What Inclusa Does

Inclusa, Inc. is a Wisconsin-based managed care organization (MCO) specializing in long-term care services and supports. Founded in 2017 and now employing between 1,001 and 5,000 people, Inclusa operates under state programs like Family Care and IRIS (Include, Respect, I Self-Direct) to administer benefits for seniors and individuals with physical or intellectual disabilities. Their core function is care coordination: they work with members to create personalized care plans, manage a network of community-based service providers, process claims, and ensure regulatory compliance. This model aims to help individuals live independently in their communities rather than in institutional settings, balancing member well-being with fiscal responsibility for publicly funded programs.

Why AI Matters at This Scale

For a mid-sized MCO like Inclusa, operating at this scale presents both a challenge and an opportunity. The company manages thousands of complex member cases, generating immense volumes of data from assessments, provider claims, care notes, and outcomes tracking. Manual processes strain care coordinators and administrators, limiting their capacity for proactive intervention. AI matters because it can transform this data deluge into actionable intelligence. At a 1,000+ employee scale, the cumulative impact of even modest efficiency gains or slight reductions in costly hospital readmissions translates into millions in savings and significantly improved member outcomes. It's a sector where predictive insights and automation are shifting from competitive advantages to operational necessities for sustainable, high-quality care.

Concrete AI Opportunities with ROI Framing

1. Predictive Risk Modeling for Proactive Care: By applying machine learning to historical claims and assessment data, Inclusa can identify members at highest risk for emergency room visits or hospitalization. A pilot targeting a specific high-cost condition, like congestive heart failure, could demonstrate ROI within a year. Early, targeted interventions (e.g., a nurse visit, medication review) are far less expensive than an inpatient stay, directly improving the medical loss ratio (MLR) and member health. 2. Intelligent Care Plan Assistants: AI can analyze outcomes data across thousands of similar member profiles to suggest evidence-based adjustments to care plans. For a care coordinator managing 80+ members, this tool acts as a decision-support system, recommending service modifications likely to improve outcomes. The ROI is realized through better goal attainment, reduced trial-and-error in planning, and more efficient use of coordinator time, allowing them to manage slightly larger caseloads effectively. 3. Automated Documentation and Compliance Reporting: Natural Language Processing (NLP) can extract key data points from unstructured care coordinator notes and call transcripts, auto-populating required state reports and reducing manual data entry by an estimated 15-20%. This directly reduces administrative overhead, minimizes reporting errors, and frees skilled staff for higher-value member engagement, offering a clear, calculable ROI on software licensing costs.

Deployment Risks Specific to This Size Band

As a mid-market organization, Inclusa faces distinct AI deployment risks. Resource Constraints: Unlike giant insurers, Inclusa likely lacks a large internal data science team, creating dependency on vendors and potential integration challenges with legacy core administration systems. Change Management at Scale: Rolling out new AI tools to over 1,000 employees, including many non-technical care coordinators, requires robust training and communication to ensure adoption and avoid workflow disruption. Data Silos and Quality: Operational data may be fragmented across departments (claims, care management, provider network). A mid-sized company must invest in data unification projects before advanced AI can be fully leveraged, an upfront cost that requires executive sponsorship. Regulatory Scrutiny: As an MCO handling Medicaid funds, Inclusa's AI tools, especially those affecting care decisions, will face scrutiny for fairness, transparency, and compliance with non-discrimination rules, necessitating careful model auditing and governance frameworks.