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.
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.
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 Also, out-of-date policies or group numbers prevent the verification of coverage.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.