Learning Theories and Diversity 

Abstract 

Healthcare providers go to work every day with the hope of improving the health of their patients. This goal can only be achieved when patient safety is the top priority of all healthcare providers. The major cause of sudden death among senior citizens in the U.S. aged 65 or over is death by falling (CDC, 2024). Around 2.8 million senior citizens are admitted to emergency rooms every year for treatment for injuries caused by falling (CDC, 2024). Confusion, low urination control, and physical ailment are some of the major factors that increase the risks of falling in senior citizens. Inpatient elderly people are also at increased risk of falling which may cause serious injuries or death. Agency for Healthcare Research and Quality asserts that the hospitalized patients’ fall rate is 700,000 to 1 million every year (Hogan et al., 2021). Research by Kim et al. (2022) showed that for every 1000 bed days, the number of falls is between 3.5 to 9.5. Mostly, elderly patients fall due to confusion, physical immobility, and urgent need for urination. 

Morris et al. (2022) research included 931 hospitalized elder people out of which, 633 were at a huge risk of falling due to physical or mental disorders. One of the adverse consequences of falling is that it can increase hospitalization days for patients so that they can be treated for injuries. For the solution of this issue, the Schmid tool was developed at OhioHeatlh to reduce the number of fallen patients. This treatment intervention first identifies the patients with the highest fall risk and then develops a treatment plan to reduce the risk of falling (Dykes et al., 2020). To assess the risk factors in patients, healthcare providers evaluate the patient’s previous number of falls, mental condition, physical condition, ability to urinate without support, and current medications they are provided. Combining the data with informatics models will provide efficient analysis for the evaluation of the Schmid tool for improving patient security and care quality. 

Introduction 

The number of adults visiting the emergency room for the treatment of injuries by falling has reached around 2.8 million per year (CDC, 2024). The rate of falls leading to injuries and hospitalization is between 700,000 and 1 million every year (Hogan et al., 2021). Patients had to face huge healthcare bills due to longer hospitalization days. To reduce this issue, the Scmid tool is used to identify high risks of falling in patients (Dykes et al., 2020). This tool determines various factors of patients such as the patient’s previous number of falls, mental condition, physical condition, ability to urinate without support, and current medications they are provided. A combination of data with informatics models can be utilized to evaluate Schmid’s tool to improve patient safety and quality.

Analyzing the Use of the Informatics Model 

The Schmid tool is commonly utilized to identify patients with the highest risk factors for falls. Nurses use four diverse categories to classify patients who are experiencing falls. One of the four categories is flexibility which provides four choices, each group with a numerical indicator. Patients are divided based on mobility such as (0) the ability to walk without support, (1) requiring support to walk, (1b) immobile, or (0a) the inability to walk overall. 0 represents alertness, 0b shows unresponsive, 1a means confused, and 1b is always confused. Number three is disposal. Patients are categorized as follows: (0)fully independent (1a) fully independent but frequent (1b)requiring support, or (1c) incontinent. The fourth category is the history of previous falls. Patients are categorized as one of the following: (0) none (1) fall before getting hospitalized (2) fall during hospitalization. The last category is medications. The drugs vary and options include (1a) anticonvulsant drugs, (1b) psychotropic, (1c) tranquilizers, (1d) hypnotics, and the last option is (0) no medication (Abell et al., 2020). 

Literature Review 

Falls in hospitals have decreased for some time but they are still a risk of death in elder patients a crucial issue for healthcare providers. It is the leading cause of harm to patients as per reports from hospitals (CDC, 2024). Hospitalized falls include huge costs and increased hospitalized days but the patients with increased injuries and adverse quality of life weigh more harm. In 2008, Medicare and Medicaid eliminated fall-related injuries for hospitalization reimbursement (Hoffman et al., 2020). Thus, hospitals must take precautionary measures to prevent patients’ fall rates to prevent financial burden. According to clinician anecdotes readmission rates due to fall injuries are highest among elder patients with traumatic injuries (Cook et al., 2023). The results of this research suggest that death and hospitalization rates from falls in elder people have been increasing. A concerning trend is that the re-admissions after a previous fall are increasing. With the increasing population of elder people, it is important to develop social support groups and preventive measures to reduce the rate of falls in elder patients (Ganz & Latham, 2020). Centers for Disease Control and Prevention states that the most common mortality cause in people aged 65 or older is falls in the United States (CDC, 2024). The risk of harmful health impacts from falls is highest in elder people in the United States because they happen often and are costly. However, prevention strategies can help in preventing falls in elder people and it is not always the natural consequence of aging. 

Conclusion

The detailed approach described above can decrease the fall rate that occurs in hospitals. In the United States, the major cause of death in elder people is falls according to previous research. The informatics model served as a guide to develop Schmid’s tool for improving quality. Several reduced falls are observed in hospitalized patients by incorporating fall risk assessment and fall scorecards. 

References

Abell, J. G., Lassale, C., Batty, G. D., & Zaninotto, P. (2020). Risk factors for hospital admission after a fall: a prospective cohort study of community-dwelling older people. The Journals of Gerontology: Series A, 76(4). https://doi.org/10.1093/gerona/glaa255 

CDC. (2024). Older adult fall prevention. CDC. https://www.cdc.gov/falls/about/index.html#:~:text=Falls%20among%20adults%2065%20and 

Cook, A., Swindall, R., Spencer, K. L., Wadle, C., Cage, S. A., Mohiuddin, M., Desai, Y., & Norwood, S. H. (2023). Hospitalization and readmission after single-level fall: a population-based sample. Injury Epidemiology, 10(1). https://doi.org/10.1186/s40621-023-00463-4 

Dykes, P. C., Burns, Z., Adelman, J., Benneyan, J., Bogaisky, M., Carter, E., Ergai, A., Lindros, M. E., Lipsitz, S. R., Scanlan, M., Shaykevich, S., & Bates, D. W. (2020). Evaluation of a patient-centered fall-prevention tool kit to reduce falls and injuries. JAMA Network Open, 3(11), 1–10. https://doi.org/10.1001/jamanetworkopen.2020.25889 

Ganz, D. A., & Latham, N. K. (2020). Prevention of falls in community-dwelling older adults. New England Journal of Medicine, 382(8), 734–743. https://doi.org/10.1056/nejmcp1903252 

Hogan, Q. B., Renz, S. M., & Bradway, C. (2021). Fall prevention and injury reduction utilizing virtual sitters in hospitalized patients. Computers, Informatics, Nursing, Publish Ahead of Print(12). https://doi.org/10.1097/cin.0000000000000773 

Hoffman, G. J., Tinetti, M. E., Ha, J., Alexander, N. B., & Min, L. C. (2020). Prehospital and posthospital fall injuries in older us adults. JAMA Network Open, 3(8), e2013243. https://doi.org/10.1001/jamanetworkopen.2020.13243 

Kim, J., Lee, E., Jung, Y., Kwon, H., & Lee, S. (2022). Patient‐level and organizational‐level factors influencing in‐hospital falls. Journal of Advanced Nursing, 78(11). https://doi.org/10.1111/jan.15254 

Morris, M. E., Webster, K., Jones, C., Hill, A.-M., Haines, T., McPhail, S., Kiegaldie, D., Slade, S., Jazayeri, D., Heng, H., Shorr, R., Carey, L., Barker, A., & Cameron, I. (2022). Interventions to reduce falls in hospitals: a systematic review and meta-analysis. Age and Ageing, 51(5), 1–12. https://doi.org/10.1093/ageing/afac077