Enabling Pharma 4.0 With Robotic Process Automation - tcs.com · planning), CRMs (Customer...
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Enabling Pharma 4.0With Robotic ProcessAutomation
Abstract
Robotic Process Automation (RPA) is an articial
intelligence (AI) driven process along with machine
learning capabilities to handle high-volume,
process-oriented repeatable tasks that previously
required a human to perform. RPA aims to
manipulate existing application software in a non-
invasive manner (e.g. ERPs (Enterprise resource
planning), CRMs (Customer relationship
management), claim applications, etc.) and replace
the repetitive non-value added tasks performed by
humans, with a virtual workforce of robotic FTEs.
It mimics user actions on the machine or application
at UI level.
Pharmaceutical sector has been working to close
the gap between rising costs in different processes
and prots in an environment of increasing
regulatory control. They face challenges in securing
approval and bringing new drugs to market with
safety, efcacy, and protability. The advent of RPA
promises to change the game, by applying the
"robots" to perform the high-volume, repeatable
tasks which are presently handled by humans.
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RPA- A Key Technology to Address the
Emerging Requirements of Business 4.0
“Over 9 in 10 (92%) senior IT decision makers agree that
process automation is a key technology to address the 1
emerging requirements of the digital business”. Also, PwC
estimates that 45 percent of all work activities eventually might 2
be automated this way. Such automation would translate into
a $2 trillion reduction in global workforce costs. Currently,
white collar labor accounts for 27 percent of costs worldwide,
according to the Bureau of Labor Statistics Employment Cost
Index. Life sciences companies have long relied upon
outsourcing to keep these costs in line. While traditional
outsourcing offers a 15 to 30 percent cost takeout, automation
technologies boast savings of 40 to 75 percent for certain 3functions (permanent cost savings).
Organizations are increasingly turning to Robotic Process
Automation to improve productivity, quality, operational
efciency, and customer satisfaction. It is part of a wider move
towards automation – here and around the globe
–organizations reporting cost savings (of 15 % +) from
automating systems and processes over the past few years.
Life sciences organizations differentiate themselves and their
products by demonstrating improved patient health outcomes.
It is critical to have reliable data related to population health
statistics, total cost of care for certain disease states, and
medication compliance patterns, as examples. While the
knowledge gathered from potential data by RPA is useful, it
becomes exponentially valuable for organizations when they
act upon analytics provided by RPA. Company can gain insight
about customers, business patterns, and industry trends,
thereby offering targeted services in a personalized way and
ensuring a more feasible business platform.
Moreover, RPA agents can access and scrape information from the
front-end of any system or application like human agents. This
enables the organization to streamline the use of applications
without heavy IT investments such as integration with a new
system or platform, and coding or migrating data from an
existing system to the cloud, thereby leveraging the ecosystem in
best economical manner. Similarly, operations like reconciliation
and generation of reports from discrete systems are another
potential area where RPA can be of great benet.
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Adoption Challenges Faced by RPA
The opportunities from RPA implementation are many — so are
the adoption challenges:
n Test Data: A variety of test data is needed not just for
testing, but also for building bots because this is a discipline
of automation in which the RPA tools need to see and feel the
objects. For example, automating the process of quality
monitoring and review of various pathology reports will
require a large amount of test data to train the IQ bot as
minute details are also of high importance in pharmaceutical
industry and are often closely related to each other, which
can lead to erroneous results, if not handled properly. What
adds to the challenge is that in the lower environments or at
lower hierarchical levels, it is difcult to nd samples that are
synchronized in all the applications involved in the process.
Capturing inputs from diverse formats and processing
unstructured limited content adds to the challenge.
n Change Management: There are frequent changes in
business rules and operating procedures at various levels as
per the changing industrial standards. Thus, IT teams along
with business teams need to collaborate and proactively
provide system and business updates to RPA support team to
update scripts once it is in production. It is difcult if multiple
applications are used in the process and any change in the
front end UI even though not impacting the processing
procedures, will impact the RPA script and the outcome.
Accommodating changes in the design at a later stage is an
expensive and risky affair.
INCONSISTENT CHANGES IN
BUSINESS RULES & OPERATING
PROCEDURES
IDENTIFICATIONOF SUITABLE
PROCESS FOR RPAIMPLEMENTATION
NEED FORVARIETY OF
SYNCHRONIEDSAMPLES IN
APPLICATIONS
n Selection of right processes: As pharmaceutical
companies face a growing responsibility to track outcomes
and adverse events related to the drugs they manufacture,
automating parts of the pharmacovigilance process will be a
nancial imperative. But, RPA implementation is especially
difcult with business processes that are non-standardized
and require frequent human intervention for execution. It is
important for companies to determine which of their
processes are suitable for RPA so that automation runs
without any obstacles. The most common mistake is to
target overly complex processes and attempt to remove
human discretion entirely from them.
Overcoming the Obstacles
Initially the challenges to adopting RPA might seem difcult to
deal with, but proper planning and consideration will allow
pharmaceutical companies to fully leverage all that RPA has to
offer. These companies can develop smart metrics & process
scorecard to optimize RPA returns and seek processes that are
the easiest and fastest to automate which offers the highest
return of investment.
When processes are complex and require a lot of human
intervention, focus should be on automating the lowest-
hanging fruit and return to the more complicated tasks later,
after gaining RPA experience. The pharmaceutical industry is
actively beginning to explore continuous learning models.
These models benet from understanding the data that are
integrated into the clinical-trial processes. The entire
randomized clinical trials lifecycle from the study design to
study start to closure is a complex data environment riddled
with numerous clinical, technical, business, and compliance
processes. Applying machine learning and RPA methods to
optimize these processes is going to be disruptive, but should
improve the efciency and success rates of clinical trials. As a
solution, an eligible patient population is established using
dynamic inclusion and exclusion criteria to evaluate the impact
of each criterion on the suitable patient cohort. This process
can be automated by using ML and RPA. RPA bots can help
speed recruitment by executing initial interactions with
prospective subjects before nal follow-up by clinical
associates. The ultimate goal is to inuence patient matching
and recruitment strategies to increase clinical participation and
success rate.
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While it’s critical to identify which processes are the best
suitable for RPA, robotics should be seen as just one
component of end-to-end process improvement. Complexity
occurs when there are too many intertwined use-cases in the
business process. We need to have feasibility assessment done
by the RPA practitioner far before the business case is
approved. Proper modularization and putting rules into look-
ups will solve much of our problems, but building rules sheets
requires specialized skills which is hard to nd.
Setting realistic expectations - Instead of seeing RPA as
solution for operational problems and broken processes,
organizations need to recognize the limits of what RPA can and
cannot do. Partial automation based on cost and benet
analysis can be considered if not full end to end automation of
a process. In such cases, proper hand-offs need to be
designed (handoffs between human agents and bots) for
optimal and seamless process execution.
RPA Gaining Ground in Pharma
RPA adoption is currently in its initial stage within the pharma
industry. Since pharmaceutical manufacturers, researchers,
and consultants typically apply standard and consistent rules
to nearly every second process, these repeatable and
consistent processes are best suitable candidates for RPA.
Today, many pharmaceutical processes are still human-
intensive and require high volumes of human effort to toggle
between multiple systems and screens to achieve “last-mile”
integration. RPA’s capability to help ensure proof of compliance
and the built-in scalability of digital automation code could
signicantly reduce the need for people-based process
execution, and can improve speed, accuracy, and compliance
at a reduced cost.
One key challenge RPA must address before being widely
adopted in pharmaceutical enterprises is that of validation. In
a regulated environment, any system that performs a decision-
making function requires validation and change control.
Specically, any change requires re-validation to show that the
system either interprets the input correctly and correctly
executes the expected resulting action, or ags the transaction
for human intervention. There are very few cases where
pharmaceutical rms apply RPA to manage validation, and
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rms continue to rely on people to perform this function.
However, because RPA systems are precisely congured and
auditable they have the potential to effectively address the
validation challenge, and in fact could do so in a more reliable
and accurate manner than humans.
Since pharmaceutical innovation enables people to live longer
and healthier lives, therefore innovation process is guided by -
regulatory authorities, posing stringent checks at every level.
By automating some of the high volume, repetitive, rule-based
and error prone tasks, RPA is enabling pharma rms to focus
on bringing safe and effective drugs faster to market and at a
lower cost. Robotic Process Automation is being used to assist
human work force and in no way considered as an alternative.
References
1. Source: “IT Modernization: Critical to digital transformation”, Research conducted by
Vanson Bourne on behalf of Avanade, March 2017.
2. Section:http://usblogs.pwc.com/emerging-technology/brieng-r
3. https://assets.kpmg.com/content/dam/kpmg/pl/pdf/2016/12/pl-intelligent-
augmentation.pdf
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About The Authors
Pankaj Khurana
Pankaj is a senior consultant at
TCS, currently part of the Life
Sciences Innovation and CTO team
- involved in the space of Robotics
Process Automation (RPA) and
Artificial Intelligence (AI), helping
Life Sciences customers to identify
opportunities of automation and
enabling automation across LS
value chain and supporting
functions. With over two decades of
experience in IT, he has managed
large accounts across industry
verticals. He holds a Master’s
degree in Computer Applications
from Institute of Management
Technology (IMT), Ghaziabad, and
an MBA from Vidyasagar University,
Midnapore, West Bengal.
Ayushi Awasthi
Ayushi Awasthi is associated with
intelligent process automation in
Life Sciences- Solutions &
Innovation. Her current role
involves contribution to the
automation practices and
frameworks in life sciences. She
has experience of working on
chatbot development and cloud
platforms. She has worked with
NLP services and RPA tools to
design, develop, and deploy
automations for clients globally.
She has completed her bachelor’s
degree from N.I.E.T, Greater Noida
in Computer Science & Engineering.
Surbhi Agarwal
Surbhi Agarwal is working with the
Life Sciences - Solutions &
Innovation department with
specialized focus on RPA, AI,
Cognitive Services, and related
areas. Her role involves working in
automation practices and
frameworks in order to drive a
culture of continuous improvement.
She is experienced in chatbot
development and working on cloud
platforms. She has done her B.Tech
in Computer Science & Engineering
from K.I.E.T, Ghaziabad.
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