The role of nurses is manifold. Beyond being caretakers, they often also take up the mantles of patient advocacy, patient education, administration, emotional support, and more. In Singapore's Tan Tock Seng Hospital (TTSH), nurses don an additional hat as innovators. The hospital is home to the Nursing Innovation Bunch (NIB), a group dedicated to creating novel solutions to address the day-to-day pain points identified by hospital staff. The NIB was established in 2020, and joins other innovation initiatives like the hospital's Centre for Healthcare Innovation Living Lab to bring the ideas of nurses, healthcare workers and other allied health professionals to life.

By using artificial intelligence, Houston Methodist researchers are able to predict hospitalization outcomes of geriatric patients with dementia on the first or second day of hospital admission. This early assessment of outcomes means more timely interventions, better care coordination, more judicious resource allocation, focused care management and timely treatment for these more vulnerable, high-risk patients. Because geriatric patients with dementia have longer hospital stays and incur higher health care costs than other patients, the team sought to solve this problem by identifying modifiable risk factors and developing an artificial intelligence model that improves patient outcomes, enhances their quality of life and reduces their hospital readmission risk, as well as reducing hospitalization costs once the model is put into practice. The study, appearing online Sept. 29 in Alzheimer's & Dementia: Translational Research and Clinical Interventions, looked at the hospital records of 8,407 geriatric patients with dementia over 10 years within Houston Methodist's system of eight hospitals, identifying risk factors for poor outcomes among subgroups of patients with different types of dementia that stem from diseases such as Alzheimer's, Parkinson's, vascular dementia and Huntington's, among others. From this data, the researchers developed a machine learning model to quickly recognize the predictive risk factors and their ranked importance for undesirable hospitalization outcomes early in the course of these patients' hospital stays.

Objectives-Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients (age 65 years and above) functional ability, physical health, and cognitive wellbeing. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases. Materials and Methods-We restricted our search to eight databases, namely PubMed, WorldCat, MEDLINE, ProQuest, ScienceDirect, SpringerLink, Wiley, and ERIC, to analyze research articles published in English between January 2010 and June 2019. We focused on studies that used ML algorithms in the care of geriatrics patients with chronic conditions. Results-We identified 35 eligible studies and classified in three groups-psychological disorder (n=22), eye diseases (n=6), and others (n=7). This review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications. Conclusion- More studies and ML standardization tailored to health care applications are required to confirm whether ML could aid in improving geriatric clinical care.

There is an unprecedented growth in the percentage of aging population throughout the world, particularly in growing economies such as Europe, Japan and China. Form 2000 to 2050, the percentage of the world's population who is 60 years of age and older will approximately double from about 12% to 22% (from 605 million to 2 billion). During the same period, the number of people aged 80 years and older will quadruple. In the USA, 14.5% of the population is 65 years or older, but by 2030 these number is anticipated to grow to 20%. This rapid aging demographic will directly affect social, economic and health outcomes for these growing economies.

An executive working in the artificial intelligence (AI) space, Shourjya Sanyal, PhD, chief executive officer of Think Biosolution, said the rapid aging of the worldwide population is opening the door to the use of AI to help care for people with chronic diseases as health care delivery adapts to increased demands. In an article written for Forbes magazine, he noted the number of people aged 80 years and older will rise from the current 14.5 percent of the U.S. population (65 and older) to more than 20 percent by 2030, with similar patterns seen across most of the rest of the Western world. As a result, health care delivery pathways "need to be readjusted, keeping in mind the prevalence of chronic diseases, comorbidities and polypharmacy requirements of the elderly and geriatric patients." There are also specific diseases related to this age cohort as well, like atherosclerosis, osteoporosis, cardiovascular diseases, obesity, diabetes, dementia, and osteoarthritis that require "quick diagnosis and continuous supervision by a professional caregiver." Added to the mix is the growing shortage of physicians and caregivers, Sanyal said.

Chatbots are used for a variety of tasks: ordering pizza, getting product suggestions via Facebook Messenger and receiving online customer support. But can they cope with death? A three-year clinical study with financial backing of more than $1 million from the National Institutes of Health is exploring whether a chatbot can help terminally ill, geriatric patients with their end-of-life care. Over the next three years, Northeastern University professor Timothy Bickmore and Boston Medical Center doctor Michael Paasche-Orlow will distribute Microsoft Surface tablets preloaded with a chatbot to about 360 patients who have been told they have less than a year to live. Designed in consultation with experts from Boston Medical Center and programmed by Bickmore and other Northeastern University researchers, the chatbot -- which takes the form of a middle-age female digital character -- is preloaded with a number of capabilities.