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A Framework for Secure and Interoperable Clinical Summarization Using the Model Context Protocol: Integrating MIMIC-III and FHIR with TinyLlama
Research Article | Published: 21 December 2025
Next-Generation Computing Systems and Technologies Volume 1, Issue 2, 2025: 91-101
Volume 1, Issue 2, Next-Generation Computing Systems and Technologies
Volume 1, Issue 2, 2025
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Next-Generation Computing Systems and Technologies, Volume 1, Issue 2, 2025: 91-101

Open Access | Research Article | 21 December 2025
A Framework for Secure and Interoperable Clinical Summarization Using the Model Context Protocol: Integrating MIMIC-III and FHIR with TinyLlama
by
Chandra Shekhar Behera Chandra Shekhar Behera 1
Swarupananda Bissoyi Swarupananda Bissoyi 1 *
1 Department of Computer Applications, Maharaja Sriram Chandra Bhanja Deo University, Baripada 757003, India
* Corresponding Author: Swarupananda Bissoyi, [email protected]
DOI: 10.62762/NGCST.2025.784852
ARK: ark:/57805/ngcst.2025.784852
Received: 04 October 2025, Accepted: 05 November 2025, Published: 21 December 2025  
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Abstract
This research presents a new framework for clinical summarization that combines the TinyLlama model with MIMIC-III and FHIR data using the Model Context Protocol (MCP). Unlike cloud-based models like Med-PaLM, our approach uses local processing to cut costs and protect patient data with AES-256 encryption and strict access controls, meeting HIPAA and GDPR standards. It retrieves FHIR-compliant data from public servers (e.g., \texttt{hapi.fhir.org}) for interoperability across hospital systems. Tested on discharge summaries, it achieves ROUGE-L F1 scores of 0.96 for MIMIC-III and 0.84 for FHIR, beating baselines like BioBERT (0.61, p < 0.001) due to efficient preprocessing and MCP’s accurate data grounding. ROUGE, BLEU and BERTScore metrics, along with visualizations, confirm its reliability. The entire pipeline code is available at \url{https://github.com/shekhar-ai99/clinical-mcp} for transparency and reproducibility.
Graphical Abstract
A Framework for Secure and Interoperable Clinical Summarization Using the Model Context Protocol: Integrating MIMIC-III and FHIR with TinyLlama
Keywords
model context protocol
tinyLlama
clinical summarization
FHIR
interoperability
MIMIC-III
healthcare AI
data security
ROUGE
BERTScore
Data Availability Statement
The source code for this study is publicly available at https://github.com/shekhar-ai99/clinical-mcp (accessed on 20 December 2025).
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
This work utilizes publicly available de-identified datasets, which do not constitute human subjects research requiring additional ethical approval.
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Cite This Article
APA Style
Behera, C. S., & Bissoyi, S. (2025). A Framework for Secure and Interoperable Clinical Summarization Using the Model Context Protocol: Integrating MIMIC-III and FHIR with TinyLlama. Next-Generation Computing Systems and Technologies, 1(2), 91–101. https://doi.org/10.62762/NGCST.2025.784852
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TY  - JOUR
AU  - Behera, Chandra Shekhar
AU  - Bissoyi, Swarupananda
PY  - 2025
DA  - 2025/12/21
TI  - A Framework for Secure and Interoperable Clinical Summarization Using the Model Context Protocol: Integrating MIMIC-III and FHIR with TinyLlama
JO  - Next-Generation Computing Systems and Technologies
T2  - Next-Generation Computing Systems and Technologies
JF  - Next-Generation Computing Systems and Technologies
VL  - 1
IS  - 2
SP  - 91
EP  - 101
DO  - 10.62762/NGCST.2025.784852
UR  - https://www.icck.org/article/abs/NGCST.2025.784852
KW  - model context protocol
KW  - tinyLlama
KW  - clinical summarization
KW  - FHIR
KW  - interoperability
KW  - MIMIC-III
KW  - healthcare AI
KW  - data security
KW  - ROUGE
KW  - BERTScore
AB  - This research presents a new framework for clinical summarization that combines the TinyLlama model with MIMIC-III and FHIR data using the Model Context Protocol (MCP). Unlike cloud-based models like Med-PaLM, our approach uses local processing to cut costs and protect patient data with AES-256 encryption and strict access controls, meeting HIPAA and GDPR standards. It retrieves FHIR-compliant data from public servers (e.g., \texttt{hapi.fhir.org}) for interoperability across hospital systems. Tested on discharge summaries, it achieves ROUGE-L F1 scores of 0.96 for MIMIC-III and 0.84 for FHIR, beating baselines like BioBERT (0.61, p < 0.001) due to efficient preprocessing and MCP’s accurate data grounding. ROUGE, BLEU and BERTScore metrics, along with visualizations, confirm its reliability. The entire pipeline code is available at \url{https://github.com/shekhar-ai99/clinical-mcp} for transparency and reproducibility.
SN  - 3070-3328
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Behera2025A,
  author = {Chandra Shekhar Behera and Swarupananda Bissoyi},
  title = {A Framework for Secure and Interoperable Clinical Summarization Using the Model Context Protocol: Integrating MIMIC-III and FHIR with TinyLlama},
  journal = {Next-Generation Computing Systems and Technologies},
  year = {2025},
  volume = {1},
  number = {2},
  pages = {91-101},
  doi = {10.62762/NGCST.2025.784852},
  url = {https://www.icck.org/article/abs/NGCST.2025.784852},
  abstract = {This research presents a new framework for clinical summarization that combines the TinyLlama model with MIMIC-III and FHIR data using the Model Context Protocol (MCP). Unlike cloud-based models like Med-PaLM, our approach uses local processing to cut costs and protect patient data with AES-256 encryption and strict access controls, meeting HIPAA and GDPR standards. It retrieves FHIR-compliant data from public servers (e.g., \texttt{hapi.fhir.org}) for interoperability across hospital systems. Tested on discharge summaries, it achieves ROUGE-L F1 scores of 0.96 for MIMIC-III and 0.84 for FHIR, beating baselines like BioBERT (0.61, p < 0.001) due to efficient preprocessing and MCP’s accurate data grounding. ROUGE, BLEU and BERTScore metrics, along with visualizations, confirm its reliability. The entire pipeline code is available at \url{https://github.com/shekhar-ai99/clinical-mcp} for transparency and reproducibility.},
  keywords = {model context protocol, tinyLlama, clinical summarization, FHIR, interoperability, MIMIC-III, healthcare AI, data security, ROUGE, BERTScore},
  issn = {3070-3328},
  publisher = {Institute of Central Computation and Knowledge}
}
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CC BY Copyright © 2025 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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