The Role of Smart Technology in Mitigating the Risk of Human Error and Improving Productivity in Pharmaceutical Quality Control
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The COVID-19 pandemic has dramatically altered lab working by giving rise to an unparalleled demand for remote and flexible working patterns. The pandemic resulted in a sea of restrictions and demanded increased efficiency in all aspects of laboratory work. In April 2020, McKinsey found that four out of five top pharma organizations they surveyed predicted “a significant increase in demand for lyophilization, as well as for mRNA and other technologies”.1 This translates to the industry looking towards alternative routes to rapidly increase capacity, as well as repurpose existing capacity in the race to new therapeutic development. The universal strive for ultimate lab efficiency therefore requires a foundation that allows for the seamless improvement and implementation of new technology to keep up with evolving demands.
The global scale of liquid chromatography (LC) analysis in regulated testing labs is significant, and the industry faces a multitude of challenges. Issues such as skilled staff shortages, high staff turnover, aging technology, frequent manual operations, and complicated onboarding are all growing concerns for quality control (QC) labs in the pharmaceutical industry, and can leave labs vulnerable to human error. Additionally, labs that are still paper-based tend to suffer from a higher rate of human errors, as well as a time- and space-consuming need for a paper filing system, which can also lead to future problems with efficiently retrieving historical results. However, many labs are now adopting more progressive ways of working. Continuous improvement initiatives and new, intelligent, and innovative ways of working are helping to reduce the possibility of errors and ensure lab professionals can maintain their productivity, focusing on important tasks, rather than spending time on error investigations, administrative processes and re-training.
One approach to reducing the frequency of errors in the laboratory is the adoption and use of smart technology. Introducing technology into the lab that can predict errors, or that can even introduce processes that dramatically lower the likelihood of human errors, is crucial to moving the pharmaceutical industry forwards. Advances in sensor technology, artificial intelligence (AI), robotics, cloud capabilities and 5G have already helped to streamline lab processes, drastically increasing productivity.
Reducing human errors and supporting compliance
Mistakes can occur at any stage of an analytical procedure – for example pipetting errors, labeling errors and deviations due to faulty equipment, which can all potentially impact compliance and, ultimately, drug safety. The significant focus that global organizations now have on introducing a quality culture is directly linked to the need to reduce errors and improve regulatory compliance.
While lab staff may know how to operate instruments, they may lack knowledge to troubleshoot problems, or to perform maintenance procedures. In these situations, adopting smart technology can also be beneficial. While automation software can be deployed to launch a troubleshooting investigation if a certain threshold is crossed, smart technology and instrumentation actively pre-empts errors, ensuring that mistakes don’t occur in the first place. When instruments become autonomous and can flag upcoming pitfalls, unplanned downtime is minimized, and throughput is increased. Monitoring software can enable the supervision of instruments and devices in real time, alerting the user when a deviation happens, preventing workflow interruptions. The software can issue a notification when an instrument stops gathering data or if it is due extra maintenance, or proactively order additional consumables ready for when they will be needed.
According to another report by McKinsey, 100% of the technologies needed to digitally enable labs are available today, meaning that the opportunity is available for labs to adopt smarter technologies that incorporate AI and predictive maintenance to reduce errors and the risk of non-compliance. Minimizing such issues can lead to fewer retests and therefore cost and time savings, with McKinsey estimating a reduction in lead times of 60-70%,2 which then improves overall time-to-market.
Intelligent innovation in QA/QC
QC laboratories have traditionally needed staff to be physically present, whether it’s to prepare samples, to physically interact with lab equipment, to receive training, or for supervision from senior colleagues. However, there is a growing demand for hybrid and remote working, particularly given the current high rates of staff turnover and skilled labor shortages. The increasingly widespread use of remote access to chromatographic software now enables users to interact with their chromatographic system from home – whether it be to check on a sample run, review data or sign off on a report, and increasing lab automation and the ability of instrumentation to make autonomous decisions will further facilitate remote working.
LC software powered by AI can be “trained” by users to detect problems faster than the human eye. Imagine, for example, that the lab professional enters data into a software program, prior to LC analysis, relating to compounds of interest or the method to be run. Based on this information, the system will flag potential issues or errors that could result in a failed sample analysis and lost productivity, before the user even starts the test. Smart technology improves productivity levels by ensuring simple mistakes or oversights are avoided.
Predictive maintenance is another aspect of smart technology that can significantly reduce downtime and improve efficiency. For example, when a system flags that a specific consumable part is wearing out, an online order of the new part is automatically triggered, and the user is informed of the maintenance required. Additionally, when an error occurs on an instrument, the system informs the user of the specific nature of the error and can also automatically suggest relevant maintenance procedures, which can be used in combination with AI training.
Human errors can be mitigated in a number of ways, including by focusing on company quality culture, where staff at all levels are committed to the pharmaceutical quality system. A quality culture is transparent and open, encouraging personnel to freely communicate failures and mistakes so that the appropriate corrective and preventative actions can be taken. However, because most mistakes are unintentional and often go unnoticed until the effects are seen, smart technology plays an essential role in minimizing mistakes before they occur.3 To keep up with demands for efficiency and innovation, collaborations between industry, regulators and analytical instrument vendors are needed to drive the continued evolution of technologies in this area.
The lab of the future
The pharmaceutical industry is heavily regulated, and as a result, laboratories have typically been slow to adopt new technology. However, innovation and regulatory compliance are not mutually exclusive goals. From AR/VR (augmented reality/virtual reality) headsets that mimic real-time walkthroughs, to “digital twin” solutions enabled with the Internet of Things (IoT) and AI for real-time quality monitoring, or deploying chromatography to set up analytical data review processes remotely4 – there is no shortage of opportunity for labs to modernize whilst maintaining regulatory compliance.
Shifting from paper records and traditional working methods to digital systems and smart technology is a learning curve, and implementing new tech requires consideration – there is little point attempting to automate processes if the software is not intuitive and easy to implement for the user. Integration of new technologies with existing systems also has its own difficulties and can be time-consuming. As well as these technical challenges there may be reluctance from the lab professionals themselves, who may not be fully aware of the benefits to their workload of adopting smart technology.
Digital transformation requires a change in thinking, along with investment and training for the workforce. To successfully adopt new ways of working and new technologies, a process of change management is necessary. This includes identifying the need to implement new tech, defining simple, clear goals for the organization, putting a rollout plan in place and creating training pilots that upskill staff. Doing this can help staff to understand the positive impact of automation and other intelligent tools, and how the benefits of adoption outweigh the initial challenges, encouraging cross-organizational collaboration (for example with IT and management staff).
Pharmaceutical companies are increasingly investing in smart technology to reduce risk and improve consistency, and to allow lab professionals to concentrate on value-added tasks, thus helping to ensure the overall efficiency and productivity of the lab and the provision of quality, safe medicines for patients.
About the author
Mike Wilson obtained both his Master's and PhD in Chemistry from the University of York, UK. After a period working in the pharmaceutical industry, he joined the Waters UK field service team in 2009. Since then, he has held various product management and product marketing roles at Waters, and now supports the QA/QC team in Milford, USA.
1. Pharma operations: The path to recovery and the next normal. McKinsey. https://www.mckinsey.com/industries/life-sciences/our-insights/pharma-operations-the-path-to-recovery-and-the-next-normal. Published May 12, 2020. Accessed February 14, 2023.
2. Digitization, automation, and online testing: Embracing smart quality control. McKinsey. https://www.mckinsey.com/industries/life-sciences/our-insights/digitization-automation-and-online-testing-embracing-smart-quality-control. Published April 14, 2021. Accessed February 14, 2023.
3. Bodmann K, Reinhard C, Mödler M, Tinson K, Johnson M. Lonza error prevention system (EPS) – changing human performance in pharmaceutical operations. CHIMIA. 2016;70(9):610-610. doi: 10.2533/chimia.2016.610
4. View: Virtual quality control's ours to tech. The Economic Times. https://economictimes.indiatimes.com/opinion/et-commentary/view-virtual-quality-controls-ours-to-tech/articleshow/91581671.cms. Published May 15, 2022. Accessed February 14, 2023.