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How Robotic Process Automation Is Changing the Life Sciences Industry

In the life sciences industry, the ever-growing demand for faster and more accurate data has resulted in the increased use of robotic process automation (RPA). RPA is a type of software that can be programmed to automate repetitive tasks. This technology is changing the way the life sciences industry operates, by providing a more efficient way to handle data.

SAS Institute, Inc.’s SAS Life Science Analytics Framework, for example, was utilised in November 2020 to support Covid-19 vaccination clinical trial management by translating data into real-time information to derive data-enriched insights.

What is Robotic Process Automation in Life Sciences

RPA is a software tool that automates repetitive tasks. However, unlike automation software, RPA doesn’t fail to perform as intended. This means that RPA will continue to work even if the programmer goes on vacation.

The life sciences intelligent automation market is predicted to develop at a 50% annual rate, reaching USD 470 Mn by 2023.

How is RPA Being Used in the Life Sciences Industry


RPA is being used by many pharmaceutical, biotechnology and life sciences companies to improve not only efficiency but also data quality and customer satisfaction.

For example, RPA can be used to streamline the process of collecting customer data from multiple surveys and other forms. This means that customer feedback can be collected more quickly and more consistently, allowing for quicker product development cycles.

The software can also automate the collection of information from external sources such as clinical research organizations (CROs), which collect medical data throughout a study. By using RPA to enter this data into a database in real time, companies can make better decisions about how to proceed with their research programs.

The global life science analytics market is predicted to develop at a compound annual growth rate (CAGR) of 7.7% from 2022 to 2030, with a value of USD 8.3 billion in 2021.

Benefits of RPA for Life Sciences

The use of intelligent automation to automate repetitive tasks in life sciences organizations can help improve business processes. Automated processes can help to improve efficiency and accuracy, and can also help to free up staff time for other tasks.

1. Improved Data Accuracy and Security:

a. Reduced human error: One of the best ways to improve data accuracy is by limiting human errors, which often result in erroneous data entry or incomplete data sets.

RPA reduces the chances of human error in several ways: It uses machine learning (an artificial intelligence process) to make decisions on what data should be entered into a database and how it should be entered. Machine learning technology is able to detect if the data entered is wrong and offers suggestions for how to correct it.

b. Reduced time spent logging data: RPA reduces errors in data entry by automating the entire process. An RPA system can be programmed to collect new data as it’s created and then log that data directly into a database.

This means there will be no more manual logging of information – a huge time-saver for life sciences companies that need to log scientific data on a regular basis.

2. Improved Project Management:

a. Increased workflow visibility: By allowing job functions to be analyzed, RPA applications can identify areas where workflow processes are inefficient and then determine how they can be improved or automated. These improvements can be implemented and tested in a matter of hours, allowing project managers to take immediate action.

b. Reduced time-to-market: This is a critical aspect of RPA’s ability to help improve project management. Many RPA solutions allow the user to schedule the execution of various tasks, allowing the completion of multiple tasks simultaneously. The end result is that many new projects can be brought to market faster than they could with human resources alone.

3. Reduced Time Spent on Redundant Tasks:

a. Increased accuracy and adherence to policies: Many RPA systems have the ability for their users to set policies for data entry and correction within their system. These policies are stored in databases and can be checked against data at any point in time. This makes it easier to identify and fix errors and also helps companies improve their compliance standards.

b. Reduced time spent on redundant tasks: RPA systems can be programmed to handle repetitive tasks, providing a more efficient way for businesses to meet compliance standards and regulations.

For example, an RPA system could be programmed to handle insurance claims by immersing information from the claim form into a digital workflow system and then identifying any missing or erroneous information before moving the claim through the workflow process.

4. Automated Auditing:

a. Reduced costs: Not only can RPA systems be programmed to perform tasks precisely and accurately, but they also allow for the automation of auditing procedures. This means that more time and less money will be required to audit a system.

b. Improved data quality: By automatically identifying incorrect information, RPA systems can help reduce the number of human errors in data entry and make it easier for organizations to meet their compliance standards.

Additionally, RPA systems can work alongside existing human resources systems to improve data quality by documenting the process by which new data is entered into a database.

5. Increased Security:

a. Reduced manual work: RPA can also be used to automate security procedures. This software can be programmed to run a database audit on a regular basis, alerting the user if any erroneous information has been entered into the system.

This not only makes it easier to identify and correct errors, but also helps to make systems more secure by reducing the chance of data breaches or unauthorized access.

b. Increased security: RPA systems are often designed with strong encryption capabilities that make them suitable for storing highly sensitive information such as medical records, financial data and other government records.

6. Improved Supplier Management:

a. Reduced costs: By automating invoice data entry and other purchasing procedures, RPA systems can reduce the time required for these tasks. This means that companies will have more time to focus on other important tasks, helping them to keep costs down.

b. Increased speed: By using RPA, life sciences companies can reduce their supplier management time frames by up to 80%, depending on the number of suppliers a company has and the department being managed by the software.

7. Increased Employee Engagement:

a. Improved productivity: By using this technology, employees are able to focus on their specialized roles while RPA automates non-value-added activities such as data entry and auditing. This enables employees to increase their productivity.

b. Improved accuracy: Employees are better able to focus on their specialized roles when their jobs are automated by RPA systems, allowing them to perform tasks more accurately and efficiently. This results in more-accurate data and ultimately a more successful business.

8 Challenges Associated with RPA in Life Sciences


The life sciences sector is highly competitive, with companies constantly striving to develop new and innovative treatments. Clinical trials are a crucial part of this process, as they allow new drugs and therapies to be tested on patients.

However, clinical trials can be complex and expensive, and often face delays due to issues with the supply chain. This can be a major problem for life science research, as new treatments may be held up for years before they can be approved for use.

The services segment dominated the market in 2021, accounting for a revenue share of more than 55.0%, owing to the growing trend of outsourcing services covering planning, training, staffing, implementation, and maintenance, as life science organisations lack the necessary expertise and resources, which is expected to boost segment growth in the coming years.

1. Organizational Change Management:

a. Recognizing potential benefits: The first challenge companies face when implementing RPA is identifying the potential benefits it can bring to their business. Typically, this means hiring a third-party service provider to perform a RPA assessment that will help identify the software’s strengths and weaknesses.

While this step is often time consuming and costly, it can be essential for companies to determine whether or not the automation software is right for them and if so, how it will fit into their existing business procedures.

b. Identifying system changes: Another challenge associated with implementing RPA solutions is identifying how they will fit into existing systems and workflows. Since RPA programs are generally built on the backs of existing systems, it can have a large impact on not only existing manual processes, but also on data management, reporting and data lifecycle solutions.

To minimize the impact it may have on existing business processes, organizations can use an RPA assessment to identify all related systems and procedures prior to implementing the new program.

2. Training Employees:

a. The value of automation: Training employees on new software and technologies is not always easy because they may not be familiar with the terms or functions associated with these tools.

This can make it difficult to implement them correctly. However, to ensure a successful implementation, an RPA assessment can be used to identify how the system may interact with other software and services. With this knowledge in hand, companies can develop training programs to help employees learn how to use new technology as effectively and efficiently as possible.

b. Employee resistance: While training employees on new technology may be challenging due to their lack of knowledge about it, employee resistance can also run rampant when automating tasks that are traditionally manual. To combat these types of problems, companies can learn about their employees’ preferences for working and the available resources by conducting an RPA assessment prior to implementing RPA into their organization.

3. Data Mapping:

a. Defining processes: Before RPA can be implemented, data must be mapped. This means that the proper procedures and workflow steps must first be defined. While this could potentially be a lengthy process, an RPA assessment can be used to save time by identifying potential problems in the automation process early on and helping to develop a solution that works for everyone involved.

b. Ensuring accuracy: Mapping data is not only important for identifying potential problems in the organization’s workflow, but it is also essential for ensuring that tasks are performed as efficiently as possible. Using an RPA assessment to identify potential issues and challenges before implementing the technology can help save time and money.

4. Data Migration:

a. Making changes across the organization: Data migration is essential for helping RPA tools function correctly and consistently across an organization’s entire business. While many companies may rely on spreadsheets to manage data, this can result in inconsistent data and limited access among other problems that will impact how well an RPA platform will work within their organization.

By conducting a data mapping assessment prior to implementing the technology, these types of issues can be identified and eliminated before they occur, saving time and money in the long run.

b. Inconsistent data: If an RPA platform is not implemented correctly, it could result in inconsistent data across the organization. This can pose problems for other automated processes and software, including ERP systems, supply chain management tools and more. By conducting a mapping assessment prior to implementing RPA, companies can identify potential issues and work to eliminate any inconsistencies beforehand.

5. Integration:

a. Identifying system capabilities: Companies must integrate their software systems with RPA platforms before they are able to automate processes within the organization. While this may be an easy process for some companies, others may experience problems when trying to do it virtually overnight.

By conducting an RPA assessment prior to implementing the technology, companies can determine how well the software will be integrated with other tools used in the organization and how it will impact specific systems. This can help companies better prepare for any potential problems that may arise during the integration process.

b. Determine project success: Integrating RPA solutions is not always easy and companies must ensure that they are set up properly before work begins on automation projects. To do this, companies need to conduct an RPA assessment that identifies the current capabilities of their systems, as well as where upgrades or patches may be required to ensure a successful implementation.

It may be difficult to know where to start when it comes to outsourcing tasks or analyzing operations, but RPA assessments can help companies get started by identifying areas of improvement and outlining potential solutions.

By conducting an RPA assessment prior to implementing the technology, organizations can ensure that their business is ready for RPA automation solutions and better position themselves for success. If you are unsure about how an RPA evaluation might work in your company, contact a company like EBS Software Inc today.

7 Trends in RPA for Life Sciences


As businesses strive to gain a competitive advantage, many are turning to artificial intelligence (AI) and automation solutions. Automation technologies can help organizations improve efficiency and productivity, while also freeing up employees to focus on more strategic tasks.

However, embarking on an automation journey can be complex and challenging. Organizations need to carefully consider their goals and objectives, and select the right solutions that will best meet their needs.

The life sciences industry relies heavily on manual processes, from data entry to biological research. automating these processes can save time and money, as well as improve accuracy and efficiency.

In the next five years, the market is expected to increase by 7.5% to 8.4% year on year.

Let’s look at what we can expect in the next couple of years based on recent developments:

1. Early Advantage for Big Pharma and Big Data:

The scaling, integration, and pre-built capabilities of enterprise RPA solutions make it an ideal fit for pharma. Pre-built applications can easily handle the industry’s quality compliance standards and the millions of datasets required for clinical trials.

With Amazon and Microsoft launching cloud services, we can expect pharma to invest in RPT. Big data types often already have a large internal development team or an offshoring company like Infosys that can develop an app for them inexpensively.

2. Mid-Sized Pharma and Medtech:

Mid-sized pharma and medtech are increasingly realizing how much they benefit from automation in other areas of their business and need a way to manage their data quality. Unfortunately, most homegrown or offshoring RPA solutions cannot scale for them.

However, many mid-sized companies have existing platforms such as Excel forms that can be augmented with new automated capabilities. Amazon and Microsoft are promoting tools to augment these “legacy systems” in the hopes they will bring on more customers.

3. Big Data and Other Publicly Traded Life Science Companies:

Big data companies are increasingly using RPA to manage their data. Most large distributors now use automated tools like InfoVista Stream and Microsoft’s R functions. In fact, this is one of the areas in which Microsoft has had the most success lately, as it can provide a service for all types of companies, not just bigger ones, who need to understand the same data structure and don’t have the in-house expertise or budget for an expensive cloud-based solution.

4. Specialty Use:

The number of life sciences organizations that require custom solutions is growing rapidly—especially larger companies that may be more likely to outsource research and development work. RPA is a natural fit for companies that want to address a specific use case or for organizations that have a well-defined and permanent set of data processing requirements.

5. Small Pharma, Medtech, and Life Science Companies:

Smaller companies typically need more custom capabilities than pre-built ones can provide. Although they may have an existing application or outsource development work, these companies are less likely to have the budget to match larger competitors in hiring an army of developer specialists.

6. Applications Replace Specialized IT from Big Pharma:

When IBM acquired The Weather Company, it developed a dashboard tool based on RPA that could be used by other large enterprises with multiple data streams for their own purposes (e.g. risk management or customer retention).

This serves as an example of a high-level application that is not dependent on a specific industry and can be used across all industries as opposed to smaller niche solutions. RPA offers life sciences enterprises the capability to design specific applications, which can accelerate the time it takes to complete repetitive tasks and enable greater levels of productivity.

7. RPA in the Cloud:

While most large pharma and medtech companies now use cloud-based RPA for part of their operations, we haven’t seen much growth in this area from smaller pharma and medtech.

Future of RPA in Life Sciences

In 2021, the on-demand delivery segment dominated the market for life science analytics, accounting for more than 50.0% of total revenue.

RPA is a rapidly emerging technology with buzzy implementations in the financial and manufacturing industries. Most people are not yet aware of its potential to transform their company’s operations, but they soon will be.

Like other tech solutions, RPA may be a fit for every life sciences organization. But, different companies are motivated to automate by different factors. For example, Fannie Mae reduced costs by half and increased speed from five days to two hours with RPA.

The company was under pressure from a new regulatory requirement for more frequent auditing of mortgage data, which is difficult for humans to do efficiently.

Last Updated on October 1, 2023 by Parina Parmar


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