Reimagining Healthcare Software in the Age of LLMs

The Death of the Form-Based Application?

Business software has followed a simple and predictable pattern: capture input, store it, and retrieve it in a new context. Whether you’re requesting an appointment, doing an intake, entering patient data into an EHR, or submitting an insurance claim, the process is fundamentally the same.

Forms > Databases > Queries > Reports.

It’s a model that has worked well—but in the era of AI-driven automation, it’s starting to feel archaic.

What if software applications no longer needed rigid forms?

What if data storage wasn’t about tables and schemas but about context and meaning?

What if retrieving information wasn’t about searching but simply asking?

We’re entering a world where AI doesn’t just enhance software—it fundamentally changes its purpose.

The Traditional Model of CRUD & Form-Based Inputs

Most software applications in healthcare are built around four essential operations: Create, Read, Update, Delete (CRUD). A user enters information through a form, it gets stored in a structured database, and when needed, it’s retrieved and presented in a new context.

This model has been the backbone of software development. But in today’s AI-driven world, it’s increasingly inefficient, restrictive, and prone to human bottlenecks.

AI as a New Model for Data Capture, Storage, and Retrieval

Large Language Models (LLMs) and Natural Language Processing (NLP) are dismantling the traditional form-based software paradigm. Instead of rigid interfaces, we’re seeing the rise of AI-driven, conversational, and autonomous interactions.

Here’s how AI disrupts each layer:

1. Data Capture → From Forms to Conversations

Instead of structured input fields, AI enables natural language interactions. Users describe their needs conversationally, and AI extracts structured data.

• Doctors dictate patient histories, and AI auto-structures data into an EHR.

• Insurance customers submit claims via chatbot, eliminating tedious form-filling.

• Financial teams ask AI for “last quarter’s revenue breakdown” instead of manually pulling reports.

This is a radical departure from traditional form-based inputs—it means software can adapt to human behavior rather than forcing humans to adapt to software.

2. Data Storage → From Rigid Schemas to Adaptive Knowledge Graphs

The way we store and organize data is evolving. Traditional relational databases require strict schemas, but AI allows for semantic knowledge graphs, vector databases, and more dynamic data structures.

• Instead of rigid EHR templates, patient records become context-aware knowledge graphs, adapting to different medical specialties.

• In finance, AI understands relationships between accounts and trends, rather than just storing static numbers.

• AI-powered databases allow health systems to search by meaning, not just keywords.

3. Data Retrieval → From Queries to Contextual Assistance

In today’s applications, retrieving data often means running a search query or filtering reports. But AI changes the game—instead of pulling raw data, it provides context-aware insights.

• Doctors no longer browse through pages of lab results—they simply ask AI, “What’s the patient’s latest trend in hemoglobin levels?”

• CFOs no longer sift through dashboards—they ask, “What’s our biggest revenue risk this quarter?” and get AI-powered recommendations.

• AI-driven systems proactively surface insights—like risk alerts in insurance claims or financial anomalies before they become problems.

4. Transformation → From Rule-Based Workflows to AI-Driven Decision-Making

Historically, software applications apply static business rules to process data. AI, however, learns and adapts—not just following rules but refining them based on patterns.

• AI detects fraud risks in insurance claims before human review.
• AI automates medical billing and coding, reducing administrative overhead.
• AI-driven clinical decision support suggests treatments based on real-time patient data and global research.

The Big Shift from CRUD to AI-Powered C-AI-R

If most software today still operates in a CRUD model, AI is replacing it with C-AI-R (Capture, AI-Transform, Retrieve).

Traditional Model (CRUD)

1. Create (Forms)
2. Read (Database Queries)
3. Update (Manual Data Entry)
4. Delete (Data Management)

AI-Powered Model (C-AI-R) (Capture, AI-Transform, Retrieve)

1. Capture (Conversations, Voice, Unstructured Inputs)
2. AI-Transform (Automated, Contextual Data Processing)
3. Retrieve (AI-Assisted, Contextual Insights)
4. AI-Driven Decision-Making (Automated Actions, Recommendations)

What This Means for Healthcare & Data Engineering

For industries like healthcare, where structured data is critical, this shift presents both opportunities and challenges.

• FHIR-based health data must be reimagined to work with AI-driven retrieval and interoperability.
• Data monetization changes when AI can generate new insights beyond static reports.
• Compliance and security must evolve as AI-driven systems interpret and act on sensitive data.

Reimagining Software in an AI-First World

If software applications are still form-based, are they already outdated?

The reality is that AI is a paradigm shift. Software can no longer be about passive data storage; it must become proactive, context-aware, and intelligent.

At Siftia, we’re continuously exploring how these ideas reshape healthcare data engineering. But the broader challenge is for everyone building software today: How will your applications adapt to this new reality?