Defining High-Quality Documentation

Last week I wrote about the importance of defining what clinical documentation is, within the scope of clinical documentation integrity (CDI) reviews. This week, I’ll be focusing on what constitutes high-quality documentation. The American Health Information Management Association (AHIMA) defines high-quality documentation as including the following characteristics:

• Legibility;
• Reliability;
• Precision;
• Completeness;
• Consistency;
• Clarity; and
• Timeliness.

Electronic medical records (EMRs) are both a blessing and a curse. They improve legibility and timeliness, but can create issues with completeness, consistency, and reliability. In 2016, the Centers for Medicare & Medicaid Services (CMS) published the Documentation Integrity in Electronic Health Records Fact Sheet that expressed concerns about how features that can auto-fill information like macros and templates can create unintended documentation errors.

Content importing technology (CIT) is the term used to describe functions like copying and pasting; automated data import from other parts of the health record; templates, or macros. Although CIT may allow providers to document faster, it also contributes to incorrect insertion of data and excessively long, bloated notes that can distract readers from essential facts. These long notes can negatively impact the clarity and consistency of the health record. The U.S. Department of Health and Human Services (HHS) Office of Inspector General (OIG) has also expressed concern that CIT could be used to facilitate fraud, waste, and abuse.

As I noted last week, CDI departments need to define the concept of clinical documentation integrity within the context of the CDI review process. However, stating that CDI efforts will be limited to documentation that impacts claims data may be insufficient, since CIT has the potential for fraudulent billing. CMS recommends the use of administrative and clinical documentation policies that include standards of conduct to provide a proper framework for appropriate use of the EMR by clinicians. This would likely be a policy developed by the medical executive committee, working in conjunction with health information management (HIM), but identifying providers who violate such a policy could be problematic. It brings us back to the question: should this be within the domain of CDI professionals? Remember, CDI is a supplemental business function that typically supports accurate billing.

Let’s consider the characteristics of reliability, consistency, and completeness. These can apply at both the patient level and the facility level. Each individual medical record should be reliable, consistent, and complete, but so should documentation across all providers within a facility. In fact, research has demonstrated that variations in EMR documentation between physicians can negatively impact patient safety, which has led to increased adoption of structured documentation, rather than narrative documentation. As CDI and coding professionals, we’ve all seen structured notes that either include templated prompts or dropdown menus/picklists. Structured notes have been shown to increase provider efficiency. They can also lead to improved reliability and consistency across notes, but can simultaneously decrease the accuracy and completeness of documentation.

CMS defines a template as “a tool/instrument/interface that assists in documenting a progress note.” Although CMS does not prohibit the use of templates to “facilitate” recordkeeping, it discourages the use of templates that provide limited options or space (i.e., check boxes and predefined answers). Their rationale is that these tools often “fail to capture sufficient detailed clinical information to demonstrate that all coverage and coding requirements are necessary.” The switch to computerize order entry demonstrates this point. When records were paper, CDI professionals would often gain insight from admission orders, because providers would document the condition necessitating the admission, which would often be the principal diagnosis. With computerized order entry, the admission order is now a background task that results in an action – the assignment of a bed – but there’s a lot less detail for CDI and coding professionals. 

Ironically, the Promoting Interoperability Program (formerly known as Meaningful Use), a program designed to promote adoption of EMRs by healthcare organizations, included a requirement for the use of a common data set (ICD-10-CM, ICD-10-PCS, SNOMED-CT, or LOINC) for a summary of care records, transactions, and discharges. This program also included the core objective of maintaining up-to-date problem lists of current and active diagnoses. Unfortunately, taken in combination, many EMRs allow providers to choose “diagnoses” from a picklist that populates an associated ICD-10-CM/PCS diagnosis code within provider notes.

Multiple problems can arise through use of provider picklists. First, in most cases, the provider is not selecting a diagnosis code, but a code title. EMRs make it more difficult for coders to know what is really provider documentation – did the provider really mean to select the ICD-10-CM code that appears within the EMR? Embedded ICD-10-CM codes are often linked to code title, so there could be an absence of supportive documentation to justify the reporting of the diagnosis code on the claim. An example of this situation is when a provider chooses “diabetes with complication.” If the provider does not fully understand coding guidelines, they may omit additional documentation that specifies the type of complication. Additionally, some complications may need to be linked to diabetes because they are not indexed under “with,” so a causal relationship cannot be assumed. Consistency can suffer because another provider may choose another diabetes code off the picklist; for example, what if another provider chooses diabetes without complications? Just to be clear, I’m not necessarily talking about diagnoses that appear only in the problem list; many EMRs include picklists of diagnoses within all provider documentation. In fact, these conflicts are less likely to be evidence, because the diagnoses don’t appear in a problem list. Another example of how this affects CDI and coding professionals is when the provider can choose CAP or HCAP in a picklist, but the default record entry is pneumonia, unspecified organism J18.9. Some CDIs or coders may feel they cannot query the provider because the documentation includes pneumonia, unspecified organism, so the provider is saying they can’t further specify the type of pneumonia, but this could be an inaccurate inference. Unfortunately, code titles are not a diagnostic statement and should not be considered clinical documentation. The associated codes should not appear on the claim unless there is supportive (narrative) documentation that meets the requirement for reporting as an other/secondary diagnosis.

The picklist itself can also be a problem. Who determines how diagnoses are ordered within the picklist? What about conditions like diabetes that have so many variations a provider could literally scroll through a hundred codes to find the most accurate code? Sure, some picklists allow the provider to type a few key letters to narrow the list, but it’s more likely that providers will chose the first diagnosis they see, even if it is the least precise condition, because they may not be aware that a more descriptive code exists (or they may not want to spend the time looking for a more precise code). Should picklists start with combination codes that include diagnoses classified as a complication/comorbidity or major complication/comorbidity (CC or MCC)? Or would that be leading the provider?

A 2016 study found picklist errors related to patient selection and medication. The researchers found interventions that require providers to take additional action, even as simple as one more click, results in decreased user satisfaction. In other words, most providers are unlikely to take additional steps entering data, even when it could result in administration of incorrect medications. Providers already feel overburdened, so the selection of the most accurate diagnosis is likely not a high priority. This can be problematic when coders assign intentionality to the provider’s diagnosis selection and allow the diagnosis title to drive coding, even if it conflicts with other evidence in the health record.

I am not advocating for CDI professionals to be the documentation police. My hope is that these articles help those in CDI leadership consider the ramifications of poorly defining the scope of documentation, as it relates to the work of CDI professionals. To support high-quality documentation, the role of CDI must be established using high-quality documentation.

Programming note:

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