The big data revolution has created a hefty demand for searching large-scale electronic health records (EHRs) to support clinical practice, research, and administration. Despite the volume of data involved, fast and accurate identification of clinical narratives pertinent to a clinical case being seen by any given provider is crucial for decision-making at the point of care. In the general domain, this capability is accomplished through a combination of the inverted index data structure, horizontal scaling, and information retrieval (IR) scoring algorithms. These technologies are also being used in the clinical domain, but have met limited success, particularly as clinical cases become more complex. One barrier affecting clinical performance is that contextual information, such as negation, temporality, and the subject of clinical mentions, impact clinical relevance but is not considered in general IR methodologies. In this study, we implemented a solution by identifying and incorporating the aforementioned semantic contexts as part of IR indexing/scoring with Elasticsearch. Experiments were conducted in comparison to baseline approaches with respect to: 1) evaluation of the impact on the quality (relevance) of the returned results, and 2) evaluation of the impact on execution time and storage requirements. The results showed a 5.1-23.1% improvement in retrieval quality, along with achieving 35% faster query execution time. Cost-wise, the solution required 1.5-2 times larger space and about 3 times increase in indexing time. The higher relevance demonstrated the merit of incorporating contextual information into clinical IR, and the near-constant increase in time and space suggested promising scalability.