Qualitative Analysis of the Interdisciplinary Interaction between Data Analysis Specialists and Novice Clinical Researchers

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

Selection Mechanisms Underlying High Impact Biomedical Research - A Qualitative Analysis and Causal Model

BACKGROUND: Although scientific innovation has been a long-standing topic of interest for historians, philosophers and cognitive scientists, few studies in biomedical research have examined from researchers' perspectives how high impact publications are developed and why they are consistently produced by a small group of researchers. Our objective was therefore to interview a group of researchers with a track record of high impact publications to explore what mechanism they believe contribute to the generation of high impact publications. METHODOLOGY/PRINCIPAL FINDINGS: Researchers were located in universities all over the globe and interviews were conducted by phone. All interviews were transcribed using standard qualitative methods. A Grounded Theory approach was used to code each transcript, later aggregating concept and categories into overarching explanation model. The model was then translated into a System Dynamics mathematical model to represent its structure and behavior. Five emerging themes were found in our study. First, researchers used heuristics or rules of thumb that came naturally to them. Second, these heuristics were reinforced by positive feedback from their peers and mentors. Third, good communication skills allowed researchers to provide feedback to their peers, thus closing a positive feedback loop. Fourth, researchers exhibited a number of psychological attributes such as curiosity or open-mindedness that constantly motivated them, even when faced with discouraging situations. Fifth, the system is dominated by randomness and serendipity and is far from a linear and predictable environment. Some researchers, however, took advantage of this randomness by incorporating mechanisms that would allow them to benefit from random findings. The aggregation of these themes into a policy model represented the overall expected behavior of publications and their impact achieved by high impact researchers. CONCLUSIONS: The proposed selection mechanism provides insights that can be translated into research coaching programs as well as research policy models to optimize the introduction of high impact research at a broad scale among institutional and governmental agencies

System Dynamics to Model the Unintended Consequences of Denying Payment for Venous Thromboembolism after Total Knee Arthroplasty

Background: The Hospital Acquired Condition Strategy (HACS) denies payment for venous thromboembolism (VTE) after total knee arthroplasty (TKA). The intention is to reduce complications and associated costs, while improving the quality of care by mandating VTE prophylaxis. We applied a system dynamics model to estimate the impact of HACS on VTE rates, and potential unintended consequences such as increased rates of bleeding and infection and decreased access for patients who might benefit from TKA. Methods and Findings: The system dynamics model uses a series of patient stocks including the number needing TKA, deemed ineligible, receiving TKA, and harmed due to surgical complication. The flow of patients between stocks is determined by a series of causal elements such as rates of exclusion, surgery and complications. The number of patients harmed due to VTE, bleeding or exclusion were modeled by year by comparing patient stocks that results in scenarios with and without HACS. The percentage of TKA patients experiencing VTE decreased approximately 3-fold with HACS. This decrease in VTE was offset by an increased rate of bleeding and infection. Moreover, results from the model suggest HACS could exclude 1.5% or half a million patients who might benefit from knee replacement through 2020. Conclusion: System dynamics modeling indicates HACS will have the intended consequence of reducing VTE rates. However, an unintended consequence of the policy might be increased potential harm resulting from over administration of prophylaxis, as well as exclusion of a large population of patients who might benefit from TKA

System Dynamics model of interdisciplinary interaction between information specialists.

<p>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.</p

The fraction % of patients with osteoarthritis who might benefit from, but not receive TKA.

<p>The fraction % of patients with osteoarthritis who might benefit from, but not receive TKA.</p

The fraction of patients harmed by HACS over time including sensitivity analysis adjustments.

<p>The fraction of patients harmed by HACS over time including sensitivity analysis adjustments.</p