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Experts Find ‘Glaring Flaws” in Data Affecting Hospital Reimbursement

Nationwide Inpatient Sample under-reports key factors

The Nationwide Inpatient Sample (NIS), which is derived from billing data, is used for everything from calculating hospitals’ risk for readmission or surgical complications to researching the effects of health-policy changes and access to care.

But there are “glaring flaws” and gaps in these data due to underreporting of patients' alcohol and tobacco use, as well as their weight and body mass, according to researchers at the Johns Hopkins University School of Medicine. Their findings were published in the journal PLoS One.

According to the NIS, the U.S. prevalence of overweight is only 0.21%, and the prevalence of obesity is 9.6%. The researchers compared the NIS data with information in the Behavioral Risk Factor Surveillance System (BRFSS), a federally sponsored telephone-administered survey in which more than 500,000 American adults answered questions about their health. According to the BRFSS data, 35.8% of Americans are overweight and 27.4% are obese.

“Why is no one coding overweight at all when it’s 33% of the population?” corresponding author Susan Hutfless, PhD, asked.

The NIS also reports that alcohol abuse affects only 4.6% of the population, compared with 18.3% in the BRFSS, and that tobacco use affects12.2% of the population, compared with 20.1% in the BRFSS.

This underreporting can have significant implications for a hospital’s bottom line, Hutfless said, since missing data can result in inaccurate risk adjustments and, therefore, unfair reimbursement.

“A very high number of patients are having their risk coded as lower than it actually is,” she said.

The information about weight, alcohol use, and tobacco use that is included in a patient’s recorded medical history doesn’t usually make it onto a hospital bill, and the NIS is based on billing data.

“When people are coding, they are coding the ‘big-picture’ conditions,” Hutfless said. “They’re coding myocardial infarction, but not necessarily that the patient is also an overweight smoker. That information would certainly affect the patient’s risk of readmission.”

Hutffless and coauthor Elie S. Al Khazi, MD, PhD, suggest that “incorporating meaningful-use measurements into datasets that comprise billing codes could enhance the completeness without adding additional coding burden.” As for possible long-term strategies, they write that “financial incentives in coding, publicly available information on items used to adjust for risk in the existing Medicare products, and random audits” could increase accuracy.

Sources: HealthLeaders Media; December 8, 2015; and PLoS One; November 4, 2015.

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