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Artificial intelligence and pharmacovigilance

Artificial intelligence and pharmacovigilance

The aim of pharmacovigilance is to protect and improve patient safety in relation to medicine. Pharmacovigilance detects problems related to use of medicine and communicate them in timely manner to regulatory authorities and healthcare professionals.

The process can be devided into two parts.

1. Collection, assessment and reporting of adverse events – ICSR processing.

2. The continuous monitoring, interpretation and communication of product benefit-risk profiles to enable Signal Detection and Benefit-Risk Management.

Marketing authorization holders and sponsors are required to collect and collate any safety event that is associated with a drug’s use regardless of whether it is drug related.

Currently each report is reviewed, compiled, and formatted for submission to the appropriate health authority using labor-intensive processes relying on a team of pharmacovigilance (PV) experts.

However there are so many challenges in the process.

  • Increase in incoming adverse event reports due to the higher number of products in development and on the market, as well as increasing public and media awareness and many cases coming from journals, patents, articles, social media, and non-standardized data sources.
  • Increasing regulatory requirements demanded by national health authorities further drives complexity

Together these two factors drive an increase in resource demands and costs.

Artificial intelligence holds great promise to address key challenges and to provide new opportunities relevant to both aspects of Pharmacovigilance. The AI Solution for Pharmacovigilance enables advanced automation by leveraging artificial intelligence and machine learning.

Benefits of artificial intelligence in pharmacovigilance:

  • Reduced Cycle Times- Enable faster case intake and processing through automation
  • Enriched Quality and Accuracy- Standardize inputs for automated case processing
  • Scalable and Futuristic Solution- Handle case volume growth and diverse types of incoming data formats effectively
  • Improved Productivity- Minimize time and effort on low-value data entry and manual process steps
  • Better Compliance: Improve accuracy and consistency of AE reporting

Machine learning algorithms: Algorithms can be used to extract and classify information from incoming adverse event reports. The extraction capability includes data elements such as patient, product, event, or reporter. This extraction is done both on structured and unstructured information. The classification capability refers, for example, to the assessment of whether a report contains a fatal or life-threatening event, thus requiring expedited processing and reporting. The artificial intelligence-extracted and classified information is then proposed to a drug safety specialist for review and ultimately confirmation or correction.

This process can also be described as “augmented intelligence” of adverse event report processing. Corrected information then serves as input for subsequent machine learning rounds, improving the algorithms over time. It is believed that in the mid- to long-term, machine learning accuracy will achieve levels that will allow for the processing of selected adverse event reports without human touch. Taken together, these advancements have the potential to deliver a significant efficiency boost to the current pharmacovigilance operating model.

Artificial intelligence systems can also support activities that require medical knowledge and expertise, and advanced analytical skills. Whereas efficiency is the focus for processing incoming safety information, Artificial Intelligence in the area of Benefit-Risk Management will open opportunities to address classification and prediction problems. This will help drive effectiveness and the generation of new insights. Classification in this context refers to leveraging Artificial Intelligence for Signal Detection. Potential signals can be identified early and be confirmed or refuted with higher confidence.

Using advanced analytics in the pharmacovigilance process will also aid better clustering of data and consequent discovery of associations, for instance, a selected patient group being more or less prone to developing an adverse event. In the long term, this also opens the opportunity to complement value-based reimbursement models with drug safety information.

Moreover, artificial intelligence-based systems have the potential to advance benefit-risk assessment through predictive capabilities. Once a risk is identified – for instance through incoming safety reports as described above – predictive algorithms could estimate the burden on a population or sub-population. In this example, the artificial intelligence-based system learns from historical data and integrates additional information to predict the effectiveness of risk-minimization measures.

Two other important features of Artificial Intelligence systems are the natural language generation (NLG) and natural language processing (NLP) capabilities. NLG could be utilized for medical writing and the generation of aggregate reports which are developed from individual case reports and the signal detection process. NLP is also useful because much information is only available in an unstructured and free-text form. For instance, medical content from additional data sources such as Electronic Health Records could be obtained by applying NLP to support Signal Verification.

Another value-adding case would be applying NLP to a broad set of data, such as free text in social media, news articles, literature, or medical records for the detection of unexpected benefits of a pharmaceutical product. This could lead to an expansion of indications for an already marketed product and provides an opportunity for Pharmacovigilance to improve patient care.

Following are the broad two categories of usage AI technologies in ICSR Processing:

Ingestion of structured and unstructured content: Comprises components for reading incoming case intake information via XML, Docx, images including PDF and PDF text including forms/tables. Here OCR/ICR along with NLP/machine learning is used to extract ICSR information from information sources in a regulatory compliant manner.

AI for decision making: Sometimes, the quality of information available in ICSR is poor. In such scenarios, semi-supervised or unsupervised learnings play a major role in devising hypothesis. For example, building Unlisted Events and Drugs Correlation, Causality Classifiers etc., specific types of Neural Networks are built and improvised with training over a period. These are faster and more accurate compared to other methods.

With artificial intelligence, instead of focusing on the resource-intensive manual and repetitive tasks of processing adverse event reports, Pharmacovigilance can now focus on more analytic tasks with greater potential to improve the lives of patients through improved benefit-risk assessment and risk management programs. In the future, we expect to analyze patient safety data more quickly and detect trends within larger volumes of data.

Faster– ideally real-time – detection of relevant safety signals will contribute towards the optimum use of therapies and enhanced patient safety. Risk minimization measures can potentially be initiated faster and, thanks to increased accuracy, the scientific evidence generated should be more robust. As a result, the application of AI in PV has the potential to further improve our ability to promote and protect the health and well-being of patients and other healthcare consumers.

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