Making the Data Work for You: How to Turn Medical Coding Audits into Actionable Insights

Making the Data Work for You: How to Turn Medical Coding Audits into Actionable Insights

Coding audit data can be extremely beneficial to the success of a healthcare organization. However, oftentimes, the vast amounts of data coding audits collect can suffocate the organization, especially those that don’t know how to cultivate value and act on the insights received from the audit process. In addition, most medical coding compliance audit programs objectively evaluate multiple data sets and generate reports on the findings. But, if the reports are not properly cleaned, analyzed, and examined, the data is not being used to its fullest potential. Thus, can’t generate strategic next steps toward the company’s quality improvement. Data and quality improvement go hand-in-hand.

So, how do organizations take these vast amounts of data and turn them into strategies?

The answer lies within this article. This article will walk readers through the entire process of how to reap the most reward from medical coding audit data. Firstly, the process outlined below begins with identifying the key performance indicators (KPIs) organizations should set in their coding audits. The process then moves to translating that data into understandable information for the company’s decision-makers.

Properly Identify & Develop Key Performance Indicators for Medical Billing Audits

1. Identify the organization’s/department’s goals. The first step in the process is to identify the organization’s core values and the objectives for each HIM department regarding coding audits and its data application. Additionally, these values and objectives need to be clear, realistic goals for the company; these will serve as benchmarks throughout the entire process. A great example goal for the HIM department could be maintaining the industry-standard MS-DRG accuracy rate of 95%. The HIM department could also strive to reach a financial breakdown of overcoded cases vs. undercoded cases that is close to 50/50.

2. Select the KPIs for the coding audits. Next, pinpoint the KPIs that will drive the organization toward achieving the desired goal or objective (see examples below). The Quality KPI Decision Matrix* that YES developed, displayed below, provides some characteristics organizations should use to identify the KPIs to use in medical coding compliance audits.

Key Characteristics to Include

These characteristics include:

• It is available and measurable.
• It aligns with a strategic company goal.
• It drives clear actionable next steps.
• It is generated in an allotted time frame.

In addition, each KPI that organizations choose should include the following details, compiled by Unanet (2019):

• Concrete, specific details about the company’s goals; including, the success criteria that clearly indicates what needs to occur in order for the stakeholders to consider the KPI has been met.
• Realistic goals for the organization.
• A way to measure the audit progress.
• A realistic deadline of completion.

The table below provides an example of the KPIs that YES measures in our client coding audits. Notice that the table also clearly identifies the objectives and success criteria for each KPI.

During this KPI selection process, it is important for healthcare organizations to understand the difference between metrics and KPIs. As noted by the founder and managing principal of Catalyst Strategic Marketing, Richard Hatheway, “The easiest way to understand the difference between metrics and KPIs is to first define them both as a quantifiable measurement of a strategic or tactical activity” (LinkedIn, 2016).

A KPI is a quantifiable or measurable value that reflects an overall business goal or objective (strategic). A metric is also a quantifiable or measurable value; it reflects how successful the activities are (tactical) in support of achieving the KPI. In other words, metrics support the KPIs.

Translate the Audit Findings Into Information to Fuel Strategic Planning

3. Clean and organize the coding audit data. Going beyond simply generating the audit data reports, the organization needs to examine the data for insights and take action. But, first, organizations need to “clean” and organize the statistical findings, so it is easily readable for analysis. Cleaning or “scrubbing” the data will directly affect the organization’s ability to analyze and provide recommendations based off the most relevant information.

Data cleansing is the process by “which you go through all of the data within a database and either remove or update information that is incomplete, incorrect, improperly formatted, duplicated, or irrelevant” (Blue-Pencil Information Security, 2018).

In the coding audits conducted by YES, the auditors cleanse the data by plugging it into a database with several automated SQL queries. This will identify the data points that the auditor has defined and separate them from hundreds or even hundreds of thousands of rows of raw data. Additionally, the auditor identifies which data points are relevant by determining those that directly support or link to a KPI.

For instance, if one of the KPIs is the MS-DRG coder accuracy rate, two relevant data points to pull would be the number of cases coded and the number of coding errors. At their discretion, organizations may deem additional data, such as patient demographics, to also be relevant.

Go Deeper to Uncover More Information

4. Perform statistical analysis. After cleaning and organizing the statistical findings, a story begins to take shape from the final reports. At this point, organizations can analyze the information. The auditor can review the relevant data points and determine if the organization met the KPIs and overall objectives.

In this stage, the auditor should review the audit data analytics and identify outliers that do not meet the KPIs. This could include a low coding accuracy rate, undercoded cases, and overcoded cases. These outliers will serve as the basis for the recommendations that will drive the organization toward achieving the KPIs.

5. Generate recommendations based off of analysis, and tailor decisions from recommendations. Based on the examples above after reviewing the audit data analytics, a recommendation to remedy a low accuracy rate may include more education and coder mentoring (if it is a single coder issue), or a policy/documentation update from the company (if it is a multi-coder issue). If there are undercoded cases, the recommendation would be to issue a rebill, and. On the other hand, if there are overcoded cases, the analyst would recommend a bill correction and refund.

Additional Information

In addition, our previous article, “Coding Compliance Audit Statistics: What to Do With Them?” provides more examples of how to generate recommendations for improvement from coding audit KPIs.

Our follow-up article discusses the next important step in leveraging coding audit data analytics, which is using the data to drive sustainable, continuous quality improvement.

Lastly, guiding healthcare organizations in understanding and acting on coding audit data is one of YES HIM Consulting’s specialties. Our coding compliance audits provide insights on outliers that affect coding quality, accuracy, revenue, ad more. Contact our team of qualified coding and auditing experts today. They’ll give you personalized recommendations that are aimed at the organization’s quality improvement.