Predictor tool may stop falls before they happen
Risk assessment at intake is key to program
Like most home care agencies, Johns Hopkins Home Health Services in Baltimore has falls among its patients. Indeed, says Amy Rader, RN, senior director of operations and clinical services, attended and unattended falls represent the No. 1 variance at the agency.
But in trying to solve the problem, Rader found there were few formal studies to help her. "When we did a literature search, we found there was very little information available on home care falls," she says. "There was a lot on acute care falls, and there are community studies, but very little on falls in the home."
For instance, according to the National Center for Injury Prevention and Control (a subsidiary of the Centers for Disease Control and Prevention in Atlanta), one in three people over the age of 65 falls each year, and for those 65-84 years old, falls are the second-leading cause of injury-related deaths. The cost of caring for people who fall was $1,400.
Going beyond traditional assessment
But none of that data specifically related to falls in the home. Rader decided Johns Hopkins should conduct its own fall study. The result is a fall predictor tool that should help caregivers prevent falls among patients.
There are no data yet available to see if the tool is working; the program only went into effect in December. But others in the home care industry say a predictor tool is a good idea.
Patricia Haley, MSA, manager of personal care at Mercy Home Care in Urbana, OH, started a fall prevention program a year ago that is put into effect following a fall. She says something put in place at the time of intake that goes beyond the traditional environmental assessment and notation of use of assistive devices can only help.
"What we have done has shown you can effect a strong decline in the number of falls after you note their occurrence," says Haley, noting that data should be available in the next month or so showing just how successful the fall prevention program is. "But if you start from the intake, maybe you won’t have to wait until someone has fallen to prevent future occurrences. I think it’s a great idea."
The initial study at Johns Hopkins had six objectives:
1. Refine predictive indicators for a fall.
Rader says she and the rest of her research team believed there were different risk factors between "fallers" and "non-fallers." To test this, they looked at previous patients and found that fallers were more likely than non-fallers to live alone, to use assistive devices, and to be on a large number of medications, especially benzodiazipenes, phenothiazenes, and antidepressants.
2. Develop a home assessment tool.
Once they determined what caused falls in the past the above predictors had an 89% accuracy rate Rader says they were able to come up with questions for nurses to answer that might help predict which patients were likely to fall. Along with the three factors, there were other risk categories, including mental impairment, history of falls, and the patients’ scores on the Mini Mental Status Exam (MMSE).
The resulting tool includes a mobility section with nine areas that are evaluated on a weighted point system, and a medication section with 12 areas for consideration. (See tool excerpts, pp. 41-42.)
3. Identify patients at risk for falls.
The tool accomplishes this with its four-tiered scoring level. Patients who have scores of two or less are considered minimally at risk. Patients with scores of two to five points have a small risk. Six to 10 points indicates a moderate risk, and 11 points or greater indicates a high fall risk.
4. Educate and train patients and their families on fall prevention.
Each of the risk levels has a related intervention to accomplish this, says Rader. Those who score under two points get no intervention. Patients with scores of two to five points have a Level 1 intervention that includes a home safety inspection and instruction and educational handouts on falls, fractures, and prevention. A score of six to 10 leads to a Level 2 intervention, which includes the Level 1 items plus an assessment and follow-up by physical and occupational therapists. A score greater than 11 points results in a Level 3 intervention that includes all items from the first two levels, daily patient checks, and consideration for a personal alarm system.
5. Compare the tool to community and acute care tools to determine differences.
6. Benchmark against community and acute care data.
Steps five and six have yet to be implemented, says Rader. The tool has only been in use since early December, and there are no data yet available. "We think we are different from them, but have no data to support that yet," she says.
While there are no numbers yet, Rader says the program offers other rewards that are already evident. "We know our staff is more aware of falls because of the tool," she says. "They seem to like the tool and it seems to be working."
In time, Rader would like to see if her results are supported by OASIS B data. "I want to take OASIS and compare myself to others."
Just doing the study is another big plus, says Rader. "Home care is still a very young industry. We are on the edge of the research initiatives that need to happen to promote and solidify our industry as a valid segment of health care." She says Johns Hopkins Home Health Services already has developed a strategy for disseminating the data from the study when it is complete. "We want to get our information out there. We want the results in the policy arena so we can share what we have learned."
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