UC32: Patient Satisfaction (HCAHPS) Analyser

The Problem

HCAHPS (Hospital Consumer Assessment of Healthcare Providers and Systems) scores drive CMS Value-Based Purchasing (VBP) reimbursement adjustments. Low HCAHPS scores can reduce hospital reimbursement by 2 to 3% annually. Survey responses are rich with free-text feedback (e.g., "nurse was rude," "wait time was too long"), but manual thematic analysis is labour-intensive. Many improvement opportunities are missed due to limited review capacity.

What the Agent Does

Processes HCAHPS survey responses in batch mode (monthly or quarterly)
Performs sentiment analysis on free-text responses: identifies positive themes (excellent care, kind staff) and negative themes (long waits, poor communication)
Clusters negative themes by unit/department and shift pattern: identifies if specific units/shifts have higher dissatisfaction
Correlates HCAHPS themes with operational data: e.g., "long waits" theme correlates with ED census >90%, suggesting staffing issue
Generates improvement action items: prioritises by theme frequency and impact on overall HCAHPS score
Tracks outcomes: monitors if interventions (staffing increase, communication training) improve HCAHPS scores in subsequent surveys

Data Requirements

Data Sources:

HCAHPS Survey Responses , Quantitative (1 to 5 scale) and free-text responses, patient demographics, discharge unit, discharge date
Press Ganey or NRC Health Data Feed , HCAHPS data normalised from survey vendor systems
Operational Data (Epic ADT, staffing systems) , Unit census, staffing levels, length of stay by unit and shift
Patient Demographics , Age, acuity, primary diagnosis, insurance type (may correlate with satisfaction)
Historical HCAHPS Scores , 12 to 24 months of data to track trends

Data Classification:

HCAHPS responses: Sensitive (may identify patients), Internal, Unstructured (free-text)
Survey metadata: Internal, Structured
Operational data: Internal, Structured
Patient demographics: Sensitive, Internal, Structured

Data Quality Requirements:

HCAHPS responses: complete and final before analysis (survey period closes 30 days after discharge)
Survey data: verified for missing/anomalous responses (Press Ganey validation)
Operational data: accurate and current (ADT, staffing logs)
Historical data: 12 to 24 months minimum for trend analysis

Integration Complexity: Medium

HCAHPS data feed from survey vendor (Press Ganey, NRC Health API)
Natural language processing (NLP) for theme extraction from free-text
Operational data integration: ADT system, staffing system
Sentiment analysis model (can use pre-trained models or custom tuning)

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	4	0.60
Error Reduction	10%	4	0.40
Cost Avoidance	10%	3	0.30
Strategic Leverage	5%	4	0.20
Data Availability	15%	3	0.45
Process Clarity	15%	3	0.45
Ease of Implementation	10%	3	0.30
Fallback Available	10%	4	0.40
Audience (Int/Ext)	10%	4	0.40
Composite	100%		3.70

Why It Scores Well

HCAHPS analysis directly impacts reimbursement: improving HCAHPS scores by 2 to 3 percentile points can reclaim $100K to $1M in VBP adjustments for large hospitals. The data is rich (free-text feedback); NLP can extract actionable themes. Outcomes are measurable (HCAHPS score trends, improvement action success).

Regulatory Alignment

CMS Value-Based Purchasing (VBP) Program: HCAHPS scores are one of four domains (cost, mortality, readmission, patient experience) affecting reimbursement
HIPAA Privacy: Patient identifiers in surveys must be handled carefully; de-identification recommended before analysis
Patient Privacy: Free-text responses may reveal sensitive information; access controls required

Sprint Factory Fit

Sprint 0 (2 weeks) + 2 build sprints (4 weeks)

HCAHPS analysis runs monthly or quarterly as a batch process: process new survey responses, generate theme reports, track improvement actions. The initial 2-week sprint focuses on HCAHPS data feed integration and basic theme extraction; a second sprint adds sentiment analysis, operational correlation, and action item generation. This is a medium-complexity use case suitable for quality/data analytics teams.

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