Most hospitals are playing fair when it comes to reporting quality data, but CMS needs to look harder for those that are gaming the system with inaccurate information, according to a recent report from the Department of Health and Human Services (HHS).
CMS requires hospitals to self-report quality data and uses that information to determine financial penalties, and by law CMS must validate that data. In 2016, CMS reviewed the data of 400 randomly selected hospitals, as well as 49 hospitals targeted for failing to report half the healthcare-associated infections they had in the previous year, or for having low passing scores in the prior year’s validation process.
Almost 99% of hospitals that CMS reviewed passed validation, CMS reports. CMS took action against the six that failed, including reducing their Medicare payments, and it offered training to hospitals to help improve the accuracy of the quality data that hospitals report.
“However, CMS’s approach to selecting hospitals for validation for payment year 2016 made it less likely to identify gaming of quality reporting (i.e., hospitals’ manipulating data to improve their scores). CMS did not include any hospitals in its targeted sample on the basis of their having aberrant data patterns,” HHS reports. “Targeting hospitals with aberrant patterns for further review could help identify inaccurate reporting and protect the integrity of programs that make quality-based payment adjustments.”
HHS notes that CMS selected none of the targeted hospitals using analysis-based criteria, such as aberrant data patterns or rapid changes in reporting. In fact, CMS identified 96 hospitals with aberrant data patterns, but did not target them for validation even though the agency can select up to 200 targeted hospitals for review, according to the HHS report.
HHS recommends that CMS make better use of analytics to ensure the integrity of hospital-reported quality data and the resulting payment adjustments. CMS could use analytics to select an increased number of hospitals in its targeted validation sample, HHS suggests, and it could analyze the data to identify outliers such as hospitals with data patterns that are substantially different from other hospitals, determine which of those outliers warrant further review, and add them to the sample.
“For example, CMS could use analytics to identify hospitals with abnormal percentages of patients who had infections present on admission; this might help identify hospitals that engage in some of the data manipulation” that is known to occur, the HHS report says.
The HHS report is available online at: http://bit.ly/2pJ0UCG.