As a matter of fact, medical sciences are highly vulnerable to various sources of bias that may be regarded as a considerable issue due to their ability to impact research outcomes. Mistakes may occur in any study and the investigation of the efficiency of the STEADI algorithm for the prevention of falls among elder patients in Florida’s urban clinic is not an exception (Centers for Disease Control and Prevention, 2019). On the basis of their classification proposed by Althubaiti (2016), errors may be the following:
- Social desirability bias. As a variant of self-reporting bias, social desirability bias occurs when participants deliberately provide incorrect answers in relation to sensitive or private topics (Althubaiti, 2016). In the case of the STEADI research, social desirability bias may occur if patients incorrectly provided information that was subsequently reflected in EHRs. Thus, the outcomes of the research may be biased due to data collected from EHRs.
- Recall bias. In contrast with social desirability bias, recall bias occurs when participants cannot accurately recall past events. In the STEADI research, this error may be observed both in EHRs and physicians’ reports. In other words, both patients and clinicians may forget or recollect some facts in a wrong way that will affect findings.
- Systematic and random measurement error bias. As the research is quantitative, measurement error bias may occur during the counting of the number of falls or due to including the results of those patients who did not visit a clinic for follow-up appointments.
- Confirmation bias. Study outcomes are frequently biased by researchers’ personal values and beliefs or the results of previous investigations. Thus, the study of the STEADI algorithm may be biased by previous research that confirms its efficiency for the prevention of fall in senior patients.
Althubaiti, A. (2016). Information bias in health research: Definition, pitfalls, and adjustment methods. Journal of Multidisciplinary Healthcare, 9, 211-217.
Centers for Disease Control and Prevention. (2019). Algorithm for Fall Risk Screening, Assessment, and Intervention.