learning revolution to the NHS on an unprecedented scale. Prof Bryan Williams, director of research at University College London Hospitals NHS Foundation Trust, said that the move could have a major impact on patient outcomes, drawing parallels with the transformation of the consumer experience by companies such as Amazon and Google. "It's going to be a game-changer," he said. "You can go on your phone and book an airline ticket, decide what movies you're going to watch or order a pizza … it's all about AI," he said.
The health ministry is considering the use of artificial intelligence to draw up care plans for nursing care insurance recipients. The ministry will launch a national survey as early as August on how AI is being developed and on examples of AI use for nursing care, ministry officials said. It also plans to compile by the end of the current fiscal year, which runs to March 2019, a report that will assess the effectiveness of AI-based care plans in alleviating the burden of care-givers and preventing worsening conditions in those who need nursing care, the officials said. The report will also list related problems to be tackled. Before an older person certified as needing long-term care can receive nursing care insurance services, a care plan needs to be drawn up.
Fujitsu Limited and Fujitsu Hokuriku Systems Limited announced on 13 February 2018 that, in partnership with Tokyo's Kita City, they are conducting a field trial to evaluate how AI can be used to streamline tasks associated with guidance and supervision of nursing-care benefit claims from service providers, which Kita City employees customarily process by hand. The field trial will utilize Fujitsu Human Centric AI Zinrai, Fujitsu's approach to artificial intelligence, and will be conducted from January through March 2018 as part of efforts to enhance social security benefits. As Japan's society continues to age rapidly, Kita City is working to create an environment where the elderly can live in health and safety. In recent years, however, the number of people recognized as requiring nursing care and the number of nursing-care facilities have both been increasing, which has consequently increased the volume of analysis and guidance tasks associated with payments for nursing-care benefit claims, leading to problems with increased burdens on such employees. In this field trial, machine learning technology developed by Fujitsu Hokuriku Systems, which combines customer business expertise and software development technology, is put to use by systems engineers to create a model to analyze the appropriateness of claim details.
At an off-site support building two miles from NewYork-Presbyterian's Weill Cornell Medical Center, registered nurses use artificial intelligence to monitor what's happening at the hospital. The space houses various remote administrative stations, including clinical monitoring setups that have computers and six screens from which nurses track real-time physiologic data of patients in the emergency department. RNs also check activity through smart-bed technology, keeps tabs on temperatures of refrigerators that house life-saving medicine, and facilitate call center functions. NewYork-Presbyterian dubbed this AI-infused shop the Clinical Operations Center, or CLOC, and the goal is to leverage artificial intelligence in a 100 percent capacity within the next 12-14 months. CLOC is already is showing the benefits of incorporating artificial intelligence into daily clinical monitoring of patient care in ways that other hospitals can learn from.
Healthcare is a numbers game and the figures are running wild. Every indicator shows the creaking NHS cannot cope with rising demand and dwindling resources. But the relentless gloom over hospital waiting lists, budgetary shortfalls and demographic time bombs is being challenged by a fresh approach that could revolutionise personal and national health. A wave of innovation driven by artificial intelligence (AI) is being hailed as both a saviour of traditional healthcare and the dawn of a new era in the public's engagement with their own health. "Healthcare is one of the highest cost areas for all modern economies, which makes it ripe for AI as providers look for efficiency to care for patients," says Dan Housman, chief technology officer at ConvergeHEALTH by Deloitte.
Would you trust a robot to help deliver your baby? Robots could eventually play integral roles in labor wards, according to findings from MIT's Computer Science and Artificial Intelligence Laboratory. Robots are currently employed in hospitals to carry out simple actions, like dispensing medication. But can they understand patient needs and make scheduling decisions? The researchers have been working for the past two years to determine whether robots can be more than just helpful companions.
Robots could eventually play integral roles in labor wards, according to findings from MIT's Computer Science and Artificial Intelligence Laboratory. Robots are currently employed in hospitals to carry out simple actions, like dispensing medication. But can they understand patient needs and make scheduling decisions? The researchers have been working for the past two years to determine whether robots can be more than just helpful companions. They've been conducting experiments to see if a robot can serve as an effective "resource nurse."
Creating android and humanoid robots to furnish companionship in the nursing care of older people continues to attract substantial development capital and research. Some people object, though, that machines of this kind furnish human–robot interaction characterized by inauthentic relationships. In particular, robotic and artificial intelligence (AI) technologies have been charged with substituting mindless mimicry of human behaviour for the real presence of conscious caring offered by human nurses. The foregoing objections and concerns can be assessed quite differently, depending upon ambient religious beliefs or metaphysical presuppositions.
In recent years genetic algorithms have emerged as a useful tool for the heuristic solution of complex discrete optimisation problems. In particular there has been considerable interest in their use in tackling problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle constraints and successful implementations usually require some sort of modification to enable the search to exploit problem specific knowledge in order to overcome this shortcoming. This paper is concerned with the development of a family of genetic algorithms for the solution of a nurse rostering problem at a major UK hospital. The hospital is made up of wards of up to 30 nurses. Each ward has its own group of nurses whose shifts have to be scheduled on a weekly basis. In addition to fulfilling the minimum demand for staff over three daily shifts, nurses' wishes and qualifications have to be taken into account. The schedules must also be seen to be fair, in that unpopular shifts have to be spread evenly amongst all nurses, and other restrictions, such as team nursing and special conditions for senior staff, have to be satisfied. The basis of the family of genetic algorithms is a classical genetic algorithm consisting of n-point crossover, single-bit mutation and a rank-based selection. The solution space consists of all schedules in which each nurse works the required number of shifts, but the remaining constraints, both hard and soft, are relaxed and penalised in the fitness function. The talk will start with a detailed description of the problem and the initial implementation and will go on to highlight the shortcomings of such an approach, in terms of the key element of balancing feasibility, i.e. covering the demand and work regulations, and quality, as measured by the nurses' preferences. A series of experiments involving parameter adaptation, niching, intelligent weights, delta coding, local hill climbing, migration and special selection rules will then be outlined and it will be shown how a series of these enhancements were able to eradicate these difficulties. Results based on several months' real data will be used to measure the impact of each modification, and to show that the final algorithm is able to compete with a tabu search approach currently employed at the hospital. The talk will conclude with some observations as to the overall quality of this approach to this and similar problems.