Hospital Data Mining Hits Paydirt

Adams speculates that the $2 million figure may have been higher still, but the data actually pointed them in the direction they needed to go in order to nip the problem at the root. Adams and his team were able to see which clinicians were missing opportunities to code additional procedures that had been performed.

For instance, the system flagged specific diagnosis which usually have lab tests associated with them if the lab test codes were missing. In doing so, they were able to capture all the charges associated with a diagnosis and then alert clinicians to be aware of their mistakes.

Plus, Adams and his team can spend less time resolving missing charges, which helps them more efficiently allocate staff resources. Since the charge capture process is no longer as time-intensive, Adams can also allocate clinical staff to higher-value activities. Moreover, the data mining tool is maximizing MultiCare's investment in their EMR because the system links the patient's record with financial information to help the system determine cost/benefit around specific care and service lines.

“This program has helped us create more lean practices and eliminate redundancy for charges,” Adams says. “We’ll always need humans for healthcare delivery, so you’re always going to have variations in the charging practices of these people...With the data mining and predictive modeling we can identify those trends and get to the root cause analysis and make the necessary adjustments quickly.”