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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