The daily email that arrived in physician Samantha Wang's inbox at 8 a.m., just before morning rounds, contained a list of names and a warning: These patients are at high risk of dying within the next year. One name that turned up again and again belonged to a man in his 40s, who had been admitted to Stanford University's hospital the previous month with a serious viral respiratory infection. He was still much too ill to go home, but Wang was a bit surprised that the email had flagged him among her patients least likely to be alive in a year's time. This list of names was generated by a machine, an algorithm that had reached its conclusions by scanning the patients' medical records. The email was meant as something of a nudge, to encourage Wang to broach a delicate conversation with her patient about his goals, values, and wishes for his care should his condition worsen.

IMAGE: Robert Gramling is the Holly and Bob Miller Chair in Palliative Medicine at the University of Vermont Larner College of Medicine. In a new paper, Gramling and his colleagues show... view more Some of the most important, and difficult, conversations in healthcare are the ones that happen amid serious and life-threatening illnesses. Discussions of the treatment options and prognoses in these settings are a delicate balance for doctors and nurses who are dealing with people at their most vulnerable point and may not fully understand what the future holds. Now researchers at the University of Vermont's Vermont Conversation Lab have used machine learning and natural language processing to better understand what those conversations look like, which could eventually help healthcare providers improve their end-of-life communication. "We want to understand this complex thing called a conversation," says Robert Gramling, director of the lab in UVM's Larner College of Medicine who led the study, published December 9 in the journal Patient Education and Counselling.

One afternoon in the summer of 2018, Bob Gramling dropped by the small suite that serves as his lab in the basement of the University of Vermont's medical school. There, in a grey lounge chair, an undergrad research assistant named Brigitte Durieux was doing her summer job, earphones plugged into a laptop. Then he saw her tears. Bob doesn't balk at tears. As a palliative care doctor, he has been at thousands of bedsides and had thousands of conversations, often wrenchingly difficult ones, about dying. But in 2007, when his father was dying of Alzheimer's, Bob was struck by his own sensitivity to every word choice of the doctors and nurses, even though he was medically trained. "If we [doctors] are feeling that vulnerable, and we theoretically have access to all the information we would want, it was a reminder to me of how vulnerable people without those types of resources are," he says. He began to do research into how dying patients, family members, and doctors talk in these moments about end of treatment, pain management, and imminent death. Six years later, he received over $1 million from the American Cancer Society to undertake what became the most extensive study of palliative care conversations in the US.

Background: Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to guarantee a minimum level of quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of risk of one-year mortality. Objectives: The main objective of this work is to develop and validate machine-learning based models to predict the exitus of a patient within the next year using data gathered at hospital admission. Methods: Five machine learning techniques were applied in our study to develop machine-learning predictive models: Support Vector Machines, K-neighbors Classifier, Gradient Boosting Classifier, Random Forest and Multilayer Perceptron. All models were trained and evaluated using the retrospective dataset. The evaluation was performed with five metrics computed by a resampling strategy: Accuracy, the area under the ROC curve, Specificity, Sensitivity, and the Balanced Error Rate. Results: All models for forecasting one-year mortality achieved an AUC ROC from 0.858 to 0.911. Specifically, Gradient Boosting Classifier was the best model, producing an AUC ROC of 0.911 (CI 95%, 0.911 to 0.912), a sensitivity of 0.858 (CI 95%, 0.856 to 0.86) and a specificity of 0.807 (CI 95%, 0.806 to 0808) and a BER of 0.168 (CI 95%, 0.167 to 0.169). Conclusions: The analysis of common information at hospital admission combined with machine learning techniques produced models with competitive discriminative power. Our models reach the best results reported in state of the art. These results demonstrate that they can be used as an accurate data-driven palliative care criteria inclusion.

SAN DIEGO--A novel smartphone app that uses artificial intelligence (AI)-based algorithms significantly reduced pain and pain-related hospital admissions in a group of patients with various metastatic, solid-organ cancers, according to results from a randomized clinical trial reported at the 2018 Palliative and Supportive Care in Oncology Symposium sponsored by ASCO (Abstract 76). The ePAL is a smartphone app that regularly monitors pain and uses AI to differentiate urgent from non-urgent issues in real time. It also collects and assesses patient-reported pain severity three times each day while providing daily tips on pain-reduction strategies. It is one of the first apps to utilize both patient-reported outcomes and AI clinical algorithms, according to the researchers. The app was developed and tested in 56 pain patients and a matched group of 56 control patients who received regular pain management care by investigators at the Massachusetts General Hospital (MGH) Cancer Center, the hospital's Division of Palliative Care, and Partners HealthCare Pivot Labs, which is a new center of excellence that focuses on human-centered preventive care and chronic pain management.

With his spiky hair and Adidas sweatshirt, Shay Murray looks like a typical 11-year-old. But he also has Pearson syndrome, an incredibly rare mitochondrial disease that affects multiple body organs. His eyesight, hearing and memory are deteriorating, his kidneys are operating at barely 60%. I'm watching Shay play video games in a big, bright social area at the Keech children's hospice in Luton, where he is a regular and very enthusiastic visitor. "Whenever he comes here, I know the staff need a rest when he leaves," says his father, Alan. In a way, the disability has made him who he is – with the family sarcasm added on."

Artificial intelligence is proving to be a revolutionary tool across many industries, but the technology is having a particularly big impact when it comes to healthcare. Researchers are using AI to combat the flu, by building improved seasonal forecasts that inform the development of influenza vaccines, and the technology is already helping to diagnose rare diseases so that patients can get the treatments they need. Now, scientists have found a new medical application for AI: predicting when a seriously ill patient admitted to the hospital will likely die. In hospitals, palliative care teams are charged with improving the quality of life of gravely ill patients and making sure their final wishes are carried out. But clinicians sometimes don't refer their patients to these specialists because they believe their patients are better off than they really are.

By using an artificially intelligent algorithm to predict patient mortality, a research team from Stanford University is hoping to improve the timing of end-of-life care for critically ill patients. In tests, the system proved eerily accurate, correctly predicting mortality outcomes in 90 percent of cases. But while the system is able to predict when a patient might die, it still cannot tell doctors how it came to its conclusion. Doctors must consider an array of complex factors, ranging from a patient's age and family history to their response to drugs and the nature of the affliction itself. To complicate matters, doctors have to contend with their own egos, biases, or an unconscious reluctance to assess a patient's prospects for what they are.

While the use of artificial intelligence to predict deaths may sound ludicrous, researchers are trying to establish the technology's potential in alerting physicians and medical professionals of patients that are at greater risks of dying in the near future. This way, doctors can administer the right end-of-life approach in dealing with the patients and their loved ones. A team at Stanford University has examined the use of artificial intelligence in palliative care in their paper "Improving Palliative Care with Deep Learning" published on the arXiv preprint server. Researchers used the machine learning technique called deep learning, which utilizes neural networks to filter and learn from massive data, in the study. What they did is come up with a model and fed its deep learning algorithm with data from the Electronic Health Records of 2 million adult and child patients admitted to either Stanford Hospital or Lucile Packard Children's hospital.

While 80% of Americans prefer to spend their final days in their home, only 20% actually do. More than 60% of deaths in the US happen in an accute care hospital, most of the patients receiving aggressive care in their final days. We build a program using Deep Learning to identify hospitalized patients with a high risk of death in the next 3-12 months by only inspecting their Electronic Health Record data. Such patients are automatically brought to the attention of the Palliative Care team with notifications. This helps the Palliative Care team to be engaged early enough to ensure patients have their Goals of Care recorded, and provide their services while it is still meaningful.