Pharmacovigilance is generally regarded as a cost centre in a pharmaceutical company, and the management will always be on the lookout for further reducing the costs without compromising compliance. One way to achieve this is through automation. There is a galore of automation opportunities available in patient safety. This can be simple automation or by use of intelligent automation, which is through Artificial Intelligence (AI) or machine learning (ML). Automation of pharmacovigilance activities is expected to have a significant impact on public health, either direct or indirect. The initial set-up costs may be high, but the return on investment is expected to be equally good if the execution is flawless. However, to materialize this, we require skilled and passionate personnel. In recent times, we have witnessed the emergence of new techno-functional roles in Pharmacovigilance that have been discussed below:

1. Subject Matter Expert (SME): This is the only role closest to the traditional pharmacovigilance roles. The SME is expected to have significant experience in Pharmacovigilance operations, with an IT mindset. The SME shall ensure that the user requirement specifications are complete and in line with the regulatory requirements.
2. Data Scientist: The data scientist is a techno-functional person with programming knowledge. The data scientist would know computer languages such as Python, SQL, R, Scala, Julia, etc. Their role is to utilize the pharmacovigilance data and develop innovative solutions. They create new ways of capturing and analyzing data for analysts to use. The skills required include analytical, statistical, and programming. The data scientist plays a vital role in safety data extraction.
3. Data Analyst: The data analysts are experts in analyzing data (even Big Data) facilitated by the data scientist. They have strong statistical analysis skills. They have significant experience with tools like Qlik Sense, Tableau, SAS, Jupyter, etc. The data analyst will be essential in signal assessment and real-time signal monitoring.
4. Data Engineer: The data engineer is generally a purely technical person who is an automation expert with limited or no subject matter expertise. They know how to use data to ensure machine learning (even deep) is appropriately executed. They can create wonders (or magic) if the necessary information is available. The data engineer would have expertise in tools such as TensorFlow, Anaconda, IBM Watson Studio, Azure Machine Learning, etc.
5. Visualization Expert: The visualization expert would focus on data presentation or interpretation. E.g., representation of signal data that is easy to understand. The Visualization expert would have experience using tools like Looker, Oracle BI, Power BI, etc. They work closely with the company’s leadership team to get a bird’s eye oversight of the pharmacovigilance activities. At the same time, they can monitor the benefit-risk of their products.
6. Quality and Testing Professional: No system is approved to go live unless vetted to be ‘ready’ by the Computer Software Assurance (CSA) team. Such personnel may have functional experience in pharmacovigilance and quality assurance (specifically validation). They would test the system’s robustness and ensure the documentation is good enough to sustain an audit or inspection. They will also identify if the desired output is available in the validation and development environment before proceeding to the production environment. Besides, the quality and testing personnel are essential during the system upgrade.

It is always a million-dollar question whether a pharmacovigilance expert should learn technical stuff or a technical person should know pharmacovigilance for effective automation! Whenever in doubt, one should prefer to have both in the team! One can expect pharmacovigilance professionals clamouring that automation is eating up their jobs, but please remember that automation is here to stay, in the interest of public health!