This paper is a literature review on the present condition of pre-natal and post-natal Maternal and Child healthcare in Rural India. This is a first step on finding the several possibilities using AI, Big Data and Telemedicine in identifying patterns and provide more structured and streamlined support to rural and semi-urban communities. Our endeavour with this research paper is to identify the pain points and attempt to find solutions using current technologies.

1. Optimizing Maternal and Child Healthcare in India through
the Integrated use of Artificial Intelligence, Big Data and
Telemedicine: A Literature Review
Vedang Tyagi1
, Skannd Tyagi2
, Mahak Agarwal3
1
Medical Intern, Shri Ram Murti Smarak Institute of Medical sciences, Bareilly, 2
Director, E Info
Solutions, 3
Junior Resident, Shri Ram Murti Smarak Institute of Medical sciences, Bareilly
ABSTRACT
Introduction: The Maternal Mortality Rate (MMR) and the Infant Mortality Rate (IMR) of India are an
alarming 174/1,00,000 live births and 34/1000 live births. This is a result of poor quality and inaccessible
healthcare. This literature attempts to review the published works offering a solution to this issue with
reference to Artificial intelligence, telemedicine, big data and their integration.
Objectives: To review the published literature regarding use of artificial intelligence, big data and
telemedicine in healthcare to offer a solution to reduce the MMR and IMR. The use of technology for
optimizing maternal and infant health-care has not been studied in India.
Method: Literature search using keywords Artificial intelligence, e-health and big data analysis was
performed.
Result and Discussion: It was seen that such integration is being utilized to improve healthcare in various
countries across the world. In India however, such application has not been envisaged.
Conclusion: The use of such integration has been beneficial in optimizing healthcare, albeit requires further
clinical evaluation.
Keywords: Artificial Intelligence, digital healthcare, technology, telemedicine, EMR, analytics, big data
INTRODUCTION
In India, the Doctor: Patient ratio is an alarming
0.68:1000 in an estimated population of 1.33 billion,
whereas the WHO prescribes a ratio of 1:10001
. This
ratio proportionately affects the quality and availability
of healthcare; despite various efforts by the government,
there is still a lag in the healthcare system. The Maternal
Mortality rate and Infant Mortality rate of India are
174/1,00,000 live births2
and 34/1000 live births3
respectively. Majority of these deaths occur due to
preventable causes in rural areas due to limited access
and poor quality of healthcare. Such variables can be
rectified with the use of technology.
The amalgamation of medicine and technology is
being explored for decades. Today, the world is looking
at Artificial intelligence, big data, telemedicine and
personalized medicine etc
Artificial Intelligence was coined in the year
1956 and has ever since made surmountable progress
in various countries. Google’s Deepmind health
project, IBM’s Medical Sieve and Watson have been
revolutionizing healthcare for both doctors and patients.
AI will become a major enabler of precision medicine
which will help doctors evaluate and treat the patient
and predict patient outcome with utmost certainty. AI in
healthcare uses algorithms and software to approximate
human cognition in the analysis of complex medical
data. In India, AI can be utilized to areas with scarce
human resources. AI can be used to carry out an efficient
Antenatal care process for the mother by managing
their data, storage of information of the mother for easy
access, prompt diagnosis and effective management of
any complication. After delivery, new-born care can also
be initiated, maintenance of documents, immunization
records, identification of danger signs and early approach
to treatment.
DOI Number: 10.5958/0976-5506.2018.00512.0

2. Indian Journal of Public Health Research & Development, May 2018, Vol.9, No. 5 97
Telemedicine is the use of electronic information
to communicate problems when the participants are at
a distance. Telemedicine can be applied to a wide array
of clinical settings, including disease diagnosis, triage,
management and follow up4
. Though telemedicine
has become very common, its integration along with
AI will solve the issue of scarce human resources and
inaccessibility.
Big data is a term that describes the large volume of
data – both structured and unstructured – that inundates a
business on a day-to-day basis. Big data can be analysed
for insights that lead to better decisions and strategic
business moves5
. The application of Big Data in the
field of medicine in India is ideal, given the enormous
volume of data, both present and future. It can be utilised
to standardize treatment and diagnostics. It will help by
increasing efficiency of monitoring of patients. This
will enable follow up and revising treatment easier and
accurate. Not to forget the recent application of big data
to curb the spread of Ebola virus in Africa6
.
We aim to analyze the scenario of such work
globally and theorize the impact that such technology
can have in India
METHODOLOGY
Literature search was performed using the key
words telemedicine, artificial intelligence, big data,
and technology in medicine. Inclusion criteria: Papers
showcasing the application of Artificial intelligence,
big data and telemedicine in healthcare. Exclusion
criteria: Papers on AI and big data out of the scope of
healthcare. The articles were reviewed on the basis of
the applicability in healthcare.
RESULT
Artificial Intelligence: Researchers at Google were
able to train an AI to detect spread of breast cancer into
lymph node tissue on microscopic images with accuracy
comparable to (or greater than) pathologists. Looking
for tiny deposits of cancer on a specimen slide can be
challenging. Whereas a human pathologist might suffer
from fatigue or inattention, the AI can process gigapixel
images without breaking a sweat.
Study conducted by Arrieta et al suggests it is
possible to reduce maternal mortality through the use
of logistic regression7
. In 2016, Atomwise launched
a virtual search for safe, existing medicines that could
be redesigned to treat the Ebola virus. This analysis,
which typically would have taken months or years,
was successfully completed in less than 1 day. Mesko
also suggests that AI lays the foundation for precision
medicine8
. In 2017 a neural network was successfully
used to differentiate images of benign and malignant
skin tumours in the US9
. An algorithm based on deep
machine learning had high sensitivity and specificity for
successfully detecting referable diabetic retinopathy10
.AI
is also suggested to augment the efficiency and accuracy
of radiologists and Imaging results respectively11
.
Korea has been a very active participant in promoting
AI in healthcare. Dr. Choi has corroborated the same
with various articles on IBM Watson12
. Similarly use
of deep learning for diagnosis of cancer using imaging
has been seen in Korea13
. According to Crawford et al,
AI has been successfully used to detect lymph node
metastasis in Prostate carcinoma using Gleason’s sum
and PSA by establishing a low cut off value14
. Telestroke
is an initiative that integrates Artificial intelligence with
telemedicine to treat stroke15
.
However, in spite of earlier optimism, medical AI
technology has not been embraced with enthusiasm. One
reason for this is the attitude of the clinicians towards
technology being used in the decision-making process16
.
There are set-backs in the application of healthcare AI
like ethical issues. The regulations to protect patient
privacy may create legal barriers to the flow of patient
data to applications.
Telemedicine: According to a study, telemedicine
has become an invaluable tool in middle and low
income group nations for diagnosis17
. The Department
of Information Technology (DIT) has formulated a
proposal to establish 100,000 common service centres
(CSCs) in rural areas, which will serve as the front end
for most government18
. The best example of effective
telemedicine was given by Amrita telemedicine
programme which performed telesurgery in remote
locations and also successfully reduced unnecessary
referrals, control of diabetes in pregnancy of mothers
in Lakshadweep and cancer treatment of patients in
Leh19
. Telemedicine also played a vital role in the 2004
tsunami20
. ISRO has been instrumental in delivering
healthcare to various rural areas by setting up satellites
to nodal centers 21
. Telemedicine has been successful

3. 98 Indian Journal of Public Health Research & Development, May 2018, Vol.9, No. 5
in delivering healthcare to children in rural areas as
per Marcin et 22
. Telemedicine has shown a decrease in
hospital emergency admission rates and mortality23
.
Telemedicine can help the patients who can’t travel
long distances24
. A computer based health care system
has shown to improve patient’s quality of life and better
dissemination of healthcare25
. Alpana et al proposed a
model that integrates telemedicine with ambulance
services, named as “Hospital-on-the-go” which promises
to deliver efficient healthcare to rural areas15
.
The challenges yet to overcome are Perspective of
medical practitioners, Patients’ fear and unfamiliarity,
financial unavailability, Lack of basic amenities, Literacy
rate and diversity in languages, Technical constraints
and Quality aspect26
.
Big data: One of the most important implications of
big data is the accurate prediction and tracking of major
outbreaks and emergencies which can help disseminate
better quality of healthcare hence, paving the way for
precision medicine27
.
Big data leads the path to emergency medicine as
shown by a study conducted by Wong et al.28
Ram et al
used big data analysis to predict the acute asthma related
emergency room admissions.29
One of the best examples
was shown by the study conducted by Ginsberg et al
who used google query data to predict epidemics of
influenza30
. Concurrently predictions for various medical
conditions can be made and pre-emptive actions taken.
Currently China is utilizing big data analytics to set up
an efficient healthcare system31
.
Big data is the future of epidemiological studies as it
can efficiently remove bias and work with both structured
and unstructured data sets32
. The Cancer Genome Atlas
utilised large sets of data and developed algorithms to
analyse it resulting in the availability of 37 types of
genomes and clinical data for 33 types of cancers33
.
Studies conducted by Barretina et al34
explore the
application of big data in pharmacogenetics through
testing drug sensitivity on cancer cells. Anticipation of
complications is another aspect big data can address,
thereby increasing preparedness. KPNC (Kaiser
Permanente Northern California) early-onset Neonatal
sepsis and emergency department composite score
pilots take advantage of big-data methodologies. Teams
of clinicians are developing work flows that integrate
big-data components (real-time risk estimates) with
traditionalcomponents(suchasclinicalexaminationsand
care pathways).35
Big data can help increase diagnostic
accuracy, decrease paper-load for a doctor, improve
prognosis and reduce work load of radiologists36
.
The potential of big data in healthcare lies in
combining traditional data with new forms of data. If,
for example, pharmaceutical developers could integrate
population clinical data sets with genomics data, it could
facilitate gaining approvals on more and better drug
therapies more quickly than in the past and expedite
distribution. The prospects for all areas of health- care
are infinite37
.
Medical big data has several distinctive features that
are different from big data from other disciplines. It is
frequently hard to access and most investigators in the
medical arena are hesitant to practice open data science
for reasons such as the risk of data misuse by other
parties and lack of data-sharing incentives. It is often
collected based on protocols and are relatively structured.
Another important feature is that medicine is practiced
in a safety critical context in which decision-making
activities should be supported by explanations. It can be
further affected by several sources of uncertainty, such
as measurement errors, missing data, or errors in coding
the information buried in textual reports. Therefore, the
role of the domain knowledge may be dominant in both
analyzing the data and interpreting the results38
.
CONCLUSION
The proliferation of technology over the past 2
decades has made it possible for humanity to solve
mass problems with the help of machines. Technologies
presented in this paper will allow the healthcare system
to work with a more streamlined and well informed
approach.
How will this help optimize maternal and child care?
Setting up of telemedicine centers in remote
locations, pregnant women and children will be screened
using AI specific for common causes of mortality and
process basic diagnostic tests. All gathered data will be
stored and analysed using big data analytics and further
plan of action can be decided. This will allow prediction
of complications by identifying set patterns which will
lay down the plan of future action.

4. Indian Journal of Public Health Research & Development, May 2018, Vol.9, No. 5 99
Further, all collected data can calculate various
indices such as maternal mortality rate and Infant
mortality rate. The collected data will be stored on the
Cloud, allowing seamless access. The patients will be
a given a registration number; they need not carry any
prescriptions/reports for future visits.
Both antenatal and postnatal care can be delivered
in remote locations with limited accessibility and low
manpower. The AI will be programmed to identify
clinical feature and analyze investigations in both
mother and child to form a diagnosis and provide scope
for treatment. Big data will process Patient particulars,
Clinical data/history, Investigations and AI will utilize
this data set to evaluate the indices and predictions.
AI, Big Data, Machine Learning combined with
corresponding dispensation mechanism once deployed
will provide an exponential spike in the current
performance indices of women and child healthcare in
India.
Conflict of Interest: None
Source of Funding: None
Ethical Clearance: Not required as it is not an
experimental study
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