Introspective Approaches Towards Issues Resolution

In a previous article, we discussed methods for clinical trial issues identification and resolution, and how the utilization of such methods can enhance issues identification, diagnosis, correction, and resolution. Throughout this process, a great deal of self-introspection is required to make optimal decisions, as the frequency of new and unusual situations in clinical trials tend to be high, and vary from one study to another. In this article, we will suggest an introspective method that can help clarify situational analysis and resolution.

The Natural Course of Human Perception when Faced with New Situations

Humans are particularly strong in pattern detection. In fact, pattern detection is so strong that our minds trick us into forging energy-efficient perceptions that are far from reality ^[1]. As a simple example, audiovisual illusions enable us to synchronize visual and auditory stimuli if they are presented within a small millisecond time range (perception), even when visual and audio stimuli are perceived at separate times (reality) ^[2]. In situational analysis, there are many more factors that can facilitate illusions in our perceptions about reality, such as incorporating emotions, experience, and critical thinking into situational interpretation. Additionally, our perceptions are oftentimes incorrect, especially when we face new types of situations that we haven’t experienced. Once we undergo situational resolution, we gain perspective and develop patterns on how to efficiently identify and resolve these situations when they present in the future. The following steps can help with properly interpreting and resolving situations.

Step 1: Appreciate Your Hypothesis

When faced with a new situation, it is easy to allow emotions to take over, causing us to jump to conclusions. It is also facile to want to feel right about interpreting a situation, and wanting to act on resolving the situation by making hasty (and ego-gratifying) decisions. Albeit it is obvious to relate with this approach while reading this article, our feelings and actions are oftentimes challenging to identify when we undergo a new situational analysis (and when experience and evidence about a situation are lacking). 

Example of Situation: You have identified two patients in EDC that exhibit similarities in data from a site led by a PI with minimal clinical trial experience. You have consistently had a bad experience with new sites when it comes to data quality.

Example of Emotion-Based Hypothesis: I think the two patients must be the same patient, site personnel made a mistake by entering data twice for the same patient, or the PI is conducting fraud. This poses a data quality issue, and I think want to close the site.

Step 2: Contradict Yourself and Discuss Hypothesis with Study Team Members

It is important to assume that your initial interpretation about a new situation is almost always wrong, and it is critical to seek advice from team members to broaden your outlook and better understand the reality of the situation, rather than basing your interpretation on illusory and emotion-based perceptions.

Example of Contradictory and Team-Led Hypothesis: I might be wrong about my initial hypothesis, and the reality right now is, we do not know why there are similarities in data. Team members have suggested many possibilities that contradicted my initial hypothesis, and agreed to verify the data by sending a monitor and/or auditor to the site to review this trend.

Step 3: Gather the Facts

At this point, you’re on a fact gathering mission by obtaining as much evidence as you possibly can from the source. Resources spent on fact gathering may also depend on the risk of the situation. To elaborate, it is advisable to expend resources on higher risk situations that could potentially result in more adverse outcomes (i.e., fraud, poor data quality, FDA 483s, etc.), and save resources on situations that do not exhibit a significant impact (i.e., issues that do not impact patient safety or data integrity).

Example of Resolution: Sponsor auditors have identified that the patients at the site were documented as identical twins in the source. Each signed a consent form, and exhibited similar physical attributes, such as weight, height, vital signs, and disease progression.

Step 4: Contradict Your Findings

In this final step, you will need to question whether the findings closely represent what is real. Although findings contain solid evidence, it is up to you to properly interpret the evidence and its impact, and determine what to do with it, which brings you back to Step 2: the initial interpretation of the evidence is likely wrong, and needs to be discussed with team members. Once the team has fully interpreted the evidence, a decision can then be made to either gather more evidence, or close the issue.

Example of Findings Interpretation and Contradiction: It is possible that the data could be from two identical twins. It is also possible that the site could be committing fraud by falsifying records, and claiming that identical twins exist. It is also possible that I could be wrong about this altogether. After running the facts by believable experts, such as QA, legal and compliance personnel, the team has decided that the risks are too high to not verify the identity of the twins; accordingly, the auditors have been requested to obtain confirmatory information that the twins are, in fact, two separate people.

Summary

In this article, we have specified that clinical trial personnel can be blinded by their own perceptions and realities, and can make poor decisions, leading to suboptimal issues identification and resolution. In combination with established issues management infrastructures, a structured interpersonal approach to issues interpretation and resolution can help reduce poor decision-making and bad outcomes.

References:

[1] Your Deceptive Mind: A Scientific Guide to Critical Thinking Skills, Steven Novella.

[2] ftp://dmz02.kom.e-technik.tu-darmstadt.de/papers/StEn93_1264_TR%2043.9310.pdf

Moe Alsumidaie, MBA, MSF is Chief Data Scientist at Annex Clinical, and Editorial Advisory Board member for and regular contributor to Applied Clinical Trials.