In a distributed information search task, data representation and cognitive distribution jointly affect user search performance in terms of response time and accuracy. Guided by UFuRT (User, Function, Representation, Task), a human-centered framework, we proposed a search model and task taxonomy. The model defines its application in the context of healthcare setting. The taxonomy clarifies the legitimate operations for each type of search task of relational data. We then developed experimental prototypes of hyperlipidemia data displays. Based on the displays, we tested the search tasks performance through two experiments. The experiments are of a within-subject design with a random sample of 24 participants. The results support our hypotheses and validate the prediction of the model and task taxonomy. In this study, representation dimensions, data scales, and search task types are the main factors in determining search efficiency and effectiveness. Specifically, the more external representations provided on the interface the better search task performance of users. The results also suggest the ideal search performance occurs when the question type and its corresponding data scale representation match. The implications of the study lie in contributing to the effective design of search interface for relational data, especially laboratory results, which could be more effectively designed in electronic medical records.