PDGM Case-Mix Optimization Case Study
How we recovered $1.8M in under-coded reimbursements by optimizing PDGM case-mix classification for a 1,400-patient home health agency across seven states.
Avg Case-Mix Weight
Annual Revenue Recovered
More Episodes Grouped Correctly
Time to Full Impact
The Challenge
A mid-sized home health agency serving 1,400 active patients across seven states had seen their operating margins erode steadily since the January 2020 implementation of the Patient-Driven Groupings Model (PDGM). Leadership suspected that PDGM reimbursement was not accurately reflecting the clinical complexity of their patient population—but they lacked the analytics to quantify the gap or identify its source.
An internal review commissioned six months prior had concluded that coding was 'adequate,' but it had examined only a small sample of claims and had not cross-referenced PDGM groupings against the full clinical record. The agency's average case-mix weight of 1.08 was significantly below the national average of 1.24 for agencies with a similar patient population mix—a disparity that translated to approximately $1.8 million in annual under-reimbursement.
The clinical complexity was genuinely there. The agency served a high proportion of patients with multiple chronic conditions, neurological diagnoses, and complex wound care needs. The problem was documentation and coding, not patient acuity.
Our Approach
PDGM Grouping Analysis Across the Full Patient Population
We analyzed the PDGM grouping for every active patient against their full clinical record. This revealed that 22% of episodes were grouped into a lower-paying PDGM category than the documentation supported. The most common miscategorization involved patients whose primary diagnoses coded to musculoskeletal groupings when the clinical record more specifically supported neuro-rehabilitation or wound care groupings—both of which carry substantially higher case-mix weights.
Diagnosis Specificity Training and Code Crosswalk Development
We developed diagnosis-specific code crosswalks mapping the most common clinical presentations at this agency to their optimal ICD-10-CM code selections under PDGM grouping logic. Certified coding specialists provided targeted training to the agency's coding team, focusing on 18 high-frequency diagnosis categories where specificity improvements had the largest impact on grouping outcomes.
AI-Assisted OASIS Functional Scoring Review
The functional impairment level—determined by four OASIS items—was a secondary source of case-mix weight suppression. Our AI tool identified that functional items were being systematically underscored by clinicians who were not accounting for safety-qualified independence. We corrected the scoring and implemented real-time validation logic that prompts clinicians to assess safe independence rather than physical capability alone.
Prospective Coding Review for All New Episodes
We implemented a prospective review process in which all new episode coding was reviewed by a Medeoan certified specialist before claim submission. This review catches miscategorizations before they become under-reimbursed claims, providing immediate revenue protection while the agency's internal team builds the skills to sustain accuracy independently.
The Results
Average Case-Mix Weight
+18% improvement vs national average
Annual Revenue Impact
$1.8M annual revenue recovered
Correctly Grouped Episodes
22% of episodes reclassified correctly
Time to Full Impact
From engagement start to sustained results
Is Your Case-Mix Weight Reflecting Your Patient Acuity?
Most home health agencies are leaving significant PDGM revenue uncaptured. Our analysis often identifies $500K–$2M+ in recoverable reimbursement within the first 30 days. Let us show you what's possible.
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