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Doctoral Student Develops User-Friendly Model To Assess Nursing Home Quality

A graduate student in the University of Iowa's Tippie College of Business has developed what could be a valuable new tool to help people assess the quality of nursing home care.

In a pair of upcoming journal articles, Tippie doctoral student Justin Goodson outlines a method of analysis that suggests a nursing home's quality of care can be determined by looking at just three factors: number of certified nursing assistants employed at the home, the prevalence of bedfast residents, and the prevalence of daily physical restraints on residents.

Goodson's formula uses an economic model called a Bayesian Network that measures not only individual factors that make up a nursing home's quality, but also how those factors relate to each other. The result is a system that takes an immense amount of data and presents it in a way that's easier to understand than existing barometers.

Goodson points out that this will be important as America's population ages and more people need to assess care quality for themselves or family members.

"The method provides a better way to manage rafts of information that can easily overwhelm anyone trying to measure a home's quality of care," Goodson said. "It provides a more accurate representation of nursing home quality through a mix of measures, and it suggests that focusing on any one variable is not as powerful in describing home care quality as analyzing the variables all at once and determining their collective influence."

Goodson developed his model using data that measured 24 factors from about 250 nursing homes in Missouri and Wisconsin. Because the model analyzes how the factors relate to each other, it provides insight that other methods cannot. Among them is the discovery that a home's overall quality can be determined by the number of certified nursing assistants, bedfast residents and residents in physical restraints.

He's not sure why those factors are so indicative, but he said the model shows a clear correlation.

At the same time, he found that other factors generally considered to be important measures, such as occupancy rate, tend to be less indicative of a home's quality.

He said the results of his analyses compared favorably to the Observable Indicator of Nursing Home Care Quality, an accepted standard measure of care quality.

Like most other assessment models, Goodson's model assigns a score to a home's quality of care. But since it uses a more flexible model, it also provides a probability that the score will increase or decrease.

"Another model might rank a home a 7 on a scale of 1 to 10, but that's all it tells me, that it's a 7," he said. "This approach can help you determine if a home's quality is improving or in decline. The score might be a 7, but the model also tells you there's a 20 percent probability it could be an 8, or a 50 percent probability it could be a 5."

Goodson said his model would be useful for more people than just those who are considering moving into a nursing home or helping a family member move. For instance, nursing home administrators can use it to more accurately assess what parts of their facility are deficient and focus on improving them. It could also help time- and money-strapped state and federal inspectors know which factors they should spend their time inspecting when visiting a home.

Goodson outlines his method in two soon-to-be-published research papers: "Assessing Nursing Home Care Quality Through Bayesian Networks," co-authored with Wooseung Jang of the University of Missouri, which will be published in a forthcoming issue of the journal Health Care Management Science; and "Nursing Home Care Quality: Insights From a Bayesian Network Approach," co-authored with Jang and Marilyn Rantz of the University of Missouri, which will be published in the journal The Gerontologist.


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