Impact of Primary Diagnosis Selection on the Quality Measures

Impact of Primary Diagnosis Selection on the Quality Measures

A well‑chosen primary diagnosis and accurate coding of all active diagnoses on the MDS does more than impact reimbursement. It shapes a resident’s clinical category, which in turn determines whether the resident is included in, excluded from, or flagged by multiple CMS Quality Measures (QMs). When the diagnosis is inaccurate, incomplete, or poorly supported, the ripple effect can distort your facility’s quality profile, affect Five‑Star ratings, and misrepresent resident outcomes.

 

Several QMs are risk‑adjusted, meaning CMS accounts for resident characteristics that influence outcomes. Diagnosis is a major component of this adjustment.

Accurate diagnoses help CMS:

• • Adjust expectations for residents with complex medical conditions.
  • Avoid penalizing facilities caring for higher‑acuity populations.
  • Compare your outcomes fairly to similar facilities.

If the primary diagnosis is incorrect or lacks specificity, the risk adjustment model may underestimate resident complexity, making your outcomes appear worse than they truly are.

The primary diagnosis coded on the MDS in Section I0020 allows CMS to determine the clinical category assignment. Clinical categories are mapped through CMS’ Hierarchical Condition Category crosswalks, which are included in the SNF QRP risk adjustment appendices. Categories include groups such as the following:

• • Major joint replacement or spinal surgery
  • Non‑surgical orthopedic
  • Acute neurologic
  • Medical management
  • Cancer
  • Cardiopulmonary
  • Complex medical conditions

 

The clinical category is one of the strongest predictors of expected functional outcomes. Each category has different expected outcomes based on national data. The diagnosis selected has a significant impact on the benchmark CMS uses to determine each facility’s performance. In addition, many high-impact SNF QRP measures utilize clinical category adjustments in their calculations. The following measures rely on diagnosis-based risk adjustments:

• • Discharge Self-Care Score – Clinical category, the interaction between the primary diagnosis and the Admission Function Score, and a multitude of comorbid diagnoses are used in risk adjustments to avoid penalizing facilities providing care to patients with a limited recovery potential.
  • Discharge Mobility Score – Utilizes data like the above to calculate the expected mobility outcomes and adjust for facilities with patients with limited expected improvement in mobility.
  • Discharge Function Score – Similar to the above measures uses the clinical category and diagnosis information to risk adjust and estimate the expected discharge function score.
  • Potentially Preventable Readmissions – Uses clinical categories and comorbidities to estimate the readmission risk.

 

These adjustments help to ensure that the expected outcomes for residents recovering from a neurological condition, such as a stroke, are not directly compared to residents admitted for care related to an elective joint replacement.

Accurate primary diagnosis selection helps ensure:

• • Residents are compared to appropriate clinical peers.
  • Expected scores reflect realistic functional outcomes.
  • Facility’s performance is represented accurately on Care Compare
  • Audit risk is reduced by aligning diagnosis, documentation, and functional performance.

 

Common pitfalls that negatively impact QM performance

• • Using a vague or non‑specific diagnosis (e.g., “weakness” instead of the underlying condition).
  • Selecting a diagnosis that is not reflective of reason for skilled care, leading to mismatched clinical categories.
  • Failing to update diagnoses when the resident’s condition changes.
  • Coding diagnoses without supporting documentation, which can lead to negative audit findings and distorted quality measures.
  • Relying on hospital problem lists without validating accuracy of active diagnoses.

 

These issues often result in residents being counted in QMs where they do not belong or being risk‑adjusted incorrectly.

 

Best practices for improving diagnosis accuracy and QM outcomes.

• • Use ICD‑10 codes that reflect the true clinical reason for skilled care in the SNF, not just the hospital’s reason for admission diagnosis.
  • Validate diagnoses with physician documentation prior to coding.
  • Ensure interdisciplinary alignment between nursing, therapy, physician notes, and the MDS.
  • Review diagnosis lists regularly to remove resolved or outdated conditions.
  • Educate MDS coordinators on diagnosis‑based QM exclusions and risk adjustment factors.

 

 

Next Steps:

• • Help your team gain the ICD.10-CM coding skills they need to drive success including QM accuracy in your facility when you join Proactive March 25-26, 2026 for our next virtual ICD-10-CM workshop.
  • Tap into critical MDS insights through the monthly Team MDS! Webinar series running throughout 2026. Gain access to January and February sessions on-demand when you purchase the full year series
  • Contact Proactive for Clinical Nurse Consulting to improve 5-star Quality Measures and MDS Consulting support on a project or annual partnership basis

 

 

 

Written By:

 

 

Christine Twombly, RN-BC, RAC-MT, RAC-MTA, HCRM, CHC

Senior Consultant

Proactive LTC Consulting