The process of handling the numerous claims that insurers face daily always begins with the entry of the claims information into their systems. A single digit could prevent the claim from being approved; hence the need for proper insurance claims data entry. Below, you will see the common errors made in data entry and ways to correct them.
What Are the Typical Mistakes in Insurance Claim Data Entry?
Most problems with claims arise from typing errors well before the payer ever sees anything. Thus, understanding where errors can be made is crucial to avoiding them.
● Inaccurate Policyholder Information and Patient Information
Name, date of birth, and address information pose problems due to the potential for minor errors to result in failed record matching. For instance, a simple error in date of birth will lead to automatic rejection. Also, out-of-date policies or group numbers prevent the verification of coverage.
● Incomplete Claims Information
Fields that appear to be empty seem harmless, but in actuality they prevent the submission of the claim from even going into the review process. Examples of such fields are missing diagnosis information, missing provider IDs, and missing service lines.
● Wrong Codes, Dates, and Claim Numbers
Even diligent typists make mistakes in entering digits into the procedure code, service date, and claim number. Therefore, a simple mistake in insurance claims data entry could easily misalign the diagnosis with the procedure. A wrong service date could also cause a claim filed beyond the deadline.
● Duplicating a Claim Entry or Making Wrong Claim Entries
There are situations where two employees key the same record, which results in duplicate records. This causes increased cost due to insurance claims processing errors, which the auditor identifies. On the other hand, a wrongly filed insurance claim directs payment into the wrong account.
● Wrong Documentation and Attachments
There has to be evidence to back the claims, such as referral letters, surgical records, and radiology reports, but the staff may send the incorrect files or may not send a document needed. In return, the payers demand additional documentation.
How to Avoid Mistakes When Entering Claims Data in Insurance?
Fortunately, the vast majority of mistakes can be remedied through consistency instead of costly changes. Thus, implement these strategies in order to ensure precision at all times.
● Adhere to Proper Claims Data Entry Methods
Develop an unambiguous standard for format, abbreviations, and field order, and distribute it to your users. For example, have one format for dates and one unit of measurement across all forms. Further, use a combination of automatic capture and manual processing.
● Utilize Data Validation
Validation errors instantly identify text in numeric data fields, value ranges outside limits, empty fields, and duplicate IDs. Date and unit validation errors are also captured before any erroneous data permeates the system. Thus, automated validation decreases rework and keeps your team’s attention on exceptions.
● Check Claims Data Prior to Inputting
Check all documents for accuracy before any typing takes place. Verify both the insurance card and the ID against the database for coverage. Lastly, make sure that out-of-date and duplicate documents are thrown away so that the incorrect one is not typed.
● Education of Staff Members Regarding Claims Data
Train your staff on common code sets, payer guidelines, and what causes denials. Furthermore, make sure to provide brief refresher courses as payer policies or coding changes occur. Thus, employees will identify issues at the keyboard, not when the denial occurs.
● Perform Quality Control on Claims Data
Start by analyzing a selection of entries each week along with the rate of errors for the various tasks. Then, make the findings public so that the entire team can improve their weaker areas. Moreover, there are some companies that employ insurance data processing outsourcing to get independent reviewers.
Conclusion
Accuracy is ensured by having clean intake, complete files, the right code, and a unique record. Standards must be set up, fields validated, employees trained, and audits done on a regular basis. When volumes exceed the capabilities of your employees, then you need to have data support.
