DIGITAL HEALTH BRIEFING: Google, researchers use AI to predict patient mortality — Mental health chatbot launches …

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Newly published
collaborative research from Google,
Stanford, the University of Chicago, and the University of
California, suggests that artificial intelligence (AI) can be
used in combination with electronic health record data to predict
mortality, readmission, and other events that have an adverse
impact on healthcare in the US. 

The study adds considerable weight to the growing body of
research in the field of big data and health analytics.

Researchers fed almost 47 billion data points, including clinical
notes, into deep learning models. These models analyzed the data
and predicted with significant accuracy things like in-hospital
mortality rates, 30-day unplanned hospital re-admissions,
prolonged length of stay, and patients’ final diagnosis.
Moreover, these deep learning models outperformed traditional
state-of-the-art predictive models in all instances.

Big data presents a significant cost-saving opportunity
for healthcare systems, and researchers are working hard to
unlock its potential.
Predictive analytics, precision
medicine, and population health could help alleviate mortality
rates, improve medication adherence, and monitor chronic
illnesses such as heart arrhythmias, which are a heavy burden on
the healthcare system. Each year in the US, healthcare-related
infections lead to more than 99,000 deaths, problems with
medication result in more than 770,000 injuries and deaths, and
unexpected hospital readmissions cost up to $17 billion, according to Google head of
product and research Katherine Chou.

There are a growing number of healthcare systems hoping
to leverage the early potential of big data to improve patient
quality of care.
Several challenges stand in the way of predictive
analytics being used en masse. However, small-scale operations
are already being explored. Last week, for example, Yale New
Haven Hospital announced a partnership with
Epic Systems with the aim of using real-time big data to enhance
the quality of healthcare. And researchers in Cleveland are
looking into using its population and health data to better
understand and address people outside of hospitals.

bii healthcare data growth


An AI chatbot designed to help people with feelings of depression
and anxiety launched a stand-alone app in the iOS App Store last
Thursday, according to Business Insider.
The chatbot, dubbed “Woebot,” is programmed to provide scripted
responses in line with Cognitive Behavioral Therapy model. Once
signed up, Woebot engages with users daily, recording things like
mood and energy levels — two symptoms associated with depression
and anxiety. Digital tools like Woebot are growing in number as
mental health professionals seek new ways to bypass the barriers
in the way of providing sufficient patient care. These include
things like a lack of resources, not enough trained
professionals, and the social stigma around mental illness. Apps
and chatbots offer around-the-clock self-monitoring, which may
help improve patient outcomes and illness management. They can
also help people who are either embarrassed about going to see a
professional, or don’t think they have time for regular check-ins
get a small-degree of help. In the US, around two-thirds of
people with a mental illness are estimated to have gone at
least a year without treatment, according to NAMI. 

During the World Economic Forum in Davos,
Switzerland last week, Israeli Prime Minister Benjamin Netanyahu
outlined plans to launch a NIS 1 billion ($295 million) digital
health project in collaboration with software giant SAP, The
Jerusalem Post reports. The initiative, which
will be carried out over five years, aims to stimulate and
support research into personalized and preventative medicine,
with a focus on the implementation of artificial intelligence
(AI) systems to facilitate analysis of Israel’s population. It
will be submitted to the Israeli government in the coming weeks
for approval. Israel is a fast-growing digital health innovation
hub in large part because of the government’s efforts to drive
and incentivize investments in the emerging field, according to a
study published in The Lancet. The
vast troves of patient data that result from nationwide digital
health systems will be the key to developing population health.
The data is necessary for AI platforms to be able to reveal
useful insights for things like predicting illness outbreaks.

Biotricity, a medical-grade wearable provider,
is developing a range of remote patient monitoring solutions
aimed at increasing patient adherence and improving patient
outcomes, according to mHealthWatch. The
company’s flagship product, Bioflux, is a mobile cardiac
telemetry device that provides physicians with real-time
monitoring and ECG information. Biotricity believes that RPM
devices can improve patient adherence rates by demonstrating how
certain choices directly impact on their health. This can include
sticking to treatment plans and engaging in healthy
lifestyle choices. Non-adherence is a massive strain on
healthcare systems, leading to increased hospital readmissions
and additional clinical visits to physicians. 

In other news

  • Allscripts was hit with a class-action lawsuit
    accusing the company of not sufficiently protecting its
    clients’ health data, according to
    FierceHealthcare. The lawsuit comes just one week after two
    Allscripts data centers were attacked by ransomware.
  • New York governor Andrew Cuomo will devote
    $664 million to building a new unified health system in
    Brooklyn, according to an announcement last week. $70
    million of the planned funding will go towards creating an
    enterprise-wide health IT platform, providing a single medical
    records system across three partnering hospitals. 

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