Evaluating for Trends and Gaps in Analyzed Data: Decrease Readmission and Infection Rates of Wounds Post Discharge
This study seeks to evaluate the trends and gaps analyzed in different articles based on the surgically related issues in the healthcare practices. Today, there are heated statistical analyses on the risk factors experienced by the patients, and the most complicated one has been the Surgical Site Infections (SSI) (Chamberlain-Salaun, Mills, & Usher, 2013). This subject has attracted many researchers who have performed different studies to examine the issue as well as its interventions in clinical practice (Anderson et al., 2014). This study will examine the trends in the current research and the gaps of knowledge noted in them. Finally, it will be crucial to investigate the effects such trends have on this project.
Example of the Data
The data provided in the recent research depicts that different authors have used qualitative data type to analyze their results. While some articles have used inferential statistics, others have utilized interactive literature reviews from IBECS, MEDLINE, PUBMED, LILACS, SciElO, and Cochrane database to obtain the data for analysis. Ovaska et al. (2013) used a qualitative research in a comparative study of the readmission rate in relation to the provision of the instructions of the wound care. On the other hand, Rosen et al. (2014) used a qualitative research to obtain the data showing the comparison between the readmission rates within a period of 30 days after discharge between the younger and older individuals. In the study by Skoufalos et al., 2012), inferential statistics was used in qualitative research to give a description of the relationship between the risk readmission and BMI. Moreover, Gillespie et al. (2015) used an inductive analysis to point out the processes or strategies that the teams of multidisciplinary healthcare utilized in the prevention of SSI. In this article, the quantitative methods were used, with one studies reviewed utilizing qualitative interviews.
Review of the Results
The results obtained were based on different statistical analyses of the data collected. In the first place, Skoufalos et al. (2012) provided the findings of the Surgical Site Infections (SSI). The authors pointed out that there was an estimation of 47 percent and 84 percent of SSI that were present after the discharge from the facility of the ambulance care, resulting in going undetected by the standard surveillance programs of SSI. The results provided by Ovaska et al. were that the deep incidence infection accounted for 6.8 percent, while the univariate analysis revealed that the odds ratio (OR) of diabetes was 2.2. Additionally, the OR of alcohol abuse, soft-tissue injury for (a Tscherne grade of ≥1, and fracture dislocation were provided based on the 95 percent confidence interval (CI) equals 1.0, 4.9. The ORs of fracture dislocation, alcohol abuse, and soft-tissue injury were OR = 2.0, 95% CI = 1.2, 3.5, OR = 3.8, 95% CI = 1.6, 9.4, and OR = 2.6, 95% CI = 1.3, 5.3. The relationship between OR for the three types of SSI aspects and their OR values has been presented in Graph 1. Rosen et al. (2014) offered the results of the rates of readmission, where there was the identification of rates accounting for 41.5 percent related to clinical practices. Additionally, the rates of heart failure and chronic obstructive pulmonary disease were 10.2 percent and 6.5 percent respectively. These results constituted the top 2 reasons for the readmissions that are clinically-related among medical discharges. Also, a percentage of 70.5 of postoperative complications of the readmissions that were clinically-related among surgical discharges was realized.
Figure 1: The Relationship between three SSI risk factors and OR values
The Trends and Gaps in the Results
The trend depicted in the results is that there have been increased concerns regarding clinical related risks factors such as surgical site infections (SSI). Firstly, Ovaska et al. (2013) have presented SSI as the most critical complications affecting the patients, and particularly the fracture of the ankle surgery. The authors have pointed out that such infections emanate from substantial morbidity, leading to a rise in the utilization of resources. The limitation of this study is that it does not provide a clear analysis of the quality measures that would effectively reduce or eliminate the risk factors under investigation in relation to clinical readmissions.
The above results are supported by the research which was conducted by Rosen et al. (2014) who suggested an attractive quality measure like readmissions of the affected people. The choice of this measure was based on the fact that it provides a wider view of the quality that exceeds in the index hospitalization in the Veterans Health Administration. Like in most studies, Rosen et al. revealed that the interventions targeted quality improvement at care processes related to the index hospitalization proved to be the most effective in the reduction of the readmissions that are clinically-related. The gap that has been observed in this study is that non-clinically related readmissions have not been clearly addressed, which may need more factors beyond the inpatient care. Added to this intervention, Skoufalos et al. (2012) recommended the development of an educational program that would improve awareness of best practices for the reduction of SSIs, which has seemed to be effective in most surgical-related readmissions. The gap of knowledge in this study is that there is no sufficient data supporting the presence SSI among patients. Gillespie et al., on the other hand, indicated that thirteen articles fulfilled the requirements of inclusion criteria while conducting an interactive interview of the research literature on the preventive interventions of SSI. The gap noted in this study is that no reviewed searches utilized strategies which considered the patient participation in SSI prevention or input of related health professionals.
The Effect of the Trend on the Project
No doubt, the identification of the trends in the above data analyses is important in the development of the strategies of preventing risk factor complications related to clinical readmissions. In a broader positive context, the increasing concerns about the quality measures have resulted in effective interventions which this project finds useful in addressing the issues related to surgical readmissions (Berenguer et al., (2010). Additionally, the data analyzed is statistically significant the researchers because it helps them predict the outcomes of the project. Nonetheless, overreliance of the data provided in the articles without further collections of the data can be misleading (Polit-O’Hara, 2010). Thus, the data analyses presented only provide a benchmark for the findings of this project.
Summary of the Results
The data analyzed focused on the risk factors related to surgical readmissions as well as the interventions used to mitigate such healthcare issues. The data collected was based on the inferential statistics and the interactive literature review from MEDLINE, LILACS, Cochrane, PUBMED, IBECS, and SciELO database. Most of the studies used qualitative research design where the description of the relationship between the risk factors and SSI was given. The key finding was that SSI was the major risk factor in the healthcare practices. The interventions that were suggested for this healthcare issue include readmission, which proved to be the most effective, and the educational initiatives for the creation of awareness to eliminate the problem. Therefore, these trends are important for the development of this research since they give the benchmarks for the outcomes of this project.
References
Anderson, D.J, Podgorny, K., Berríos-Torres, S.I, et al. (2014). Strategies to prevent surgical site infections in acute care hospitals: 2014 update. Infect Control Hosp Epidemiol., 35(6), 605–627.
Berenguer, C.M., Ochsner, M.G Jr,, & Lord, S.A., Senkowski, C.K. (2010). Improving surgical site infections: Using National Surgical Quality Improvement Program data to institute Surgical Care Improvement Project protocols in improving surgical outcomes. J Am Coll Surg., 210(5), 737–741.
Chamberlain-Salaun, J., Mills, J., & Usher, K. (2013). Terminology used to describe health care teams: an integrative review of the literature. Journal of Multidisciplinary Healthcare, 6, 65–74.
Gillespie, B.M., Kang, E., Roberts, S., Lin, F., Morley, N., Finigan, T., Homer, A., & Chaboyer, W. (2015). Reducing the risk of surgical site infection using a multidisciplinary approach: an integrative review. Journal of Multidisciplinary Healthcare, 8, 473–487
Ovaska, M.T., Mäkinen, T.J., Madanat, R., Huotari, K., Vahlberg, T., Hirvensalo, E., Lindahl, J. (2013). Risk factors for deep surgical site infection following operative treatment of ankle fractures. Journal of Bone Joint Surg Am., 95(4), 348-353. doi: 10.2106/JBJS.K.01672.
Polit-O’Hara, D. (2010). Statistics and Data Analysis for Nursing Research. Boston: Pearson.
Rosen, A.K., Chen, Q., Shin, M.H., O’Brien, W., Shwartz, M., Mull, H.J., Cevasco, M., & Borzecki, A.M. (2014). Medical and surgical readmissions in the Veterans Health Administration: what proportion are related to the index hospitalization? Med Care, 52(3), 243-249. doi: 10.1097/MLR.0000000000000081
Skoufalos, A., Clarke. J.L., Napp, M., Abrams, K.J., Berman, B., Armellino, D., Schilling, M.E., & Pracilio, V. (2012). Improving awareness of best practices to reduce surgical site infection: A multistakeholder approach. Am J Med Qual., 27(4), 297-304. doi:s10.1177/1062860611422122
