Dr. Noah Payne shook his head in disbelief: the practice reimbursements shrank instead of climbing in response to the recent hiring of Dr. Inna Ternist. The new doctor clearly added to the total number of patients seen yet overall payments did not reflect the added charges. Perhaps the new claims were not created, submitted, or paid? Dr. Noah remembered noticing the growing pile of rejected and denied claims accumulating dust on his desk - he never had the time to review them...How many of these claims are clean? How many of them require manual review and correction?

Dr. Noah looked at his Vericle screen and began analyzing the numbers. The system showed 58 percent clean claims (PCC). In other words, almost every second claim required manual correction. Who could be causing such a high level of problems: the practice, the billing service, or the payer? Dr. Noah's instinctively felt that perhaps the billing service was negligent about data entry process and kept introducing massive data errors. But the service manager was quick to explain a rigorous quality assurance process for data entry. What else could be causing such a high level of manual work in a seemingly streamlined process?

A quick review shows that PCC varies along several dimensions:

1. 19 and 70 percent for financial class
2. 37 and 66 percent for month of service
3. 55 and 59 percent for physician
4. 29 and 70 percent for various CPT codes

Trying to discover a pattern, Dr. Noah looked for a root cause dimension. He drilled into 99213 - the single largest frequency CPT code for his practice. Vericle showed 3,135 claims and the above average 62 PCC carrying charges and payments for 99213 code.

Having isolated the single most frequent CPT code, Dr. Noah was thinking about other dimensions that influence PCC. He hypothesized that if all doctors in his practice had the same coding skills, and assuming uniform distribution of errors, he should observe no PCC variance across the doctors. Yet, a quick click on a Vericle screen yielded a spread, confirming his suspicion that different doctors maintained slightly different coding skills:

1. Dr. Ted 1,554 claims and PCC = 63%
2. Dr. Lori 865 claims and PCC = 62%
3. Dr. Inna 194 claims and PCC = 61%
4. Dr. Noah 516 claims and PCC = 60%

Next, Dr. Noah switched his attention to distribution of PCC across the financial classes. Again, he hypothesized that if all payers used the same rules to deny claims then there should be no difference in the average PCC for different payers, subject to a uniform distribution of errors over a large sample of submitted and paid claims. Yet the numbers showed a significant (30 percent) variation of PCC for the same CPT code: UHC - 82, Blue Cross Blue Shield - 73, Oxford - 64, Aetna - 59, Medicare - 59, and Cigna - 51, confirming his conclusion that various payers used various rules to deny and underpay claims.

Dr. Noah recalled reading an article about PacifiCare, a Californian insurance company being fined upon an audit. The joint Department of Managed Health Care and Insurance Department recently analyzed 1.1 million paid claims from June 2005 to May 2007 that covered about 190,000 members in PacifiCare's HMO plans and PPO coverage [Gilbert Chan , "PacifiCare fined record $3.5 million," www.sacbee.com , January 30, 2008]. They discovered 30 percent of the HMO claims wrongly denied and 29 percent of the disputes with doctors were handled incorrectly. PacifiCare paid out over $1 million and was fined additional $3.5 million. Dr. Noah's findings roughly matched PacifiCare audit - the insurance companies were failing anywhere between twenty to fifty percent of his claims and each insurance company showed a different failure rate, depending on a system used to fail submitted claims.

Finally, Dr. Noah thought of the billing service operation. Is his billing service systematically working to discover failed claims and improve its response to such discoveries? Is there a pattern of an occasional drop of PCC reflecting its deterioration in response to various payer's initiatives? Conversely, is there any evidence for a systematic improvement effort? A chart of the distribution of a single CPT-code clean claim percentage over the entire year must answer his question. In his mind, PCC should iterate between drops and climbs, hopefully each time at a higher level. Vericle confirmed his expectations, showing an overall improvement of PCC over the year (46% 1-07, 39% 2-07, 52% 3-07, 55% 4-07, 63% 5-07, 67% 6-07, 72% 7-07, 69% 8-07, 72% 9-07, 68% 10-07, 74% 11-07, 73% 12-07)





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