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Healthcare in India

 

Overview

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Healthcare has
become one of India’s largest sectors both in terms of revenue &
employment.
India is
expected to rank amongst the top 3 healthcare markets in terms of
incremental growth by 2020.
Healthcare has
become one of India’s largest sectors – both in terms of revenue and
employment. Healthcare comprises hospitals, medical devices, clinical
trials, outsourcing, telemedicine, medical tourism, health insurance and
medical equipment. The Indian healthcare sector is growing at a brisk pace
due to its strengthening coverage, services and increasing expenditure by
public as well private players
In FY17,
Indian healthcare sector stood as the 4th largest employer as the sector
employed a total of 319,780 people.

 

Market size

 

Deloitte Touche
Tohmatsu India has predicted that with increased digital adoption, the
Indian healthcare market, which is worth around US$ 100 billion, will
likely grow at a CAGR of 23 per cent to US$ 280 billion by 2020. The
healthcare market can increase three fold to US$ 372 billion by 2022.
The low
cost of medical services has resulted in a rise in the country’s medical
tourism, attracting patients from across the world. Moreover, India has
emerged as a hub for R activities for international players due to
its relatively low cost of clinical research.

(Business Standard, Ministry of
External Affairs, Ministry of External Affairs (Investment and Technology
Promotion Division))

India is
experiencing 22-25 per cent growth in medical tourism and the industry is
expected to double its size from present (April 2017) US$ 3 billion to US$
6 billion by 2018. Medical tourist arrivals in India increased more than
50 per cent to 200,000 in 2016 from 130,000 in 2015.
The Healthcare
Information Technology (IT) market is valued at US$ 1 billion currently
(April 2016) and is expected to grow 1.5 times by 2020 (3)

 

Healthcare Segments

 

Healthcare is being operated through following majorly –

       
i.           
Hospitals (Government and
Non-Government)

     
ii.           
Diagnostic, Medical Equipment and Consumable

–         
The
medical devices market size, valued at US$ 4 billion in 2016, is expected to
reach US$ 11 billion by 2022, backed by rising geriatric population, growth in
medical tourism and declining cost of medical services.

   
iii.           
Pharmaceutical

–         
Cost of
developing new drugs is as low as 60 per cent of the testing cost in the US.

–         
About 60
per cent of global clinical trials is outsourced to developing countries.

   
iv.           
Medical Insurance

–         
In FY18
(till September 2017), gross direct premium income from health insurance stood
at 23.90 per cent of overall gross direct premium income for non life insurance
segment  Health insurance is gaining
momentum in India; witnessing growth at a CAGR of 23.6 per cent during FY15-17.

–         
Gross
healthcare insurance premium in the month of September 2017 stood at US$ 2.7
billion

–         
As of
2016, less than 15 per cent of the Indian population is covered through health
insurance

     
v.           
Telemedicine

–         
Rural
India, which accounts for over 70 per cent of population and is set to emerge
as a potential demand source.

–         
Only 3
per cent of specialist physicians cater to rural demand.

 

 

Role of AI in Diagnostic Imaging

 

AI may offer a
paradigm shift in how clinicians work in an effort to significantly boost
workflow efficiency, while at the same time improving care and patient
throughput. 
Newer AI methods, such as “deep
learning,” could pave the way for quantitative, standardized, yet also
personalized imaging, while helping to prevent diagnostic errors and, at
the same time, enabling sustained productivity increases.
Machine learning
software will serve as a very experienced clinical assistant, augmenting
the doctor and making workflow more efficient.
Challenges

–         
This
includes imaging data, exam and procedure reports, lab values, pathology
reports, waveforms, data automatically downloaded from implantable
electrophysiology devices, data transferred from the imaging and diagnostics
systems themselves, as well as the information entered in the EMR, admission,
discharge and transfer (ADT), hospital information system (HIS) and billing
software.

–         
 In
the next couple years there will be a further data explosion with the use of
bidirectional patient portals, where patients can upload their own data and
images to their EMRs. 

–         
It also
will include medication compliance tracking, blood pressure and weight logs,
blood sugar, anticoagulant INR and other home monitoring test results, and
activity tracking from apps, wearables and the evolving Internet of things
(IoT) to aid in keeping patients healthy.

–         
It is very difficult or impossible to go
through the large volumes of data to pick out what is clinically relevant or
actionable.

–         
In
addition, the benefits of a medical imaging test rely on both image and
interpretation quality, with the latter being mainly handled by the
radiologist; however, interpretation is prone to errors and can be limited,
since humans suffer from factors like fatigue and distractions. This is one
reason patients sometimes have different interpretations from various doctors,
which can make choosing a plan of action a stressful and tedious process.

–         
Not
least, diagnostic errors are an unresolved problem. Studies show that erroneous
interpretations occur in about 4% of all radiology diagnoses, with the error
rate varying individually and depending heavily on the procedure.

Solution

–         
 AI will be augmenting their
ability to find the key, relevant data they need to care for a patient and present
it in a concise, easily digestible format.

–         
When a radiologist calls up a chest computed tomography
(CT) scan to read, the AI will review the image and identify potential findings
immediately — from the image and also by combing through the patient
history  related to the particular anatomy scanned. If the exam order is
for chest pain, the AI system will call up:

o   
All the relevant data
and prior exams specific to prior cardiac history;

o   
Pharmacy information
regarding drugs specific to COPD, heart failure, coronary disease and
anticoagulants;

o   
Prior imaging exams
from any modality of the chest that may aid in diagnosis;

o   
Prior reports for
that imaging;

o   
Prior thoracic or
cardiac procedures;

o   
Recent lab results;
and

o   
Any pathology reports
that relate to specimens collected from the thorax.

–         
Patient history from prior reports or the EMR that may be
relevant to potential causes of chest pain will also be collected by the AI and
displayed in brief with links to the full information (such as history of
aortic aneurism, high blood pressure, coronary blockages, history of smoking,
prior pulmonary embolism, cancer, implantable devices or deep vein thrombosis).
This information would otherwise would take too long to collect, or its
existence might not be known, by the physician so they would not have spent
time looking for it.   

AI in
Healthcare

Access to vast
quantities of patient data and images is needed to feed the AI software
algorithms educational materials to learn from. Sorting through massive
amounts of big data is a major component of how AI learns what is
important for clinicians, what data elements are related to various
disease states and gains clinical understanding. 
The first step in
machine learning software is for it to ingest medical textbooks and care
guidelines and then review examples of clinical cases. Unlike human
students, the number of cases AI uses to learn numbers in the millions.
In medicine, there
are so many variables it is difficult to always arrive at the correct
diagnosis for people or machines. However, percentage wise, experts now
say AI software reading medical imaging studies can often match, or in
some cases, outperform human radiologists. This is especially true for
rare diseases or presentations, where a radiologist might only see a
handful of such cases during their entire career. AI has the advantage of
reviewing hundreds or even thousands of these rare studies from archives
to become proficient at reading them and identify a proper diagnosis.
Also, unlike the human mind, it always remains fresh in the computer’s
mind.
AI systems are trained
using vast numbers of exams to determine what normal anatomy looks like on
scans from CT, magnetic resonance imaging (MRI), ultrasound or nuclear
imaging. 
AI can be trained
to search through all the prior exams on record in the health system to
help identify patients that may have a particular disease. For example,
all the prior exams of chest CT can be recalled to identify lung cancer.
This type of retrospective screening may apply to other disease states as
well, especially if the AI can pull in genomic testing results to narrow
the review to patients who are predisposed to some diseases.
AI can help
automate qualification and quickly pull out related patient data from the
EMR that will aid diagnosis or the understanding of a patient’s condition.

 

 

Current
Status

 

Ministry of
Electronics and IT, Govt of India has initiated various AI based R
projects under its folds. However, sector specific AI/DL(deep learning)
based advance applications are yet to be worked upon.
Global healthcare
leaders expect the role of AI in monitoring and diagnosis to expand.
GE has also announced a 3-year partnership with UC San
Francisco to develop a set of algorithms that help its radiologists
distinguish between a normal result and one that requires further
attention. This effort is in addition to another GE partnership with
Boston’s Children Hospital to create smart imaging technology for
detecting pediatric brain disorders.
IBM/Merge,
Philips, Agfa and Siemens have already started integrating AI into their
medical imaging software systems.
Philips uses AI as a
component of its new Illumeo software with adaptive intelligence, which
automatically pulls in related prior exams for radiology. The user can
click on an area of the anatomy in a specific MPI view, and AI will find
and open prior imaging studies to show the same anatomy, slice and
orientation.
For oncology
imaging, with a couple clicks on the tumor in the image, the AI will
perform an automated quantification and then perform the same measures on
the priors, presenting a side-by-side comparison of the tumor assessment.
This can significantly reduce the time involved with tumor tracking
assessment and speed workflow.  
IBM Watson has been cited for the past few
years as being in the forefront of medical AI, but has yet to
commercialize the technology. Some of the first versions of
work-in-progress software were shown at HIMSS by partner vendors Agfa
and Siemens.
Agfa
showed an impressive example of how the technology works. A digital
radiography (DR) chest X-ray exam was called up and Watson reviewed the
image and determined the patient had small-cell lung cancer and evidence
of both lung and heart surgery. Watson then searched the picture archiving
and communication system (PACS), EMR and departmental reporting systems to
bring in:

o    Prior
chest imaging studies;

o    Cardiology
report information;

o    Medications
the patient is currently taking;

o    Patient
history relevant to them having COPD and a history of smoking that might relate
to their current exam;

o    Recent
lab reports;

o    Oncology
patient encounters including chemotherapy; and

o    Radiation
therapy treatments.

When the radiologist opens the
study, all this information is presented in a concise format and greatly
enhances the picture of this patient’s health.
Agfa said the goal is to improve the
radiologist’s understanding of the patient to improve the diagnosis,
therapies and resulting patient outcomes without adding more burden on the
clinician. 
IBM purchased Merge Healthcare in
2015 for $1 billion, partly to get an established foothold in the medical
IT market. However, the purchase also gave Watson millions of radiology
studies and a vast amount of existing medical record data to help train
the AI in evaluating patient data and get better at reading imaging exams.
IBM Watson is now licensing its software through third-party agreements
with other health IT vendors. The contracts stipulate that each vendor
needs to add additional value to Watson with their own programming, not
just become a reseller. Probably the most important stipulation of these
new contracts is that vendors also are required to share access to all the
patient data and imaging studies they have access to. This allows Watson
to continue to hone its clinical intelligence with millions of new patient
records.  
Lunit, a South Korean startup
established in 2013, uses its DL algorithms to analyze and interpret X-ray
and CT images. Lunit’s system is able to provide interpretations in 5
seconds and with 95 percent accuracy, an achievement that has attracted
investments of $2.3 million through international startup incubation
programs in just 3 years.
Another South Korean startup
established in 2014, Vuno, is also helping doctors
in medical image interpretations. Vuno uses its ML/DL technology to
analyze the patient imaging data and compares it to a lexicon of
already-processed medical data, letting doctors assess a patient’s
condition more quickly and provide better decisions.

 

 

Applications

 

In the
next five to 10 years, artificial intelligence is likely to fundamentally
transform diagnostic imaging. This will by no means replace radiologists,
but rather help to meet the rising demand for imaging examinations,
prevent diagnostic errors, and enable sustained productivity increases.
There are
vast number of application of AI in Healthcare and following are limited indication
of the long-ranging ML/DL impact in the medical imaging –

 

o    Cancer Detection, Tracking and Monitoring

–        
To
detect the tumor, the DL algorithm learns important features related to the
disease from a group of medical images and then makes predictions (i.e.
detection) based on that learning.

–         
The
software can, for example, determine how the volume of a tumor changes over
time and supports the detection of new tumors,

 

o   
Blood
Flow Quantification and Visualization

–         
Magnetic
Resonance Imaging (MRI) allows for the non-invasive visualization and
quantification of blood flow in human vessels, without the use of contrast
agents. 

–         
Arterys, a DL medical imaging technology company,
recently partnered with
GE Healthcare to combine its quantification and medical imaging technology with
GE Healthcare’s magnetic resonance (MR) cardiac solutions. Arterys’ system
enables a much more efficient visualization and quantification of blood flow
inside the heart, alongside a comprehensive diagnosis of cardiovascular disease. 

–         
Arterys’
DL software techniques have made it possible for cardiac assessments on GE MR
systems to occur in a fraction of the time of conventional cardiac MR scans.

 

o   
Medical
Interpretation

–         
The
benefits of a medical imaging test rely on both image and interpretation
quality, with the latter being mainly handled by the radiologist; however,
interpretation is prone to errors and can be limited, since humans suffer from
factors like fatigue and distractions. This is one reason patients sometimes
have different interpretations from various doctors, which can make choosing a
plan of action a stressful and tedious process.

–         
A DL
algorithm is then trained to detect the presence or absence of the disease in
the medical images (i.e. radiology reports), helping doctors come up with
better interpretations.

 

o   
Diabetic
Retinopathy

–         
As with
a many debilitating diseases, if detected early DR can be treated efficiently.
A recent study published in 2016 by a group of Google researchers in the Journal of the American Medical Association
(JAMA), showed that their DL algorithm, which was trained on a large fundus
image dataset, has been able to detect
DR with more than 90 percent accuracy.

 

 

 

Proposal

 

At present, R at Medical Electronics
needs to be redefined. Medical Electronics thrust areas can be re-defined to AI
 robotics,

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Source:

 

(1)Export.gov –  U.S. Department
of Commerce’s International Trade
Administration collaborates with 19 U.S. Government agencies to bring
you Export.gov.

 

(2) India Brand Equity Foundation
(IBEF) and Aranca.

https://www.ibef.org/industry/healthcare-india.aspx

 

(3) National Association of Software and Services Companies
(NASSCOM))

 

(4) Frost & Sullivan, LSI Financial Services, Deloitte

 

(5) Imaging Technology News

(https://www.itnonline.com/article/how-artificial-intelligence-will-change-medical-imaging)

 

(6) Statistical data include unofficial estimates from trade
sources and industry. As this industry has not been well documented in the
Indian context, the estimates of industry size vary significantly across
different sources

 

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