Modern low-code platforms are often linked to the shadow IT myth, which paints a picture of chaos: departments independently creating apps that are insecure, non-compliant, difficult to maintain, and difficult to use, leading to redundancies and inefficiencies. This can be true if low-code platforms are mismanaged, but modern low-code platforms are designed to prevent these issues.
The fear of low-code platforms spiralling out of control and creating “shadow IT” has persisted since these tools entered the mainstream. IT departments often worry about losing oversight as business units develop apps independently, raising concerns about security, compliance, reliability, and data integrity – especially in sensitive industries like healthcare.
However, these fears come from a misunderstanding of modern low-code platforms and their robust governance capabilities rather than inherent flaws in the approach. Today’s advanced low-code platforms, particularly those built on standardised data models and now complemented by AI, have evolved to address these concerns, enabling secure, compliant, and collaborative innovation.
Who does what, how can they do it, and what is the role of AI?
Modern low-code platforms offer robust governance tools and leverage AI to enhance their capabilities. By prioritising a data-first approach and utilising open standards, these platforms address key challenges associated with low-code development while maintaining security, compliance, and scalability in healthcare IT. IT departments can retain visibility and control through:
- Role-based access controls: These establish a structured environment where user roles define access to specific functionalities and data. Unlike non-standardised platforms, modern low-code solutions enforce granular permissions aligned with organisational policies, ensuring only authorised users can make changes or access sensitive information. This minimises risks while enabling tailored access for clinicians, administrators, and developers.
- Data governance: In healthcare, data is the foundation of every clinical decision. Low-code platforms with a data-first design, such as those based on openEHR, provide a standardised and interoperable data layer.
This approach ensures:
- Consistency: Applications operate on a unified, clinically validated data model, eliminating redundancies and errors.
- Compliance: Built-in adherence to healthcare regulations ensures security and legal alignment from the outset.
- Interoperability: The standardised data model enables seamless sharing and reuse of data across applications, avoiding silos common in non-standardised systems.
This level of governance transforms data into a reliable, scalable asset while maintaining integrity across the ecosystem.
- AI-augmented governance: AI goes beyond simple monitoring to actively support governance processes. It identifies security vulnerabilities, detects compliance risks, and optimises app performance. Additionally, AI enhances data management by:
- Assisting in maintaining data consistency across apps and modules, e.g. when creating new data models (avoiding duplication).
- Providing predictive analytics to anticipate system bottlenecks or data usage spikes.
- Recommending workflow adjustments to improve efficiency and ensure compliance.
- Assisting and accelerating the development by writing custom widgets, building clinical forms, and assembling screens, all while being compliant with the platform data governance model.
This isn’t about restricting creativity but rather enabling a framework where innovation thrives safely. By combining a data-first philosophy with AI and robust governance, modern low-code platforms allow IT teams to empower business units while maintaining oversight and ensuring that every app operates within a secure, compliant, and interoperable ecosystem.
Modern low-code platforms in healthcare: AI-driven + data-centric + low-code
Healthcare is uniquely complex, with stakes for data security, accuracy, and interoperability higher than almost any other industry. In this environment, AI-enhanced, data-driven governance ensures that low-code platforms avoid the pitfalls of Shadow IT while unlocking new opportunities for transformation.
The future of data standardisation with AI
Low-code platforms based on openEHR and FHIR offer standardised data models, embedding clinical knowledge and expertise into the foundation. AI enhances these standards by:
- New data models: Assisting or fully building new models based on the existing models within the repository.
- Streamlining data validation: Automatically identifying inconsistencies and ensuring apps operate from a unified, validated clinical model.
- Predictive compliance: Continuously assessing applications for adherence to healthcare regulations.
- Smarter data utilisation: Assisting developers in querying and leveraging data efficiently, minimising redundancy and fragmentation.
For example, instead of fragmented cardiology apps built on disparate proprietary databases, an AI-enhanced, data-driven, low-code platform ensures that all apps pull from the same validated clinical model. This enables interoperability, allowing seamless data sharing and collaboration within and across organisations.
Ensuring proper governance: A methodology
Low-code, especially when paired with AI, doesn’t mean no rules. Governance must be embedded into the development process to ensure success in healthcare.
- Adopt a standardised data model: Start with platforms based on openEHR or FHIR for clinical validity and interoperability, enhanced with AI-driven data management.
- Define roles and responsibilities: Clearly outline who can build, deploy, and maintain apps. AI tools can simplify role assignments and enforce governance policies.
- Use centralised tools for oversight: Dashboards with AI integration provide predictive monitoring, flagging potential issues in security, compliance, and app performance.
- Encourage collaboration: Foster partnerships between IT and business units. Low-code and AI empower IT to focus on high-value tasks while enabling business-side innovation.
- Iterate and improve: Regularly review apps for usability and performance. AI can automate feedback collection and suggest enhancements, ensuring the ecosystem evolves dynamically.
An ecosystem, not a jungle
With AI and data-driven low-code development, the fear of “too many apps” becomes irrelevant. Why? Because these apps aren’t standalone silos, they are part of a cohesive ecosystem.
Key benefits:
- Microservices over monoliths: AI helps create lightweight, purpose-built apps tailored to distinct user groups, such as clinicians, administrators, or patients.
- Vendor-neutral longevity: The data model, not the app, becomes the enduring entity. Apps may change, but the data persists, ensuring continuity throughout a patient’s lifetime.
- Ecosystem intelligence: AI fosters app collaboration by analysing usage patterns and suggesting integrations or optimisations.
This transition from a “feral system” to an organised, intelligent ecosystem isn’t theoretical. It is already powering national projects like the Slovenian Shared Care Record and the London Universal Care Plan, and AI will continue to amplify their success.
What’s next?
At Better, we are committed to advancing AI-powered, data-driven low-code development. Our tools don’t just help build apps faster—they ensure every app is secure, compliant, and aligned with clinical best practices. Low-code isn’t the future—it’s the present. And with AI and data-driven governance, it is a present that is ready to scale, secure, and transform healthcare.
