Pulling Back the Curtain: When Documentation Becomes a Legal Question (Part IV of a Five-Part Series)

In the first three parts of this series, we explored how payers analyze claims data, identify cases for additional scrutiny, and interpret the medical record during clinical review.

But sometimes, the scrutiny of documentation does not stop with payer review.

In some cases, the documentation process itself becomes the focus of regulatory investigation or legal challenge.

When that happens, the question is no longer simply whether a diagnosis was clinically valid.

Instead, investigators may ask a different question:

How was the diagnosis generated and documented in the medical record?

This shift from evaluating clinical accuracy to examining documentation processes is becoming increasingly relevant for everyone who contributes to the medical record. Physicians, advanced practice providers, nurses, dietitians, therapists, coders, and clinical documentation integrity (CDI) professionals all play a role in creating the documentation that ultimately becomes part of a claim submitted for reimbursement.

A recent False Claims Act case illustrates how documentation workflows themselves can become the focus of scrutiny.

When the Documentation Process Becomes a Legal Question

In United States ex rel. Smith v. Mercy Health, a relator alleged that hospital documentation practices related to malnutrition diagnoses resulted in the submission of false claims to Medicare.

The case centered on the use of an electronic medical record (EMR) Best Practice Advisory (BPA) that appeared when physicians opened a patient’s chart after a dietitian documented clinical indicators suggesting possible malnutrition.

The alert allowed physicians to either add a malnutrition diagnosis to the patient’s problem list or decline the recommendation.

The relator alleged that the BPA functioned as a leading query, effectively prompting physicians toward a diagnosis without presenting supporting clinical indicators in a compliant query format.

Industry guidance has long emphasized that documentation clarification queries must remain non-leading and be supported by clinical indicators. The American Health Information Management Association’s (AHIMA’s) Guidelines for Achieving a Compliant Query Practice state that queries should not direct providers toward a particular diagnosis and must present the relevant clinical information that prompted the question. In an environment where electronic health record (EHR) workflows increasingly incorporate prompts and decision-support alerts, the design of these tools has become an important compliance consideration.⁶

Importantly, the dispute did not primarily center on whether patients clinically had malnutrition. Instead, the allegations focused on whether the documentation workflow used to generate the diagnosis aligned with regulatory expectations and industry guidance regarding compliant query practices.

The federal government declined to intervene in the case, leaving the relator to pursue the litigation independently under the qui tam provisions of the False Claims Act.¹

As the litigation progressed, the defendants filed a motion seeking judgment on the pleadings or dismissal for lack of standing. The motion raised broader constitutional questions regarding whether private entities may pursue enforcement actions on behalf of the federal government under the False Claims Act.

In 2026, the case concluded after significant litigation, including motions challenging both the documentation practices at issue and broader constitutional questions surrounding False Claims Act qui tam enforcement.¹

While the dismissal resolved the specific allegations, the case nevertheless highlights an important lesson for healthcare organizations: documentation workflows within EMRs may themselves become the focus of scrutiny when diagnoses ultimately support reimbursement claims.

The case also underscores an emerging reality. Documentation may be challenged not only on clinical grounds, but also based on how the documentation supporting the claim was generated in the medical record.

For organizations building increasingly automated documentation workflows within EHRs, the case serves as a reminder that the processes used to generate diagnoses may eventually be examined just as closely as the diagnoses themselves.

Automation and the Expanding Documentation Environment

EHRs have introduced numerous tools intended to improve communication and documentation efficiency.

These include templated documentation, copy-forward functionality, automated prompts, clinical decision support alerts, structured data-entry fields, and increasingly artificial-intelligence- (AI)-assisted documentation tools.

These technologies can improve documentation efficiency and help clinicians recognize potential diagnoses supported by clinical indicators.

However, they also introduce new considerations regarding documentation transparency and accountability.

External reviewers, whether payer clinicians, auditors, or regulators, do not observe the internal workflow that produced documentation in the medical record. Instead, they see the final documentation as it appears in the record when evaluating medical necessity, coding accuracy, and reimbursement claims.

Every entry in the medical record ultimately becomes part of the narrative that external reviewers interpret.

When automation assists in the creation of documentation, the responsibility for verifying that the documentation accurately reflects the patient’s clinical condition remains with the individual signing the note.

AI and Coverage Review

At the same time, healthcare organizations are adopting automated documentation tools, and insurers are increasingly using analytics and AI to evaluate claims and identify cases for review.

Health insurers routinely analyze large volumes of claims data using predictive models designed to identify utilization patterns that may warrant further scrutiny.²

These systems can flag claims that appear inconsistent with expected clinical patterns, triggering additional medical review.

The growing use of automated review tools has raised concerns about transparency, algorithmic bias, and the role of human oversight in coverage decisions.²

Policy experts have warned that automation in utilization review may amplify errors if algorithmic systems operate without appropriate clinical oversight.²

These concerns have begun to attract the attention of policymakers and regulators.

Legislative Responses to AI in Healthcare

Several states have begun introducing legislation addressing the role of AI in healthcare decision-making.

California has emerged as one of the most active states in this area.

Recent legislation – including Assembly Bill 3030, Assembly Bill 489, and Senate Bill 1120 – reflects growing attention to the role of AI in healthcare documentation and decision-making.

Assembly Bill 3030, enacted in 2024, requires healthcare providers that use generative AI to communicate clinical information to patients to disclose when AI was used in communication.³

Assembly Bill 489 addresses oversight of algorithmic tools used in healthcare decision-making, including utilization management processes.⁵

Senate Bill 1120 further emphasizes transparency and accountability when algorithmic systems are used in healthcare operations and decision-making.⁴

Although these laws primarily address patient communication and utilization review, they reflect a broader regulatory trend: ensuring that AI tools support, but do not replace, clinical judgment.

The Signature Still Matters

Despite advances in automation, one fundamental principle remains unchanged: the individual signing the medical record entry is responsible for the content of that documentation.

Templates, automated prompts, and AI-supported tools may assist clinicians in documenting patient care.

But the clinician signing the entry confirms that the documentation accurately reflects the patient’s condition and the clinical decisions made during the encounter.

This responsibility extends beyond physicians alone. As noted, modern medical records include documentation created by multiple members of the care team, including nurses, advanced practice providers, dietitians, therapists, and other clinicians.

Each entry contributes to the clinical narrative that external reviewers later evaluate.

Checkbox documentation and templated entries may support efficiency, but they cannot replace clear clinical reasoning. External reviewers often focus less on the presence of documentation elements and more on whether the record clearly explains why a particular diagnosis or level of care was appropriate.

Looking Ahead

As healthcare documentation continues to evolve, expectations placed on the medical record continue to expand.

Documentation is no longer evaluated only within the walls of the hospital. It is reviewed by payer clinicians, analyzed by automated systems, examined through compliance audits, and occasionally, scrutinized in court.

Understanding how these different layers of review interact is essential for organizations seeking to maintain accurate and defensible documentation.

In Part V, the final installment of this series, we will explore what may be the most important challenge facing healthcare organizations, moving forward: how to shift the culture of documentation so that it is recognized not as an administrative burden, but as an essential component of patient care.

Because in the end, documentation is not simply a regulatory requirement; it is the clinical narrative that explains the care provided, and increasingly, the evidence that determines whether that care is validated, reimbursed, or challenged.

References

¹ United States ex rel. Smith v. Mercy Health, Case No. 18-cv-3267 (W.D. Mo. 2026). Order of Dismissal.

² Mello MM, Trotsyuk AA, Mahamadou AJD, Char DS.
The AI Arms Race in Health Insurance Utilization Review: Promises of Efficiency and Risks of Supercharged Flaws.
Health Affairs. 2026.
https://www.healthaffairs.org

³ California Assembly Bill 3030 (2024) – Artificial Intelligence in Health Care Services.
https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=202320240AB3030

⁴ California Senate Bill 1120 (2024) – Artificial Intelligence Oversight in Health Care Decision-Making.
https://leginfo.legislature.ca.gov

⁵ California Assembly Bill 489 (2024) – Utilization Review and Artificial Intelligence Oversight.
https://leginfo.legislature.ca.gov

⁶ AHIMA. Guidelines for Achieving a Compliant Query Practice (Updated 2019).
https://library.ahima.org/doc?oid=301848