Pharmacovigilance (PV), the process of identifying, tracking, evaluating and preventing negative outcomes from drug therapies, is a sector that has seen huge growth in recent years. According to a Global Market Insights report, the PV market is predicted to exceed US$8 billion by 2024,  with its growth attributed to factors such as: a rising number of adverse drug reactions (ADRs), an ageing global population and rising chronic disease burden; an increasing number of pharmaceutical companies and the emergence of personalised medicine. While PV starts during clinical development, it is not limited to clinical trials alone as post-market surveillance is crucial to monitoring a drug’s safety after it has been approved.

There are a range of challenges the pharmaceutical industry faces when establishing and maintaining increasingly complex PV systems. The evolving regulatory environment in a progressively global industry places demand on pharmaceutical companies to manage PV activities more efficiently than before. With the ongoing pressures of optimising costs, traditional PV strategies must be revised and revitalised with smarter spending in mind. The focus is now shifting from primarily safety operations to proactive risk management, personalised medicine, and completely transparent data between pharma, patients, healthcare providers and regulatory agencies.

Technological advances are playing a major role in pharmaceutical PV strategy updates. For example, more companies are looking towards cloud-based solutions, mobile applications, robotic automation, artificial intelligence (AI) and big data analytics as a vital part of clinical, safety and regulatory operations in the pharmaceutical industry. Applying innovative technology automation tools and processes to PV strategies is now a critical requirement for managing the safety of pharmaceutical products.

Optimising efficiency

As one of the fastest growing life science disciplines, PV strategies must be optimised for peak efficiency. A well-established principal information technology (IT) framework provides organisations with high performance and scalability, together with system validation and information security, for effective design and distribution of automation initiatives. However, many of the problems surrounding PV systems are not IT issues, but are actually down to the processes or individuals managing the system holding onto personal preferences rather than absolute requirements. Addressing process improvements and organisational requirements in parallel with IT solution improvements will enable operational efficiency to go further and drive a proactive PV strategy.

AI has the potential to fill the gaps that traditional PV services currently leave, such as the ability to assimilate large volumes of cloud-based data and map patterns, in order to effectively predict ADRs. Genetic information and real-world patient data can also feed into this more streamlined approach to make PV more of a predictive science. Integrated IT solutions that combine scientific and technological expertise are capable of delivering high operational efficiency, quality and regulatory compliance.

The four stages of automation

Many current IT systems and applications are capable of automating case processing and reporting activities, but the overall process still requires significant manual effort, particularly regarding case intake and data entry. There are multiple levels of automation that can serve to make end-to-end safety processes more streamlined and strip down redundant, non-value added steps in existing processes, while increasing human labor efficiency.

The first stage is basic process automation, which involves tracking and monitoring tasks and enables the collection of continuous metrics. Basic automation provides reporting and dashboards, and automates a workflow that involves multiple roles, but still requires manual entry, processing and analysing of safety data into a database or system. Robotic process Automation (RPA) is the next level, and helps to reduce or eliminate these manual tasks. RPA is often combined with the subsequent level, cognitive automation, which leverages Natural Language Processing (NLP) to assist human decision-making. The system engages in human interaction, whereas the final level, AI, requires little or no human interaction and self-learns through experience, to make predictions based on patterns observed in large volumes of data with the help of machine learning (ML).

Regulatory responses to industry change

Ever increasing volumes of drug data places an urgent need on the development and implementation of technology capable of providing a secure, integrated big data repository. For example, all AEs, regardless of their degree of severity and source, should be stored in a single drug safety database.

Cloud-based capture and reporting is a key trend in the PV space, and is now being used to bring a fully-integrated database to all stakeholders. Integrating cloud technology can further optimise data intake, storage and analysis, and even provide regional and temporal insights into ADR patterns. Healthcare providers, physicians, users and research institutions can store and access drug safety information, whether reported during clinical trials or post-market experience. Cloud-based systems can also reduce latency between reporting and analysis, leading to more timely and qualitatively improved regulatory decision-making surrounding crucial public health issues.

Source: https://www.epmmagazine.com/opinion/the-future-of-pharmacovigilance-and-impact-of-automation/