Evidence-Based Practice: Literature Support
Brown et al., (2014) seeks to assess the effects of continuous patient monitoring in the medical-surgical unit in preventing unplanned transfers and length of stay in ICU as well as medical-surgical units. In assessing the effect of close monitoring, the study compared a 33-bed medical-surgical unit to a “sister” control unit where a 9-month pre-implementation and post-implementation period of equal length were compared. All the beds that were in the intervention unit were equipped with monitors. The monitors were responsible for the continuous assessment of heart rate and respiration rate for the patient. Patients charts were used which were 7643 and out of the total, 2314 were continuously monitored while the rest 5329 were used as control arms. The keywords used in the study included Heart arrest, Clinical alarms, Intensive care unit and Hospital rapid response teams.
From the research, the average length of stay of patients who were hospitalized before the implementation of monitors on the intervention unit was higher compared to patients who were in the intervention unit after the implementation of monitors. The decrease noted was from 4.0 to 3.6. Also, the total intensive care unit days were reduced after the implementation for monitors. The rate of transfers remained the same for both pre-implementation and post-implementation. To reduce the total length of stay, continuous monitoring on a medical-surgical unit was essential as it also lowers the intensive care unit days for the transferred patients as well as reduces code blue rates. The research can be applied to this study as it offers a solution to the high death rate among medical-surgical unit patients. The only weakness of the research is that it does not provide supporting studies and other databases describing the use of frequent monitors to reduce the mortality rate in medical-surgical units.
Chukuezi & Nwosu (2010) focus on the mortality pattern in the surgical wards where a five years review was used. They wanted to understand the cause of deaths in surgical wards and come up with changes that could improve patient’s surgical care as well as their outcome. The study was retrospective where available data was used in assessing mortality patterns. In assessing the cause of surgical care deaths, theatre operation registers, case notes, and ward registers were used which belonged to all patients admitted in surgical wards at Federal Medical Centre located in Owerri. The data comprised of all surgical patients regardless of whether the surgery was elective or emergency. The period of the reports was between January 1997 and December 2001 for the reliability of the results. The keywords used in this study included Pattern, surgical wards, and Review.
Although the information from the operation registers, case notes and ward registers was not compared with other resources, it was reliable as it was complete. Between 1997 and 2001, 4583 surgical admissions were recorded where 2751 were males while 1823 were female patients. Among them, 419 deaths were noted where the overall death per admission crude mortality rate was 9.14%. The study revealed that the leading causes of surgical deaths were acute abdomen followed by RTA with a head injury and the last cause was malignancies. Further analysis was also conducted as to which population is greatly prone to surgical deaths. Among the population, men had higher chances of death as compared to women with a ratio of 2.61: 1 (Chukuezi & Nwosu, 2010). To reduce the mortality rate among medical-surgical wards patients, healthcare campaigns, improvement of healthcare facilities as well as health education are among the things that will reduce in-hospital surgical mortality rates. Also, increased access to healthcare facilities as well as political will by the government to promote surgical infrastructure will also benefit surgical patients and reduce mortality rates. The research is significant in improving the outcome of surgical patients.
Krishnamurthy et al. assessed the mortality pattern and trends of deaths in surgery wards. A five-year retrospective study was used which was based in Hassan district hospital located in Karnataka India. All patients who were admitted to the surgery department of Hassan district hospital were assessed as from January 1st, 2011 to 31st December 2015 (Krishnamurthy et al., 2016). Databases were used which contained admission data of those patients. Also, data of expired patients was collected in detail from hospital databases to aid in the research. Some of the retrieved data from the databases included gender, age, surgical diagnosis, co-morbid conditions, and the procedures performed. The data was aimed to provide an accurate number of deaths in surgical wards for that period. The keywords used in the study were Tertiary care, Cause of death, In-hospital surgical mortality and Retrospective study.
The sample used in the study included 8962 patients who were from all surgical wards in that hospital. Among these patients, 5540 were male while 3422 were females. The assessment revealed that men are more prone to a surgical procedure and are at risk of deaths compared to their female counterparts. The crude mortality rate was 6.5%, and according to the information from databases, road traffic accidents were the leading cause of death due to head injuries that were approximated to be 27.86%. Burns followed by a percentage of 27.17 (Krishnamurthy et al., 2016). Among the deaths reported, men had a higher percentage compared to women. The study revealed a reduction in the number of deaths within the study period and the reduction was attributed to preventable causes such as GIT related deaths, trauma, and sepsis. The findings of the study explain the significance of proper care that can improve the outcome of surgical patients and reduce cases of deaths. What the study did was not compared to related studies to justify the findings hence a weakness.
Li et al., (2016) wanted to establish the risk factors for predicting mortality in critically ill medical-surgical patients who are under heparin thromboprophylaxis treatment. A randomized controlled trial was used in the building of a new prediction model for both hospitals and ICU mortality. The keywords used in the study were Critical care, Mortality, APACHE, Intensive care unit and prediction model. The study included 3746 critically ill patients who were not traumatized. The model used in predicting the 60-day hospital mortality was APACHE II score. The model was the best due to data collected, and it was well calibrated for effective data collection (Li et al., 2016). The results of the study showed that the APACHE II score was effective in predicting accuracy for hospital and Intensive Care Unit. The study was weak as it did not identify the risks for predicting mortality for surgical patients who are critically ill.