Many rev cycle KPI top performing practices are only average when it comes to their EPH (encounters per hour). The main reason why they cannot catch the top performers is the false choice fallacy.
Old workflows die hard. Many rev cycle teams believe coding accuracy and coding productivity are mutually exclusive.
In a real-world scenario, doctors make coding mistakes. After all, they didn't go to medical school to become coders. Thankfully, we have processes that support the doctors and empower the coders to catch and correct these mistakes. Coders are not only identifying encounters that could have caused a denied claim, but finding missed revenue opportunities. For example, they may identify an established patient visit where the patient has not been seen in the past 3 years and, as a result, can be changed to a new patient visit. Or they may find an injection which was documented but not coded.
Through our work with 100+ practices over the past 4 years, we can now quantify these types of coding changes. The net impact is an additional $1.24 of reimbursement per encounter.
The White Glove Analytics team reviewed over 8.5 million encounters looking for coding changes that impacted expected reimbursement. Overall, we identified 825,498 (9.7%) encounters with a coding change that increased (or decreased) the revenue generated from a particular encounter. Only a small percentage of encounters had a coding change that impacted reimbursement. These encounters had an average change of $12.75.
Now that we can quantify the amount of revenue captured through the post-encounter coding process, we can evaluate the Coding Accuracy vs. Coding Productivity comparison.
