Microsoft Cloud for Healthcare design principles
To ensure the solution meets the highest standards, Microsoft offers a set of design principles for Microsoft Cloud for Healthcare and Life sciences. These design principles build on the five pillars of architectural excellence in the Azure Well-Architected Framework and guide building sustainable and scalable solutions. This article explores these design principles and how you can apply them to create effective and efficient solutions.
| Design principle | Description | Pillar |
|---|---|---|
| Build for the needs of business | A business requirement must justify every design decision. For an application to be appropriately reliable, it must reflect the business requirements surrounding it. | Reliability |
| Assign least privilege | Implement least privilege throughout the application and control plane to protect against data exfiltration and malicious actor scenarios. Make necessary configurations to show/hide any customer information relevant to each persona. | Security |
| Configure-first approach | Prioritize built-in configurations and settings available within the solution and Power Platform before using custom code or complex customization. Reserve custom coding or complex development efforts for situations where no suitable prebuilt, low-code, or third-party solutions exist. | Performance efficiency |
| Integration and interoperability | Enable seamless integration with existing systems and applications commonly used in the healthcare services industry. Support APIs and industry standards to facilitate data exchange between platforms and services. | Reliability |
| Centralized operations and monitoring | Standardize and centralize logging and auditing for each solution component. Use Azure Monitor Application Insights and Azure Log Analytics to collect on-premises, hybrid cloud, platform as a service (PaaS), and Power Platform logs. | Operational excellence |
| Monitor the health of the entire solution | Understand the scalability and resiliency of the infrastructure, application, and dependent services. Regularly gather and review key performance counters. | Operational excellence |
| Bring in only necessary data for usage | Selectively determine which data elements are essential for mapping or extending in health data model and avoid unnecessary data proliferation. | Performance efficiency |
| Data privacy and consent management | Prioritize data privacy by implementing robust mechanisms for managing user consent and controlling access to sensitive information. Implement privacy-by-design principles, pseudonymization techniques, and data anonymization to protect customer data while enabling data-driven insights and personalization. | Security |
| Security and compliance first | Prioritize robust security measures, data encryption, access controls, and compliance with industry regulations. | Security |
| Foster data-driven decision making | Healthcare organizations rely heavily on data-driven decision-making. Use, configure, or integrate with data visualization tools to provide insights into customer behavior, risk management, and overall business performance. | Operational excellence |
| Use native solutions/connectors instead of building your own | Use one of the native connectors for data ingestion along with transformation capabilities instead of building your own connectors. | Cost optimization |
| Optimize costs | Operational data can grow significantly with time. Monitor the growth of your Dataverse tables by time and apply data retention policies for data, logs, and files in Dataverse. | Cost optimization |
| Select the right licensing strategy | Establish the right licensing assignment strategy to optimize your platform licensing costs. | Cost optimization |