Transforming the Operational Landscape of Life Sciences with Robotic Process Automation | HCL Blogs

Transforming the Operational Landscape of Life Sciences with Robotic Process Automation

Transforming the Operational Landscape of Life Sciences with Robotic Process Automation
August 10, 2020

Challenges that life sciences companies face today and efforts to deliver value to the patient by going “beyond the pill” demand a considerably different operational response – improved efficiency and effectiveness, optimized costs, increased responsiveness and agility, enhanced user experience – to stay competitive and profitable. For that, it is important to look at operational transformation opportunities wherever possible. Traditional transformation approaches have become an unaffordable luxury against the backdrop of today’s operating realities. Product lifecycles are decreasing, truncating both launch times and time to peak sales; heightened regulatory pressures and increasingly stringent compliance specifications are causing further pain points for life sciences enterprises.

In such a scenario in the life sciences industry, companies need to reinvent their operational transformation approach and shift to an automation approach that drives value while optimizing costs. In short, a gig of new operational transformation technique is of utmost necessity and embracing emergent technologies is the need of the hour. Artificial intelligence (AI), machine learning (ML), cognitive sciences, robotics, and virtual reality are some of the technologies that can help enterprises navigate challenges and emerge prepared to optimize opportunities.

Robotic process automation (RPA) in particular is poised to play a critical role in the life sciences industry. The heatmap view below details automation hotspots, potential areas for robotization, and areas where robotics fused with cognitive intelligence and AI can deliver greater value.

Robotic process automation (RPA)

Robotic process automation (RPA) in particular is poised to play a critical role in the life sciences space.

Mentioned below are some of the ways in which organizations can achieve operational transformation by improving efficiency and optimizing cost with the help of robotic process automation.

Clinical Trials Optimization

The randomized clinical trials (RCT) lifecycle is a highly complex data environment that requires meticulous study and is further compounded by various technical, clinical, business, and compliance requirements. It involves disparate systems, such as accounting, document stores, spreadsheets, and clinical trial management systems (CTMS). With manual steps and dependency on employee capabilities the only method in practice right now, the challenges are many. They include humane errors, ignorance of best practices, lack of training and compatibility, and cultural resistance. 

Almost 90% of successful trials see delays of six weeks or more due to the failure of meeting enrollment timelines

Almost 90% of successful trials see delays of six weeks or more due to the failure of meeting enrollment timelines, and given the high costs of RCTs, any delay in trials adds significantly to the costs and stretches the budget. The resultant increase in time-to-market causes further losses of revenue and opportunities. With so much riding on RCTs, using RPA in life sciences in conjunction with ML can make a telling difference to life sciences enterprises. Improved efficiency and success rate of RCTs are just two of the positive effects of robotic process automation. Let us look at how that can happen.

  • Transforming scanned documents and images into usable data
  • Monitoring submission requirements and tracking documents with the help of rule-based automation
  • Document creation and product strategy development with the help of ML
  • Increased efficiency of data collection, statistical data entry, regulatory submission, and compliance.

Some of the critical business endpoints that can be optimized using robotics and ML includes:

Patient Matching

  • Patient Matching: ML and RPA can successfully automate identification, screening, and enrollment of patients. Manual intervention can be reduced by almost 85% with the help of robotization, resulting in significant cost and time savings. Robots are also capable of ascertaining root causes for acceptance and rejection of patients for trials through historical data analysis. RPA is critical for speeding up recruitment before clinical associates do the final follow up.
  • Selection of Site: RPA-ML can analyze failures specific to the sites and derive insights into their relevance to their target study. Site success probability can be predicted by an ML-based learning model. Computation of overall success probability on the site is also made possible by ML.
  • Ascertaining Investigators: Availability, experience, and training for target study can also be ascertained by RPA and ML techniques. They can also predict investigator delays and failures.
  • Competition: Furthermore, complex competitive landscape can be analyzed by the RPA-ML model for the proposed investigational new product based on several clinical and commercial dimensions.

Pharmacovigilance and Compliance

It is paramount to ensure that there are no adverse reaction of the medicines even after they have been licensed to use as there can be serious ramifications toward patient health. In order to ensure watertight pharmacovigilance, cognitive automation must be used more extensively by life sciences enterprises. Rule-based automation is not only crucial to monitoring data, but also ensures its complete accuracy. Cognitive automation also helps with quantitative findings, medical report analysis, and quality assurance. RPA-ML’s capability of offering vital insights by analyzing unstructured data is crucial for enterprises to enhance operational efficiency and improve quality. Additionally, cognitive automation’s traceable and auditable actions ensure regulatory compliance.

Strategic Application of Data and Analytics

Life sciences enterprises can offer better healthcare by being able to access reliable data and statistics. That also plays a crucial role in offering a competitive advantage in a saturated market. Actionable data pertaining to population size, healthcare costs, and compliance requirements are important for enterprise, and in order to access them swiftly and accurately, digitalizing the management of such data is paramount. With huge amounts of data within the healthcare ecosystem – supply chain, facilities, procurement, among others – cognitive automation can offer real-time insights and useful interpretations for informed decision making

Commercial Operations

RPA has a crucial role to play in operational functions and maintain the sanctity of data by preventing third-party interferences. Aggregate spends reporting, chargeback and rebate processing, salesforce effectiveness, marketing fulfillment, accounting analysis, and master data management are some of the functions that can benefit immensely from the use of RPA. It can also enrich the quality of the data received from third parties.

Rebate/Chargeback Processing

Paying back rebates and chargebacks to wholesalers is an important element of the life sciences business. This process is still run manually in most companies, but RPA can be utilized to cut down the time taken considerably. Enterprises are at various stages of RPA deployment to simplify and speed this process. In due course, fully-automated chargeback processing is a distinct possibility.