Radio frequency identification (RFID) technology has seen increasing adoption rates in applications that range from supply chain management, asset tracking, Medical/Health Care applications, People tracking, Manufacturing, Retail, Warehouses, and Livestock Timing. This technology is used in many applications for data collection. The data captured by RFID readers are usually of low quality and may contain many anomalies. Data quality has become increasingly important to many organizations. This is especially true in the Medical/health care field because minute errors in it can cost heavy financial and personal losses. In order to provide reliable data to RFID application it is necessary to clean the collected data. SMURF is a declarative and adaptive smoothing cleaning technique for unreliable RFID data. However it does not work well when tag moves rapidly in and out of reader's communication range. The errors need to be cleansed in an effective manner before they are subjected to warehousing. Factors such as inter tag distance, tag-antenna distance, number of tags in the read range of antenna, reader communication range, velocity of tag movement affect the data cleaning result. Our proposed algorithm considers these factors and also the missing tag information, tags that are mistakenly read as present dynamically in determination of the size of slide window. Simulation shows our cleansing approach deals with RFID data more accurately and efficiently. Thus with the aid of the planned data cleaning technique we can bring down the health care costs, optimize business processes, streamline patient identification processes and improve patient safety.