Inpatients staying for a long time in hospitals have received tremendous support from the authorities in the health care system through the development of strategies to reduce falls. This is especially focused on helping the elderly inpatients to reduce the risk of falls, which increase death risks and injuries. However, there is little evidence to show that the short-stay patients have received similar attention in the reduction of patient falls. Dykes, Carroll, Hurley, Lipsitz, Benoit, Chang, and Middleton (2010) embarked on a quantitative study to highlight the viability of integrating a fall prevention tool kit with health information technology to reduce patient falls. This paper looks into the study with a close focus on the study design.
The quantitative study assumed a cluster-randomized design. The researchers identified 4 health care facilities in the urban areas of the United States, and they singled out 8 units that provided regular care to patients. 4 units that handled 5104 patients throughout the experiment were observed without the interference of the safety conditions. 4 units were fitted with interventional measures, and they handled 5160 patients during the entire period of the study. The research entailed the development of a comparison between the numbers of falls recorded in the respective units (Dykes et al., 2010).
Strength and Weaknesses of the Design
The cluster-randomized design of the structure meant that the collected data was viable in studying the interventions. This is one of the advantages associated with the design. The researchers could positively identify the differences between the two experimental groups without propagating biases from the participants. Another strength of the design is that the researchers developed to control and intervention groups from the same health care facilities to eliminate the non-dependence factor of the statistical model.
One of the weaknesses of the study design is that the statistical power is compromised when the sample space is small (Armitage, Berry & Matthews, 2008). The researcher had to include a very large number of participants to gain the required statistical power. In addition to this, the viability of the study design depended on the ability to replicate results by the respective participants.
Cluster randomized sampling is one of the easiest and most reliable sampling methods because it is cheap and easy to execute. Instead of gathering participants from every health care facility in the nation, the researchers identified 4 facilities to represent the entire nation. Random clusters also eliminate researcher bias in the intervention groups. The precision of analyzing data from individual participants is quite challenged in this type of sampling, but the availability of a large sample space compensates for the errors. The researchers also ensured that the clusters represented the urban population, which means that the results are only applicable to health care facilities in urban areas.
One of the demerits of the sampling method used in the research is that the participants in the respective clusters might be similar, resulting in a misrepresentation of the population. The study should have used more than 4 health care facilities to enhance the reliability of the sample. Cluster randomization also faces challenges in the validity of the results because it is difficult to predict the specific size of the sample that brings the most accurate findings (Marsden & Wright, 2010).
The researchers identified that there was a clear difference in the number of falls between the control (n=87) and intervention (n=67) groups (P=.02). The control groups indicated a rate of 4.18 falls at a confidence interval of 95%, whereas the intervention groups indicated a fall rate of 2.08 at a 95% confidence interval, 0.61-3.56. Each statistic was based on ‘per 1000 patient-days. These findings revealed that the integration of a fall prevention tool kit in health care facilities has a significant positive effect in reducing falls among inpatients.
Dykes et al. (2010) conducted a quantitative study to identify the reliability of a fall prevention tool kit in reducing falls among patients in both short and long stay in health care facilities. The cluster-randomized design used by the researchers was the most reliable sampling method because it ensured that the researchers secured a large sample space from selected facilities. The large sample space of 10264 participants enhanced the validity of the data. Essentially, cluster-randomized sampling shares merits with simple random sampling and stratified sampling methods; hence, it was a relatively good design for the study.
However, the demerits of the sampling method also reduced the accuracy of the findings. A larger sample space would have provided statistical findings with higher accuracy (Eldridge, Ashby & Kerry, 2006). The size of the control and intervention groups was relatively equal; hence, the data collected by the researchers was reliable. The risk of propagating participant and researcher bias was eliminated by the sampling method and the analysis process. In conclusion, the deductions of the study are reliable, but their validity should be tested through the development of similar studies in the future to obtain the same results.
Armitage, P., Berry, G., & Matthews, J. N. (2008). Statistical methods in medical research. New Jersey: John Wiley & Sons.
Dykes, P. C., Carroll, D. L., Hurley, A., Lipsitz, S., Benoit, A., Chang, F., & Middleton, B. (2010). Fall prevention in acute care hospitals: a randomized trial. Jama, 304(17), 1912-1918.
Eldridge, S. M., Ashby, D., & Kerry, S. (2006). Sample size for cluster randomized trials: effect of coefficient of variation of cluster size and analysis method. International journal of epidemiology, 35(5), 1292-1300.
Marsden, P. V., & Wright, J. D. (2010). Handbook of survey research. Bradford: Emerald Group Publishing.