Relevant data is helpful in aiding a clinician in decision making, which requires the capture of information efficiently. However, retrieval of responses from various sources like electronic health records (EHRs) and other related documents is still a difficult task. In solving this problem, a system called Natural Language Querying for Clinical Information Extraction (NLQ-CIE) has been developed using a trained model of PubMed BERT which answers user queries in a suitable and context-dependent manner. In this model, a clinician is responsible for providing a patient’s medical data alongside pertinent queries from which answers need to be fetched. The system manages the questions using tokenization and word embeddings, and the medical information undergoes a set of procedures including data cleansing, tokenization, and standardization. Afterwards, these models are trained for the first time using the Biomedical Clinical Question Answering dataset (BCQA) which has been aimed towards supporting the biomedical question answering process. This guarantees that relevancy to the biomedical NLP domain is preserved, as well as providing the opportunity to assess the model performance. Thereafter, model specialization on the defined tasks is completed to enhance the model's capability of retrieving patient clinical data efficiently. During the extraction phase, quality assessment is performed relating to the data validation. For measuring model performance, some relevant model evaluation metrics like Exact Match (EM) and F1 Score are applied, proving the usefulness of the system. Using domain knowledge for clinical use and customizing PubMed BERT, the system proves useful in extracting important clinical information and thereby aiding clinicians to make better decisions.