[Paper Review] Applying Software Patterns to Address Interoperability in Blockchain-based Healthcare Apps

Peng Zhang, Jules White+2 more|arXiv (Cornell University)|Jun 5, 2017

Blockchain Technology Applications and Security3 references96 citations

TL;DR

The paper argues for using foundational software design patterns (Abstract Factory, Flyweight, Proxy, Publisher-Subscriber) to modularize and extend blockchain-based healthcare apps, demonstrated via the DASH case study to address interoperability challenges.

ABSTRACT

Since the inception of the Bitcoin technology, its underlying data structure--the blockchain--has garnered much attention due to properties such as decentralization, transparency, and immutability. These properties make blockchains suitable for apps that require disintermediation through trustless exchange, consistent and incorruptible transaction records, and operational models beyond cryptocurrency. In particular, blockchain and its smart contract capabilities have the potential to address healthcare interoperability issues, such as enabling effective interactions between users and medical applications, delivering patient data securely to a variety of organizations and devices, and improving the overall efficiency of medical practice workflow. Despite the interest in using blockchain for healthcare interoperability, however, little information is available on the concrete architectural styles and patterns for applying blockchain to healthcare apps. This paper provides an initial step in filling this gap by showing: (1) the features and implementation challenges in healthcare interoperability, (2) an end-to-end case study of a blockchain-based healthcare app we are developing, and (3) how applying foundational software patterns can help address common interoperability challenges faced by blockchain-based healthcare apps.

Motivation & Objective

• Motivate and define healthcare interoperability in the context of blockchain and smart contracts.
• Identify architectural features and implementation challenges of blockchain-based healthcare apps.
• Demonstrate how software design patterns can address interoperability challenges in blockchain healthcare apps.
• Provide an end-to-end case study (DASH) to illustrate pattern-driven solutions.

Proposed method

• Describe blockchain concepts and Ethereum smart contracts relevant to healthcare interoperability.
• Analyze interoperability challenges in healthcare (evolvability, data storage, security, and scalable notification).
• Present an end-to-end case study (DASH) and document implementation challenges encountered.
• Apply four foundational patterns (Abstract Factory, Flyweight, Proxy, Publisher-Subscriber) to DASH to address interoperability issues.
• Discuss how patterns interact and the trade-offs of pattern-based design in Ethereum-based healthcare apps.

Experimental results

Research questions

• RQ1 How can software patterns be applied to improve modularity and interoperability in blockchain-based healthcare apps?
• RQ2 What interoperability challenges in healthcare can be addressed by Abstract Factory, Flyweight, Proxy, and Publisher-Subscriber patterns?
• RQ3 What are the practical design and implementation considerations when applying these patterns to Ethereum smart contracts in a healthcare context?
• RQ4 How does pattern-guided design impact evolution, storage efficiency, security, and scalability in DASH-like applications?

Key findings

• Foundational patterns can modularize and extend blockchain healthcare apps, reducing tight coupling and enabling evolution.
• Flyweight helps minimize on-chain storage by sharing common intrinsic data across many patient accounts while keeping extrinsic data separate.
• Proxy provides a way to balance data immutability on-chain with privacy and upgradability through off-chain or proxy-backed access patterns.
• Publisher-Subscriber enables scalable, event-driven notifications to care teams without burdening the blockchain with constant event processing.
• The DASH case study illustrates initial challenges (tight coupling, duplicated resources, scalability) and shows how pattern adoption addresses these issues.