AI is more predictive of death than doctors, how will hospice care improve?
Using artificial intelligence to predict mortality will make it possible to optimize current palliative care. Although this topic is a bit heavy, artificial intelligence does have the potential to help health care providers and doctors improve the quality of care for critically ill patients. At the end of life, receiving reasonable treatment is far more important than we think. If there is not enough treatment or there is no right medicine, the patient will become very painful. And excessive treatment can result in unnecessary medical expenses, amounting to hundreds of thousands of dollars, even if the patient has medical insurance, which is still a small expense for them. For critically ill patients, especially those over the age of 65, choosing a reasonable treatment plan is crucial. Because the use of specific treatment options to treat a particular patient can help save a lot of medical costs. Artificial intelligence technology can help patients and doctors diagnose diseases in time and prepare for treatment plans and budgets as quickly as possible. A recent study by NPJ Digital Medical Magazine shows that artificial intelligence technology will soon help doctors improve patient care in a timely manner. Researchers use artificial intelligence to scan electronic health records (EHR) and identify potential clinical and health risks through patient records left by doctors. Artificial intelligence systems predict patient mortality and final diagnosis more accurately and quickly than doctors. So how does it work? Use deep learning to predict the patient's condition In the NPJ study, researchers provided nearly 48 billion data points (including physician patient records, patient demographics, procedures, drugs, laboratory results, and vital signs) to deep learning models. The model analyzes these data and predicts some medical issues with 90% accuracy, such as mortality, length of hospital stay, unplanned readmission rate, and final diagnosis of the patient. Compared with traditional predictive models, deep learning models are more accurate and have a wider range of predictions. For example, a woman in advanced breast cancer came to a city hospital where her lungs had accumulated fluid. Two doctors looked at her case and advised her to do a radiological scan. The hospital's traditional predictive model tested her data and predicted her chance of dying in the hospital was 9.3%. A new algorithm created by Google detected the woman's data, with approximately 175,639 data points, and predicted her actual death rate was 19.9%. The patient died in a few days, which proved that the algorithm model is more accurate than the traditional prediction model. The accuracy of the deep learning model is increased by 10% compared to the traditional method. The system is able to screen data that was previously unavailable, which helps it provide more accurate mortality. Instead of just detecting some risk factors, the model detects the patient's entire electronic health record, including comments hidden in the PDF and scribbled on the old chart. Deep learning models help future doctors save lives and provide better patient care. Save lives and save on medical costs So what can we do with this information? By more accurately predicting patient mortality, hospitals and physicians can adjust treatment plans in time to optimize patient care services and predict them before the disease worsens. In addition, medical staff does not have to spend more time entering patient data into a standard, easy-to-read system. For example, a Futurism report states that Ultramics is an AI diagnostic system developed in the UK that can diagnose heart disease more accurately than doctors. It also pointed out that a startup called Optellum is working on an AI system that can diagnose lung cancer by analyzing the cell mass found in the scan. The system is expected to diagnose 4,000 additional lung cancer cases each year and is earlier than doctors are currently able to confirm. These AI diagnostic systems not only save lives, but also help hospitals save on medical costs. In an interview with the Futurism report, Optellum's chief science and technology officer, Timor Kadir, said that artificial intelligence systems could cut the cost of the healthcare industry by $13.5 billion. John Bell, Chairman of the UK Health Research Strategy Office, added: “The cost of medical services in the National Health Service is about $2.97 billion. With the advent of artificial intelligence, costs can be directly reduced by half.†Predicting mortality for better care Current research indicates that only 8% of patients who require palliative care receive the treatment. Because sometimes doctors make inaccurate or overly optimistic predictions about patients. Dr. Kenneth Jung, a research scientist at Stanford University School of Medicine, said: "Doctors may not have thought about using palliative care because they are too concerned about managing their health problems, and this idea will not even appear in their minds." These patients who are not sure if palliative care is needed may eventually worsen or even die. If the patient's health suddenly drops, they may not receive a reasonable treatment plan in the last few days, hoping to extend their lives for a few weeks. However, research shows that about 80% of Americans prefer to die at home, not in the hospital. Sadly, the report also stated that 60% of these people died of emergency care in hospitals. In these cases, AI can help identify patients with severe illness and may survive palliative care. Early predictions of mortality in these patients can help them get the treatment they need faster. And it can allow patients to stay at home for the last few days, not at the hospital. While some may question the future of artificial intelligence in the healthcare arena, the purpose of artificial intelligence systems is to play a supporting role in the healthcare industry. These AI systems will be powerful tools to help doctors and other health care professionals deliver higher quality care and timely delivery of palliative care. Jewelry Safes,Jewelry Safe,Best Jewelry Safe,Jewelry Safe Box Hebei Yingbo Safe Boxes Co.,Ltd , https://www.ybsafebox.com