Question answering (QA) is one of the oldest research areas of AI and Computational Linguistics. QA has seen significant progress with the development of state-of-the-art models and benchmark datasets over the last few years. However, pre-trained QA models perform poorly for clinical QA tasks, presumably due to the complexity of electronic healthcare data. With the digitization of healthcare data and the increasing volume of unstructured data, it is extremely important for healthcare providers to have a mechanism to query the data to find appropriate answers. Since diagnosis is central to any decision-making for the clinicians and patients, we have created a pipeline to develop diagnosis-specific QA datasets and curated a QA database for the Cerebrovascular Accident (CVA). CVA, also commonly known as Stroke, is an important and commonly occurring diagnosis amongst critically ill patients. Our method when compared to clinician validation achieved an accuracy of 0.90(with 90% CI [0.82,0.99]). Using our method, we hope to overcome the key challenges of building and validating a highly accurate QA dataset in a semiautomated manner which can help improve the performance of QA models.