Qualitative analysis of the interdisciplinary interaction between data analysis specialists and novice clinical researchers

Abstract

Background: The inherent complexity of statistical methods and clinical phenomena compel researchers with diverse domains of expertise to work in interdisciplinary teams, where none of them have a complete knowledge in their counterpart's field. As a result, knowledge exchange may often be characterized by miscommunication leading to misinterpretation, ultimately resulting in errors in research and even clinical practice. Though communication has a central role in interdisciplinary collaboration and since miscommunication can have a negative impact on research processes, to the best of our knowledge, no study has yet explored how data analysis specialists and clinical researchers communicate over time.

Methods/principal findings: We conducted qualitative analysis of encounters between clinical researchers and data analysis specialists (epidemiologist, clinical epidemiologist, and data mining specialist). These encounters were recorded and systematically analyzed using a grounded theory methodology for extraction of emerging themes, followed by data triangulation and analysis of negative cases for validation. A policy analysis was then performed using a system dynamics methodology looking for potential interventions to improve this process. Four major emerging themes were found. Definitions using lay language were frequently employed as a way to bridge the language gap between the specialties. Thought experiments presented a series of "what if" situations that helped clarify how the method or information from the other field would behave, if exposed to alternative situations, ultimately aiding in explaining their main objective. Metaphors and analogies were used to translate concepts across fields, from the unfamiliar to the familiar. Prolepsis was used to anticipate study outcomes, thus helping specialists understand the current context based on an understanding of their final goal.

Conclusion/significance: The communication between clinical researchers and data analysis specialists presents multiple challenges that can lead to errors.

Figure 1. System Dynamics model of interdisciplinary interaction between information specialists.

The boxes (“stocks”) represent an accumulation of an element over time. For example: ‘number of information specialists’ represents a stock. Thick arrows (flows) represent the rate of influx or efflux of a stock over time and the thin arrows represent the relationship between the elements of the system. The +/− sign at the end of arrows indicate a positive/reinforcing and negative/balancing effect respectively. For example: Each of the themes – definitions, prolepsis, metaphors and analogies and thought experiments have a positive effect on communication while a difference in mental models has a negative effect on communication.