access-friction-detector

name	Access Friction Detector
description	Systematically identify and quantify barriers to healthcare access across scheduling, geographic, financial, cultural, and digital dimensions using CMS access standards and health equity frameworks.
metadata	{"display_name":"Access Friction Detector","short_description":"Detect and quantify healthcare access barriers by type","default_prompt":"Review my access friction and highlight top risks and next actions","version":"1.0.1","tags":["healthcare"],"icon_path":"assets/icon.png"}

Access Friction Detector

Overview

This skill detects and quantifies friction points that prevent or delay patients from accessing healthcare services. It evaluates access across five core dimensions aligned with the Penchansky-Thomas Access Framework: availability, accessibility, accommodation, affordability, and acceptability. The analysis incorporates CMS network adequacy standards, ADA compliance requirements, and health equity benchmarks to produce actionable findings with regulatory context.

When to Use

• Evaluating access barriers after patient complaint trend analysis reveals scheduling or availability themes
• Preparing for NCQA accreditation or CMS network adequacy reviews
• Assessing health equity impact of service changes (clinic closures, hour reductions, telehealth transitions)
• Investigating disparities in appointment wait times, no-show rates, or patient leakage by demographic
• Supporting Community Health Needs Assessment (CHNA) access components
• Designing patient access center workflows or digital front-door strategies

Required Inputs

Input	Description	Format
appointment_data	Scheduling records with request date, appointment date, type, location, provider	De-identified CSV/JSON
patient_demographics	Aggregate demographic data (zip code, language, insurance, age, disability)	JSON array
facility_locations	Addresses and service hours for all access points	JSON array
payer_mix	Insurance type distribution across patient population	JSON object
complaint_data	Access-related complaints categorized by theme	JSON array
telehealth_utilization	Virtual visit adoption rates by service and demographic	JSON object
referral_data	Referral completion rates and time-to-appointment	JSON object

Methodology

Step 1: Availability Analysis

• Calculate third-next-available appointment (TNAA) by provider, specialty, and location
• Benchmark against specialty-specific access standards:
  • Primary care: 7 days or fewer for routine, 48 hours or fewer for urgent
  • Specialty care: 14 days or fewer for routine referrals
  • Behavioral health: 10 business days or fewer (per NCQA)
  • Post-discharge follow-up: 7 days or fewer (per CMS readmission reduction)
• Identify capacity bottlenecks by day of week, time of day, and provider panel saturation
• Flag providers operating above 85% panel capacity as at-risk for access degradation

Step 2: Accessibility Analysis (Geographic and Physical)

• Compute drive-time and public-transit-time isochrones from patient zip code centroids to facilities
• Apply CMS time-distance standards:
  • Primary care: 30 min / 15 miles (urban), 60 min / 60 miles (rural)
  • Specialty: 60 min / 30 miles (urban), 120 min / 75 miles (rural)
• Assess ADA physical accessibility for each facility (parking, entrance, exam room, equipment)
• Evaluate public transportation proximity (within 0.5 miles from transit stop)
• Map geographic deserts where access standards are not met

Step 3: Accommodation Analysis

• Evaluate scheduling flexibility:
  • Extended hours availability (before 8 AM, after 5 PM, weekends)
  • Same-day and walk-in capacity
  • Online self-scheduling adoption rate
  • Cancellation and reschedule ease (number of steps, channels available)
• Assess communication accommodation:
  • Language services availability (interpreter access within 10 minutes per LEP standards)
  • TTY/TDD availability for deaf and hard-of-hearing patients
  • Health literacy accommodations in intake and instructions

Step 4: Affordability Analysis

• Analyze cost-related access barriers:
  • Percentage of patients with high-deductible health plans (HDHP)
  • Financial assistance and charity care application rates and approval rates
  • Copay and cost-sharing transparency at point of scheduling
  • Prescription affordability barriers flagged in clinical notes (de-identified aggregate)
• Identify insurance-related friction:
  • Prior authorization denial rates and average turnaround time
  • Out-of-network referral frequency
  • Medicaid acceptance gaps across specialties

Step 5: Acceptability Analysis

• Evaluate cultural and trust barriers:
  • Provider demographic concordance rates (race, ethnicity, gender, language)
  • Patient-reported experience scores segmented by demographic group
  • Trust indicators from community health assessments
  • Cultural competency training completion rates among staff
• Assess digital acceptability:
  • Patient portal adoption by age group and digital literacy level
  • Telehealth utilization disparities by race, age, rurality, and insurance
  • Digital divide indicators (broadband access rates by patient zip code)

Step 6: Friction Scoring and Prioritization

• Assign composite friction scores per dimension (0-100):
  • 0-25: Low friction (meeting standards)
  • 26-50: Moderate friction (approaching risk)
  • 51-75: High friction (below standards, action needed)
  • 76-100: Critical friction (regulatory or equity risk)
• Aggregate into an overall Access Friction Index (AFI)
• Rank barriers by population impact (number of patients affected multiplied by severity)

Output Specification

access_friction_report:
  analysis_date: date
  population_scope: string
  overall_afi_score: number
  dimension_scores:
    availability: number
    accessibility: number
    accommodation: number
    affordability: number
    acceptability: number
  critical_barriers:
    - dimension: string
      barrier_description: string
      affected_population: string
      patient_count_estimate: number
      severity: string
      regulatory_reference: string
      recommendation: string
  geographic_gaps:
    - area: string
      population: number
      nearest_facility_minutes: number
      standard_exceeded_by: number
  equity_disparities:
    - metric: string
      advantaged_group_value: number
      disadvantaged_group_value: number
      gap_percentage: number
  recommendations:
    - action: string
      priority: string
      estimated_impact: string
      timeline: string

Analysis Framework

Use the Penchansky-Thomas 5A Framework enhanced with equity stratification:

Dimension	Core Question	Key Metrics
Availability	Is there enough supply?	TNAA, panel size, provider FTE
Accessibility	Can patients get there?	Drive time, transit time, ADA compliance
Accommodation	Does the system flex for patients?	Hours, channels, language services
Affordability	Can patients pay?	HDHP rates, PA denials, charity care
Acceptability	Will patients engage?	Concordance, cultural competency, digital divide

Examples

Example: Urban Health System Access Audit

• Availability: TNAA for dermatology = 47 days, CRITICAL (benchmark: 14 days)
• Accessibility: 12% of Medicaid patients live more than 45 min from nearest PCP, HIGH
• Accommodation: Online scheduling available for only 3 of 12 specialties, MODERATE
• Affordability: PA denial rate for behavioral health = 31%, HIGH
• Acceptability: Telehealth adoption among 65+ = 8% vs. 45% overall, Equity gap flagged
• Overall AFI: 62/100, HIGH friction, priority intervention recommended

Guidelines

• HIPAA Compliance: All analysis must use de-identified or aggregate data. Geographic analysis should use zip code or census tract level, never individual addresses. Complaint data must be stripped of PHI before ingestion.
• Regulatory Alignment: Reference CMS network adequacy standards (42 CFR 438.68), ADA Title III requirements, and Section 1557 language access provisions in findings.
• Equity-First Approach: Always stratify findings by race, ethnicity, language, insurance type, disability status, and rurality. Report disparities explicitly.
• Actionability: Every identified barrier must include at least one specific, implementable recommendation with timeline and responsible party.
• Community Input: Validate findings with patient advisory councils and community health workers when possible.

Validation Checklist

• All five access dimensions analyzed with quantitative metrics
• Benchmarks sourced from CMS, NCQA, or evidence-based standards
• Geographic analysis uses time-distance standards appropriate to urban/rural classification
• Equity stratification completed across minimum 4 demographic dimensions
• No PHI present in any output artifact
• Regulatory references cited for all standard comparisons
• Recommendations are specific, actionable, and time-bound
• Access Friction Index calculated with transparent methodology
• Findings validated against patient complaint data for consistency
• Report formatted for executive and operational audiences