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Robert D. Lieberthal

Publications and source records attributed to Robert D. Lieberthal.

2 recordsLinked to original sources

An Actuarial Cost and Revenue Model for Helicopter Emergency Medical Services: Estimating Population-Based Coverage and Sustainability Thresholds

Helicopter emergency medical services (HEMS) provide rapid access to critical care but are costly to operate and difficult to sustain financially. A clear understanding of these costs is essential for evaluating the feasibility and design of population-based funding or policy strategies. We developed a two-part model: (1) a cost framework capturing capital and operating expenses (e.g., aircraft, equipment, labor, facilities), and (2) an actuarial revenue model using healthcare encounter data and payer reimbursement rates. The model was applied to a commercially insured Massachusetts population (3.9M lives), using provider charge data and Medicare fee schedules. We analyzed breakeven transport volumes under varying reimbursement and labor cost assumptions, including sensitivity scenarios. Under optimistic assumptions (full charge realization, minimal overhead), breakeven is reached with approximately 90 annual transports. More realistic scenarios, incorporating commercial reimbursement at 50% of charges and full 24/7 staffing, require 184 transports. If labor costs are doubled or Medicare rates are used exclusively, breakeven thresholds exceed 1,000 transports per year. A Monte Carlo simulation (10,000 iterations) confirmed the robustness of these thresholds: the median simulated breakeven was 190 transports under commercial reimbursement, closely matching the deterministic base case. The 90th percentile reached 304 (commercial) and 1,066 (Medicare) annual transports. HEMS programs are highly sensitive to labor costs and payer reimbursement levels. Sustainable operation requires more transport volume than previously estimated, especially when reimbursement is constrained or staffing costs increase. This model provides a transparent, replicable tool to inform financial planning, policy evaluation, and payer negotiations for air medical services.

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Leveraging LLMs for Unstructured Claims Data Analysis

Actuaries rely primarily on structured numerical data for reserving and ratemaking, while valuable predictive information in unstructured text including medical records, adjuster notes, and call transcripts remains largely unused. Manual processing of these documents is time-consuming, inconsistent across reviewers, and unscalable. We present a proof-of-concept framework using large language models (LLMs) to extract structured actuarial variables from unstructured claims data. We implement a two-stage processing architecture separating document-level extraction (Stage 1) from claim-level synthesis (Stage 2). A modular four-script Python pipeline processes synthetic FHIR-based claims data and real claims documents, extracting 36 actuarial variables across reserving, ratemaking, and claims management categories. We validate 14 core variables using two independent clinical expert reviewers scoring 20 synthetic claims on a five-point Likert rubric, achieving mean scores above 4.0 and a weighted kappa of 0.53. Integration with chain ladder reserving demonstrates practical actuarial value: severity-segmented analysis reduced reserve estimation error from 6.5% to 4.0%. The open-source implementation includes audit trails and confidence scoring, providing a replicable foundation for LLM-based actuarial variable extraction in property-casualty insurance.

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