Attributing manufacturing problems to “human error” is an oversimplification that can mask deeper failures, according to Ann Ryan, head of business development at Ireland-based training and technology institute, Innopharma Education.
Drug manufacturing is complex, involving multiple linked operations performed in sequence. Errors or deviations at any stage of the process can impact the safety, efficacy, and quality of the finished product.
In a bid to avoid errors, drug makers routinely try to determine the root causes of any incidents that occur. However, says Ryan, investigations often fall short with many simply blaming “human error” rather than identifying actionable underlying issues.
“Documented research states that human error has been attributed to approximately 50% of quality incidents and related problems within the pharmaceutical manufacturing industry. This statistic is echoed by the BioPhorum Operations Group (BPOG) which estimated that half of the deviations seen in the biopharmaceutical industry are due to human error.
“This is a significant percentage, however if biopharma manufacturers drilled into the issues in greater detail, this percentage would be much lower. Often the true root causes are related to a systems issue, organizational failures, or human factors,” Ryan tells GEN, adding, “The easy answer is to just attribute it to human error.”
Human errors are most often attributed to deficiencies in training and corrective measures usually focus on re-training.
In contrast, human factors, which Ryan explores in a new paper, take into account things that influence behavior, such as time pressure, inadequate equipment, and poor facility design or ergonomics.
By looking at human factors rather than just errors, firms can identify the specific circumstances in which an incident occurs and take corrective actions. The approach could also help technology suppliers, Ryan says.
“Greater consideration could be given by technology suppliers to the end users, for example, the importance of considering cultural diversity in the design of production systems and aligning systems to diverse workforces. I’m not saying that each supplier must consider every end user, however, they should consider it where possible, taking account of production ergonomics and the end users’ capabilities.
“An example of a lack of consideration of human factors and ergonomics is the Bhopal disaster [in India in 1984]. Poorly trained staff were charged with operating advanced technological systems designed by other humans with very different educational, cultural, and psychosocial backgrounds.”
The holistic, human factors approach will better prepare drug companies to embrace AI and automation, Ryan says.
“Automation and AI-based process modelling and control systems have significant potential to reduce accidents and errors in biopharma manufacturing by minimizing manual interventions, enhancing real-time monitoring, and enabling predictive maintenance.
“Human intervention will be required in different more complex ways, with increased focus on managing, interpreting, and optimizing AI-driven and automated systems, ensuring quality control, making critical decisions, and handling exceptions that machines cannot resolve autonomously.”
The post Blaming Just Human Error Can Mask Deeper Bioprocess Problems appeared first on GEN - Genetic Engineering and Biotechnology News.
Drug manufacturing is complex, involving multiple linked operations performed in sequence. Errors or deviations at any stage of the process can impact the safety, efficacy, and quality of the finished product.
In a bid to avoid errors, drug makers routinely try to determine the root causes of any incidents that occur. However, says Ryan, investigations often fall short with many simply blaming “human error” rather than identifying actionable underlying issues.
“Documented research states that human error has been attributed to approximately 50% of quality incidents and related problems within the pharmaceutical manufacturing industry. This statistic is echoed by the BioPhorum Operations Group (BPOG) which estimated that half of the deviations seen in the biopharmaceutical industry are due to human error.
“This is a significant percentage, however if biopharma manufacturers drilled into the issues in greater detail, this percentage would be much lower. Often the true root causes are related to a systems issue, organizational failures, or human factors,” Ryan tells GEN, adding, “The easy answer is to just attribute it to human error.”
Human factors
Human errors are most often attributed to deficiencies in training and corrective measures usually focus on re-training.
In contrast, human factors, which Ryan explores in a new paper, take into account things that influence behavior, such as time pressure, inadequate equipment, and poor facility design or ergonomics.
By looking at human factors rather than just errors, firms can identify the specific circumstances in which an incident occurs and take corrective actions. The approach could also help technology suppliers, Ryan says.
“Greater consideration could be given by technology suppliers to the end users, for example, the importance of considering cultural diversity in the design of production systems and aligning systems to diverse workforces. I’m not saying that each supplier must consider every end user, however, they should consider it where possible, taking account of production ergonomics and the end users’ capabilities.
“An example of a lack of consideration of human factors and ergonomics is the Bhopal disaster [in India in 1984]. Poorly trained staff were charged with operating advanced technological systems designed by other humans with very different educational, cultural, and psychosocial backgrounds.”
Innovation
The holistic, human factors approach will better prepare drug companies to embrace AI and automation, Ryan says.
“Automation and AI-based process modelling and control systems have significant potential to reduce accidents and errors in biopharma manufacturing by minimizing manual interventions, enhancing real-time monitoring, and enabling predictive maintenance.
“Human intervention will be required in different more complex ways, with increased focus on managing, interpreting, and optimizing AI-driven and automated systems, ensuring quality control, making critical decisions, and handling exceptions that machines cannot resolve autonomously.”
The post Blaming Just Human Error Can Mask Deeper Bioprocess Problems appeared first on GEN - Genetic Engineering and Biotechnology News.