Intermediary Organization Landscape

State-by-State Analysis

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Purpose and Analytical Framework

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This technical document catalogs intermediary organizations across states receiving RHTP funding, assessing their capacity, roles, and value contribution to rural health transformation. The document serves as a reference for understanding the intermediary landscape and identifying patterns in how states structure transformation implementation.

Analytical Value: Data organization reveals patterns in value-add versus overhead, intermediary reliance versus direct provider engagement, and accountability structure variations across states.

Limitations: Subaward values represent estimates based on state applications, budget narratives, and comparable programs. Actual awards pending state procurement may differ. Capacity assessments reflect available evidence and may not capture recent organizational changes.

Section 1: Intermediary Inventory by State

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Priority States (Three-Part Treatment)

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Standard States (Single-Article Treatment)

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Northeast States

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Section 2: RHTP Subaward Distribution Analysis

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Intermediary Funding Concentration by State

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Patterns Observed:

High Intermediary Reliance (50%+ via intermediaries): Arkansas, Missouri. These states have established intermediary infrastructure (UAMS AHEC network, ToRCH hospital network) that pre-dates RHTP. State agencies leverage existing relationships rather than building new channels.

Moderate Intermediary Reliance (30-50%): Texas, California, Ohio, Georgia, North Carolina, Tennessee, Kentucky, Alabama, West Virginia, Oklahoma, Wisconsin, Louisiana. Most states fall in this range, balancing intermediary expertise with direct provider engagement.

Lower Intermediary Reliance (<30%): Texas approaches the lower bound, emphasizing competitive procurement and direct provider awards over intermediary pass-through.

Pass-Through Efficiency: States with higher intermediary reliance tend to show lower pass-through percentages, suggesting overhead absorption increases with intermediary involvement. Missouri’s ToRCH model shows 50-60% pass-through despite 60-70% intermediary channeling, indicating substantial overhead retention.

Section 3: Value Assessment Framework

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Hospital Association Assessment

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Primary Care Association Assessment

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AHEC Assessment

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RHIO/HIE Assessment

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Section 4: State Approach Classification

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Intermediary Reliance Models

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Accountability Structure Assessment

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Section 5: Cross-State Pattern Analysis

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Intermediary Type Predominance by Region

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Southeast (TX, LA, MS, AL, GA, TN, KY, WV, NC, SC): Hospital associations receive largest intermediary shares. PCAs play secondary roles. AHECs vary significantly (strong in NC, limited in MS). RHIOs generally underdeveloped. Pattern reflects historical hospital dominance in rural health policy and weaker safety-net infrastructure compared to other regions.

Midwest (OH, IN, IL, MI, WI, MN, IA, MO, KS, NE, ND, SD): More balanced intermediary portfolios. Stronger RHIO infrastructure (Indiana INPC, Michigan MiHIN, Wisconsin WISHIN). Hospital associations and PCAs both significant. Agricultural economy creates distinct rural health challenges addressed through cooperative models (Wisconsin Rural Health Cooperative example).

Mountain/West (MT, WY, ID, CO, UT, NV, AZ, NM): AHEC networks often strongest intermediary due to vast distances requiring distributed training infrastructure. Hospital associations moderate capacity. PCAs strong in border states (AZ, NM) with significant FQHC presence serving migrant populations. RHIOs generally limited except Colorado CORHIO.

Pacific Northwest (WA, OR): CCO integration creates unique intermediary landscape where coordinated care organizations subsume functions other states assign to separate intermediaries. Strong hospital and PCA infrastructure. AHECs moderate. RHIO development ongoing within CCO framework.

Northeast: Strongest overall intermediary infrastructure reflecting longer organizational development history. Mature RHIOs in some states (RI CurrentCare, VT VITL, MD CRISP). Strong hospital associations and PCAs. AHEC coverage comprehensive. Bi-State PCA model (VT/NH) demonstrates cross-state coordination.

State Characteristics Associated with Intermediary Effectiveness

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Factors Correlating with Higher Intermediary Value:

1. Outcome-based contracts: States specifying transformation metrics rather than activity deliverables show better intermediary performance
2. Competitive intermediary landscape: States with multiple organizations capable of similar functions discipline performance through competition
3. Independent evaluation: States with university or third-party evaluation capacity assess intermediary contribution more accurately
4. Pass-through requirements: States requiring 65%+ pass-through to providers limit overhead absorption
5. Community governance requirements: States mandating community representation in intermediary governance improve alignment with community needs

Factors Correlating with Lower Intermediary Value:

1. Single dominant intermediary: Monopoly position reduces accountability pressure
2. Activity-based reporting: Focus on deliverables rather than outcomes enables value extraction
3. Self-reporting without verification: Intermediary self-assessment overstates contribution
4. Historical relationships over performance: States awarding subawards based on existing relationships rather than demonstrated capacity perpetuate mediocrity
5. Subaward scope exceeding capacity: Awards larger than organizational budget strain administrative systems

Geographic Patterns in Intermediary Capacity

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Strong Capacity Clusters:

• Upper Midwest (WI, MN, MI) with mature cooperative traditions
• Mid-Atlantic (MD, VA, PA) with robust organizational infrastructure
• Pacific Northwest (WA, OR) with integrated CCO models

Limited Capacity Clusters:

• Northern Plains (WY, SD, ND) with small populations limiting organizational scale
• Gulf states (MS, LA) with historical underinvestment in intermediary infrastructure
• Interior Mountain (NV, ID) with geographic barriers to coordination

Emerging Capacity:

• Texas border region with growing FQHC infrastructure
• Appalachian states with recent investment in regional coordination
• Southwest tribal areas with developing IHS-intermediary partnerships

Section 6: Recommendations for State Reference

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When Assessing Intermediary Proposals

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Capacity Indicators to Examine:

1. Staff expertise relevant to proposed functions
2. Historical performance on comparable programs
3. Current budget relative to proposed subaward scope
4. Organizational stability and leadership tenure

Value-Add Indicators to Require:

1. Specific outcomes beyond what state could achieve directly
2. Member/stakeholder relationships state cannot replicate
3. Technical expertise state lacks internally
4. Cost efficiency compared to direct provision

Risk Indicators to Monitor:

1. Member protection versus transformation alignment
2. Overhead absorption without outcome justification
3. Activity reporting substituting for outcome evidence
4. Sustainability models requiring perpetual need

Intermediary Selection Framework

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