A Detailed Guide On Building a Unified API Platform

The race to connect health data to AI is accelerating faster than most teams manage to build integrations. OpenAI and Anthropic both launched healthcare-focused AI products in January 2026, meaning users now expect to ask AI assistants about their sleep, heart rate, and fitness trends. 

To keep up, health applications need reliable, normalized data from dozens of devices. This guide walks through choosing your integration approach, designing for data normalization, ensuring compliance, and preparing for AI-powered features.

Define your integration strategy before writing code

Before writing any code, map out which health data sources your users actually need. 

1. Which devices do your target users own?
   Survey your users or analyze your market segment. Fitness and wellness apps typically prioritize platforms like Apple Health, Garmin, and Fitbit. Clinical or medical applications often require integrations with glucose monitors or lab systems.
2. What data types drive your core features?
   Activity, sleep, heart rate variability, nutrition, and lab data each come with different integration and processing requirements. Focus on the data types that directly support your primary use case before expanding coverage.
3. How real-time does your data need to be?
   Workout tracking and coaching apps often require near-real-time synchronization, while trend analysis or health reports can rely on batch updates. Your latency requirements will directly influence your integration architecture.

Weigh building versus buying

Before anything else, you need to decide whether to build custom integrations in-house or use a unified API platform.

Building in-house gives you full control but comes with high costs. A single wearable integration can take up to 2-3 months, as each manufacturer uses different authentication flows, data formats, and versioning schemes. Supporting 10-15 device brands can quickly turn into 1-2 years of integration work.

However, the true hidden cost is maintenance. Every manufacturer updates their API on their own schedule, which means your engineering team ends up spending more time on upkeep than innovation. 

Using a unified API platform addresses this by removing device-specific complexity behind individual integrations. Instead of maintaining dozens of custom pipelines, teams integrate once and receive standardized data across many devices. Authentication handling, schema changes, and ongoing maintenance are also managed by the API provider, dramatically reducing engineering overhead and time to market. OuiLive cut support tickets by 40% after switching from in-house integrations to Spike API.

Evaluate platforms on their track record, compliance certifications, and whether they offer dedicated implementation support.

Prioritize data normalization from day one

Data normalization is the difference between a health platform that scales and one that collapses under technical debt. 

Device data varies widely across devices, in units, timestamps, and field names. Even the same provider can return data at different granularities: one device might give you HRV readings every hour throughout the night, while another returns only a nightly average. Without normalization, you will need to write custom parsers for every device, which break whenever manufacturers update APIs. Skipping this step often means maintaining pipelines instead of building features.

The solution is to refine your data model before writing integration code. 

This upfront investment pays off in two ways. First, your application logic stays organized because it only needs to understand one data format. Second, you're ready for AI features that require consistent, structured inputs. Whether you build normalization yourself or use a platform that provides normalized, AI-ready data, the structure needs to exist before you scale.

Build your compliance foundation

Health data carries strict regulatory requirements that must be built into your architecture from the start.

• HIPAA: Required if handling US-protected health information. Ensure Business Associate Agreements (BAAs), encryption in transit and at rest, and access logging.
• GDPR: Governs EU user data with strict consent and deletion requirements. For global apps, design for GDPR first.
• SOC 2: Demonstrates security controls; often required by health systems and insurers.

If you use a unified API platform, verify that they maintain these certifications. 

Prepare your infrastructure for AI

Recent healthcare-focused AI launches have made personalized, AI-driven health insights an expectation rather than a differentiator. If your app does not offer such features, users will churn. 

Your data architecture determines whether you can leverage these capabilities. If your health data arrives in fragmented formats from different devices, you'll spend months on preprocessing before shipping any AI features.

Build with AI in mind by adopting standards like the Model Context Protocol (MCP) for connecting health data to AI applications. Platforms that offer MCP integration let you add AI-powered features without rebuilding your data layer. 

How Spike API accelerates your build phase

Here's why teams choose Spike API over building in-house or other platforms:

Ship in weeks, not months. Every team gets a dedicated implementation engineer who works with you to ensure a smooth launch. Most teams go live in 2-4 weeks.

One integration, 500+ devices. Connect once to access data from wearables, IoT devices, nutrition apps, and lab reports. Spike handles authentication and maintenance across all sources.

AI-ready from day one. The MCP layer connects health data directly to any LLM of your choice without any preprocessing required. You get both raw and normalized data formats depending on your use case.

Compliance infrastructure included. HIPAA and GDPR compliance with European data centers built into the platform, not bolted on later.

If you want to see how a unified, AI-ready health data platform would work for your specific use case, book a personalized demo.