Every skilled nursing facility sees denied or rejected claims. The question is whether each one is treated as a one-time puzzle or as a data point. Teams that track denials systematically often find that a small number of root causes account for much of the rework, and that fixing those causes upstream is far more efficient than appealing claim after claim.
Start with a simple structure
You do not need a complex system to begin. A shared log or report with a consistent set of fields is enough.
- Date of service and date of denial
- Payer and payer class
- Claim amount
- Denial or rejection reason, in standardized categories
- Responsible area, such as admissions, MDS, therapy, nursing documentation, billing or payer
- Status, such as open, appealed, corrected and resubmitted, paid or written off
- Date resolved
Consistency matters more than detail. If two people categorize the same denial in different ways, patterns will blur.
Create useful reason categories
Payers use many codes, and a raw list quickly becomes unmanageable. Group them into categories your team can act on.
- Eligibility and enrollment: coverage not active, wrong payer or primary and secondary mix-ups.
- Authorization: missing, expired or not matching the services.
- Documentation: missing or incomplete supporting records.
- Coding and data errors: mismatches in dates, identifiers or assessment data.
- Timing: late submission or late assessment.
- Payer policy or dispute: disagreements about coverage or medical necessity.
- Other.
Review the "other" category regularly. If it grows, the structure needs refinement.
The questions to ask
Where do denials originate?
A denial is discovered in the business office, but it is often created elsewhere. If most denials in one category trace to admission paperwork, the fix belongs there. Map each category to the department with the most influence, and share the results with that team.
Which categories repeat?
Rank categories by count and by dollars. A frequent small denial and an infrequent large one may need different attention. Both belong on the list.
How long does resolution take?
Measure the time from denial to resolution. Slow resolution may point to an unclear owner, a complicated appeal process or a lack of a standard approach.
What is the outcome?
Track how many denials are ultimately paid, adjusted or written off. A category with a low recovery rate deserves prevention effort, since appeals are not working there.
A hypothetical example
Suppose a hypothetical building sees that the largest category of denials over a quarter is "authorization," and most involve a single managed care payer. A closer look finds that authorizations are often requested correctly but the approved dates are not tracked, so the stay runs past the authorized period without a new request. The team creates a shared list of authorization end dates, assigns an owner and reviews the list twice a week. This does not change how care is delivered. It prevents a predictable administrative miss.
Build prevention into the workflow
- Checklists at admission for eligibility and authorization.
- Alerts for expiring authorizations.
- Feedback loops that send denial categories back to the responsible department each month.
- Training based on real examples, with identifying details removed.
Keep privacy in mind. Share patterns and counts widely, and restrict resident-level details to those who need them.
Make it a monthly conversation
Bring business office, admissions, MDS and clinical leaders together monthly for a short review: top categories, trends since last month, actions completed and next steps. Celebrate reductions. Prevention is invisible, so make it visible.
Pitfalls
- Working denials without recording them. The lessons are lost.
- Blaming the biller. Many causes sit upstream.
- Over-categorizing. Too many categories make patterns hard to see.
- Ignoring payer feedback. Payers sometimes provide guidance that explains recurring issues.
Where CarePulse fits
CarePulse can organize denial and remittance data by category, payer and responsible area, and show trends month over month. If you would like to see what a denial dashboard could look like using your own data, a demo is a good place to start.