MLR Review Process Automation in the Pharm Industry
Artificial intelligence has emerged as a powerful tool for MLR automation and enhancing the review process.
A marketing team at a leading pharmaceutical company has just finished developing promotional material for a new, life-saving drug. The deadline to release this content is tight, but before it can be published, it must undergo the Medical Legal Regulatory (MLR) review process. The marketing team submits the content for review, and what was once a hopeful timeline becomes a drawn-out process filled with revisions, legal clarifications, and regulatory back-and-forth. This is the reality many pharma companies face today—a reality tangled by manual reviews and resource constraints. But what if there was a way to make this critical, yet heavy, process faster, more accurate, and less frustrating? Enter the age of MLR automation.
The Current State of MLR Review: A Complex Compliance Landscape
The MLR review process plays a vital role in ensuring that all promotional and educational content in the pharmaceutical and life sciences industries adheres to strict regulatory standards. This process typically involves multiple stakeholders: medical experts providing the scientific accuracy of claims, legal teams verifying compliance with laws, and regulatory bodies reviewing adherence to specific country and industry guidelines. Theoretically, the goal is simple: ensure the content is accurate, compliant, and ethically sound. In practice, however, this review process is anything but straightforward. The following challenges often bog down the MLR review process:
Manual and Time-Consuming Processes
Reviews can involve hundreds of documents, multiple rounds of approvals, and detailed cross-checking. Each stakeholder involved in the process has their timelines and criteria to meet, which leads to significant delays in getting materials to market. Companies like LivaNova, Baxter, and Cardiovascular Systems, Inc. (CSI) faced delays and inefficiencies due to inconsistent review processes, where handling hundreds of documents, cross-checking versions, and managing approvals created significant bottlenecks. For instance, Jim Wilson, Senior Director of Digital Marketing at CSI, highlighted that inconsistent workflows and outdated technology often led to confusion and re-reviewing of previously approved content. This caused delays in getting marketing materials approved and resulted in missed deadlines, impacting time-to-market for important pharmaceutical products.
Regulatory Complexity
The pharma industry is one of the most heavily regulated in the world. Navigating different regulations across global markets means teams must constantly stay updated on a shifting regulatory landscape. Any mistake or oversight could lead to non-compliance, resulting in hefty fines or product recalls. The complexity of handling multiple regulatory frameworks has been highlighted by Bain & Company, which pointed out that compliance requirements have surged over the last two decades, creating a “perfect storm” for pharma companies. This complexity often leads to delayed filings, missed regulatory updates, and, in severe cases, costly product recalls. Resolving regulatory non-compliance, such as addressing a warning letter, can range from $2 million for a simple fix to as much as $20 million when significant production changes are needed.
High Risk of Human Error
Given the complexity of regulations and the high volume of content that passes through MLR review, the risk of human error is significant. A misplaced claim or an overlooked regulatory detail can have major legal consequences. Jim Wilson from Cardiovascular Systems, Inc. highlighted that handling large volumes of marketing materials and ensuring they adhere to strict regulatory guidelines can lead to confusion and errors. He noted that when a small team reviews hundreds of documents monthly, inconsistencies in reviews or forgotten details are inevitable, increasing the chance of mistakes like overlooked claims or missed regulatory requirements.
Fragmented Communication and Workflow
The process often involves multiple departments working in silos, leading to inefficiencies. Miscommunication or lack of visibility into other departments’ progress can slow down the entire review process. A real-life example comes from a project undertaken by EMMsphere. In this case, a biopharmaceutical company faced significant challenges due to the lack of standardized workflows and fragmented communication between departments. The process was often stalled, with work being misrouted or delayed, causing inefficiencies across markets. One key issue was the inability of departments to share real-time progress updates, leading to miscommunication and further slowing down the entire review process.
As a result, life sciences and pharma companies often face process inefficiencies, delayed go-to-market strategies, and increased costs. To stay ahead in the evolving market, these organizations are turning to MLR automation.
How can AI Transform the MLR Review Process?
Artificial intelligence has emerged as a powerful tool for MLR automation and enhancing the review process. By leveraging AI-driven technologies, pharmaceutical companies can reduce review times, eliminate errors, and ensure compliance with regulatory and legal requirements. Here’s how:
Automated Content Screening for Regulatory Compliance
AI systems are capable of scanning through promotional material and educational content for potential regulatory breaches in real-time. Machine learning models are trained on vast datasets of regulatory guidelines and compliance protocols, allowing them to detect inconsistencies faster than human reviewers. For example, Indegene has collaborated with major global pharma companies to implement AI and NLP (Natural Language Processing) in MLR reviews. Their MLR automation helped reduce cycle times and improve accuracy by quickly identifying potential content errors and compliance issues. One of their pharmaceutical clients saw a 30% reduction in turnaround times after integrating this technology, significantly improving the efficiency of their content review process.
Ensuring Scientific Accuracy with NLP Algorithms
NLP algorithms are a game changer when it comes to validating scientific claims in content. AI can automatically cross-reference data in marketing materials with approved scientific literature, ensuring that all statements are evidence-based and accurate. This reduces the time medical experts spend on manual verification and minimizes the chances of incorrect or misleading information being published. Boris Braylyan, Vice President and Head of Information Management at Pfizer has discussed how AI and machine learning are being used to streamline regulatory processes. Specifically, Braylyan explained that AI can help predict the types of queries regulators may raise during drug submissions, allowing Pfizer to prepare responses proactively. This predictive capability can save significant time, reducing the back-and-forth between the company and regulatory bodies, thus speeding up the overall approval process for new drugs.
“In the future, we believe that AI may help us predict what queries regulators are likely to come back with,” says Boris Braylyan, Vice President and Head of Information Management at Pfizer. “We may then be able to improve our submissions by predicting in advance what regulators are likely to ask, and coming prepared with those answers ahead of time.”
Accelerating Legal Reviews with Contract Analysis Tools
Legal teams tasked with reviewing MLR content often deal with complex contractual and regulatory language. Traditional methods involve manual review, which can be time-consuming, prone to human error, and costly. AI-powered contract analysis tools can quickly analyze these documents, highlighting key clauses, terms, and potential compliance issues. This allows legal teams to focus on high-value tasks while automating the repetitive aspects of content review. These tools utilize NLP and machine learning algorithms to quickly scan and analyze contracts or regulatory documents. They can identify, extract, and highlight key clauses, terms, and potential compliance risks, such as anti-bribery regulations, intellectual property terms, and data protection laws.
By MLR automation, legal teams can focus on higher-value tasks, such as risk mitigation, strategic decision-making, and final approval of content. A real-life example of this can be seen with platforms like Kira Systems and Luminance, which are widely used by law firms and life sciences companies to expedite contract review, increasing both accuracy and efficiency.
Streamlined Collaboration Through AI-Powered Platforms
One of the biggest challenges in MLR review is the fragmentation of communication between teams since each is responsible for different aspects of compliance, legal scrutiny, and medical accuracy. Teams worked in silos, and miscommunications frequently led to rework, further slowing down the process of approving content for market release. MLR automation platforms help centralize the review process, ensuring that all stakeholders have visibility into the content’s progress. Notifications and real-time updates ensure that everyone is on the same page, minimizing delays and miscommunication.
The Future of MLR Review: AI as a Critical Partner
As the pharmaceutical industry continues to evolve, the pressure to streamline compliance processes will only increase. The benefits of MLR automation are clear: reduced review times, increased accuracy, minimized human error, and more strategic use of resources. However, the true potential of AI goes beyond automation. AI can be a critical partner in ensuring that content is not only compliant but also tailored to meet the ever-evolving needs of the global regulatory landscape.
Furthermore, as AI technologies continue to advance, we can expect even more sophisticated solutions in the future, such as the ability to adapt to real-time regulatory changes, provide automated feedback, and even self-learn from past review experiences to improve accuracy and speed.
The MLR review process is a cornerstone of compliance in the life sciences and pharmaceutical industries. However, it is also one of the most challenging and time-consuming processes, often acting as a bottleneck to getting crucial content into the hands of healthcare providers and patients. With MLR automation, the future of the process looks brighter. By incorporating machine learning, NLP, and predictive analytics, pharmaceutical companies can transform this traditionally manual process into a streamlined, efficient, and compliant workflow. The result is not just faster time-to-market but also enhanced regulatory adherence, ultimately benefiting both the industry and the patients it serves.
AI is no longer a futuristic concept but a critical tool for compliance teams striving to navigate the complex landscape of pharmaceutical regulations. By embracing AI, pharma companies can focus on their core mission: delivering safe, effective, and compliant treatments to patients worldwide.
