Data-Driven Decisions in a Patient-Centered World

Author: Brian Taylor, Au.D.

Regardless of your political persuasion, it is likely that you were somewhat surprised by the results of the 2012 presidential election. That is unless you were reading Nate Silver. Mr. Silver, a statistician, runs the political blog FiveThirtyEight.com. He is also the author of the bestselling book The Signal and the Noise. By aggregating data from more than 20 national polls and systematically evaluating trends from past elections, Silver was able to correctly predict within a few percentage points the outcome of the election in 49 of 50 states. He did this by relying on something called Baye’s theorem. In its basic form, Baye’s theorem is a mathematical way to make predictive insights using three known variables and one unknown one. Although there is not space here to go into the details, Baye’s theorem has a lot of utility for audiologists. For example, if we know the current return rate and the probability of a patient experiencing significant benefit because we conducted the fitting using a set of best practice guidelines, we could be more confident in the results of our recommendations of new hearing aids to an individual. You will need to read Silver’s colorful example in his book on how probability is actually calculated.

One data point that should give us more confidence that using best practices make a big difference, comes from Abrams et al (2012). Abrams and colleagues compared a “first fit” method, (use of the audiogram to determine a prescribed target based on patient preference) to using probe mic measures to verify an independently-derived target (in this case the NAL-NL1). Using the APHAB to measure benefit, the researchers found that using probe mic measures to verify the “closeness” of the fit to the prescription target – a best practice recommended by many expert panels -- is more likely to result in higher patient outcomes. Using Silver’s methodology, the results of this study suggest that using probe mic measures used to match an independently-derived target increases the probability of a better outcome for the patient.

Why does this matter? In a world where people can easily evaluate everything from the quality of food and service at a local restaurant on Yelp.com to the quality of healthcare delivery from a physician on Healthgrades.com, it forces us to place the patient at the center of our universe. In this age that we live in, we are constantly being judged by everyone from third-party payers to patients. Making data-driven decisions is one way to maintain and continually improve quality in our practices. This issue is dedicated to that emerging reality.

References
Abrams, H. et al (2012). Initial-fit versus verified prescription: comparing self-perceived hearing aid benefit. Journal of the American Academy of Audiology. 23, 10, 768-778.

Silver, N. The Signal and the Noise: Why so Many Predictions Fail- but Some Don’t. New York, NY: Penguin Press, 2012.