Proper clinical documentation and coding must go hand in hand to avoid documents being returned by Quality Assurance (QA) and prevent possible claim rejections.
For a timely reimbursement, clinicians must document their findings as precisely as possible so that the subsequent coding will accurately capture the conditions noted during the assessment. Clinicians and coders need to know these frequent documentation and coding errors that can bottleneck claims processing.
Common Documentation Error: Description
When it comes to any Outcome and Assessment Information Set (OASIS) assessment, it is vital to comprehensively assess each body system from head to toe. Etiology, laterality and site designations are important factors for documentation and coding.
One of the more common causes of confusion and improper coding is the inappropriate description of wounds by the clinician. It is vital to identify the correct etiology of wounds, whether it is a venous stasis ulcer, pressure ulcer, non-pressure ulcer, diabetic foot ulcer and the like because it impacts coding.
There have been instances where clinicians indicate the wound on the foot as a pressure ulcer or an unspecified wound, but the patient has a history of diabetes or a peripheral vascular disease, such as chronic venous insufficiency. A helpful tip is to browse over the included history and physical examination documentation, the doctor clinic notes and the discharge summary, which might reveal more insight regarding the ulcer.
How to Code This Patient
In this case, the codes used should be diabetes with foot ulcer (E10.621 or E11.621), followed by the ulcer code with its proper laterality and severity. Ulcers in the foot are assumed to be related to diabetes unless otherwise stated by the medical provider.
For chronic venous insufficiency, diabetes with diabetic peripheral angiopathy without gangrene (E10.51 or E11.51) should be coded first, followed by I87.2 venous insufficiency (chronic) (peripheral) and then the ulcer code with its proper laterality and severity.
Common Coding Errors: Conflicting Descriptions
Another documentation and coding error would be improper coding on laterality or designation.
For example, in the history and physical examination, the wound was indicated on the left, while in the OASIS, the wound was found on the right. When this occurs, you should query the clinician for the correct site, and if possible, request a wound picture for supportive documentation.
Another example would be a patient having osteoarthritis on the left knee as indicated in the OASIS, but the coding submitted was osteoarthritis on the right knee. The locations should ideally be reviewed by the coder before sending to Quality Assurance (QA) or for submission of claim.
A useful tip when it comes to a situation like this is to request any x-rays, ultrasounds, MRI results or any other laboratory reports that can justify the coding.
Common Coding Errors: Excluding Diagnoses
When it comes to coding, there might have been rejections based on an inappropriate use of diagnosis.
Sometimes this occurs not because the codes are incorrect, but because of the Type 1 Excludes coding rule, which indicates that certain codes cannot be coded together. For example, lumbago with sciatica (M54.4-) cannot be coded together with lumbar intervertebral disc disorder (M51.1-) since (M51.1-) already encapsulates the pain induced by sciatica with this code.
Another common Type 1 Excludes error is coding both bipolar (F31) and depressive disorders (F32 and F33), especially if the client reports to have depression or scores highly on the depression screening. Individuals with bipolar disorder may have alternating mania and depression. Therefore, depression should not be coded since it is already included in the F31 code.
These documentation and coding tips might seem simple, but they are often overlooked. If both clinician and coder are fully equipped with the right knowledge and excellent tools, there would be fewer chances for documents to be returned by QA and claims to be rejected.
Axxess Home Health, a cloud-based home health software, includes a built-in OASIS scrubber for optimal coding accuracy.
