Manage data challenges in life sciences using AI technology

Introduction

Data is the lifeblood of the life sciences industry. Access to high-quality, comprehensive data is essential for identifying potential drug targets, designing effective clinical trials, and ensuring patient safety. Real-world data (RWD) and real-world evidence (RWE) have been gaining increasing recognition from drug developers and regulatory agencies as significant tools for enhancing drug development from target identification to clinical trials.

While data holds immense potential for driving innovation and improving patient outcomes, the life sciences industry faces significant challenges in managing and harnessing its full value. Issues such as data silos, poor data governance, and integration challenges often hinder the ability to maintain high-quality data. With the introduction of AI technology, the life sciences industry now can significantly improve data management, reduce costs, streamline operations, and increase return on investment (ROI).

This white paper discusses key data challenges facing life science companies and how artificial intelligence can be used to address these issues. The paper also provides an overview for building a successful data management program, shows how AI can work within the life sciences industry, and introduces EXL Health’s solution for life sciences.

Key challenges to effectively utilizing data

Maintaining data integrity amid massive data growth

Contrary to the common belief that “more is better” when it comes to data, the reality is that the more data you have, the greater the risk for maintaining data quality and integrity. All life sciences companies have experienced a tremendous growth in data, and more is coming due to the surge in clinical trial data, genomic sequences, and real-time patient monitoring.

Unstructured data, such as medical imaging, clinical notes, and audio recordings, is one of the fastest-growing types of data and requires advanced tools for processing and analysis. With massive volumes of unstructured data generated daily from drug research, patient trials, and laboratory results, maintaining data integrity is a top priority for many companies. This is especially true for life sciences organizations that must adhere to stringent regulatory requirements. Ensuring accurate, reliable, and consistent information throughout its lifecycle is crucial.

Complying with data governance and regulatory requirements

The life sciences industry is heavily regulated, with strict guidelines for data management and reporting. Rapidly evolving regulatory standards, influenced by global events and shifting governmental policies, add layers of complexity to data management. Ensuring compliance with these regulations while managing vast amounts of data can be daunting. Robust compliance frameworks and comprehensive quality risk management practices are essential to navigating this complex landscape.

Addressing data fragmentation to improve decision-making Life sciences companies often operate in silos. Various different departments and systems can independently generate and store data. The fragmentation of data leads to difficulties in integrating and accessing comprehensive datasets that are essential for informed decision-making and innovation. To combat fragmentation, companies should develop a protocol to break down data silos and foster a culture of data sharing to enhance operational efficiency and drive better outcomes.

The increasing need for scalability and analytics

The rapid growth of data is driving the need for scalable technology solutions for data storage, processing and analysis. Traditional systems with legacy databases and manual processes are struggling to keep up with the volume and complexity of data.

Life science companies will soon have no choice but to implement advanced analytics tools like predictive analytics and AI-based solutions. These will be the only tools that can handle large volumes of structured and unstructured data and provide real-time insights. Implementing these technologies at scale remains a challenge due to integration complexities, data privacy concerns, and the need for substantial investment in infrastructure and training.

How AI meets the challenge of data management issues

AI technology, such as machine learning (ML) and natural language processing (NLP), will continue to play a pivotal role in enhancing data quality in the life sciences industry. AI-powered technology solutions automate data cleansing, identify and resolve data anomalies, and ensure data consistency across various systems. AI algorithms detect patterns and inconsistencies in large datasets, flagging potential errors for further investigation. These capabilities are particularly valuable for the life sciences industry to maintain data integrity and comply with regulatory standards.

Furthermore, AI technology streamlines data integration by harmonizing data from disparate sources, reducing the risk of data silos. For instance, AI-powered tools automatically map and transform data to ensure compatibility across different systems, facilitating seamless data flow and accessibility.

Additionally, AI-backed technology enhances data governance by providing real-time monitoring and reporting capabilities, enabling life sciences companies to proactively address data quality issues and maintain compliance with regulatory requirements. AI technology also supports predictive analytics, allowing life sciences companies to anticipate and mitigate potential data quality issues before they impact operations. By leveraging AI-driven insights, life sciences companies can implement proactive measures to ensure data accuracy and reliability, ultimately improving decision-making and operational efficiency.

Five key features to building a successful data management program

The life sciences industry must develop a process to embrace AI technology while continuing to meet industry and regulatory requirements. EXL Health, a leader in data management and healthcare solutions, works with major U.S. healthcare organizations to implement technology solutions that improve patient outcomes, reduce costs and drive better business results. Based on that experience, we know these five key features are needed for an effective life sciences data management program:

Prioritize data integrity

Prioritize data integrity: Ensuring data accuracy and consistency is crucial. This involves implementing robust data governance policies and regular audits to maintain data quality.

Break down silos

Break down silos: Encourage collaboration across departments by improving data accessibility. This can be potentially achieved by integrating data systems and fostering a culture of data sharing.

Embrace scalable analytics

Embrace scalable analytics: Utilize scalable analytics platforms to handle large volumes of data efficiently. This allows for better data analysis and decision-making to deliver better patient outcomes.

Leverage digital transformation

Leverage digital transformation: Adopt digital tools to enhance data management capabilities. This includes using AI and machine learning to automate data processes and gain deeper insights.

Leverage digital transformation

Data modernization: Update your data infrastructure to support advanced analytics and real-time data processing. This involves migrating to cloud-based platforms, updating legacy systems, and implementing modern data architectures like data lakes and data warehouses.

AI technology at work in the life sciences industry

Improving the pharmacovigilance function

The primary objective of patient safety is to minimize risk and identify hazards related to life sciences products. This involves conducting comprehensive drug safety and pharmacovigilance audits to ensure compliance with FDA laws and regulations. By using advanced analytics, life sciences companies can examine large and varied data sets containing safety information to uncover hidden patterns, unknown correlations, trends, patient preferences, and other useful information. AI-driven technology solutions enable organizations to make more informed business decisions.

Signal detection for adverse events is another key area that can benefit from AI technology, to ensure high quality data and proper data integration. By implementing AI technology, life science companies can improve accuracy, completeness, and reliability of data used for regulatory reporting. AI technology can harmonize data across different platforms and technologies, maintain data consistency, and address potential research biases.

Using technology to analyze RWE and systematic literature review

The primary goal of RWE is to gather and analyze real-world data to demonstrate the value of life sciences products and improve market access. Systematic literature review (SLR) is a feature of RWE that provides a rigorous, structured way to collect, analyze, and synthesize evidence from existing studies, which enhances the reliability and context of real-world evidence. This work is a perfect fit for integrated advanced analytics and innovative technology. By leveraging genAI technology within RWE and SLR, life sciences companies can gain insights into the effectiveness, safety, and value of their products in real-world settings, which is crucial for regulatory submissions, market access, and post-market surveillance.

EXL Health for life sciences

EXL Health’s solution for life sciences leverage domain expertise and artificial intelligence to transform real-world data into meaningful insights. We empower life science companies to improve clinical effectiveness and patient outcomes. EXL Health provides a tailored service offering including analytics, AI pharma solutions, data assets, data management and cloud services. We take a patient-centric approach, ensuring high-quality data integration and regulatory compliance.

Specifically for data management, we offer consulting services to evaluate existing data management practices, including data governance, data quality, and data integration. We implement benchmarking and road map development, comparing against industry standards and best practices. We assist in roadmap implementation and regularly monitor progress to ensure continuous improvement.

Our healthcare technology platform is based on EXL’s leadership in data management and AI-driven data solutions. By utilizing these solutions, life sciences companies can make informed decisions, improve patient outcomes, and achieve regulatory compliance more efficiently and effectively. Key life science solutions include:

  • EXL DocSage complements RWE with SLR and provides advanced AI-driven literature review capabilities, integrating literature insights to provide comprehensive evidence on the safety and efficacy of life sciences products
  • EXL Vigilane detects and analyzes adverse drug reactions more effectively from clinical records
  • EXL XTRAKTO.AI™ is a proven tool for extracting and managing unstructured data that is heavily used in other regulated industries
  • EXL Clinical Library is a rule-based medical platform featuring disease flags, clinical measures, and cost/utilization categories, updated quarterly by clinical informatics experts

Conclusion

The life sciences industry stands at a crossroads, where the rapid growth of data presents both opportunities and challenges. By prioritizing data integrity, breaking down silos, embracing scalable analytics, and leveraging digital transformation, life science companies can unlock the full potential of its data assets.

AI technology is here to stay. To retain competitive advantage, life science companies must find a way to safely and effectively introduce this innovation into their data management program. Industry leaders know that the best way to implement change is to work with organizations that know their business and know how to make change happen.

At EXL Health, we understand the complexities of life sciences. Together, we can build innovative, data-driven solutions to enhance patient safety through pharmacovigilance, expedite time to market with advanced AI tools, and drive better patient outcomes while optimizing cost structures. To learn more about EXL Health’s life sciences solution, visit our website.