Advances in healthcare data management and analytics have opened new horizons for healthcare providers such as cost effective treatments, ability to detect medical fraud, and diagnose diseases at an early stage. Central to these abilities is the need for fast ad-hoc query processing of large volumes of complex healthcare datasets. End users who work with healthcare databases spend enormous effort in data exploration since exploration is the first step to any subsequent predictive modeling to generate actionable insights for patients, providers and physicians. Unfortunately, unlike other domains the complexity and volumes of claims (ICD9 or 10) as well as clinical (HL7) healthcare datasets results in data exploration solutions being extremely slow and cumbersome when attempted using traditional disk resident data warehousing approaches. In this paper we describe the first ever attempt of real-time data exploration for healthcare datasets using in-memory databases. We benchmark and compare two such in-memory database systems to study responsiveness and ability to handle complexity of typical health data exploration tasks. We share our work in progress results and outline key issues that need to be addressed for forthcoming advances in this very important big data vertical.