Referenced in the Institute of Medicine’s book Keeping Patients Safe and derived from more than 20 years of research, the H2Model is used in national and international care settings to identify patients at risk for falls. Use of the H2Model in your healthcare facility meets guidelines of agencies such as The Joint Commission and American Nurses Credentialing Center (ANCC), which awards Magnet status.
The H2Model effectively and efficiently identifies at-risk patients and facilitates fall prevention in patients across the healthcare continuum.
The H2Model is part of the Upright™ Fall Prevention System that is supported by a complete and comprehensive Resource Guide; an interactive, computer-based education program; tools and forms; and partner support. Clinicians are trained not only to identify risk factors, but also to consider their root causes. This approach enables clinicians to develop effective fall prevention interventions – with precision and in real time.
The H2Model is based on a series of studies leading up to the prospective randomized study by Hendrich et al., published in 2003. This study of patients was drawn from a 750-bed acute care hospital over a 2-year period. Concurrent fall cases were identified from hospital incident reports as well as from reports of care coordinators and unit-based registered nurses, who notified the investigators of reported falls in their caseloads. Patient assessments were performed either by the principal investigators or by 1 of 4 trained registered nurses who worked as research assistants. More than 600 patient and fall-specific variables were included in the Hendrich Falls Assessment Tool (HFAT).
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The H2Model incorporates the results of the study into a useful tool that is effective and efficient for nurses to use in clinical practice at the point of care with their patients. The H2Model contains only 8 risk factors and requires only a few minutes to complete. Moreover, the H2Model is accurate: It is both sensitive (74.9% of high-risk patients were correctly identified in the study) and highly specific (73.9% of patients not at high risk of falling were correctly identified). When one considers that as many as 10% to 15% of falls are not predictable (e.g., drop attacks, seizures, arrhythmias, and strokes), the reliability of the H2Model is increased significantly.
Though myriad factors were examined in the study, only 8 factors were found to be independent risk factors:
1. Confusion/disorientation/impulsivity
2. Symptomatic depression
3. Altered elimination
4. Dizziness/vertigo
5. Gender (male)
6. Any administered antiepileptic medications
7. Any administered benzodiazepine medications
8. Compromised ability to rise from a sitting position
Though other conditions may contribute to 1 of the risk factors on the list above, such conditions alone are not statistically significant. We must identify and respond to true fall risk – not those conditions that merely hold the potential to become a risk factor
The Hendrich et al. 2003 study put to rest some fundamental misunderstandings about fall risk. First and significantly, the study found that age alone is not a risk factor for falls. Understanding this research-based fact also is essential to understanding the H2Model. An elderly person is no more likely to fall than a younger person, unless the elderly person’s age is paired with true fall risk factors.
Though research shows significant fall rates among the elderly, the Hendrich et al. 2003 study brought more clarity to the relationship between falls and aging. No longer can we view age as a single, independent fall risk factor.
A second finding in this study was the effects of medications on falls. Conventional wisdom suggests an increased fall risk always occurs with an increase in the number of medications a patient takes. This belief was found to be variable and not always accurate. Though polypharmacy can produce side effects that could place a person at risk for falling, such side effects are irrelevant unless the patient experiences them. The only 2 drug classes that proved to be exceptions to this finding were antiepileptic and benzodiazepine medications. These 2 classes proved to carry a statistical, independent fall risk. Thus, a patient taking 1 of these drugs could be at greater risk of falling than a patient taking 5 or 6 medications outside these drug classes.
The third significant finding the study addressed is the importance of the patient’s history of falls. Many researchers found past history of falls to be predictive of future falls. However, this is not necessarily true. The history of a fall must be carefully examined to determine the true root cause and/or the identification of the risk factor(s) that caused the fall in the first place. Simply put, a predictable fall always has an underlying risk factor(s) and it may or may not signal current risk, depending upon whether the risk factor(s) is still present. For example, if a patient fell due to an underlying condition, such as confusion/dementia or limited ability to rise from a chair, and this risk factor(s) is a chronic condition, it could lead to another fall. In this instance, the previous fall is not the risk factor. Confusion and the limited ability to rise from a chair (impaired gait/balance) are the true risk factors.
What can be more important than preventing an injurious patient fall or even the loss of a life? Does your organization meet today's patient safety standards and the rigorous national regulatory guidelines and quality improvement initiatives?