Agency for Healthcare Research and Quality (AHRQ), Essay Example

The exploration of medication errors would require a predominantly qualitative design. In order to determine the baseline number of medication errors at my institution, it will be necessary to search hospital records and supplement these findings with a self-report of nurses in order to gain a clearer picture of the amount and frequency of medication occurs that occur (James, 2013). Baseline measurements will be compared to figures that are collected after the implementation of the intervention for six months, and this analysis will rely on primarily quantitative methodology (Houser, 2015).

The validity of the data collection is somewhat questionable because nurses will be self reporting some of the data (AHRQ, 2013). Many individuals do not admit to making medication errors, so it is possible that this method could confound the data (Houser, 2015). However, this can be resolved by indicating that all survey identifiers will be removed at that reporting honestly will benefit the hospital. Nurses are responsible for ensuring that their research practices are designed in a manner that will confer benefit to their institution. It appears that this is possible with this particular project.

While evidence is known with regards to the frequency and types of medical errors at most institutions, there is little knowledge as to how these errors could be prevented (Houser, 2015). Many interventions have been proposed, such as the use of electronic health record systems and enhanced training programs, but the results of these interventions have demonstrated inconsistency (Grossman et al., 2014). The search terms used in these instances were “medication”, “error” “EHR”, “electronic health record system”, and “training”. Sources of information were deemed to be credible if authors had many publications available on the topic and all information was consistent or built upon these previous findings.

Inferential statistics is important in research because it helps researchers uncover new trends (James, 2013). Descriptive statistics are used only to summarize information that is present, not to predict new figures (Houser, 2015). An example of descriptive statistics would be plotting the relationship between the number of medical errors and time to determine the trend. However, an example of inferential statistics would be to predict the amount of medical errors at a point in time that goes beyond the data available in the graph. Examples of descriptive statistics include determining the degree of central tendency, in addition to measuring the mean, median, mode, and rage. A knowledge of central tendency could then be used to calculate inferential values, allowing known information about data trends to be used to predict an unknown variable. This is beneficial in the health care setting because a patient’s demographics can be used to predict his or her risk of getting a particular disease.

Nurses know if knowledge in a research study is usable for practice based on confirming these findings with other sources of literature. If these findings have been unanimously confirmed, this information is safe to use (Houser, 2015). Before a switch is made, it is important for the reader to determine whether the clinical situations in which this evidence was created is similar to his or her own clinical practice (James, 2013). It is important for sources to be credible for them to be implemented, as a lack of credibility generally indicates a questionability of generated information.

References

Agency for Healthcare Research and Quality (AHRQ). (2013). Funding and grants. Retrieved   from: www.ahrq.gov

Grossman J.M., Gourevitch, R., Cross D.A. (2014). Hospital Experiences Using Electronic Health Records to Support Medication Reconciliation. National Institute for Health Care   Reform, 17: 1-12.

Houser, J. (2015). Nursing research: Reading, using, and creating evidence (3rd ed.). Sudbury, MA: Jones and Bartlett.

James, J.T. (2013). A new, evidence-based estimate of patient harms associated with hospital care. J Patient Saf., 9(3): 122-8.