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Enhancing Economic Efficiency in U.S. Healthcare: A Human-in-the-Loop AI Pipeline for Regulatory Compliance and Cost Assurance in Life Sciences
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 79 |
| Year of Publication: 2026 |
| Authors: Rahul Kumar Thatikonda, Sucharitha Donepudi |
10.5120/ijca2026926346
|
Rahul Kumar Thatikonda, Sucharitha Donepudi . Enhancing Economic Efficiency in U.S. Healthcare: A Human-in-the-Loop AI Pipeline for Regulatory Compliance and Cost Assurance in Life Sciences. International Journal of Computer Applications. 187, 79 ( Feb 2026), 1-4. DOI=10.5120/ijca2026926346
Abstract
Organizations managing large volumes of service agreements in life sciences and biotechnology face persistent revenue leakage and compliance failures because critical billing-relevant terms—payment schedules, volume discounts, late-payment penalties, and renewal escalations—are embedded in unstructured legal language. By automating the financial governance of clinical research and supply chain agreements, this framework addresses a critical source of administrative waste that contributes to rising costs in the broader U.S. healthcare system. This paper presents an implementable, human-in-the-loop architecture for contract ingestion, clause segmentation, term extraction, and billing rule generation with full traceability. The system was evaluated on a dataset of 100 expertly curated and densely annotated biotech/clinical research contracts using a 5-fold cross-validation protocol. Results demonstrate: (1) 89.3% precision and 93.1% F1-score in clause classification, (2) 92.0% overall extraction accuracy across five key billing fields, and (3) a 75% reduction in downstream billing error rates compared to manual workflows. The approach combines supervised learning for extraction with deterministic rule-based logic for normalization, ensuring the auditability required for regulated environments.
References
- H. Zhong, C. Xiao, C. Tu, T. Zhang, Z. Liu, and M. Sun, “How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence,” arXiv preprint arXiv:2004.12158, 2020.
- I. Chalkidis et al., “LexGLUE: A Benchmark Dataset for Legal Language Understanding in English,” in Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics, 2022, pp. 4310–4330.
- S. Donepudi and R. K. Thatikonda, “AI-Driven Contract Analysis for Ensuring Compliance and Optimizing Billing,” Zenodo Preprint, Dec. 2024. doi: 10.5281/zenodo.14511240.
- F. Ariai, J. Mackenzie, and G. Demartini, “A Survey of Classification Tasks and Approaches for Legal Contracts,” arXiv preprint arXiv:2507.21108, 2025.
- A. Ekeh et al., “Using AI to Ensure Reliable Supply Chains: Legal Relation Extraction for Sustainable and Transparent Contract Automation,” Sustainability, vol. 17, no. 9, 2025.
- M. M. Rahman et al., “Natural Language Processing in Legal Document Analysis Software: A Systematic Review,” International Journal of Innovative Research and Scientific Studies, vol. 8, no. 3, pp. 5026–5042, 2025.
- A. Ekeh, “Automating Legal Compliance and Contract Management: Advances in Data Analytics,” ResearchGate Preprint, 2025.
- Z. Shaheen, G. Wohlgenannt, and E. Filtz, “Large Scale Legal Text Classification Using Transformer Models,” arXiv preprint arXiv:2010.12871, 2020.
- M. Z. May, N. H. Thanh, S. Ken, S. Saku, and N. Fumihito, “Information Extraction from Lengthy Legal Contracts: Leveraging Query-Based Summarization and GPT-3.5,” in Legal Knowledge and Information Systems, 2023, pp. 177- 186.
- ContractEval Team, “ContractEval: Benchmarking LLMs for Clause-Level Legal Risk Identification in Commercial Contracts,” arXiv preprint arXiv:2508.03080, 2025.
- I. Chalkidis, M. Fergadiotis, P. Malakasiotis, and I. Androutsopoulos, “Neural Contract Element Extraction Revisited: Letters From Sesame Street,” arXiv preprint arXiv:2101.04355, 2021.
- S. Liu, J. Cao, R. Yang, and Z. Wen, “Long Text and Multi- Table Summarization: Dataset and Method,” in Findings of the Association for Computational Linguistics: EMNLP 2022, 2022.
- H. T. Nguyen et al., “Enhancing Legal Document Retrieval: A Multi-Phase Approach With Large Language Models,” arXiv preprint arXiv:2403.18093, 2024.
- M. Siino, M. Falco, D. Croce, and P. Rosso, “Exploring LLMs Applications in Law: A Literature Review on Current Legal NLP Approaches,” IEEE Access, vol. 13, pp. 18253–18276, 2025.
- Y. Zhang et al., “LLMAuditor: A Framework for Auditing Large Language Models Using Human-in-the-Loop,” arXiv preprint arXiv:2402.09346, 2024.
- M. J. R. Cheekaramelli, “Using Natural Language Processing (NLP) to Identify Fraudulent Healthcare Claims,” International Journal of Computing and Engineering, vol. 7, no. 3, pp. 34-53, 2025.
- Insights AI, “Transforming Healthcare: Fraud Detection And Risk Management With NLP,” Insights AI Blog, Jan. 2025.
- N. Guha et al., “LegalBench: A Collaboratively Built Benchmark for Legal Reasoning,” arXiv preprint arXiv:2308.11462, 2023.
- A. Blair-Stanek and B. V. Durme, “Evaluations of LLMs in the Legal Domain,” arXiv preprint arXiv:2502.17638, 2025.
- F. Ariai, J. Mackenzie, and G. Demartini, “Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges,” ACM Computing Surveys, vol. 58, no. 6, pp. 1–37, 2025.
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