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WVU researchers pinpoint factor that predicts unplanned hospital readmissions

Man in blue scrubs in an operating room
New research out of WVU suggests that whether a hospital patient is on Medicaid can help predict whether he or she will be readmitted to the hospital shortly after discharge. A widely used index to assess readmission risk does not consider this variable—a blind spot that may lessen its accuracy in rural or impoverished areas.
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New research from West Virginia University suggests a widely used index to assess hospital patients’ risk of readmission may have a blind spot.

Physicians and nurses use a tool called the “LACE index” to identify which patients are most likely to be readmitted to the hospital because symptoms come back or complications arise. But research out of the Health Sciences Center suggests the index fails to consider a key variables that could improve predictions in West Virginia: whether patients are on Medicaid.

“LACE was validated and tested in Ontario, Canada,” said Jennifer Mallow, an associate professor in the School of Nursing and member of the research team. “The LACE index didn’t look at things like payor because they have universal healthcare.”

LACE stands for length of stay, acuity, comorbidity and emergency department—the four factors the index takes into account. Higher scores in these four areas usually indicate an increased risk of unplanned readmission.

For example, two women undergoing the same surgery to fix a broken wrist might score very differently on the LACE index. One woman would have a high risk of readmission if she had diabetes and dementia, visited the emergency room three times in the past six months, went straight from the ER to the hospital for her wrist surgery, and took four days to recuperate in the hospital. However, the LACE index would not calculate a high risk for a diabetic woman—with no other conditions—who scheduled her surgery in advance, was discharged the same day, and hadn’t been to the ER in years.

Mallow and her colleagues wanted to assess the predictive value of the index in West Virginia, where healthcare is privatized, poverty is endemic and the landscape is more rural. The team—which was led by Andrea Bailey, team lead for the Transitional Care Coordination Team in the School of Medicine, and included Laurie Theeke, director of the Ph.D. program in the WVU School of Nursing, and Karen Clark, chief and medical director of care management and utilization review in the WVU School of Medicine—reviewed the medical records of adult patients admitted to WVU Medicine’s J.W. Ruby Memorial Hospital between January 1, 2014, and December 31, 2015. They compared patients’ 30-day readmission rates to their LACE index scores, insurance status, functional issues such as illiteracy and substance misuse, and other characteristics.

In the patient population the group studied, the only LACE variable that correlated to increased readmission rates was comorbidities, and the correlation was not particularly strong. Length of hospital stay, being admitted through the ER and visiting the ER frequently in the months leading up to admission had no significant effect.

In fact, LACE scores in general were higher for patients who did not return to the hospital, even though the index’s design would lead one to expect the opposite.

The factor that did have a significant relationship to readmission rates was insurance payor type. The LACE index, however, does not account for this factor. And why would it? In Canada, where the index originated, everyone’s healthcare is publicly funded.

“You can’t take the payor out of the equation because there is likely a connection between payor type and health disparities,” said Bailey. “What we are finding is, you can teach someone about managing their clinical needs or making sure that they have this specific medication to treat their disease, but if they don’t have transportation to the pharmacy to pick up their medication, they’re going to fail.”

Including insurance status or more reliable measures of health disparities in the LACE index—or a similar tool used routinely—might help healthcare providers in rural areas predict which patients are in the most danger of readmission. Armed with that information, they can direct scarce resources with more precision and to the greatest benefit of patients. They might explain to a Medicaid patient how to reserve a free ride to the doctor for follow-up care, to name one example.

“We can’t just keep adding care, adding questions, adding burden,” said Mallow. “We have to be good stewards of the resources that we have, and we have to use them appropriately with patients.”

-WVU-

see/01/14/19

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