A Blockchain-Based Model for Storing and Retrieving Patient Healthcare Records

Imagine a world where your medical records follow you seamlessly from one hospital to another, secure from hackers, accessible only to authorized doctors, and updated in real-time without bloating massive databases. Sounds like science fiction? It’s not. Enter the revolutionary known as DHC, designed specifically for healthcare data management.

In the era of exploding patient data volumes, traditional centralized systems are crumbling under the weight of privacy breaches, slow access times, and scalability nightmares. Blockchain technology steps in as the hero, promising decentralization, immutability, and trust. But standard blockchains? They’re too clunky for healthcare’s high-stakes, high-volume needs. The DHC model changes that by introducing smart off-chain and on-chain structures that slash storage, boost speed, and lock down privacy tighter than ever.

Why Healthcare Data Storage is a Blockchain Battleground

Patient records fuel everything from diagnoses to treatments and even pandemics like COVID-19. Yet, siloed hospital databases mean doctors waste time hunting data, patients risk privacy leaks, and systems choke on sheer volume. Cloud solutions? They’re vulnerable to hacks and don’t guarantee integrity.

Blockchain flips the script: data as tamper-proof transactions in blocks, verified by consensus, distributed across nodes. But challenges persist:

• Scalability: Healthcare generates mountains of data; full chains explode storage needs, killing full nodes.
• Retrieval Speed: Linear scans take O(n) time—unacceptable for emergencies.
• Privacy: Public ledgers expose sensitive info.
• Off-Chain Pitfalls: Hashes on-chain ensure integrity, but off-chain data scalability and trust lag.

The DHC model tackles these head-on with innovative structures like Huffman tree-based blocks, dynamic chains, and a two-layer consensus.

Breaking Down the DHC Model: Key Components

At its core, DHC blends local efficiency with global security. Here’s how it works:

1. Wards Huffman Tree (WHT) and Local Dynamic Blocks (LDB)

Traditional Merkle trees treat all data equally—inefficient for healthcare where ward priority matters. DHC uses a Wards Huffman Tree (WHT), inspired by data compression genius Huffman coding. Wards get weights based on importance (e.g., ICU higher than outpatient). Lower-weight wards merge first, placing critical ones higher for faster access.

Daily, patient data from visited wards populates the WHT, forming a Local Dynamic Block (LDB). LDB header: patient ID, prev hash, WHT root, timestamp, last flag (discharge?), threshold (max days before on-chain commit). Body: WHT with raw data.

Result? Latest LDB holds all hospitalization data, keeping chains lean—no redundancy.

2. Local Dynamic Chain (LDCh) and Trimming Magic

Each hospital runs its own LDCh, chaining daily LDBs. When threshold hits or discharge flags, final LDB becomes a Ready Block (RB) with hashed ward data (raw on local IPFS). RBs queue in FIFO Ready Queue (RQ).

Scalability hack: LDCh trimming. Post-RB submission, scan for first non-candidate block (NCB). Keep 6 prior blocks (Bitcoin-style integrity) + all after; prune rest. Chains stay lightweight on fog nodes.

3. Global Blocks (GB), Global Chain (GCh), and Two-Layer Consensus (TLC)

RBs verify via TLC: Local Raft consensus per hospital (fast, trustworthy fog nodes), then global Raft on leaders. Validated? RB becomes GB on GCh.

GB header: patient ID, LDCh ID, prev hash, HWHT root, wards count, timestamp, Count (block num, status) for O(1) traversal. Body: HWHT hashes. Retrieval? Start from latest GB, recurse via Count—no full scans.

4. Privacy-Preserving Access Control

Smart contracts (SCs) rule: Indices Database (IDB) tracks patients/physicians/wards. Physicians sign scripts with hashed ward public keys. SC fetches GBs via IDB, filters by signature, pulls from IPFS. O(1) privacy-compliant access.

Security: Fort Knox for Health Data

DHC isn’t just fast—it’s bulletproof:

• Attacks Mitigated: DDoS (SC auth), Sybil/Eclipse (one-vote nodes), Forks (sequential RQ).
• CIA Triad + More: Auth via SC, integrity via hashes/6-block rule, confidentiality via signatures, ownership hospital-held, traceability via IDs/Count.

Beats rivals: Full privacy, ownership, scalability where others falter.

Performance: Numbers Don’t Lie

Tested on WHO COVID data (Python 3.9, i5, 6GB RAM):

Insights: DHC retrieval scales with days (d), IPFS local-only saves space, TLC faster than PoW, block creation efficient despite denser data.

Why DHC Matters for Blockchain in Healthcare (and Crypto)

Beyond tech specs, DHC paves Web3 health: Tokenize access? NFTs for records? DeFi for insurance? It’s consortium-ready, energy-efficient (Raft over PoW), IPFS-integrated. Future: ML-optimized Huffmans, game theory for weights.

As crypto evolves, healthcare adoption signals mass scalability. DHC proves blockchain isn’t just Bitcoin—it’s life-saving infrastructure.

Conclusion: The Future of Secure Health Data is Here

The DHC redefines healthcare records: faster retrieval, ironclad security, infinite scalability. Hospitals worldwide should pilot this now. What’s stopping your chain from going dynamic?

Ready to dive deeper? Explore blockchain’s health revolution.

Key Takeaways:

• WHT + LDB/LDCh = Off-chain scalability with integrity.
• TLC + GCh = On-chain speed and security.
• SCs = Privacy without compromise.
• Beats benchmarks in storage, throughput, attacks.

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