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www.biopharminternational.com Quality and Regulatory Sourcebook March eBook 2023 BioPharm International ® 9 Pharmacovigil ance desired format, so the organization will need to clean and standardize it according to the software require- ments. Technology 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. Prepa r i ng t he g roundwork for automat ion i n- cludes mapping how it moves through the system, determining 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 mea- suring performance. The outcome of this phase will determine 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 automation of the PV activities. This requires a heightened focus on high-priority workflows that deliver time, cost, and quality benefits through automation. One should begin building a project framework and governance model for all future AI deployments that captures best prac- tices 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 sim- ply a way to ease the human resource burden. Consider the entire workflow through a tech-enabled lens to iden- tify real obstacles and determine how to use technology to eliminate problems. During this phase, companies typically reach the point of being ready to use AI to support all key decisions in the safety workflow. AI is embedded across departments, with fully automated data entry, built-in reporting rules governed by machine learning algorithms, and a suite of applications 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 suppor ting 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. • P ush ing vendors to create new solut ions a nd to reimagine their own technolog y roadmaps to improve processes within the pharmacovig- ilance environment. The pharmaceutical industr y has seen an explo- sion of data, much of it f rom dev ices and sources that did not impact pharmacovigilance in the past. Data f lowing from wearable devices, social media, audio recordings, and shared images can a ll now be u sed to mon itor t he sa fet y of ph a r m acolog ic products. This transformation will only continue, which means safety sur veillance must continue to push the envelope on data relevance and the use of AI to capture, translate, and interpret it. Results of automating safety surveillance processes Compa n ies t h at h ave i nt roduced autom at ion i n their PV processes have seen numerous improve- me nt s i n e f f ic ie nc y. A I a d d s v a lue to d at a a nd creates new i n sig ht s a nd k nowledge. It replaces mu lt iple processes a nd moves t he orga n i z at ion f rom a t ra n s ac t ion a l v ie w of t he world to a d a- ta-driven v iew of t he world. It cha l lenges t he en- tire business model and the business case that un- derpins it, freeing up data and enabling the use of knowledge in new ways. Moreover, AI will create new data that we do not k now today. We are not going to ask for it; it wi l l happen because that is what AI does. In the longer term, AI will start to answer its own questions. The possibilities for creating new value for compan ies are end less and w i l l force t hem to rethink many of their current operation methods. Moving to an AI-driven organization To become a fully AI-enabled organization, organi- zations will need to spend a lot of time looking at their processes, reviewing their business models, and really understanding how AI ideally supports a l l t he cr it ica l decisions t hey ma ke. For cha nge- ma kers, t he key is to f ig ure out where your com- pany is today and where it wants to go. Reference 1. Fonseca, S. Utilizing a Safety Automation Maturity Matrix to Achieve Your Efficiency Goals, Webinar. On line. DIAGlobal.org. 2022.https://engage.dia- global.org/SPW_22203_IQVIA_LP.html (accessed Dec. 29, 2022) ■