Quality data is essential to assessing patient’s health status and promoting health initiatives. With the unprecedented technological advancement, individuals now have better access to health information. However, just because data has become more accessible does not imply that all the data is accurate and reliable. Several conditions need to be met for data to be reliable. A reliable data is characterized by its totality of features that bear its ability to meet the requirement that results from the planned use of the data (Raghupathi & Raghupathi, 2014, p.3). These features include completeness, accuracy, interpretability, relevancy, and timeliness. However, some clinical circumstances can force a healthcare provider to make a decision using data that are not accurate, timely or complete.An example where I made a decision using data that was not accurateDuring a shift change in the hospital where I secured my first job, the outgoing nurse was giving a report on two patients. I was working at a computer but I had been assign to take care of a patient in another room. The outgoing nurse finished charting but entered the patient’s data in in the wrong electronic health record which I had already opened. I later realized that the patient whose record was opened had two infusions of the same hypertension medication. One infusion had been started in the emergency department. I made the decision to discontinue the other infusion when I discovered the error. The patient become hypotensive. Wrong record selection is a common problem especially when nurses are performing multiple tasks such as documenting in patient’s records, and giving an end-of-shift report (Gai et al. 2015). Wrong patient record selections can also lead to using inaccurate data to make a clinical decision.ReferencesGai, K., Qiu, M., Chen, L. C., & Liu, M. (2015, August). Electronic health record error prevention approach using ontology in big data. In High Performance Computing and Communications (HPCC), Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), 3.