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PharmTech.com Quality and Regulatory Sourcebook March eBook 2023 Pharmaceutical Technology ® 11 Pharmacovigil ance desired format, so the organization will need to clean and standardize it according to the software require- ments. Technolog y will fail if the data are not cor- rectly prepared and available in the proper format. A company must also invest in f lexibility and cre- ate an organization willing to accept change. While technology can adapt, vendors have a specific view of the world that does not necessarily include knowl- edge of an individual organization's culture. Be pre- pared to adjust the company goals during the pro- cess. This could mean bringing in different groups or people as implementation progresses and the focus changes to alternative areas. Preparing the groundwork for automation includes mapping how it moves through the system, deter- mining the impact on roles and responsibilities, and preparing employees to understand and accept the changes. Once the data and workf low are ready, the team can roll out its pilot project and begin measur- ing performance. The outcome of this phase will de- termine the company's way forward. Phase 3: enabling automation Reaching this phase means the company is ready to begin reaping the benefits of AI through automa- tion of the PV activities. This requires a heightened focus on high-priority workf lows that deliver time, cost, and quality benefits through automation. One shou ld beg i n bui ld i ng a projec t f ra mework a nd governance model for all f uture AI deployments t hat capt ures best practices related to timelines, stakeholder support, vendor management, and change efforts. This process will help streamline projects, reduce the time to deployment, and prevent common mistakes from recurring. Change the company's business model to embrace safety as an automation-driven process, rather than simply a way to ease the human resource burden. Con- sider the entire workflow through a tech-enabled lens to identify real obstacles and determine how to use technology to eliminate problems. Dur i ng t h is phase, compa n ies t y pica l ly reach t he point of being ready to use AI to suppor t a l l key deci sion s i n t he sa fet y work f low. A I i s em- bedded across departments, with fully automated data ent r y, bui lt-i n repor t i ng r u les gover ned by machine learning algorithms, and a suite of appli- cations that enable automated case translation, data analysis, and decision-making as part of the safety surveillance workflow. Phase 4: innovation and improvement Companies that reach this Matrix stage have achieved full automation of their PV activities and are ready to consider future possibilities. These include: • Establishing an AI-first culture that actively seeks new ways to use technology to enhance, accelerate, and act on safety data. • Focusing on supporting a continuous state of improvement by appointing a dedicated team to monitor digital innovations and constantly question how, when, and where new technolo- gies could add value for the organization, even if they are not yet available for use. • Pushing vendors to create new solutions and to reimagine their own technology roadmaps to improve processes within the pharmacovig- ilance environment. The pharmaceutical industr y has seen an explo- sion of data, much of it from devices and sources that did not impact pharmacovigilance in the past. Data f lowing from wearable devices, social media, audio recordings, and shared images can all now be used to monitor the safety of pharmacologic products. This transformation will on ly continue, which means safety surveillance must continue to push the enve- lope on data relevance and the use of AI to capture, translate, and interpret it. Results of automating safety surveillance processes Companies that have introduced automation in their PV processes have seen numerous improvements in efficiency. AI adds value to data and creates new in- sights and knowledge. It replaces multiple processes and moves the organization from a transactional view of the world to a data-driven view of the world. It chal- lenges the entire business model and the business case that underpins it, freeing up data and enabling the use of knowledge in new ways. Moreover, AI will create new data that we do not know today. We are not going to ask for it; it will hap- pen because that is what AI does. In the longer term, AI will start to answer its own questions. The possibil- ities for creating new value for companies are endless and will force them to rethink many of their current operation methods. Moving to an AI-driven organization To become a fully AI-enabled organization, organiza- tions will need to spend a lot of time looking at their processes, reviewing their business models, and re- ally understanding how AI ideally supports all the critical decisions they make. For changemakers, the key is to figure out where your company is today and where it wants to go. Reference 1. Fonseca, S. Utilizing a Safety Automation Maturity Matrix to Achieve Your Efficiency Goals, Webinar. Online. DIAGlobal.org. 2022.https://engage.dia- global.org/SPW_22203_IQVIA_LP.html (accessed Dec. 29, 2022) ■