Average medical practice may lose as much as 11% of its revenue due to underpayments. But underpayment recovery potential rates only 5% of revenue and involves costually appealing process. To avoid unrecoverable losses, some providers discontinue servicing patients insured by the worst performing payers. Unfortunately, such a drastic loss reduction measure may boomerang and increase losses depending on complexity of referral relationships. This article outlines limits of traditional database queries used to identify payer candidates for contract termination and demonstrations alternative decision choices with superior performance in terms of revenue and risk management, facilitated with On Line Analytical Processing (OLAP) technology.
First Order SQL Queries for Accounts Receivable Analysis
Traditional accounts receivable analysis includes identification of payers that systematically underpay and refuse denial appearances. Such analysis is based on simple questions, designed to identify the best CPT code or the worst payer in absolute terms:
- Comparison of revenue for various CPT codes for a given time-period
- Comparison of underpayments for various payers for a given time-period
- Comparison of denials for various payers for a given time-period
A single key database indexing is a standard measure to improve time performance of such questions. It builds an ordered relationship within the data elements based on the value of the selected metric. But single key indexing precludes implementation of more complex questions like "who is the payer that underpays the most for the best CPT code," or "who is the worst referring physician for my worst payer?" and require complex SQL programming skills because of the need to store and process intermediate results. Therefore, ranking the data elements along a single attribute, forces a limited choice for management decision:
- Ignore the problem,
- Renegotiate the contract with the payer, or
- Stop serving patients insured by the worst payer.
But to find more subtle solutions the office manager requires the ability to aggregate and drill into data and formulate queries in real time, in response to observed results to the previous queries. Specifically, a low frequency under performing payer with a high degree of underpayment may not be as detrimental to the office as a high frequency under performing payer with a low degree of underpayment. Contract termination with a wrong payer may achieve the opposition result to practice goals in terms of revenue maximization and workload reduction. Additionally, a decision to stop servicing patients insured by any one payer may cause reduction of referral volume of other patients across all payers for a particular referral physician.
Combinatorial (Second Order SQL) Queries for Accounts Receivable Analysis
Fortunately, modern database query technology can address both limitations by enabling "second order SQL" queries, which allow data manipulation based on multiple criteria and using functions of combinations of such criteria.
In our case, second-degree SQL queries allow finding the worst payer for best revenue generating code.
Such a discriminatory approach allows focusing on higher priority items first, resulting in more effective management. In general, the manager performs a custom comparison of payers according to the following four-step sequence:
- Select metrics (eg,% paid,% accounts receivable beyond 120 days,% denials)
- Select dimensions (providers, payers, CPT codes, ICD-9 codes, referring physicians)
- Partition
- Aggregate, drill-down, pivot
Worst Payer Query
To find a payer with the highest amount of underpayments for the most-frequent CPT code, a second order SQL query can be written along the following lines:
For a given time-interval,
Select payers
Where sum of underpayments over
(all CPT codes Where Revenue> Revenue Threshold)> Underpayment Threshold
Worst Referring Physician Query
To avoid the risk of losing referrals from better-performing payers, the manager may consider sever referral relationship with some referring doctors instead of payers. In such a case, distribution of patients across various payers plays an important role for each referral physician. A single combinatorial query may fetch the Worst Referring Physician as follows:
For a given time-interval,
Select referring doctors Where Revenue for the Worst Payer> Threshold
Summary
Underpayment management involves all forms of claims processing and requires powerful Vericle-like computing platforms for exhaustive comparisons of payments versus allowed amounts and subsequent appeal management. OLAP allows better analysis of accounts receivable and more effective management because of the ability to handle queries with functions of multiple attributes and dimensions. Note that in the absence of native OLAP mechanism, effective Vericle-like billing platforms allow similarly powerful analysis by introducing intermediate steps. Such steps may add insight to analysis and improve decision quality.