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EXCLUSIVE - Using cloud computing and big data to address a poorly understood medical condition

EXCLUSIVE – Using cloud computing and big data to address a poorly understood medical condition

How can digital technology help
clinicians in tackling a poorly understood, neglected disease, with a lack of comprehensive
clinical information and agreed-on guidelines for diagnosis? How can technology
facilitate international collaboration between clinicians separated by long
distances across multiple countries and produce results benefiting patients
around the world?

Recently, OpenGov learnt about the
Collaborative Ocular Tuberculosis Study (COTS),
a global initiative to address ocular tuberculosis (TB) leveraging cloud
computing and big data.

COTS is led by uveitis [1]
experts from 25 multinational Ophthalmology centres including Tan Tock Seng
Hospital (Singapore), Postgraduate Institute of Medical Education and Research
(India), Moorfields Eye Hospital (UK), Byer's eye centre in Stanford (USA), and
many more. Participating centres in this international work group originate
from over 10 countries spanning Singapore, India, UK, US, Turkey, Tunisia,
Switzerland, Saudi Arabia, Italy, Spain, and Brazil.

Most people would think of TB as
a disease affecting only the lungs. But few know that it can also affect other
parts of the body, including the eye.

Uveitis could be indicative of
latent TB. But the difficulty in diagnosing ocular TB often leads to delayed or
missed diagnoses, resulting in poor clinical outcomes and missed opportunities to
address TB infection at an early stage. The COTS group was created to address
these problems.

OpenGov spoke to Dr Rupesh
Agrawal (below left), a consultant ophthalmologist at Tan Tock Seng Hospital (TTSH) and
communicated via e-mail with Dr Dinesh Visva Gunasekeran (below right), two of the leads on
the study to learn more about the technology platforms used.

Dr Dinesh said that COTS-1 was effectively
a pilot trial for the use of cloud-based data aggregation platform to
facilitate this multinational clinical research collaboration.

Dr Agrawal started research on
ocular TB in London in 2012. His team reviewed case records of more than 300
patients and published reports in different scientific journals. But this was a
single centre study. Then he conducted a similar study in Singapore, looking at
60 patients over 5 years.

He said, “The conclusion was that
we do not know anything about the disease. Let’s form a group, a consortium.”

The initiative was taken from
Singapore and soon centres from US, Australia, India, the Middle East and more
were on-board. Clinicians everywhere were facing the same challenges so joining
forces and collaborating was seen as the way ahead.

The idea was proposed in 2015 and
it took nearly three years, with 10-12 hours of time committed every week, to
set up the consortium and complete the first stage of the study.

Designing a Smart Form

One of the key challenges was to
design a secure, multi-user data aggregation system with a
centralised data repository accessible by an administrator user. The data
collection had to be uniform and standardised, so that data clean-up is
minimised. In addition, patient confidentiality cannot be breached.

Data privacy requirements in the
different geographies, such as the Personal Data Protection Act (PDPA) in Singapore, have
to be complied with. Then the data has to be encrypted, to prevent anyone
entering into the database and manipulating the data.

Finally, the form had to be
simple enough for people to be motivated to do the data entry. Training
requirements would have to be minimal, as clinicians are busy with seeing
patients and they have limited time on their hands. Taking into account that
research assistants and junior clinicians also help with the data entry, the
form has to be straightforward with no scope for ambiguity.

After exploring multiple options,
a cloud-based platform called Cognito
Forms
was selected. The team designed their own template for protocolised data
entry, based on retrospective records by trained interpreters. The protocol
was developed by the international experts on the steering committee of this
study group, and was used by the team to design the ICT platform
accordingly. 

The form incorporated dropdown
menus where possible and minimised the use of free text fields, so as to avoid
situations where different jargons are used (e.g. one person enters right eye, another
RE).

The form also had embedded
logic to minimise keystrokes by modifying the questions presented for
data entry based on responses to earlier questions (so that only relevant
data is collected i.e. depth of relevant data instead of breadth of all data).
For instance, if a patient informs that only one eye is involved, then
automatically all entry fields related to the other eye become inactive.

The form also has embedded
prompts to reinforce relevant criteria during data entry in accordance
with the study protocol as well as important instructions, such as the process
of anonymising patient data that is entered to the form.

The instructions specified how each
site would code their patients, but no patient identifiers had to be entered. Each
clinician would thus have access to the identity of the patients they have
entered from their centre, but that information is not accessible by other
sites and is also not required for the research project.  

Each participating centre had to
get their own ethics clearance, before they could start doing data entry. Only
Dr Agrawal and Dr Dinesh have access to the central data repository.

We asked Dr Agrawal if they
considered extracting data from Electronic Medical Records (EMRs). He replied
that the team considered using machine learning or big data analytics to
extract the data. But each centre uses a different EMR system.

“There is no common language in
which these platforms can talk to each other. If they do not speak the same
language, you cannot extract the data,” explained Dr Agrawal.

“Particularly in uveitis or TB,
we do not have the same language, everyone writes their own language. So there
needs to be a person for entering the information.”

Data processing and analytics was
embedded into the form itself. A program for automated data
processing/analytics was developed by a statistician using R-program based
on the Microsoft Excel data file output of the data aggregation form.

A trial run was conducted with
100 patients. On conducting the automated analysis, a few important fields were
found to be missing. Then the form was modified and it was then necessary to
re-enter those information from the trial. Several iterations were required to
arrive at the final form.

Dr Agrawal said, “We finally
managed to get the form the way we wanted it to be. That form has given us a
lot of data from 25 participating centres in 10 countries. With that data, we
published our first report
in the journal, JAMA Opthalmology
. There are other reports in the pipeline,
two more papers. One more is published and another will be published very soon.
Three more are under review.”

Impact of the technology

Dr Dinesh told us that the form
facilitated the international collaboration and overcame many limitations
of existing scientific descriptions of this disease which are limited to
small cohorts of patients from singular localities.

Some of its key benefits were
ease of coordination of a multinational clinical investigation and protocolised
data aggregation to extract meaningful data on a poorly understood subject
matter. 

Before this form, such a collaboration would have been incredibly
difficult to coordinate. Many difficulties would have been encountered including
insufficient data entry, security concerns with the transfer of large amounts
of patient data between centres, and fatigue for data entrants.
 

Over 200
variables were collected for each study subject and without the smart form
logic, this would have been a highly taxing exercise for the data entrants, who
were often clinicians themselves with limited time. 

Insights gained and future plans

The results of the study included
novel findings that improve understanding of the disease and question existing
doctrines that are based on limited available evidence from existing
literature.

For example, the
results revealed geographic variations in the way affected patients may
present, and also suggest unusual behaviour of the condition in certain defined
populations that will direct future study.

Around 80% of the patients
entered into the records did very well after they were given anti-tubercular
therapy, while 20% still had recurrences.

Another finding was that there
was a particular subtype of uveitis which involves the back of the eye, called choroiditis, which is a strong indicator of TB.

PCR (polymerase chain reaction),
a test which the researchers thought could be a diagnostic gold standard, was
found to be very useful in diagnosing ocular TB. Similarly, another test QuantiFERON-TB
gold, produced false positives.  

Dr Agrawal said that through these
insights, they realised that the disease needs to be studied in greater detail.
The team is exploring a genetic study to see whether there is any genetic
pre-disposition. They are also looking into novel ways to diagnose this
disease.

COTS-1 was a retrospective study.
Now the group is planning to launch a COTS registry for COTS-2, which will be a
prospective study.

An app is being created, so that
the participating centres can enter the data of any patient they see easily
into the registry. The number of entries will be minimised to reduce demands on
clinic time. This is also being done using the Cognito Forms based smart form.

“We have also created COTS-1A
which is a survey questionnaire kind of form which we have circulated to
general practitioners and to specialist doctors for their opinions. We recently
got an education grant from a pharma company, for 45000 dollars. This is for a
survey study called COTSCon (COTS consensus group meeting),” Dr Agrawal said.

For COTSCon, again a smart form
has been created for circulation among experts with more than 10 years of experience
in managing ocular TB from around the world.

All this will help accumulate
evidence. At a closed-door meeting in PGI Chandigarh, India on November 15 this
year, 25 experts from across the world will gather to discuss and generate evidence
statements for this disease, which will guide all ophthalmologists in managing
patients with uveitis.

To further ease data entry and
make it less tedious and more enjoyable, Dr Agrawal said that gamification
ideas are being implemented.  

We enquired if images have been
uploaded and whether the team considered using artificial intelligence
algorithms on the images for diagnosis. Dr Agrawal replied that images were
uploaded as part of the data entry. However, as the prevalence of the disease
is not very high, it is difficult to gather the large number of images required
to the train the system. Moreover, pattern recognition on the images is insufficient,
unlike diabetic
retinopathy
.

But eventually, a machine
learning algorithm will have to be built. It will have to go beyond the images
to take into consideration multiple risk factors, such as geography and
endemicity, and figure how the 200 variables correlate.

At the moment, the COTS group is
looking to continue expanding on its collaborations, with technology solution
providers, more ophthalmology centres, and more clinicians. Only by pooling
together resources, knowledge, experience and data, can the understanding of
this medical condition be improved.  

[1] Inflammation of the middle layer of the
eye that consists of the iris, ciliary body and choroid.

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