Biopharma Investigations

4 min read

The main premise of the article is about biopharma investigations that focus on investigating deviations involving human error. Some biopharma sites are experiencing high numbers of these types of deviations, which may be indicative of less than effective causal analysis efforts. These investigations and subsequent actions taken to prevent recurrence are not as productive as they would like, and that there are obstacles to achieving human performance improvements.



As a causal analysis practitioner for over 30 years in the nuclear industry, I wanted to thank the panel for their excellent insights into how Pharma approaches these investigations. The (well written) article struck a chord with me and prompted me to make a few observations.

There are a few common themes that consistently surface when I read articles from aerospace, healthcare and pharma. One theme is that the traditional root cause analysis (RCA) approaches do not often lead to the deepest-seated causes, and corrective actions tend to fall short. I would like to offer a few reasons based on my experience as to why many of these traditional approaches fall short.

After decades of conducting RCAs, audits and assessments at nuclear power plants, the US National Laboratories and the nuclear weapons complex, I determined that the classic definition of a “root cause” has been allowing investigations to stop too soon. Most industries have adopted a variation of the following: a root cause is a causal factor that, if eliminated, the problem will not recur. With this traditional definition, RCA efforts stop as soon as they identify the root causes for the specific event being investigated (i.e. the event root cause). True root causes are deeper than that, so consider a revised definition: Root causes are the deepest-seated causes for an event or condition, which, if corrected or eliminated, would preclude repetition of, not only the event or condition being analyzed, but also many other conditions affecting past and future performance. With this definition, methods will have to be adjusted to probe deeper.

Another explanation is that the traditional tools and techniques used to conduct causal analysis in energy, aerospace, healthcare and pharma are not as effective as you might think. (I use the term causal analysis to envelope RCA and all other levels of effort). Many organizations have compiled a RCA “tool kit” comprised of a number of approaches that are used as appropriate. I’m familiar with most of those tools, having taken many problem-solving courses in my career such as Kepner-Tregoe, Phoenix, TapRoot, MORT, Lean Six Sigma, Kaizen Team Leader Training, Human Performance Evaluation System (HPES) and others. If your organization is still using the Fishbone, the Five Why’s, Barrier/Task/Change Analysis tables and Events and Causal Factors Charts, you will not get the best results. And if you do, it likely took a huge effort with a number of smart people on the team. I liken it to driving a horse and buggy while some of us are driving a Ferrari. Today’s working environment values (demands) speed, accuracy, flexibility and efficiency. These traditional tools are used separately and then brought together like pieces of a puzzle, which can be time consuming and difficult at best.

Lastly, there is a tendency to view problems in silos. For example, we tend to classify issues as human errors, equipment failures or by other trend codes. We then seek a specific tool to address that particular problem area. For human errors, we break out one of the traditional and proven methods such as HPES. However, please consider the following perspective. There are complex problems that are solved by math and science, so I am not speaking to those. There is another universe of complex problems that are difficult to solve because they involve humans, and I call those “complex, human-centric problems.” Complex, human-centric problems are a complex combination of human errors, equipment/tool/material interface issues, and organizational and programmatic weaknesses. These problems require critical thinking and complex problem-solving skills that are not typically taught in mainstream academia, requiring a deep understanding of causal analysis methods and techniques. However, traditional methods/tools that are currently in use in most industries are not equipped to manage and solve those kinds of problems with one seamless and efficient approach. It takes a method like BlueDragon Hyper-Integrated Causal Analysis (HCA) to solve those types of problems, using an integrated set of tools that attacks these problems from multiple perspectives. HCA arrives at the deepest-seated causes of much more than the event being investigated, and does so in a matter of hours and days and not weeks and months. HCA is a modern version of RCA that is in wide use at the National Labs and the nuclear weapons complex.

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