Here are 3 ways AI will change healthcare by 2030
Here are 3 ways AI will change healthcare
y 2030
07 Jan 2020
Carla Kriwet
Chief Executive Officer, Connected Care and Health Informatics, Royal Philips
'In 2030, health systems are able to deliver truly proactive, predictive
healthcare'
Image: REUTERS/Regis
Duvignau
This article is part of the World Economic Forum Annual Meeting
By 2030, AI will access multiple sources of data to reveal patterns in disease
and aid treatment and care.
Healthcare systems will be able to predict an individual's risk of certain diseases
and suggest preventative measures.
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AI will help reduce waiting times for patients and improve efficiency in hospitals
and health systems.
It’s a typically cold day in January 2030 and the peak of flu season. At this time of year a
decade ago, clinics and doctor’s offices would be overflowing with sick people waiting to be
seen; today, clinicians and patients move easily through the system.
So what’s changed? Connected care has become a reality, driven by years of immense
pressure on global healthcare systems without enough skilled medical professionals to care fo
their rapidly growing and ageing populations and
eakthroughs in powerful technology
enablers, such as data science and artificial intelligence (AI).
AI can now reveal patterns across huge amounts of data that are too subtle or complex fo
people to detect. It does so by aggregating information from multiple sources that in 2020
emained trapped in silos, including connected home devices, medical records and,
increasingly, non-medical data.
The first big consequence of this in 2030 is that health systems are able to deliver truly
proactive, predictive healthcare.
1. AI-powered predictive care
AI and predictive analytics help us to understand more about the different factors in our lives
that influence our health, not just when we might get the flu or what medical conditions we’ve
inherited, but things relating to where we are born, what we eat, where we work, what our local
air pollution levels are or whether we have access to safe housing and a stable income. These
are some of the factors that the World Health Organization calls “the social determinants of
health” (SDOH).
In 2030, this means that healthcare systems can anticipate when a person is at risk of
developing a chronic disease, for example, and suggest preventative measures before they get
worse. This development has been so successful that rates of diabetes, congestive heart
failure and COPD (chronic obstructive heart disease), which are all strongly influenced by
SDOH, are finally on the decline.
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eleases/2013/health-workforce-shortage/en
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www.who.int/social_determinants/en
2. Networked hospitals, connected care
Alongside predictive care comes another
eakthrough related to where that care takes place.
In 2030, a hospital is no longer one big building that covers a
oad range of diseases; instead,
it focuses care on the acutely ill and highly complex procedures, while less urgent cases are
monitored and treated via smaller hubs and spokes, such as retail clinics, same-day surgery
centres, specialist treatment clinics and even people’s homes.
These locations are connected to a single digital infrastructure. Centralized command centres
analyse clinical and location data to monitor supply and demand across the network in real
time. As well as using AI to spot patients at risk of deterioration, this network can also remove
ottlenecks in the system and ensure that patients and healthcare professionals are directed
to where they can best be cared for or where they are most needed.
The glue that binds this network together is no longer location. Instead, it is the experiences of
the people it serves – which
ings us to the third big difference in 2030.
3. Better patient and staff experiences
Understanding the size of the global healthcare challenge Image: Philips
Why are experiences so important? For patients, research has long shown that they can have
a direct effect on whether they get better or not. For clinicians, better work experiences
ecame increasingly urgent – a decade ago they started suffering from huge rates of burnout,
mainly caused by the stress of trying to help too many patients with too few resources.
In 2030, AI-powered predictive healthcare networks are helping to reduce wait times, improve
staff workflows and take on the ever-growing administrative burden. The more that AI is used
in clinical practice, the more clinicians are growing to trust it to augment their skills in areas
such as surgery and diagnosis.
By learning from every patient, every diagnosis and every procedure, AI creates experiences
that adapt to the professional and the patient. This not only improves health outcomes, but
also reduces clinician shortages and burnout, while enabling the system to be financially
sustainable.
This networked system spans communities and is powered by connected care, uniting people,
places, hardware, software and services – creating true networks of care that improve lifelong
health and well-being.
Back to reality
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Back in 2020, we’re still a long way from achieving this vision. Unrelentingly complex
technology, IT and data systems still impede staff workflows and threaten the continuity of
care in the clinical areas in which they are used to help diagnose, treat, monitor and, hopefully,
prevent and cure diseases.
Nevertheless, I see clear signs that all three of these ideas can one day become reality.
Intelligent systems are already capable of performing expert tasks and augmenting human
capabilities. Examples include AI that can detect cancerous lesions on an image, analyse and
quantify physician notes or optimize patient flow in emergency care. Inside hospitals, the
application of AI-enabled predictive analytics is already helping to save lives in intensive care
units. Outside of hospitals, it is helping to identify certain at-risk groups so that pre-emptive
primary or community care can reduce the need for hospital admissions.
But it’s a long and complex journey which no single company or organization can take alone. I
elieve that governments, health systems and private companies must continue working
together in order to ensure that AI systems are fully interoperable and transparent and prevent
ias and inequality. As healthcare continues to globalize, the need for international standards
that protect the way in which AI uses personal data will become an urgent priority.
Perhaps most of all, I believe we must keep in mind that AI’s most powerful use is to enhance
human capabilities, not replace them. The heart of connected care isn’t new technology, it’s
people: the people who need to be cared for and the people who work so tirelessly to deliver it
to all of us.
License and Republishing
Written by
Carla Kriwet, Chief Executive Officer, Connected Care and Health Informatics, Royal Philips
The views expressed in this article are those of the author alone and not the World Economic Forum.
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FHJv6n2-Davenport.indd
94 © Royal College of Physicians 2019. All rights reserved.
FUTURE Future Healthcare Journal 2019 Vol 6, No 2: 94–8
XXXXXXXXXXDIGITAL TECHNOLOGY The potential for artificial intelligence
in healthcare
Authors: Thomas Davenport A XXXXXXXXXXand Ravi Kalakota B
XXXXXXXXXXThe complexity and rise of data in healthcare means that
artificial intelligence (AI) will increasingly be applied within
the field. Several types of AI are already being employed by
payers and providers of care, and life sciences companies. The
key categories of applications involve diagnosis and treatment
ecommendations, patient engagement and adherence, and
administrative activities. Although there are many instances
in which AI can perform healthcare tasks as well or better
than humans, implementation factors will prevent large-scale
automation of healthcare professional jobs for a considerable
period. Ethical issues in the application of AI to healthcare are
also discussed.
KEYWORDS : Artifi cial intelligence , clinical decision support ,
electronic health record systems
Introduction
Artificial intelligence (AI) and related technologies are increasingly
prevalent in business and society, and are beginning to be applied
to healthcare. These technologies have the potential to transform
many aspects of patient care, as well as administrative processes
within provider, payer and pharmaceutical organisations.
There are already a number of research studies suggesting that
AI can perform as well as or better than humans at key healthcare
tasks, such as diagnosing disease. Today, algorithms are already
outperforming radiologists at spotting malignant tumours, and
guiding researchers in how to construct cohorts for costly clinical
trials. However, for a variety of reasons, we believe that it will be
many years before AI replaces humans for
oad medical process
domains. In this article, we describe both the potential that AI
offers to automate aspects of care and some of the ba
iers to
apid implementation of AI in healthcare.
Types of AI of relevance to healthcare
Artificial intelligence is not one technology, but rather a collection
of them. Most of these technologies have immediate relevance
to the healthcare field, but the specific processes and tasks they
A
B
ST
R
A
C
T
support vary widely. Some particular AI technologies of high
importance to healthcare are defined and described below.
Machine learning – neural networks and deep learning
Machine learning is a statistical technique for fitting models
to data and to ‘learn’ by training models with data. Machine
learning is one of the most common forms of AI; in a 2018
Deloitte survey of 1,100 US managers whose organisations
were already pursuing AI, 63% of companies surveyed were
employing machine learning in their businesses. 1 It is a
oad
technique at the core of many approaches to AI and there are
many versions of it.
In healthcare, the most common application of traditional
machine learning is precision medicine – predicting what
treatment protocols are likely to succeed on a patient based on
various patient attributes and the treatment context. 2 The great
majority of machine learning and precision medicine applications
equire a training dataset for which the outcome variable (eg onset
of disease) is known; this is called supervised learning.
A more complex form of machine learning is the neural
network – a technology that has been available since