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Show HEALTH SCIENCES LEAP PROGRAM SPRING 2013 Conclusion: The development of a data capturing tool to analyze instances of medication error is vital to any present, or future health care providers. The data compiled by this tool has the potential to aid clinicians in creating a didactic training system for nurses in the hospital setting. In addition, data collected by this tool may be used in other academic areas that aim to recognize and minimize medication errors. Discussion: It is very probable that patients will interact with one or more nurses during a typical stay at a hospital. As such, many lives are entrusted to the hands of skilled nurses whose job includes delivering life-saving medications in a fast-paced environment. It is currently estimated that medication administration errors occur in 20 % of all of the doses given in a hospital. Of the 2 0 % given, 2-7 % lead to an adverse drug event in which the patient may suffer from a series of harmful reactions including: allergic response, cytotoxic overdose, and permanent disability a m o n g others (Doig, 2013). For these reasons, it is important that the attention of nurses is on the task at hand and that the safety of the patients is ensured. Implementing the use of data collection tools to aid clinicians in tracking error rates has the potential to improve patient safety as well as reduce medical malpractice lawsuits. Implications: If the data capturing tool, along with didactic simulation-based training system, are found to be effective in reducing violation and serious medication errors, then the training protocols will be compiled and disseminated to other research-based facilities. "The goal of this project is to create a concise, high impact training protocol that can be incorporated into simulation-based nursing education, new nurse orientation or residency programs, and continuing education/training for all nurses" (Doig, 2013). References: 164 Doig, Alexa. Development of a human factors training to reduce the negative effects of nursing work interruptions. University of Utah, 2013. |
