When a claim comes back rejected or sits unpaid, the billing office usually gets the call. But in long-term care, many of those problems were created days or weeks earlier: a missing authorization, an eligibility detail that changed, a documentation gap, an assessment that closed late. Analytics can help you move the conversation upstream, where fixes are cheaper and faster.
Why upstream matters
Every touch of a claim after the first submission is rework. Someone has to find the cause, correct it, resubmit, and wait again. Meanwhile, the money you earned is tied up. Looking only at aging buckets tells you how much is late. It does not tell you why, and it does not tell you what to change.
The useful shift is from "how much is outstanding?" to "where in the process do problems begin?"
The upstream checkpoints worth measuring
Admission and eligibility
Was payer eligibility verified before or at admission, and was it re-verified when it needed to be? Track the share of admissions with eligibility confirmed within a set number of days. Track how many later required correction.
Authorization and notice steps
For payers that require authorization, how many days passed between admission and authorization being secured? Which payers produce the most delays? A simple list by payer, with average days to authorization, often shows where relationships or processes need attention.
Assessment timing
Because PDPM classification flows from the MDS, late or incomplete assessments can hold up billing. Measure the share of assessments completed in their windows and the days between assessment completion and billing release.
Documentation completeness
Where your system allows, track items that commonly hold up billing, such as missing physician signatures or incomplete orders, and how long they stay open.
Charge capture
Are ancillary services and supplies captured on time? Late charges are easy to miss and slow to correct.
Building a simple pre-bill dashboard
A useful view lists claims or billing periods that are not yet sent, along with the reason each is waiting. Group them: waiting on assessment, waiting on authorization, waiting on documentation, ready to send. That grouping alone changes the conversation. Instead of "billing is behind," the team can say "eleven items are waiting on documentation, and eight of those are the same two requirements."
Add a trend line showing the number of items held, week over week. If it keeps growing, something upstream is slipping.
Tracking rework, not just rejections
Where possible, track the share of claims that required any correction before payment, and categorize the reasons. After a few months, a pattern usually appears. A small number of root causes typically explain a large share of rework. That is the hypothesis worth testing in your building: a few repeat causes, not many random ones.
A hypothetical example
Imagine a hypothetical multi-facility group where the business office notes that payments from one payer type are slower than others. A pre-bill view shows that authorizations for that payer are frequently confirmed after the first billing cycle has begun. The fix is not in billing at all: the admissions coordinator begins confirming authorization status in the first days of the stay and flags missing items to the business office early. Over the next few months, the team compares days-to-payment for that payer against its earlier trend. The example is illustrative, but the logic is real: the data pointed upstream.
Making it a shared responsibility
Revenue cycle performance involves admissions, nursing, the MDS coordinator, therapy, medical records and the business office. Consider a short monthly meeting where each sees the upstream view and owns their part:
- Admissions: eligibility and authorization timing
- MDS: assessment completion
- Clinical teams: documentation items
- Business office: pre-bill holds and rework categories
Keep the tone problem-solving rather than blaming. People respond when they can see how their step affects the result and when the data is accurate.
Pitfalls to avoid
- Measuring only what is paid. By then the problem is old.
- Mixing payer types. Medicare, Medicaid, managed care and private pay behave differently. Segment them.
- Counting rework without categories. A total tells you there is a problem, not where.
- Over-engineering. Start with three or four upstream measures and expand carefully.
Getting started
Choose the two upstream checkpoints that most often cause delays in your building and track them weekly for a month. If you would like to see how EHR and billing data can be combined into a pre-bill view, CarePulse can walk through it in a demo with your own numbers.