Quality data is essential to assessing patients’ health status and promoting health initiatives. With 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. Reliable data is characterized by its totality of features that bear the 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 accurate
During 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 assigned to take care of a patient in another room. The outgoing nurse finished charting but entered the patient’s data 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 becomes hypotensive. Wrong record selection is a common problem, especially when nurses are performing multiple tasks, such as documenting patient 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.
References
Gai, 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.
Cite This Work
To export a reference to this article please select a referencing stye below:
Academic Master Education Team is a group of academic editors and subject specialists responsible for producing structured, research-backed essays across multiple disciplines. Each article is developed following Academic Master’s Editorial Policy and supported by credible academic references. The team ensures clarity, citation accuracy, and adherence to ethical academic writing standards
Content reviewed under Academic Master Editorial Policy.
