Sepsis is one of the common and preventable hospital-based issues associated with negative patient outcomes and considerable financial losses for healthcare facilities as well as the entire healthcare system. In the USA, sepsis leads to approximately 215,000 deaths and $17 billion losses each year (Armen et al., 2014). This paper includes a brief analysis of two studies related to sepsis rate reduction and the mitigation of potential adverse effects linked to the health issue.
One of the articles under consideration dwells upon the program aimed at early recognition and treatment of sepsis. The project implied the use of early sepsis identification techniques, early antimicrobial administration, as well as staff training (Armen et al., 2016). The study involved 1401 patients staying in the hospital during the control period, and 1331 participants were treated during the intervention period. It has been found that the odds of dying reduced by 30%, patients had 1.07 fewer days of staying in the intensive care unit and 2.15 fewer admission days (Armen et al., 2016). Although the researchers have reported a $1949 reduction in hospital costs, this effect has not been statistically significant, as concluded by Armen et al. (2016).
Thus, the favorable effects of the program have been identified, and early identification can be seen as an important mitigating factor related to sepsis mortality and longer hospital stay. The strengths of this study are a large sample, a considerable duration (approximately two years), and the use of statistical analysis. The major limitation is the fact that the hospital in question participated in several quality management initiatives.
Another study to be considered is concerned with the evaluation of the effectiveness of a machine learning-based sepsis prediction system. McCoy and Das (2017) concentrated on the intensive care unit, emergency department, and hospital floor units. After the implementation of the program, the sepsis-related mortality rate decreased by over 60%, and sepsis-related hospital stay reduced by almost 10%, while the 30-day readmission rate was lower by over 50% (McCoy & Das, 2017).
These findings suggest that the use of machine-based identification instruments can contribute to the reduction of sepsis mortality and the duration of the sepsis-related hospital stay in the hospital setting. The sample size was appropriate (1328 cases) and can be regarded as a strength of the study. The sound statistical tools to measure the outcomes of the intervention is another strength. However, the generalizability of data is limited since only one hospital was involved, and no randomized design was developed.
Nevertheless, irrespective of the limitations of the two studies, they contribute to the advancement of nursing practice in the field of sepsis rate reduction and sepsis management. Both studies shed light on the importance of early identification of sepsis to reduce negative outcomes such as complications, readmission, or death. Staff training was also analyzed and proved to have a favorable effect on patient outcomes. McCoy and Das (2017) explore the consequences of the use of a machine-based system, while Armen et al. (2016) examine a quality improvement project entailing a set of clinical procedures.
Both studies provide insights into the ways the quality of provided care can be improved. It is possible to conclude that a combination of incentives and procedures facilitated by the use of technology should be implemented in healthcare facilities in order to ensure the provision of high-quality care.
References
Armen, S. B., Freer, C. V., Showalter, J. W., Crook, T., Whitener, C. J., & West, C., Terndrup, T. E., Grifasi, M., DeFlitch, C. J., & Hollenbeak, C. S. (2016). Improving outcomes in patients with sepsis. American Journal of Medical Quality, 31(1), 56-63. Web.
McCoy, A., & Das, R. (2017). Reducing patient mortality, length of stay and readmissions through machine learning-based sepsis prediction in the emergency department, intensive care unit and hospital floor units. BMJ Open Quality, 6(2), 1-7. Web.