It is necessary to note that a broad range of data analysis methods has been developed over the years, and some of the methods are especially effective when it comes to health care. The role of new technologies has been increasing in the industry, and they can be utilized to store and interpret data. Moreover, it is a core part of clinical needs assessment because it helps to identify strengths and weaknesses, and it is easier to justify expenses on a particular project.
Some of the core data analysis methods should be discussed to determine their advantages and which one is the most appropriate in this situation. For instance, a t-test may be viewed as an efficient approach because it is not complicated most of the time. It helps to get a better understanding of internal and external problems. Moreover, it is possible to identify if there are significant differences between benchmarks and actual performance. The focus on outcomes is critical, and the most attention should be devoted to the needs of patients (Davis & LaCour, 2016). Also, it is necessary to mention that graphing would also be beneficial in this case because it will help with a visual inspection, and it is easier to determine particular patterns. Factor analysis may also be considered because it can be utilized to get a better understanding of relationships between various points. Such information is vital most of the time, and it is possible to identify which areas must be prioritized and how to use resources that are available efficiently. Another one that also needs to be discussed is the framework method because its use in the industry has been increasing and many consider it to be the most efficient approach. It is important to understand that it has several limitations. Complications may occur when it comes to decision-making and perception of a particular issue. On the other hand, it can be used to determine the cause of a problem. Another advantage that needs to be mentioned is that it is incredibly flexible, and it is possible to make several alterations if it is necessary. However, the experience is critical most of the time, and the process may be quite complicated. Furthermore, it may be important to consult with a professional in this area to get valuable tips (Gale, Heath, Cameron, Rashid, & Redwood, 2013).
It is important to understand that the process of analysis has several phases. First of all, it is imperative to assess the quality of the data that has been collected and if it can be used with the use of a particular evaluation method. Moreover, the data needs to be cleaned to ensure that there are no mistakes related to spelling, address missing information, and exclude everything that is not necessary (Cleophas & Zwinderman, 2015). It is important to build a particular model that would help to correlate the data with core objectives or goals. Also, a lot of attention is devoted to the identification of trends. Interpretation is also a vital step in the process, and it is paramount to ensure that all of the core aspects are taken into account during the process. Microsoft Excel has numerous features that can be used to analyze the data. For instance, the ability to build charts can be quite beneficial, and it is necessary to utilize pivot tables (McKinney & Hess, 2012). Analysis ToolPak may also be used, and it provides many additional analysis tools. They can be used to look at data from different perspectives, and re-creation of tables does not take too much time (Adams & Garets, 2014). Another benefit of this approach is that it is not a hard task to alter the methodology if complications are introduced. Statistical analysis should be used for closed-ended questions and answers may be sorted depending on their frequency of occurrence, and it is possible to determine if significant differences between responses are present. Sorting and grouping are preferred when it comes to the other type. It is important to rank them, assess trends, and it may be necessary to analyze responses statistically (Harris, Roussel, Thomas, & Dearman, 2015). The information that was received must be reviewed and checked to ensure that there are no mistakes because it could lead to severe complications in some cases. The role of presentation also should not be overlooked because the information that is presented must be well-structured and comprehensive depending on the audience. Such tools as graphs and tables will be utilized, and it may be necessary to use Microsoft PowerPoint.
In conclusion, it is imperative to note that it would be beneficial to select an analysis method that would be flexible and can be altered if it is required because it is entirely possible that numerous complications would occur during the process. Microsoft Excel has been chosen as an analysis tool in this case because it has proven its effectiveness, and it has all the necessary tools to analyze the data. Overall, it is important to focus on such aspects as graphing and identification of relationships between different factors.
Adams, J., & Garets, D. (2014). The healthcare analytics evolution: Moving from descriptive to predictive to prescriptive. In R. A. Gensinger (Ed.), Analytics in healthcare: An introduction (pp. 13-21). Chicago, IL: HIMSS Books.
Cleophas, T. J., & Zwinderman, A. H. (2015). Machine learning in medicine – a complete overview. Berlin, DE: Springer.
Davis, N. A., & LaCour, M. (2016). Foundations of health information management. Philadelphia, PA: Elsevier Health Sciences.
Gale, N. K., Heath, G., Cameron, E., Rashid, S., & Redwood, S. (2013). Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Medical Research Methodology, 13(1), 117.
Harris, J. L., Roussel, L. A., Thomas, T., & Dearman, C. (2015). Project planning & Management: A guide for nurses and interprofessional teams (2nd ed.). Burlington, MA: Jones & Bartlett Learning.
McKinney, C., & Hess, R. (2012). Implementing business intelligence in your healthcare organization. Chicago, IL: HIMSS Books.