Health care uses PICO(T) questions and an evidence-based approach.
Evidence-based practice (EBP) has become one of the most important parts of nursing work in recent years. One of its most important parts is using a PICO(T) framework to solve clinical issues. The letters in the PICO(T) system stand for Patient population, Intervention, Comparison, Outcome, and Time. It has been shown to help doctors, nurses, researchers, and many other people who work in health care get results that are useful and trustworthy. With the help of the PICO(T) framework, we look at a practice problem that has to do with elderly patients who fall while they are in the hospital. We also look at sources of data that might be able to help us answer a PICO(T) question about geriatric patient falls. There is also an explanation of the results from papers or other sources of evidence, as well as an explanation of how the results from certain sources of evidence can be used to make decisions about a PICO(T) question.
The PICO(T) Approach for Older People Who Fall
Patient falls are a big safety problem not just in the U.S. but all over the world. Even though there are now interventions based on evidence-based practice, this safety problem has not been fully fixed. Only in the U.S., the number of falls per 1000 patient days ranges from 3.3 to 11.5. Lelaurin and Shor (2019) say that 700,000 to 1 million patient falls happen every year in U.S. hospitals. One group of people who are more likely to fall in a hospital is the elderly. About 45% of the time, older people who fall get hurt, and 10% of the time, they get hurt so badly that they die (Khalifa, 2019). These numbers only show how bad it is for elderly patients to fall, as well as the fact that falls in hospitals are still a big safety issue that needs to be fixed. The PICO(T) method would help a lot to solve this safety problem because it would include interventions that are based on facts. This might be the missing piece of the puzzle when it comes to trying to solve the problem of hospital falls.
In order to use the PICO(T) method to solve the problem of senior patient falls, we need to come up with a PICO(T) question that can be answered in a reasonable way. So, our question will be: “Do bedside rails make older people less likely to fall when they are in the hospital compared to when they don’t use bedside rails?”
Sources of Evidence
To answer a PICO(T) practice question, you need to use sources that are reliable and appropriate. EBP requires nurses to be able to do study and find credible, evidence-based, scholarly sources. This is one of the core skills that nurses learn in nursing school. Some websites and databases that have this kind of information are the Cumulative Index to Nursing and Allied Health Literature (CINAHL) database by EBSCO publishing, the American Cancer Society (ACS), the PubMed website (https://pubmed.ncbi.nlm.nih.gov/), the Journal of Pain by the American Pain Society (APS), and the International Journal of Pain and Relief. It is also important to make sure that the information from these sources is current, reliable, and relevant to the topic, which in this case is geriatric patient falls.
The TAARP criterion would be a good way to figure out how recent, reliable, and important information is. (BYU, 2021) TAARP stands for time, authority, audience, relevance, and perspective. The evidence you use to answer a PICO(T) question must be the most recent and, if possible, no more than 5 years old. This is called “timeliness.” Journals should be reviewed by other experts, and writers should list their credentials to show that they are qualified to write about patient falls. The authors also need to think about who the piece is for so that they can get their message across to the right people. When nurses check the relevance of a source, they have to think about whether the source backs up their ideas and answers their study questions (BYU, 2021). Last but not least, everyone has some bias. So, it would be smart to choose a source that helps you understand both sides of the issue.
Findings
The idea that bedside bars can keep hospitalized patients from falling out of bed is as old as nursing itself. This has always been done, as far back as nurses can remember. It has stood the test of time because it works well to keep bedridden people in their beds. But Marques et al. (2017) haven’t found much evidence that it works better than bedside bars or any other kind of restraint to keep people from falling in hospitals. This may be in part because people don’t only fall in hospitals when they are in bed or near their beds. Patients, especially older patients, fall because of a number of things that make them more likely to do so. (Timmons et al., 2019) Some of these are getting older, having multiple health problems, a history of falling, being a woman, having a problem with how you walk, losing your mind, and other things in the hospital like slippery or wet floors and weak beds.
But people have come up with a number of ways to deal with elderly patients who fall. The most important of these has been making the patient’s surroundings safer with things like flooring, beds, and rails, as well as with wearable devices and floor and room sensors (Timmons et al., 2019). Both methods have been successful on their own, and tests are currently being done to see what happens to the number of patient falls when they are combined. Bed and bedside chair pressure sensors tied to radio pagers have already been tried with some success (Timmons et al., 2019) to prevent bedside falls in older people. The fact that the papers used to research this topic came from EBSCO Publishing’s Cumulative Index to Nursing and Allied Health Literature (CINAHL) database shows how reliable and useful the database is for health and nursing information.
How the results matter
The results of this study show that our PICO(T) question is still an area that needs more research. Marques et al. (2017) found that, despite the size and importance of the problem, there aren’t many studies about the use of bedrails to keep elderly patients from falling. This may be because of the social side of the issue. Because of this, asking this question gives experts the chance to try to find answers through studies. But since this is a pretty old practice, it might be better to put more time and money into the study by Timmons et al. (2019). This is because the study is the most important because it keeps up with the latest technological advances, which is the way the world is going. More research should be done to find out how well new technologies can be combined with old ways of doing things, like using bedside bars.
Conclusion
Using EBP interventions, the PICO(T) strategy is a good way to answer a difficult clinical question. When using the method, it is important that nurses find reliable and relevant sources from which to get evidence. They should also use the TAARP criterion to figure out how reliable journals and articles used as evidence are. The question of whether bedside rails can help keep older people from falling out of bed needs more study. Another option is to combine bedside rails with modern technology.
References
Brigham Young University (2021). Step-by-Step Guide & Research Rescue: Evaluating Credibility. Retrieved from https://guides.lib.byu.edu/evaluating-credibility
Khalifa, M. (2019). Improving Patient Safety by Reducing Falls in Hospitals Among the Elderly: A Review of Successful Strategies. In ICIMTH (pp. 340-343). Retrieved from https://doi:10.3233/SHTI190088
LeLaurin, J. H., & Shorr, R. I. (2019). Preventing Falls in Hospitalized Patients: State of the Science. Clinics in Geriatric Medicine. May; 35(2): 273-283
Marques, P., Quieros, C., Apostolo, J., & Cardoso, D. (2017). Effectiveness of bedrails in preventing falls among hospitalized older adults: a systematic review. JBI Database of Systematic Reviews and Implementation Reports. Retrieved from https://doi:10.11124/JBISRIR-2017-003362
Timmons, S., Vezyridis, P., & Sahota, O. (2019). Trialing technologies to reduce hospital in‐patient falls: an agential realist analysis. Sociology of Health and Illness, 41(6), 1104-1119. Retrieved from https://doi.org/10.1111/1467-9566.12889
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