Discuss the basic principles of data interpretation, including the validity and reliability of the data used to translate outcomes into practice. What are common barriers t

DQ 1

Assessment Description

The basic principles of data interpretation should be understood by the DNP learner to implement the DPI project. This includes the validity and reliability of the data used to translate into practice. Basic principles of data interpretation are vital for nursing leaders today. Data provides the knowledge to identify gaps and process improvement opportunities in nursing. Data interpretation helps to demonstrate if the data is reproducible, adaptable, and transparent (Molder et al., 2021). Validity and reliability are crucial to translate information into daily practice. The validity of a tool or study demonstrates that it measures what it is intended to measure. (Sylvia & Terhaar, 2019). The reliability of a tool or study demonstrates that it will continue to produce the same results (Sylvia & Terhaar, 2019). Common barriers that occur for translating outcomes into practice include the following: lack of time, education, lack of evidence-based practice guidelines or polices at the organizational level, specific clinician behavioral and the culture of the organization (Capobianco, 2020).

Implementation of evidence-based practice requires buy in from the team and effective communication. Establishment that the tools are reliable and valid helps increase the team's buy and understanding for the need to implement the change. It is inherent that the literature be appraised for findings to support the translation into research. Reviewing the level of evidence along with appraising the findings of similar studies is a crucial start to support the translating of evidence into practice. The ability to speak and present the data analysis and evidence supporting the change will increase stakeholder buy-in. A lack of supporting literature from the critical appraisal would encourage one to continue to review the evidence.  As one develops a plan for implementation, it is important to consider barriers and challenges that may arise to plan appropriately.

As I plan for my proposed DPI Project of listening to music to reduce numeric self-reported pain scores, I must also anticipate for barriers that may arise. A review of the evidence notes that barriers may include interruptions, noise, equipment that is not working and gaps in education with either the nursing team or the patient and family. I also anticipate that consistency and reminders as it is a new practice will be critical. This may be addressed by celebrating the wins and positive reinforcement. A sign may also be placed on the door when the patient is listening to music to prevent interruptions if this is supported by the team. I have presented the proposed project to the leader and clinical supervisors for buy in and support as they are critical.  

References

Capobianco, E. (2020). Imprecise data and their impact on translational research in medicine.  Frontiers. 

Mölder, F., Jablonski, K. P., Letcher, B., Hall, M. B., Tomkins-Tinch, C. H., Sochat, V., … & Köster, J. (2021). Sustainable data analysis with Snakemake.  F1000Research,  10. 

Sylvia, M. L., & Terhaar, M. F. (2018).  Clinical analytics and data management for the DNP. Springer. ISBN-13: 

Bonnie Flores In 200 Words

Data interpretation is a valuable aspect of nursing research and quality improvement projects. It enables the doctoral learner to analyze the data and make conclusions; therefore, it must be accurate, valid, and reliable. Additionally, it should lend insight to the question that was asked. Thus, it is essential that evidence-based and valid tools are used to collect data and ensure that accurate methods are used (Vadiveloo et al., 2020). Since the goal of our DPI projects is to show clinical significance in the improvement of patient outcomes, it is necessary to consider potential barriers that may arise during this arduous process. As stated in previous discussions, there may be resistance to change from staff who do not see the big picture, or, rather, lack the willingness to put forth additional effort (Melnyk et al., 2018). Another potential barrier could be a staffing shortage that does not allow for extra nurses to perform additional complex interventions (e.g., mobility while on a ventilator) (Melnyk et al., 2018).

Finally, all the data from my DPI will come from the electronic health record (EHR). This will provide this doctoral learner with a wealth of information about the interventions implemented and the patient’s response to the interventions. There are, however, barriers that can arise from this as well. For example, biased results may occur from incorrect interpretation of the medical record (Capobianco, 2020). If providers read the medical records and misinterpret the data or overestimate the effects of treatment, then this may lead to reporting results that are different than what occurred (Capobianco, 2020). Perhaps the biggest barrier that may arise is in the evaluation of the data to determine if the intervention had the intended effect; data evaluation may require as much as one year of data post-implementation, rather than the 8-weeks of data and few months that we will be given (Sylvia & Terhaar, 2018). Thus, as we enter the stage where we are to begin putting our projects into practice, we need to be prepared for all barriers that may lead to delays in intervention implementation and reporting outcomes.

References