case study

Industry: Healthcare

Insurance Capture

FOCUS: Updating Patient Insurance Information


Insurance Capture Buffer (ICB) is a system that captures the insurance on file for a patient. Within this system, a daily list is created for patients needing review and updated. These reviews are required every six months for patients seen within the facility. When insurance capture does not occur when required, it creates an ICB Exception. As of September 2015, the Healthcare System has a 23% ICB Exception Rate, equating to 11,126 missed opportunities, which is well above the goal of less than 15% These missed opportunities results in a significant loss of revenue.

With these exceptions, the Hospital System did not have the correct insurance information on file, which in turn could create erroneous billing statements for patients or other insurance companies. Patients are billed based on the insurance on file. If the insurance is incorrect the hospital cannot bill the patients and/or insurance companies correctly and ensure that they receive the revenue for the care provided. Therefore, with higher rates of ICB Exceptions, it not only could lead to dissatisfied patients, but could impact the revenue cycle for the healthcare system as well.


To ensure a marked improvement in the rate of ICB Exceptions, the above team set up the goal to improve the rate of ICB Capture to 85% from 77% and decrease the Exceptions to 15% from 23%.  Analysis of the current ICB process indicates a lower insurance capture rate than what is acceptable by standards. The rolled throughput yield (RTY) is 0.5184, indicating a need for improvement. This conveys that that there are many process deviations.

Six root causes were identified.  There was a lack of insurance card scanners, many inoperable scanners and software, no buy-in from all of the intake staff, lack of discipline, and process deviations, all of which leads to a poor understanding of the importance and actual process of insurance capture.

Having a keen understanding of the root causes which resulted in the higher exception rate led the team to develop a list of improvements. These improvements included purchasing 70 additional insurance card scanners, having the information technology department troubleshoot inoperable scanners and install scanner software, adding insurance capture duties to the intake staff’s functional statement, adding insurance capture performance goals to the intake staff’s fiscal year performance plan, meeting with all clinical services to discuss the operational and fiscal goals tied to insurance capture and retraining all intake staff on correct ICB processes.

A small subgroup came together and 1) educated all clinic management and intake staff on operational and fiscal goals, 2) discussed compliance rate and how it affects the intake staff’s FY16 performance measures, 3) distributed additional scanners throughout the Healthcare System, 4) troubleshooted and fixed inoperable scanners and 5) educated intake staff on proper ICB processes, and 6) reviewed intake staff clinic’s for accuracy in the ICB software. As a group, it was determined that a pilot for the ICB processes would be run for two weeks in three different clinical areas implementing the ICB processes and the use of the Standard Operating Procedure (SOP) as resources. It was decided that the three clinics would include high and low ICB exception percentages and overall ICB opportunities. Supervisors for the three clinics were trained in ICB procedures and were provided all of the resources needed for the pilot.


These solutions were piloted in three clinics for two weeks which resulted in a significant improvement (93%) in the overall ICB Exception Rate. The ICB Exception rate was monitored for two weeks pre- and post-implementation. Upon completion of this pilot, the team saw a positive difference of 40% in the ICB Exception rate for these 3 clinics (47% to 7%). Using these results allowed the team to analyze how accurate and sustainable the new process is. First, a C-chart was produced clearly showing that post-pilot there was significantly less variation in the data as it was much closer to the lower control limit (0%) if not exactly on the line Second, a T-Test was run allowing the team to have 99.9% confidence that the rate of exception will improve with the implementation of the changes tested in our pilot.

* Due to nondisclosure agreements, the organization referenced in this example cannot be disclosed.