Updesh Dosanjh

With the continuous proliferation and increasing commonality of internet usage around the world, it is easier than ever to share information with others. The volume of data created by the growing usage of the internet has raised the challenge of managing pharmacovigilance data. As internet usage grows, so does the amount of data that needs to be monitored, rendering manual pharmacovigilance efforts useless. Drug interactions, adverse drug reactions (ADRs), and prescription errors can all be found and addressed with technology that automates and enhances pharmacovigilance activities. Automation-enabling technology is essential for spotting these adverse effects, improving drug safety and effectiveness, and promoting confidence in the healthcare system.

In addition to data volume challenges, pharmacovigilance faces several other obstacles, one of which being under-reporting. Many healthcare professionals and patients do not report adverse reactions, often due to a lack of awareness or knowledge of pharmacovigilance processes. This under-reporting can lead to delays in identifying potential risks and adverse effects, which could potentially harm patients in the long run.

The intricacy of pharmaceuticals is another issue. It gets more challenging to discover potential hazards and negative effects as medicines become more complicated. This is especially true for biological medications and gene therapies, which are extremely complex and monitorable only with professional training.

As mentioned prior, pharmacovigilance also faces difficulties with data management and data quality. Advanced data management tools and procedures are needed to collect and analyze enormous volumes of data from multiple sources. Making educated decisions on the safety and effectiveness of medicines also depends on the quality and accuracy of the data.

A vital part of drug research and patient safety is pharmacovigilance, yet the amount of data collected in this area is growing, making it difficult to extract insights and identify unfavorable events quickly and affordably. Luckily, technological developments have made it feasible to overcome these obstacles and increase the effectiveness of pharmacovigilance procedures.

Natural language processing (NLP) is one of several technological developments that has transformed pharmacovigilance in recent years. NLP technology allows computers to comprehend and interpret human language, making it possible to extract and analyze vast amounts of unstructured data, including adverse event reports and medical records quickly and effectively.

NLP helps computers identify adverse events more precisely and efficiently than human analysts, which is why many might argue that it is particularly useful in pharmacovigilance. To identify adverse occurrences more rapidly and accurately, this system can evaluate hundreds of case files and spot trends that human analysis would miss.

The ability of NLP to learn and improve analyses over time, increasing its accuracy and effectiveness, is another benefit. This means that the more data that is analyzed using NLP, the better the program grows, making it more successful at recognizing adverse occurrences and enhancing patient safety. Together with NLP, other technologies like machine learning (ML), data analytics, and artificial intelligence (AI) are also enhancing pharmacovigilance effectiveness. In order to swiftly identify potential safety risks and create suitable actions, analysts can use ML algorithms, for instance, to help identify patterns in massive datasets.

Overall, technology is changing the pharmacovigilance industry and enhancing patient safety and well-being. Pharmaceutical businesses are now able to evaluate data more quickly and precisely, which improves patient outcomes.

NLP technology enables computers to harvest and analyze data simultaneously, assisting analysts in more quickly and accurately identifying adverse events. It may also learn and develop over time, making it increasingly effective and efficient.

In the area of pharmacovigilance, NLP offers speed, affordability, and quality. The system analyzes massive amounts of unstructured data faster and more accurately than human analysts. As a result, NLP is an effective tool for enhancing patient safety and spotting safety hazards before they spread widely. To protect the general public's health, pharmacovigilance monitoring and reporting of drug safety is essential. Having efficient systems in place to recognize and manage any potential safety issues is crucial given the continuous development and distribution of new medications.

Technological developments like AI and ML can improve pharmacovigilance efforts by enhancing data collecting and analysis, more effectively identifying safety signals, and increasing the accuracy and timeliness of adverse event reporting. To understand and take appropriate action on the results produced by these technologies, however, human judgment and knowledge may still be needed.

However, efficient pharmacovigilance requires cooperation between healthcare practitioners, regulatory bodies, pharmaceutical firms, and patients. Working collaboratively, stakeholders may exchange information, spot potential safety issues, and take the necessary steps to stop drug damage. Modern pharmacovigilance technology is essential for guaranteeing the security and effectiveness of medications. Although pharmacovigilance efforts can be strengthened by technological improvements, human skills, and teamwork are still essential for preserving public health.

NLP, ML, and other technologies are enabling faster analysis of massive data sets and quicker identification of potential safety concerns, while simultaneously improving patient outcomes and making the healthcare system safer. Technology is set to play a crucial role in guaranteeing patient safety and enhancing the effectiveness of drug development procedures as the area of pharmacovigilance technology continues to develop.

Pharmacovigilance teams must adjust their approaches to take advantage of new opportunities and technologies as the healthcare sector develops. The growing use of digital health technology, such as electronic health records (EHRs), mobile health apps, and wearables, is an important trend in pharmacovigilance. Healthcare providers can more rapidly identify potential hazards and negative consequences with real-time data on patient health and prescription use provided by these devices.

The increasing emphasis on individualized medicine is another trend. The goal of personalized medicine is to adjust a patient's care to their unique traits, such as their genetics, way of life, and surroundings. By enabling the identification of potential hazards and unfavorable side effects specific to each patient, pharmacovigilance helps to assure the safety and effectiveness of personalized medicine.

The significance of patient involvement in pharmacovigilance is also becoming more widely acknowledged. Patients are the most important source of data regarding the efficacy and safety of medications, and their opinions can help to raise the caliber of pharmacovigilance data. Patients can actively participate in ensuring the safety of medications by disclosing adverse effects and other drug-related issues to healthcare providers and medical professionals.

Looking to hear more regarding how pharmacovigilance platforms can decrease the time, expense, and risk involved in manual reporting? Dosanjh elaborates on these processes in the latest episode of 

Updesh Dosanjh is practice leader for the pharmacovigilance technology solutions business unit at IQVIA. He recently joined Pharmaceutical Commerce on an episode of its 

Articles

Why automation-enabling tech—such as NLP and ML—can help improve medicine effectiveness.

Transforming Drug Safety: The Future of Pharmacovigilance and Technology

Getting critical treatments approved and delivered to market is no easy feat. Currently, only 15% of drugs successfully make it from clinical trials to U.S. FDA approval—with nearly 75% of failures attributed to safety and efficacy concerns. Once drugs do make it to market, keeping them there requires in-depth pharmacovigilance (PV) practices.

Adverse event (AE) reporting is a key area undergoing major changes as new sources of information become available. Now, patients are directly sharing their experiences with drugs. These patient-reported outcomes are shared in unstructured formats, including social media, doctors’ notes captured in electronic health records, call centers, and from more structured information coming from clinical trials.

Pharmaceutical companies are seeking opportunities for digital transformation to capture these additional unstructured, natural language-based data sources. The goal is to marry these new disparate data sources with information from more traditional structured sources. All these sources have data that was commonly ignored due to the complexity associated with accessing and analyzing them. The potential for tapping into rich safety insights is expansive but not without its challenges—with 80% of healthcare data currently residing in unstructured formats.

While the industry has traditionally been reticent to adopt artificial intelligence (AI) technology in compliance, new regulations and expectations are shifting many viewpoints on the promise and power of automation—from a perceived risk to the status quo to a vital business tool.

 is one such tool that biopharma companies are adopting, particularly as it pertains to PV and AE identification.

The hurdles of gathering data from patient-reported outcomes

Taking in and processing patient-reported outcomes comes with major challenges; first, the sheer amount of data that needs to be processed can seem monumental, and, second, performing quality checks on that data can be equally time-intensive.

The availability of health data has exploded in recent years—by  878% since 2016

, according to statistics compiled by Dell EMC in 2019. This growing volume and complexity—combined with growing expectations from regulators to perform PV activities in near real-time to boost patient safety—has made analysis and reporting of data a near-impossible undertaking for humans to perform without any NLP/tech-related assistance. Put simply, humans cannot accomplish this alone—there are simply not enough trained professionals available to manage all the data that needs to be processed, sorted, and shared at the speed that is needed for modern drug development.

Further, all data, once processed, must be quality-checked to verify that nothing has been missed and that no inadequate or irrelevant information makes its way into a report. Quality assurance (QA) practices have historically been  significantly time-intensive for pharma professionals. Traditionally, QA professionals must read every individual document and manually extract critical insights. Even with productivity hacks like keyword searches, it can take hours to contextualize patient-reported outcomes—often requiring knowledge and memory of complex rules across various therapeutic areas to accurately spot an adverse event.

Operating in this way is unproductive in terms of driving broader innovation and value. Sixty percent or more of time and resources spent on PV activities are currently dedicated to data extraction and quality checking. This estimate does not account for time spent on staff training—let alone troubleshooting to ensure data interpretation consistency across staff members and handling evolving reporting requirements.

NLP is a technological operation by which unstructured text-based documents are mined and converted into structured information that can subsequently be analyzed by a computer. It is not a new technology for the pharmaceutical industry: there have been strong use cases pointing to the value of NLP in recent years, particularly in clinical development, such as patient recruitment for clinical trials (through the evaluation of data available from electronic medical records and previous clinical studies). In one example working to identify patient cohorts for a clinical study, Drexel University was able to increase relevant identified patients by 67% while reducing manual review by 80% and overall time spent from five months to less than a week.

In these instances, evaluation criteria for finding prospects for trials remain relatively static. However, AE monitoring is a different story. Evaluating the accuracy, severity, and relevance of one consumer’s mention of an unexpected side effect from a commercially available drug, for example, is much harder to qualify and contextualize than feedback from a patient engaged in a clinical trial. Therefore, in working off of patient-reported outcomes, the context and chronology of input become critical differentiators when determining whether a specific drug caused an AE and needs to be reported to authorities—or whether that reported outcome was an outlying, unique case in which the AE was misreported, misunderstood, or influenced by other external factors.

The intricacies of natural language must be taken into consideration, such as slang and exaggeration, especially in looking to social media and other online repositories for this information. For instance, if a patient reports a bad night’s sleep, it would not necessarily be interpreted or properly codified as sleeplessness, and thus reported as an AE.

Further, near real-time processing is critical to make the best use of NLP data for AE monitoring. NLP technology must process data and flag relevant AE information, moving with the speed of a computer processing unit while “thinking” more complexly to better assist humans. It has taken years of refining and honing algorithms to get to this point of accuracy and consistent reporting with NLP.

Today, NLP can solve the problems of context and timeliness. Its true power lies in the ability to combine and compare decades of static legacy data (such as previously published medical literature) with constantly updating patient data, which it captures and processes in near real time. By cross-referencing text with billions of pieces of legacy medical data and the Medical Dictionary for Regulatory Activities (MedDRA)

, NLP is able to standardize and report potential AEs with a high degree of accuracy. In one example, CSL Behring, the rare disease drug developer, was able to double its accurate auto-coding of AE from 30% with one-to-one verbatim text matching alone, to more than 60% with NLP technology.

The future of NLP for compliance and beyond

NLP technology for AE monitoring is still in its nascent stages. However, NLP’s inherent value is starting to resonate with the pharmaceutical industry.

NLP becomes still more important as we think about the trajectory of additional data sources likely available in the years to come. For example, biometric data from wearable devices is already starting to be captured. These data sets are anticipated to grow significantly as more people adopt wearable devices and as more applications leveraging wearable devices with patient input become available. With more available data sources, we can expect the quality of manually processed data to become more challenging to accurately track without NLP assistance. Here, NLP becomes even more important for continuously solving data processing challenges and quality-related issues.

Looking further ahead, other related emerging technologies in the industry, like AI and machine learning, will start to be more fully integrated with NLP. With that, we may start to see how AI tools can help to determine the level of scrutiny a given AE should receive and how that factors into a product’s risk profile.

Looking beyond compliance, a newfound understanding of natural language data sources, enabled by NLP, will offer biopharma companies a valuable competitive edge in identifying new opportunities for clinical development. Through the same channels that may lead to uncovering AEs, companies may discover entirely new indications for which their products prove effective. This value will undoubtedly be even more important for organizations looking to spearhead innovative new treatments such as personalized medicine and cell and gene therapy.

Ultimately, NLP will become an essential tool for PV departments to help them continuously and consistently meet the rising pressures of compliance, while effectively managing cost and complexity. Freeing up these resources from manual reporting challenges will enable companies to reinvest in activities that drive true business value, with the ability to analyze the depth and breadth of previously untapped data to fuel future R&D activities.

NLP has the potential to help manufacturers drive toward fulfilling the industry's most important value: improving the safety and quality of treatments available today, while advancing the promise of untapped opportunities on the horizon.

Updesh Dosanjh, Practice Lead, IQVIA Vigilance Platform, 

; Jane Reed, Head of Life Science Strategy, 

The emergence of natural language processing in pharmacovigilance