Healthcare’s new frontier: combining blockchain, IPFS and edge computing to handle critical data flow

In a time when healthcare systems are under ever-growing pressure from rising data volumes, patient privacy regulations and the need for real-time responsiveness, a team of Indian researchers have unveiled a bold new architecture intended to future-proof medical data management. The study, led by Soubhagya Ranjan Mallick, Veena Goswami, Rakesh Kumar Lenka and colleagues, introduces a solution called LIVER (Lightweight Infrastructure for Verifiable Electronic Records) that merges distributed ledger/blockchain technology, the InterPlanetary File System (IPFS), edge computing and queueing-theoretic models for the healthcare domain. SpringerLink

The impetus for LIVER comes from the twin problems of scale and latency: modern healthcare devices — sensors, wearables, Internet-of-Medical-Things (IoMT) systems — generate vast volumes of data. Traditional centralised systems often falter in terms of throughput, interoperability, trustworthy sharing and timely processing. As the paper points out, “Traditional healthcare organisations struggle to secure significant amounts of sensitive patient data. Data privacy, interoperability, and scalability are three areas where centralised systems have generally failed.” SpringerLink+1

What LIVER proposes
The core of LIVER is a hybrid architecture. At its base: an edge-computing layer handles data close to the source (for example within hospital premises or near medical IoMT devices), reducing network latency and off-loading burdens from central servers. Then, for storage and persistent integrity, LIVER uses IPFS to distribute medical records (such as reports, images, sensor logs) across peer nodes, while the blockchain layer stores lightweight metadata (hashes, timestamps, identifiers) to ensure immutability, traceability and tamper-resistance. SpringerLink

Specifically, the authors design a dual-blockchain model: a public blockchain to anchor patient records in an immutable ledger (accessible by multiple stakeholders: patients, physicians, insurance, government) and a private blockchain within a healthcare organisation that handles internal functions such as billing, staffing, inventories. This hybrid ensures data sharing across organisations without compromising internal confidentiality. SpringerLink

A further innovation: LIVER integrates queueing-theoretic techniques, notably M/M/1-type queue models with balking behaviour (users or requests may leave if waiting is too long) to model, prioritise and manage healthcare data flows. This aspect is relatively rare in blockchain/IoT healthcare literature. The queueing mechanism is used to manage incoming tasks (data requests, transactions) and service times in the edge/blockchain/IPFS system, thus tackling bottlenecks and improving responsiveness. SpringerLink

According to the authors, the key contributions are:

1. “A unique integration of blockchain technology with IPFS to guarantee secure, decentralized, and lightweight medical record storage, reducing storage requirements and delays.” SpringerLink

2. “Edge processing and real-time decision‐making with low latency and minimal network traffic while keeping the network secure and trustworthy.” SpringerLink

3. “A novel queueing technique to optimize data processing and resource allocation … ensures real-time healthcare data processing, reduces system congestion and improves response time.” SpringerLink

4. “Demonstration via experimental evaluation showing improvements in ledger size, data sharing, system efficiency, scalability, security and performance.” SpringerLink

Experimental outcomes and performance
In their evaluation, the authors implemented the LIVER architecture on an Ethereum-based platform integrated with IPFS and IoMT devices via edge nodes. They measured key metrics: latency (execution time from data generation to block anchoring/record retrieval), throughput (transactions per second, or TPS) under varying file sizes and number of nodes, queueing parameters (arrival/service rates, balking functions) and scalability. SpringerLink

Key findings:

• Execution latency increases as number of nodes or file sizes increase, but after a threshold the growth rate slows — suggesting the design begins to stabilise as load increases. SpringerLink

• TPS is higher for smaller file sizes because less time is required for encryption, storage and retrieval. As file size grows, throughput drops somewhat — yet the system still achieves higher throughput compared to conventional centralised models. SpringerLink

• From the queueing model: the so-called “balking” behaviour is modelled by two functions (e.g., $f(n)=1/(n+1)$ and f(n)=e−nf(n)=e^{-n}f(n)=e−n). The results show that with higher balking (i.e., fewer tasks joining long queues) the system achieves improved waiting times and numbers in system. SpringerLink

• Overall, the authors report reduced latency, improved throughput, enhanced scalability (due to edge and decentralized storage) and improved resource utilisation compared to traditional architectures. SpringerLink

Why this matters
For healthcare, the stakes are unusually high: delayed responses, lost or tampered medical records, interoperability failures can all have severe consequences for patient safety and outcomes. The authors thus emphasise that in healthcare “where decisions made in real-time might have a life-or-death impact, queueing techniques are essential for efficient resource allocation and traffic management.” SpringerLink

Furthermore, by distributing storage (via IPFS) and processing (via edge) while anchoring trust via blockchain, LIVER addresses several interlinked concerns: avoiding large centralized storage (and its vulnerabilities), reducing latency (critical in real-time monitoring), ensuring auditability and immutability (vital for medical records), and enabling scalable handling of many devices and patients. The hybrid blockchain (public/private) adds flexibility for diverse healthcare stakeholders.

In regions with limited infrastructure (for example remote or rural healthcare facilities), the lightweight nature of edge deployment and decentralized storage may be especially beneficial. As the authors note: “In healthcare systems with inadequate infrastructure or in remote areas, when immediate responses are needed but not always possible, edge deployment can be extremely beneficial.” SpringerLink

Caveats and next‐steps
Of course, like any research work, LIVER has its limitations. The experiments are conducted under simulated environments (edge nodes, sensor devices, IPFS and blockchain combining) and may not capture all the complexities of a real-world hospital, regulatory context, or large-scale IoMT deployment. The queueing model, while useful, still represents simplified assumptions (e.g., M/M/1 queue, Poisson arrivals) that may not fully capture heterogeneous healthcare request patterns. SpringerLink

The authors themselves highlight future work directions: “Future work will incorporate machine learning techniques to better distribute workloads and investigate the possibility of dynamic queue changes dependent on patterns in real-time data.” SpringerLink

Implications for policy and practice
If architectures such as LIVER are adopted in practice, healthcare organisations could benefit in multiple ways. Real-time monitoring from IoMT devices (for example in ICU, wearable patient monitoring, remote consultations) could integrate more seamlessly, with lower latency and higher trust in data integrity. Insurance companies, regulators, hospitals could more reliably share certain metadata without exposing full patient records, thanks to hybrid blockchain layering. Remote and underserved healthcare settings could leverage edge+IPFS to reduce dependence on high-bandwidth/cloud infrastructure.

However, deploying such systems will require staff training, regulatory alignment (especially around data privacy, cross-institution data sharing), standardisation (for interoperability, e.g., the authors mention use of FHIR protocol for EHR integration) and robust security audits of the entire stack (blockchain nodes, edge devices, IPFS nodes, IoMT gateways). SpringerLink

Conclusion
In sum, the LIVER framework offers a next-generation blueprint: secure, lightweight, scalable healthcare data architecture that unites blockchain, IPFS, edge computing and queueing theory to meet the demands of modern healthcare environments. While further validation and real‐world roll-out remain ahead, the research opens promising paths for handling the twin pressures of data volume and real-time responsiveness in medical settings. For healthcare providers, technologists and policymakers alike, LIVER suggests a future where digital health systems are not just larger, but smarter — designed for critical, time-sensitive, trustworthy care.

As one of the authors’ affiliations is in Hyderabad (India) and given that many healthcare systems in India, Southeast Asia and other emerging markets face similar constraints (bandwidth, infrastructure, device proliferation), the regional relevance is clear. The challenge now is translating laboratory architecture into hospital corridors, clinics, remote care centres — and making sure the promise translates into improved patient outcomes.