Health is wealth a wise old saying. In the hectic day-to-day lives of modern days, our health and
self-care has taken a back seat. How many days do you just rush out the door to work, head out
for an interview, or just do some chores? And end up either starving yourself or eating food
that is junk and processed. Many times, a week right. Unless it’s a weekend or you are an
extremely organized person who gets up at 6 and packs lunch for yourself chances are you are
a part of the hustle lifestyle most of us live.
It is quintessential for you to look after your health. It is no wonder that technology can do
several complex tasks for us. From monitoring your sleep patterns, heart rate to identifying
underlying health issues Artificial Intelligence is a common element in healthcare
developments. Machine learning is used to manage the population’s health. The system known
as Population Health Management (PHM) helps in the understanding of public health using big
data, patient engagement, and care delivery. PHM systems focus on strengthening primary care
and delivering care closer to home, which can address the growing demand pressures for care
Technology is also shaping the way elder care is provided. AI-enabled Virtual assistants build
conversations with people and help them keep their minds sharp even at an old age. AI has
paved its way into End of Life Care systems as well. Stanford and several other reputed world-class
universities have research departments dedicated to developing AI that will aid in
One interesting development in health care sector is AI algorithms can help clinicians figure out
which patients are at high risk of death so that intense discussions on what needs to be done
and end-of-life care options can be had with patients.
The AI algorithm model is built around the patients’ previous medical records, electronic health
records extracted and relies on several machine learning techniques. and insurance claim
records. Few AI models are also built around socio-economic data and info gathered from
claims of insurance. The Algorithm is trained and tested on thousands of data points from
patients who’ve been treated before, including their diagnoses, their medications, and whether
they deteriorated and died. The system flags patients at high risk so clinicians can sit down with
patients and decide options of palliative care.