The Date-Information-Wisdom Model Paper

The Date-Information-Knowledge-Wisdom model is a model that is essentially aimed at providing the nursing profession with a framework to support the practice of nursing and the delivery of nursing care (Ronquillo, 2016). This framework encompasses the idea that data, information, knowledge, and wisdom can be useful in the world of nursing informatics, and these four aspects can act as a guide to identify solutions to many nursing problems (Ronquillo, 2016). When dissecting the model into four parts, each component contains a different meaning, or use. Data means concrete information that has not been interpreted yet, information means organizing or interpreting the data, knowledge means relationships are formed from the data itself, and wisdom

However, the data provided was not sufficient enough to make a claim/conclusion, so further data, such as what measures were taken before surgery, and what measures were taken after, are needed in order to apply this model (Sewell, 2016). This paper will further apply this model to the data provided by author Jeanne Sewell, as well identify the goals of the data analysis. The first two concepts within this model consist of data and information. With that being said, author Jeanne Sewell provided data, but not much information about the presence of post-surgical infections among different surgeons (Sewell, 2016). The data table presented different

Knowledge, in this case, would need to be targeted at understanding how the different data obtained could contribute to post-surgical infections. For example, it would be ideal to know how much higher of a risk it is to develop a post-surgical infection if the antimicrobial prophylaxis was given after one hour (Anderson et al., 2014). Also, having knowledge about how much staff should be in a room during certain surgeries would allow for the individuals analyzing the data to determine whether or not a room was appropriately staffed to have the highest chance of reducing infection (Anderson et al., 2014). In terms of wisdom, understanding and applying the data, information, and knowledge is essential in this component. In this particular case, educating the surgeons, perioperative personnel, and the hospital about the data that was found and educating them about ways to decrease the risk of post-surgical infections would be necessary for all involved (Andersen, 2018). The ultimate goal of this particular data analysis is to find a way to decrease the incidence of post-surgical infections. Although there should be emphasis placed on the surgeon that has shown more patients with post-surgical infections, the overarching goal is to identify why these infections could be occurring as often as they are and how to