Home Health Payment Structure
Home health agencies are reimbursed under a prospective payment system using the Patient-Driven Groupings Model (PDGM), which pays based on 30-day periods of care rather than the 60-day episodes used under the prior payment model.
PDGM Case Mix Variables
- Admission source: whether the patient was referred from the community or from an institutional post-acute setting, which affects payment because institutional referrals typically indicate higher acuity
- Timing: whether the 30-day period is "early" (the first period in a sequence) or "late" (subsequent periods)
- Clinical grouping: based on the principal diagnosis reported, which must be specific enough to support a valid clinical group assignment
- Functional impairment level: derived from OASIS assessment items measuring the patient's functional status
- Comorbidity adjustment: additional payment for qualifying secondary diagnoses that increase resource needs
OASIS Assessment
The Outcome and Assessment Information Set (OASIS) is the standardized assessment tool used to collect clinical and functional data on home health patients. OASIS data drives both PDGM payment classification and publicly reported home health quality measures.
Coding Considerations Specific to PDGM
- The principal diagnosis must fall into one of PDGM's clinical groups; vague or unspecified diagnosis codes may not group appropriately and can result in a claim return for more information
- Comorbidities must be coded to the highest specificity to capture applicable comorbidity adjustments
- Coding accuracy directly ties to OASIS accuracy since both draw from the same clinical documentation
Low Utilization Payment Adjustment (LUPA)
When the number of home health visits in a 30-day period falls below a clinical-group-specific threshold, payment reverts to a lower per-visit rate rather than the full episode payment, reflecting reduced resource utilization.
Exam Tip
Understand that PDGM eliminated therapy visit thresholds as a payment driver in favor of clinical characteristics, and be able to identify the OASIS-based data elements that influence functional impairment level.