Managing Errors in Demographic/Clinical Information: Using Tracking Mechanisms

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All insurance accounts fall into one of two groups: full resolution (at negotiated fees, where the credits only consist of cash, contract adjustment, and probable bad debt, if any patient balance cannot be collected) or full write-off (where the practice did not comply with a payor-based rule).

Past ImagingBiz.com articles illustrate how billing systems should organize information on billed results. One article¹ focuses on reports that can reveal lost revenues attributed to payor rules and on the importance of developing compliance-based coding. A second article² deals with reports that a billing company should be able to provide to track the dictation patterns of practice members, for exams that can be either complete or limited, to determine whether all members are consistent in articulating clinical findings between the simple and complex cases.

There is a third component that a practice needs from its billing company or internal system. How does it manage exams that have missing or flawed demographic and/or clinical information that prevents the case from being billed?

Ongoing Information Challenges

Even though billing for radiology interpretations has been going on for more than 30 years, it can never be assumed that the new generations of radiologists know how to play the compliance game. Medical schools do not have the time or inclination to teach their students how to conduct their clinical affairs according to the rules set by the insurance companies. It is up to the administrative arms of the practice both to understand these rules and to put infrastructure in place to play by them.

It has been stated many times that billing for the hospital-based component of a radiology practice involves different requirements than those of an office-based system. This has to do with reliance on the hospital for all clinical and demographic information and the volumes of new accounts per month. The radiology practice, in a hospital system, is the largest submitter of insurance claims in that system—by a factor of 10. A practice that does not account for the combination of volume and information needs will spend needless amounts on the billing process with, at best, mixed collection results.

It is becoming an absolute, in these times, that billing systems must be able to interface with the hospital information system (HIS), both for admission demographic data and for clinical findings. The implication is that the system is highly dependent on the hospital for the quality of account data. There will be definite winners and losers in this market; hospitals all have differing systems and coverage populations.

Data-field Editing

Billing vendors focus on three fundamental operations on the front end of the billing process. The first is the classification of critical demographic and clinical field data from the hospital systems, resulting in daily file extractions via virtual private network (VPN) connection. The second is the performance of automated edits on the field data to isolate missing or erroneous information, and the third is the provision of enhanced tools, to certified coders, that enable them quickly to populate exception lists that document records requiring clinical feedback from the practice of origin. This operation uses RIS accession numbers to help the practice locate the source documents.

Timing is a critical component in this process because the clock is moving toward a filing-date deadline. Most non-Medicare payors have become aggressive in requiring providers to file claims within a narrowing window after the date of service. This is especially troublesome in radiology because there are already sequenced time delays built into the department’s RIS; these delays are also built in when the hospital populates demographic files that the billing company can extract.

An outline of problem fields can help in managing the important follow-up requirements (see Figure 1).

    I. Demographic problems

  1. Date-of-birth mismatch
  2. Patient-name mismatch
  3. Missing complete demographic record
  4. Missing patient record
  5. Missing responsible-party information
  6. Noncurrent patient information (more than 30 days old)
    II. Duplicate procedures

      III. Referring-physician problems

    1. Blank last name for referring