• Kristen L Swartzell, Janet S Fulton, and Barbara Manz Friesth.
• Ortholndy, Indianapolis, IN, USA.
• Medsurg Nurs. 2013 May 1;22(3):180-7.

IntroductionFalls are a common clinical problem in the acute care setting, and fall-related injuries can include fractures, subdural hematomas, excessive bleeding, and even death (Hitcho et al., 2004). Several instruments are used clinically to estimate a patient's risk of falling. The STRATIFY (Oliver, Britton, Seed, Martin, & Hopper, 1997), the Morse Fall Scale (Morse, Black, Oberle, & Donahue, 1989), and the Hendrich II Fall Risk Model (Hendrich, Bender, & Nyhuis, 2003) are three instruments widely used in clinical practice by nurses. To be clinically useful, a fall risk assessment instrument should be easy to use with only a small number of items, perform consistently across target populations, and have evidence-based scoring and good inter-rater reliability. Oliver (2008), author of the STRATIFY tool, questioned the merits of any instrument used to assess fall risk in hospital inpatients in the absence of interventions to modify the risk factors. Too often, patient assessment and assignment of a score become required tasks and resulting data do not drive interventions.PurposeThe purpose of this study was to explore the relationship between scores on the Hendrich II Fall Risk Model (HIIFRM) and fall occurrence as recorded in the medical record for patients diagnosed with diabetes mellitus, stroke, or heart failure in an acute care inpatient setting.MethodTo determine if a relationship existed between the occurrence of a fall and the HIIFRM score, the study used a random sample of patients who fell during admission and a matched control group of patients who did not fall. Fall cases were identified based on an admission Medical Severity-Diagnosis Related Group (MS-DRG) (Schmidt & Stegman, 2008) of stroke or secondary International Classification of Diseases (9th revision) (ICD-9) code (Hart, Stegman, & Ford, 2009) of heart failure or diabetes. Non-faller matched controls were selected at random from the same admission MS-DRG or secondary ICD-9 code as the fall case and matched for admission month/year.DiscussionThis study found HIIFRM scores to be related significantly to falls in the sample of patients with diabetes, but not in the sample of patients with heart failure. Although the HIIFRM demonstrated statistically significant mean differences in scores between patients who fell and those who did not, clinically the instrument failed to identify 44% of patients who did fall as being at high risk for falling. Given the negative consequences associated with falling, not identifying 44% of high-risk patients can have significant clinical implications.ConclusionsIn this study, HIIFRM scores were related to falls among inpatients in an acute care hospital who had a diabetes diagnosis, but not a heart failure diagnosis. The differ ences between patient groups based on medical diagnoses suggest the instrument does not perform equally across patient groups, nursing skill levels, or clinical units. Though the findings are statistically significant, the clinical concemrn remains that a large percentage of patients who fell were scored as low risk using the HIIFRM instrument. At some level, every patient admitted to an acute care hospital is at risk for falls. Patients sick enough to be in the hospital have underlying disease, are receiving physiologically altering medications and treatments, and are likely experiencing pain, fatigue, anxiety, sleep disturbance, and other symptoms that interfere with cognitive and physical functioning. The key to preventing falls among hospitalized patients may lie in addressing how the hospital environment creates risk. Nurses should continue to improve the ability to assess fall risk and implement interventions that modify or eliminate risk when possible.

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