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Custom Healthcare Software Development: 2020 Approach Vs. 2024 Approach

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4 years – the world has changed completely and so has the approach to custom healthcare software development.

And to achieve success (and of course greatness), knowing how it evolved is crucial.

The post-pandemic era was all about keeping up with the latest trends blindly.

But now, it has shifted towards value-driven innovation that directly addresses the needs of healthcare providers, patients, and administrators.

Hence, in this blog, we’ve compared the 2020 and 2024 approaches to building healthcare software.

So, let’s get started.

Custom Healthcare Software Development: 2020 Vs. 2024 Approach

  • Traditional, document-centric approach.
  • Primarily on requirements gathering through interviews, workshops, and documentation.
  • Limited use of data analytics, user journey mapping, and prototyping tools.
  • Static project scope creation.
  • Agile, data-driven, and user-centric approach with rapid prototyping and iterative development.
  • Deeper user understanding through data analysis, user journey mapping, and rapid prototyping.
  • Leverage AI-powered tools for user persona development, sentiment analysis, and journey mapping.
  • Dynamic and data-driven product vision document
  • Monolithic architectures were common.
  • On-premise deployments were still prevalent.
  • The focus was often on internal functionality, with APIs as an afterthought.
  • Containerization (Docker, Kubernetes) was gaining traction.
  • Microservices architectures dominate.
  • Cloud adoption (public, private, hybrid) is widespread.
  • API-first design is crucial now.
  • Serverless computing, AI/ML integration, and IoT integration are transforming architectures.

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  • Manual testing methodologies.
  • Limited automation, mostly for regression testing.
  • Testing is performed in isolated environments, leading to longer testing cycles.
  • Limited use of AI/ML for testing purposes.
  • Shift towards automated testing.
  • Adoption of AI-driven testing tools.
  • Integration of testing into DevOps and Agile methodologies.
  • Utilization of cloud-based testing platforms for scalability and flexibility.
  • UX focused on basic functionality and navigation.
  • Static interfaces, limited data integration, focus on desktop or web.
  • Patient portals with basic functions and limited user research.
  • Limited use of interactive elements and animations in UI.
  • UX emphasizes personalization and intuitive workflows.
  • Dynamic interfaces, AI-powered personalization, multi-device accessibility.
  • Empathetic design, data-driven personalization, gamification for engagement.
  • Voice interfaces, micro-interactions, AR/VR integration, intuitive navigation.
  • Basic optimization techniques like code profiling, caching, and database indexing.
  • Primarily on-premise infrastructure, limited focus on cloud scalability.
  • Focus on optimizing code and infrastructure for baseline performance.
  • Reactive performance testing, limited scalability in infrastructure.
  • AI/ML-driven performance analysis, proactive identification of bottlenecks.
  • Leveraging cloud infrastructure for dynamic scaling and microservices architecture.
  • Focus on UX and continuous improvement with AI/ML-driven optimization.
  • Proactive performance analysis with AI, cloud-based infrastructure for elastic scaling.
  • Limited scalability due to monolithic architecture.
  • Relational databases that struggled with unstructured data like medical images.
  • Required manual intervention to add resources.
  • Costly and had limitations in handling sudden spikes in traffic or data volume.
  • Custom healthcare software development emphasizes microservices architecture.
  • Efficiently handle large volumes of unstructured data with NoSQL databases.
  • Leverages cloud platforms like AWS or Azure for elastic scaling.
  • Automated tools to manage resource allocation based on predefined rules.
  • Focus on legacy security protocols
  • On-premise data storage is common, increasing the attack surface.
  • Focus on known vulnerabilities, leaving blind spots for new threats.
  • Firewalls and intrusion detection systems formed the main defense.
  • Shift to stronger protocols and post-quantum cryptography readiness.
  • Cloud-based storage with robust provider security features is preferred.
  • DevSecOps that implements security into the development process, not bolted on later.
  • Encryption throughout the data lifecycle (at-rest, in-transit, in-use).
  • Relied heavily on traditional version control systems SVN.
  • Limited branching options
  • Change tracking and compliance checks primarily manual
  • Manual integration with compliance tools.
  • Increased use of Git for distributed version control
  • Advanced branching strategies and tools (e.g., Gitlab, Github) for complex workflows.
  • Automated tracking and compliance
  • CI/CD pipelines integrate version control with automated testing and security scans.
  • CI/CD pipelines were manually configured and maintained.
  • Limited automation resulted in slower release cycles and error-prone.
  • Testing and deployment processes were typically separate.
  • Legacy build tools, limited use of cloud platforms, static infrastructure.
  • CI/CD pipelines are fully automated and orchestrated using advanced DevOps tools and platforms.
  • Automated testing is seamlessly integrated into the CI/CD pipeline.
  • IaC principles are employed for defining and managing infrastructure resources.
  • Utilization of Docker and Kubernetes for consistent deployment across different environments.
  • Primarily paper-based documentation or static PDFs.
  • Limited collaboration due to manual updates and version control is cumbersome.
  • Emphasis on detailed compliance checklists and reports.
  • Restricted access within healthcare organizations.
  • Cloud-based platforms facilitate real-time access and updates.
  • Tools enabling team-based document creation and modification.
  • Automated compliance with built-in checks and reports ensures regulatory adherence.
  • Secure access for authorized users across organizations
  • Maintenance focused on fixing bugs and issues after they arose.
  • Updates and patches are applied manually, leading to potential delays and downtime.
  • Limited monitoring due to relying on user reports for issue identification.
  • Manual testing and deployment, increasing maintenance costs and human error.
  • Predictive analytics are used to anticipate and prevent issues.
  • Tools and scripts for automated updates, patches, and deployments.
  • Continuous monitoring with AI-driven algorithms.
  • Automated testing and deployment pipelines for faster, more reliable updates.
  • Error handling in custom healthcare software development typically relies on basic try-catch blocks and manual error logging.
  • Debugging and troubleshooting were time-consuming.
  • Manual error recovery processes with limited automation.
  • Reactive error handling. Catching and logging errors to identify them later.
  • Advanced error handling techniques, leveraging automated monitoring and proactive error detection mechanisms.
  • AI-driven anomaly detection and predictive analysis.
  • Leverages self-healing mechanisms and API resilience techniques for automatic recovery.
  • Proactive error handling. Predicting and preventing errors before they occur.
  • Relied on experienced developers to meticulously check code.
  • Basic static analysis tools for initial checks, but deeper scrutiny is done manually.
  • Reviews are often restricted to smaller teams or individual developers.
  • Detailed comments and reports outlining findings.
  • Increased use of automated tools for code quality, security, and performance checks.
  • AI-powered assistance to identify complex issues and automate repetitive tasks.
  • Collaborative code review and real-time feedback within larger teams.
  • Metrics and automated reports provide clearer insights and track progress over time.
  • Focus on readability and maintainability
  • Ensuring individual code blocks function as intended.
  • Teams rely on human analysis for code quality.
  • Limited use of static analysis tools, primarily for basic checks like syntax and potential bugs.
  • Enhanced focus on code quality assurance and improvement
  • Streamlining the testing process and improving coverage.
  • Greater emphasis on writing testable code.
  • Integration of automated code analysis tools and AI-driven code review systems.

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  • Prevalent use of tools for manual dependency installation and updates.
  • High risk of version conflicts leading to compatibility issues and bugs.
  • Dependencies specific to projects, hinder code reuse across projects.
  • Less focus on dependency security vulnerabilities.
  • Increased adoption of containerization tools like Docker and Kubernetes for isolated and controlled environments.
  • Dependencies packaged within containers, eliminating version conflicts.
  • Utilizing microservices architecture, promoting modularity and code reuse.
  • Integration of automated dependency vulnerability scanning and patching tools.
  • Primarily focused on identifying issues after they occurred.
  • Limited log data capture, mostly basic logs like errors and user actions.
  • Relied on manual inspection of logs for root cause analysis.
  • Logs are stored on local servers, limiting scalability and accessibility.
  • Utilizes real-time analytics to predict and prevent potential issues.
  • Captures detailed logs including system metrics, API calls, and user behavior.
  • Utilizes machine learning for automated issue identification and root cause analysis.
  • Cloud-based storage that enables scalable and centralized log management.
  • Monolithic architecture was prevalent.
  • Limited scalability and flexibility due to the dependency on a single codebase.
  • Challenges in updating or adding new features without disrupting the entire system.
  • Traditional relational databases like SQL were commonly used for data storage.
  • Microservices architecture is widely adopted.
  • Enables scalability and flexibility by allowing each service to be developed, deployed, and updated independently.
  • Simplifies integration of new features and technologies without affecting the entire system.
  • Adopts NoSQL databases for efficient management of diverse healthcare data at scale.
  • Relied heavily on established unit testing frameworks like JUnit, NUnit, and XUnit.
  • Testing primarily focused on individual units of code in isolation.
  • Limited support for integration testing and end-to-end testing within the same framework.
  • Emphasis on manual testing for system-level validation.
  • Adoption of more advanced unit testing frameworks such as Jest, Mocha, and Cypress.
  • Shift towards Test-Driven Development (TDD) and Behavior-Driven Development (BDD) methodologies.
  • Integration of unit tests with CI/CD pipelines for automated validation.
  • Increased use of containerization for environment consistency in testing.
  • Relational databases like SQL were predominantly used for custom healthcare software development.
  • Emphasis on normalization to reduce redundancy and maintain data integrity.
  • Performance optimization through indexing and query optimization techniques.
  • Limited scalability due to the rigid structure of relational databases.
  • Adoption of NoSQL databases for more flexibility in handling diverse and unstructured healthcare data.
  • Integration of graph databases for complex relationships in healthcare networks.
  • Emphasis on distributed databases for scalability and fault tolerance.
  • Utilization of ML algorithms for dynamic database optimization and predictive analytics.
  • Prevalent use of RESTful APIs due to their simplicity and widespread adoption.
  • FHIR was an emerging standard but adoption was not yet widespread.
  • Security primarily focused on authorization.
  • Some legacy systems still rely on SOAP APIs for data exchange.
  • FHIR has become the dominant standard for healthcare data exchange due to its flexibility and interoperability.
  • API gateways are used for complex management and security.
  • Growing compliance concerns like GDPR and HIPAA lead to stronger data privacy measures.
  • Deeper integration with serverless computing for scalability and cost-effectiveness.
  • DevOps relied on manual intervention for CI/CD processes.
  • Proactive monitoring of applications and infrastructure for performance issues and errors.
  • Automated tasks like testing, deployment, and infrastructure provisioning for efficiency and reduced human error.
  • DevOps emphasizes a manual approach to resource management and scaling decisions.
  • AIOps utilizes ML algorithms during CI/CD processes to identify patterns, anomalies, and potential issues.
  • AI analyzes performance data to identify bottlenecks and suggest optimizations.
  • AI predicts potential hardware failures based on sensor data.
  • AIOps leverages ML algorithms to analyze real-time data and predict resource demands.

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What’s Next for Your Custom Healthcare Software Development Endevours?

The development is divided into three terms – which was, which is, and which will be.

You have to learn from the past to profit from the present, and from the present, to build a healthier tomorrow!

At Azilen, we’ve actively participated in the evolution of healthcare software development from 2020 to 2024.

In 2020, the focus was primarily on digitizing healthcare processes, improving efficiency, and enhancing patient experience.

These days, it’s all about AI, machine learning, blockchain, IoT, and so on.

In essence, it’s not just digitizing anymore; it’s becoming smart, predictive, and totally personalized.

Moving forward, our commitment to innovation and engineering excellence remains unwavering.

So, if you’re ready to take your healthcare services to the next level, let’s connect.

Together, we’ll innovate, collaborate, and transform the future of healthcare delivery!

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