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Causal inference may be defined as the process in which the effect of a certain phenomenon is determined. As a matter of fact, people navigate the world every day on the basis of knowledge received from causal inference. Causes inferred from all types of data help solve problems. Thus, this process may be applied to the evaluation of the efficiency of the STEADI algorithm for the prevention of falls in senior patients in a small clinic in Florida.
In general, randomized controlled trials may be regarded as the standard for the establishment of causal inference. An ideal experiment implies the division of participants into treatment groups, and the average effect will be estimated through the averages of variables in groups. At the same time, randomized controlled trials may be expensive, time-consuming, or practically impossible, especially in small clinical settings. Thus, for a small outpatient clinic, causal inference may be inferred from observational data.
The STEADI algorithm includes three core elements – screening, assessment, and intervention – to identify vulnerable individuals, at-risk factors, and methods of fall prevention on the basis of received information. In order to evaluate its efficiency, the algorithm should be implemented in relation to adults aged 65 or older. First of all, they should be screened annually with a 12-question tool to distinguish patients vulnerable to falls (Eckstrom et al., 2017). Subsequently, their fall history and all risk factors, including medications, comorbidities, poor visual acuity, feet and footwear issues, and potential home hazards, should be assessed.
Finally, together with a patient, a specific intervention plan should be created to reduce identified risk factors. A period of follow-up may last up to 3 months; during it, a health care provider may address barriers if they appear to improve patient receptiveness (Centers for Disease Control and Prevention, 2019). After one year, it will be possible to compare the number of falls before and after the implementation of the STEADI algorithm to evaluate its efficiency. Referring to causal inference, it will be clear whether a particular cause (the algorithm) has an effect (reduced number of falls).
References
Centers for Disease Control and Prevention. (2019). Algorithm for Fall Risk Screening, Assessment, and Intervention.
Eckstrom, E., Parker, E. M., Lambert, G. H., Winkler, G., Dowler, D., & Casey, C. M. (2017). Implementing STEADI in academic primary care to address older adult fall risk. Innovation in Aging, 1(2), 1-9.
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