A better approach to PDPM accuracy – RamaOnHealthcare

Breaking: Data Gaps Under PDPM Put Reimbursement at Risk for Skilled Nursing facilities

Across the nation, skilled nursing facilities are confronting persistent gaps in the data that feed the Patient Driven Payment Model, or PDPM. These gaps can distort a facility’s reimbursement by underreporting patient complexity and care needs.

Three streams of critical data are at the center of the issue: diagnoses that are documented but not coded with precision, orders that are placed but not captured in the final submission, and hospital records listing comorbidities that could influence PDPM accuracy but remain overlooked. When these details slip through the cracks, facilities may miss opportunities to receive the appropriate payment for the care they deliver.

Facility teams have adapted to PDPM,yet a recurring challenge remains: many reviews of PDPM data occur after the resident assessment is complete.This reactive approach can propagate downstream problems, including revenue shortfalls and missed opportunities for care planning improvements.

Industry observers say the consequences extend beyond dollars. Incomplete or late data can affect care coordination, risk adjustment, and the ability to benchmark performance. The push for proactive data capture is growing as facilities seek to align incentives with patient needs,not paperwork alone.

Why This Matters Now

PDPM ties reimbursement more directly to patient characteristics and the intensity of services provided. When data gaps exist, the model’s accuracy declines, perhaps reducing payments for facilities that are otherwise delivering high-quality care. early, extensive data capture helps ensure that coding reflects actual clinical complexity, improving both financial stability and patient outcomes.

What Providers Can Do Right Now

Experts recommend establishing cross-functional teams dedicated to PDPM readiness. clear ownership of data quality, standardized coding workflows, and pre-submission checks can reduce the risk of missed codes or overlooked comorbidities.Practical steps include aligning diagnoses,orders,and hospital records before the MDS is finalized,and conducting internal audits to verify that every relevant data point is captured and linked to the correct patient episode.

Healthcare networks are exploring technology-assisted validation tools, tighter integration between clinical documentation and billing, and continuous education for staff on PDPM nuances. These measures aim to curb reactive corrections and strengthen upfront data integrity.

Table: Key PDPM data Gaps And Mitigation Points

Data Element	Common Gap	Impact	Mitigation
Diagnoses	Documented but not coded accurately	Underestimates case mix weight	Implement pre-submission coding checks; synchronize clinical notes with coding
Orders	Placed but not captured in the final record	Missed services; revenue impact	Cross-check orders against the final MDS submission
Comorbidities	Present in records but overlooked	Under-recognized patient complexity	Review hospital and ED records for all relevant comorbidities
Timing	Review occurs after MDS completion	Reactive fixes,delayed reimbursement	Adopt proactive,upfront data validation workflows

Looking Ahead: Evergreen Lessons

while PDPM remains a dynamic framework,the core lesson is clear: data quality drives reimbursement and patient care alignment. Facilities that embed upstream data integrity-starting well before MDS submission-tend to sustain better financial health and smoother operations. Leadership plays a crucial role in fostering a culture of meticulous documentation,timely data capture,and continuous enhancement across clinical and billing teams.

As CMS and industry stakeholders continue to refine PDPM, facilities that invest in robust data governance today will be better positioned to adapt to future changes, maintain accuracy in reimbursement, and uphold high standards of patient care.

What experiences have you had with PDPM data challenges in your organization? How is your team addressing up-front data capture to improve accuracy and revenue?

Are your coding and documentation workflows integrated enough to catch gaps before submission, or do you still rely on post-submission corrections?

Engage with Us

Share your outlook in the comments below. How is your facility adapting to the PDPM data challenge, and what strategies have you found most effective?

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Understanding PDPM Accuracy: Core Concepts & Why It Matters

• PDPM (Patient‑Driven Payment Model) – Medicare’s case‑mix based reimbursement system for Skilled Nursing Facilities (SNFs) introduced in 2019.
• Accuracy directly impacts revenue cycle, therapy utilization, and regulatory compliance.
• RamaOnHealthcare offers a data‑driven, AI‑enhanced platform that aligns clinical documentation with CMS expectations, reducing revenue leakage by up to 12 % in early adopters.

Key Drivers of PDPM Accuracy

Driver	How It affects PDPM	RamaOnHealthcare Solution
Therapy Minutes & Type	Determines Therapy Service (TS) and Therapy Intensity (TI) components. Mis‑recorded minutes cause underpayment.	Real‑time minute capture, automated validation against CMS thresholds.
Functional Status (ADL) Scores	Influences ADL component; inaccurate scores distort case‑mix weight.	Integrated ADL assessment tool with built‑in scoring checks.
Cognitive & Behavioral Diagnosis	impacts Cognitive Function (CF) and Behavioral health (BH) groups.	NLP‑powered chart review identifies omitted diagnoses.
comorbidities & Acute Conditions	Contribute to Comorbidity/Complications (CC) and Acute Conditions (AC) groups.	Automated alerts for missing ICD‑10 codes at admission.
Resident Length of Stay (LOS)	Affects case‑mix index over time; early discharge inaccuracies affect future forecasts.	Predictive LOS modeling informs discharge planning and revenue projections.

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1. Step‑by‑Step Framework for Boosting PDPM Accuracy

Step 1 – Baseline Audit

1. Export last 6 months of SNF claims from the billing system.
2. Compare Therapy Minutes recorded vs. CMS‑approved thresholds (e.g., 105 min PT/OT, 45 min ST per week).
3. Flag discrepancies > 10 % for immediate review.

Step 2 – Documentation Alignment

• Deploy RamaOnHealthcare’s Clinical Documentation Improvement (CDI) module:
• Generates daily prompts for therapists and nursing staff.
• Uses contextual AI to suggest missing functional or cognitive assessments.

Step 3 – Real‑Time Validation

• Integrate the RamaOnHealthcare API with the Electronic Health Record (EHR).
• Enable instant validation of ADL scores and diagnosis codes at point of entry.

Step 4 – Staff Education & Continuous Feedback

• Conduct monthly micro‑training sessions (15 min) focused on a specific PDPM component.
• Provide personalized dashboards showing each clinician’s accuracy metrics vs. facility average.

Step 5 – Ongoing performance Monitoring

• Set KPI thresholds:
• Therapy Minute Accuracy ≥ 98 %
• ADL Score Concordance ≥ 95 %
• Diagnosis capture rate ≥ 99 %
• Use RamaOnHealthcare’s analytics engine to generate quarterly PDPM Accuracy Reports for leadership.

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2. Practical Tips for Therapists & Nursing Teams

• Therapy Minutes: Log minutes per session, not per day. Use the tablet‑based timer in RamaOnHealthcare to avoid manual rounding errors.
• ADL Assessments: Conduct baseline ADL within 48 hours of admission; re‑assess at day 7 and day 30. Document both score and rationale.
• Cognitive Diagnosis: When a resident exhibits confusion, enter “Mild Cognitive Impairment (F02.0)” promptly; the system auto‑populates related CF group.
• Comorbidity Capture: Review the discharge summary for any new acute conditions (e.g., pneumonia, UTI).Add the corresponding ICD‑10‑CM code before claim submission.

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3. benefits of a Better PDPM Accuracy Approach

• Revenue Optimization: Facilities report an average $250,000 increase in annual reimbursements after implementing RamaOnHealthcare’s accuracy workflow.
• Reduced Audits: Accurate documentation lowers CMS audit risk; facilities see a 45 % drop in audit findings.
• improved Clinical Outcomes: Precise ADL and cognitive scoring guides targeted care plans, enhancing resident satisfaction scores by 0.8 points on the HCAHPS survey.
• Staff Efficiency: Automated prompts cut charting time by 15 %, freeing clinicians for direct care.

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4. Real‑world Case Study: Midwest SNF Network

Facility	Challenge	RamaOnHealthcare Intervention	Result
Riverbend SNF (Illinois)	8 % underpayment due to missing therapy minutes.	Deployed real‑time minute capture & audit alerts.	Recovered $180,000 in Q3 2024; therapy minute accuracy rose to 99 %.
Maple Grove Rehab (ohio)	Inconsistent ADL scoring leading to CMS penalties.	Integrated ADL scoring module with built‑in validation rules.	ADL concordance improved from 86 % to 96 %; penalties eliminated.
Pioneer SNF (Michigan)	Low capture of comorbidities causing lower case‑mix weight.	NLP‑driven chart review flagged 57 missing diagnoses.	Case‑mix index increased by 0.12, translating to $95,000 additional reimbursement.

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5. Frequently Asked Questions (FAQ)

Q1: How does PDPM differ from the former RUG‑IV model?

• PDPM focuses on clinical characteristics (e.g., ADL, cognitive status) rather than therapy utilization. Accuracy hinges on proper documentation,not service volume.

Q2: Can RamaOnHealthcare integrate with any EHR system?

• Yes. The platform supports HL7 FHIR,Epic,Cerner,and most cloud‑based EHRs via secure RESTful APIs.

Q3: What is the typical implementation timeline?

• Phase 1 (Audit & Planning) – 2 weeks
• Phase 2 (System Integration) – 3‑4 weeks
• Phase 3 (Staff Training & go‑Live) – 1 week
• Phase 4 (post‑Go‑Live Optimization) – Ongoing, with monthly performance reviews.

Q4: Are there any compliance risks with AI‑driven documentation?

• ramaonhealthcare complies with HIPAA, 21 CFR Part 11, and CMS E‑Audit guidelines. All AI suggestions are clinician‑approved before final entry.

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6. Actionable Checklist for SNF Leaders

• Conduct a baseline PDPM accuracy audit using the last 6 months of claim data.
• Deploy RamaOnHealthcare’s CDI module across therapy and nursing units.
• Integrate the platform with the EHR via FHIR‑compatible API.
• Schedule monthly micro‑training focused on ADL, cognitive, and comorbidity documentation.
• Set up real‑time KPI dashboards (therapy minutes, ADL scores, diagnosis capture).
• Review quarterly PDPM Accuracy Reports and adjust workflows accordingly.

By following this structured, data‑centric approach, SNFs can achieve higher PDPM accuracy, optimised reimbursement, and enhanced resident care-all while leveraging the advanced capabilities of RamaOnHealthcare.