The Importance of Clinical Validation Queries: Part II

The Importance of Clinical Validation Queries: Part II

Last week I mused about how clinical validation queries do not appear to be keeping up with the volume of clinical validation denials. Perhaps one reason for this discrepancy is how difficult clinical validation queries are to construct. Consider the following:

  1. Query professionals are used to asking providers to add a diagnosis based on clinical evidence, not to remove a diagnosis due to a lack of clinical evidence.
  2. Providers are used to agreeing with queries by selecting a high-value diagnosis.  Providers may think that clinical documentation integrity (CDI) wants the diagnosis confirmed, because we usually want to add a diagnosis to the health record, rather than rule it out.
  3. What kind of clinical indicators should be included in a clinical validation query?
  4. What kind of multiple-choice options should be included in a clinical validation query?

Another consideration that may negatively impact the volume of clinical validation queries is the technology available to query professionals. Most software tools that track query rates (i.e., response rate, agreement rate, etc.) were designed for queries where the goal is to clarify a vague or incomplete diagnosis – or to add a diagnosis when one is missing, based on the associated clinical evidence. Clinical validation queries are often the opposite: removing a diagnosis that lacks sufficient clinical evidence.

To make the query process easier, many organizations use query templates. Most CDI and coding departments have a multitude of query templates, with the goal of adding a missing diagnosis, but they may only have one query validation template.

Constructing compliant clinical validation queries requires a strong clinical background, in addition to excellent writing skills. When the same query templates are used to both add a missing diagnosis or clinically validate a diagnosis, the results can be confusing to the provider, who usually doubles down on the diagnosis, not realizing that the goal of the query is to have the diagnosis ruled out.

Superseded practice briefs once instructed query professionals to ask the provider to add documentation to support a diagnosis with incomplete clinical indicators. This guidance probably contributed to the confusion about the purpose of clinical validation queries, because it created a mixed message.

Provider documentation (e.g., interpreting presentation, diagnostics, etc.,) can be helpful to support a diagnosis, but for most diagnoses, additional documentation will not make it clinically valid due to the absence of objective criteria (e.g., diagnostic indicators that help define the condition.)

Additional documentation can be helpful if typical clinical indicators don’t apply to the patient. Remember, all queries should be individualized to the patient and the clinical scenario in the current encounter. For example, treating someone with chronic obstructive pulmonary disease (COPD) or chronic hypercapnic respiratory failure with high-flow oxygen without monitoring caron dioxide levels (PaCO2) is not the standard of treatment, because it could affect their drive to breathe.

If an organization has a definition for acute respiratory failure that requires a particular volume of oxygen, the definition may lead to clinical validation issues for patients with COPD. It is helpful for a provider to document when they deviate from usual practices due to patient needs, e.g., not using antibiotics within certain protocol because the patient is allergic. There can always be an exception to the rule, in which case provider documentation can add credence to the documented diagnosis.

However, more often than not, additional provider documentation will not validate a clinically questionable diagnosis. Many diagnoses are determined based upon objective diagnostic criteria, so it can be hard to defend a diagnosis when objective measures are not met (unless the patient is atypical, and the deviation can be explained).

There are also many diagnoses that have a standard of care, which can be used to clinically validate a documented diagnosis unless the treatment is prophylactic. In these situations, the goal of the clinical validation query is to have the diagnosis ruled out to avoid it being included on the claim.

Reporting the outcome of a clinical validation query can also be challenging. If the provider rules out the diagnosis with insufficient clinical indicators, is that considered “agree,” even though it may result in lower reimbursement? Or is it “agree” if the provider confirms the inadequately supported diagnosis because there MS-DRG remains unchanged?

In other words, is the agreement rate based on responses that lead to higher reimbursement, or responses that reflect a more accurate medical record? These are not necessarily the same thing.

Our industry still has a lot of work to do with regard to clinical validation queries. Providers need more education, so they understand the purpose of clinical validation queries. Hospital-based CDI and coding professionals need training specific to the clinical validation process.

Topics that should be addressed include the following:

  • Identifying when a clinical validation query is warranted;
  • Clinical criteria associated with high-risk diagnoses; and
  • Guidance on how to construct a compliant clinical validation query:
    • How are clinical indicators used in a clinical validation query, compared to the typical physician query?
    • How should the clinical validation question be posed?
    • What choices should be included if a multiple-choice format is used?

As the volume of clinical validation denials continues to grow, it is imperative that CDI professionals understand the importance of clinical validation queries and become more comfortable using them.

Programming note:

Listen live when Cheryl Ericson reports this story on Talk Ten Tuesday with Chuck Buck and Angela Comfort, 10 Eastern.

EDITOR’S NOTE:

The opinions expressed in this article are solely those of the author and do not necessarily represent the views or opinions of MedLearn Media. We provide a platform for diverse perspectives, but the content and opinions expressed herein are the author’s own. MedLearn Media does not endorse or guarantee the accuracy of the information presented. Readers are encouraged to critically evaluate the content and conduct their own research. Any actions taken based on this article are at the reader’s own discretion.

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