Digital twins are emerging as a valuable tool for short-term decision-making as well as for long-term strategic planning across numerous domains, including process industry, energy, space, transport, and healthcare. This paper reports on our ongoing work on designing a digital twin to enhance resource planning, e.g., for the in-patient ward needs in hospitals. By leveraging executable formal models for system exploration, ontologies for knowledge representation and an SMT solver for constraint satisfiability, our approach aims to explore hypothetical "what-if" scenarios to improve strategic planning processes, as well as to solve concrete, short-term decision-making tasks. Our proposed solution uses the executable formal model to turn a stream of arriving patients, that need to be hospitalized, into a stream of optimization problems, e.g., capturing daily inpatient ward needs, that can be solved by SMT techniques. The knowledge base, which formalizes domain knowledge, is used to model the needed configuration in the digital twin, allowing the twin to support both short-term decision-making and long-term strategic planning by generating scenarios spanning average-case as well as worst-case resource needs, depending on the expected treatment of patients, as well as ranging over variations in available resources, e.g., bed distribution in different rooms. We illustrate our digital twin architecture by considering the problem of bed bay allocation in a hospital ward.

A joke telling cleaning robot is being rolled out to hospital wards in a bid to'put a smile on patients' faces' and help in the fight against the deadly coronavirus. Autonomous bot Ella, built by LionsBot, has already proved her worth during a demo at Tunbridge Wells Hospital in Kent where she kept the reception'spick and span'. As a result of the successful demo, Maidstone and Tunbridge Wells NHS Trust expressed an interest in leasing two of the gadgets for use on a paediatric ward. She interacts with anyone she comes into contact with on the wards by telling'hilarious gags and even singing', according to LionsBot. The trust say they will use the robots in hospital reception areas to mop the large floors and free up human cleaners to focus on potential Covid-19 touch points.

The coronavirus pandemic has taken a severe toll on industries, health systems and lives since the outbreak began with doctors, scientists and ordinary people racing to find ways to tackle the contagion. From robots to a virus-killing snood and a portable isolation capsule, these new prototypes demonstrate what humans are capable of in the face of adversity. Here are some of the innovations developed to combat the current outbreak that has killed more than 217,000 people and infected 3.1 million. COVID-19 attacks people's lungs making it hard for them to deliver oxygen to the blood. Ventilators, which feed oxygen into the lungs, are a crucial tool to keep people with the virus alive.

A new hospital ward run entirely by robots has opened in Wuhan, China, in a bid to protect medical staff from contracting the coronavirus. On 7 March, about 200 patients exhibiting early symptoms of covid-19 were ushered into the new ward, which is in a converted sports centre in Wuhan, the city where the coronavirus outbreak started. The robots deliver food, drinks and drugs to the patients, and keep the ward clean.

Reported today (8 May) in PLOS Genetics, the machine learning tool could be useful for flagging dangerous bacteria before they cause an outbreak, from hospital wards to a global scale. As the cost of genomic sequencing falls, scientists around the world are using genetics to better understand the bacteria causing infections, how diseases spread, how bacteria gain resistance to drugs, and which strains of bacteria may cause outbreaks. However, current methods to identify the genetic adaptations in emerging strains of bacteria behind an outbreak are time-consuming and often involve manually comparing the new strain to an older reference collection. The group of bacteria known as Salmonella includes many different types that vary in the severity of the disease they cause. Some types cause food poisoning, known as gastrointestinal Salmonella, whereas others cause severe disease by spreading beyond the gut, for example Salmonella Typhi which causes typhoid fever.

Hospital-acquired infections are a pesky problem and around one in 25 hospital patients have at least one healthcare-associated illness at any given time. To combat this issue, a research team based at Stanford University turned to depth cameras and computer vision to observe activity on hospital wards -- a system that could be used to track hygienic practices of hospital staff and visitors in order to spot behaviors that might contribute to the spread of infection. The work is being presented at the Machine Learning in Healthcare Conference later this week. The researchers placed depth cameras in various places -- in hallways, patient rooms and around hand sanitizing dispensers -- across two different hospital wards. Video was collected over the course of one very busy hour in the hospital and 80 percent of the video was used to train tracking algorithms while the other 20 percent was used to test the algorithms post-training.

We develop a personalized real time risk scoring algorithm that provides timely and granular assessments for the clinical acuity of ward patients based on their (temporal) lab tests and vital signs. Heterogeneity of the patients population is captured via a hierarchical latent class model. The proposed algorithm aims to discover the number of latent classes in the patients population, and train a mixture of Gaussian Process (GP) experts, where each expert models the physiological data streams associated with a specific class. Self-taught transfer learning is used to transfer the knowledge of latent classes learned from the domain of clinically stable patients to the domain of clinically deteriorating patients. For new patients, the posterior beliefs of all GP experts about the patient's clinical status given her physiological data stream are computed, and a personalized risk score is evaluated as a weighted average of those beliefs, where the weights are learned from the patient's hospital admission information. Experiments on a heterogeneous cohort of 6,313 patients admitted to Ronald Regan UCLA medical center show that our risk score outperforms the currently deployed risk scores, such as MEWS and Rothman scores.

"Coming to Terms With the Computer" by Edward H. Shortliffe reprinted with permission from The Machine at the Bedside, Eds. You are asked to assist a major teaching hospital in the assessment of a large computer system that was installed 3 months ago to help with doctors' orders, laboratory test reporting, nursing schedules, and bed control. Because of mixed reviews of the system's effectiveness, the hospital has decided to bring in outside experts to assess the computer's strengths and weaknesses. The computer system was installed by a vendor of large-scale hospital information systems (HIS). The company had developed the programs over several years, but this is its first major commercial installation.