The selection of variables to collect in empirical pilot studies is critical due to time, financial and sample size limitations. The collection of patient data in the healthcare system poses additional concerns due to patient privacy, incomplete data and clinical feasibility. The existence of these intrinsic pilot study and healthcare constraints requires a strategic selection of study variables to maximize the variability explained in the patient population and knowledge gained from data analysis. We propose a value tree analysis to select data collection strategies (combination of study variables) that produce the highest preference value for the researcher while also taking into account the interests of other study stakeholders (patient, provider, clinical staff, Institutional Review Board, etc.). In the first stage (problem structuring), the decision context is framed to define the pilot study environment and stakeholder values that may impact the study design. The study objectives, decision alternatives and attributes are then generated to consider tradeoffs among conflicting criteria and stakeholder interests. In the second stage (preference elicitation), decision attributes are weighted using Analytical Hierarchy Processing and a value function is used to elicit the decision maker‟s preference for each variable combination. Value tree analysis is a low cost and low computation approach for time-sensitive variable selection in empirical pilot healthcare studies. The proposed approach provides a structured and strategic decision-making process that accounts for pilot study and patient data constraints and maximizes value for study stakeholders.