The Patient-Driven Groupings Model (PDGM) replaced the prior Home Health Prospective Payment System (PPS) in January 2020, changing the payment unit from 60-day episodes to 30-day periods and shifting the reimbursement basis from therapy minutes to clinical complexity. Under PDGM, payment is determined by the HIPPS code — a composite of clinical group (12 categories based on primary diagnosis), functional impairment level (low/medium/high based on OASIS functional scores), and comorbidity adjustment (none/low/high based on secondary diagnoses). Agencies that managed revenue under the old therapy threshold model had to completely rebuild their financial analytics for PDGM.
LUPA (Low-Utilization Payment Adjustment) is the single most impactful operational risk under PDGM. When a 30-day payment period falls below the visit threshold for its clinical group, the episode is paid at a per-visit rate instead of the case rate — typically 40-60% lower. LUPA thresholds vary by clinical group (from 2 to 6 visits). An agency whose clinical operations team doesn't track which episodes are approaching LUPA threshold in real time is systematically destroying revenue that a simple visit scheduling intervention could protect.
OASIS accuracy drives both clinical quality reporting and PDGM payment accuracy simultaneously. An OASIS functional score that underestimates patient impairment doesn't just misrepresent clinical status — it assigns a lower functional impairment level in the HIPPS code, reducing reimbursement for the entire 30-day period. Most home health agencies do not have analytics that cross-reference OASIS accuracy rates with payment outcomes.
The Home Health Quality Reporting Program (HHQRP) requires quarterly OASIS data submission to CMS. OASIS-based quality measures are publicly reported on Home Health Compare, including outcome measures (functional improvement, hospitalization) and process measures (depression screening, fall risk assessment, medication reconciliation). Home Health Compare also displays HHCAHPS patient survey star ratings — agencies with high HHCAHPS scores demonstrate better care coordination and communication outcomes that managed care organizations increasingly require for network inclusion.
The Home Health Value-Based Purchasing (HHVBP) model expanded nationally in 2023 after a multi-year pilot in nine states. Under HHVBP, Medicare payments are adjusted based on quality performance relative to peers — agencies in the top quintile receive payment increases, agencies in the bottom quintile receive payment reductions. HHVBP measures include OASIS-based clinical quality measures, HHCAHPS patient experience measures, and claims-based measures including hospitalization and emergency department use.
Industry Platform Footprint
Vizier connects to the major EHRs and specialty platforms running in home health agencies via FHIR R4, HL7 v2, OAuth-based APIs, or scheduled exports. Each row links to the connector documentation where one exists.
How Home Health Agencies Customers Use Vizier
Illustrative scenarios drawn from the patterns we see across home health agencies operators, networks, and provider groups. Not specific customer stories.
A multi-branch home health agency on HCHB wants to validate OASIS-E item accuracy before each transmission cycle. Vizier compares the OASIS responses against the medical record and prior assessment patterns, flags inconsistencies, and surfaces clinicians whose error rate is significantly above peer mean for targeted retraining.
A home health operator needs to understand how its case mix has shifted under PDGM — institutional vs. community admission source, early vs. late period mix, clinical grouping distribution, comorbidity adjustment. Vizier surfaces the case-mix distribution and identifies the patient cohorts driving margin variance.
A national home health agency wants HHCAHPS trend by branch alongside the all-cause acute hospitalisation rate. Vizier joins survey results with claims-based rehospitalisation data, decomposes drivers by clinical grouping, and identifies the branches with patterns that should not be averaged out at the national level.
Upload your OASIS data and billing records, ask 'which open episodes are at LUPA risk this period?' and get a ranked list with intervention windows — before the revenue is lost.