Patient Gripes Poor Indicators of Quality Care

Hospitals that have the best survival outcomes are not doing the best job in the area of patient satisfaction, says a healthcare economist. Better indicators of quality are the number of beds and patient volume.

While quiet hospitals with friendly doctors and nurses who are attentive and quick to answer bedside buzzers might boost patient satisfaction scores, they don't necessarily correlate with quality care.

A far more important indicator of quality outcomes is the number of beds in the hospital and patient volume, says Robert D. Lieberthal, lead author of the study appearing in Risk Management and Insurance Review.

"There is a lot of information patients can use to select a hospital," says Lieberthal, a healthcare economist and assistant professor at the Jefferson School of Population Health in Philadelphia. "However, this is usually a laundry list of indicators that may not mean much for the lay person or that they may be unaware even exists. Our method compares hospitals directly, so that a patient choosing between two or three hospitals can easily compare them and choose the highest quality facility."

Lieberthal's findings are based on a statistical methodology known as PRIDIT that was originally designed to detect automobile insurance fraud. He reconfigured the model and uses it to establish a predictable scale for hospital quality so that actuaries could map out reimbursement rates over years for programs such as  Medicare and the Patient Protection Affordable Care Act.

"We took what is simply a statistical method and put in quality data and what that method told us was which hospitals were of higher or lower quality," Lieberthal says.

"Then we validated that by looking at whether the quality scores in one year predicted the outcomes in the next year. We created a score for 2010—these are the higher and lower quality hospitals—and then we validated that that was correlated with mortality in 2011.  We both applied the method and validated it by saying that it could predict future outcomes."

Lieberthal's study relied on Medicare data available on Hospital Compare and other data from the American Hospital Association.

"Our method was designed to take these different types of data, hospital characteristics, process measures of care, outcome measures, and satisfaction and then from those measures, to determine the score. We didn't decide ahead of time what was important. Our statistical method told us which were the important measures and which ones were correlated with quality," he says.

"Hospitals that scored the highest or 'always' on satisfaction measures such as patients received help as soon as they wanted it or nurses or doctors communicated well, that was correlated with lower quality as measured through our method."

In other words, a patient in a large, high-volume hospital that is highly rated under Lieberthal's model might dislike the noise and bad food but will survive a life-threatening heart attack. "Based on this study the hospitals that have the best survival outcomes are not doing the best job of satisfying patients," Lieberthal says.

Lieberthal believes his method could be used by the federal government as a way to correlate the different quality measures that they collect and put them into a single quality score. "For example, right now Medicare has a model that they use to do mortality risk adjustment. The hospitals that tend to see sicker patients get an adjustment for that and the mortality scores that are reported in Hospital Compare," he says. "We would definitely see a value in Medicare applying this model to all of the data they generate, not just the data they put in Hospital Compare but in their much larger set of claims and other data that they generate as a large health insurer."

The study was funded by the Society of Actuaries.

"I was commissioned by them to develop a way to predict the quality of hospitals so that insurance companies and hospitals could plan their reimbursement rates," Lieberthal says.

"We see an implication of this study using the overall scores that we developed to pay more for the hospitals that were better or include the hospitals that were better in preferred provider networks. For the hospitals that didn't do as well, and some of that was because of these satisfaction measures, insurance companies might want to consider not including them in a preferred provider network."