Case Study

EHR EMR Integration for an AI-based IVF Success Prediction Platform

In the world of assisted reproduction, the fusion of technology and medicine is creating exciting new possibilities. This case study dives into a cutting-edge project that combines EHR and EMR with an AI-based platform designed to predict IVF success rates.

At a Glance

A HealthTech platform analyses the Embryos developed in Labs and predicts the success rate and flags the ones that may fail to fertilize further. Thus, avoiding patients undergoing repetitive IVF cycles.

AI can only predict based on the right and relevant data of the patient with real-time status available at the point of Analysis.

Azilen enabled this through EHR EMR integration. With a cloud-hosted serverless integration layer, the system was enabled with a secure data pipeline to exchange real-time anonymized patient data with HIPAA-compliant formats.

Key Highlights

Auto Triggering / Auto Invocation

Data Aggregation & Movement

Data Conversion

Data-Driven Decision Making

Data Quality with Consistency

Multi Systems Integrations

Challenges

Data Synchronization
Data Normalization
Data Conversation
Multiple Integrations

About Client

The client is a DeepTech start-up specializing in clinical embryology, working on a platform supporting in-Vitro Fertility (IVF) treatments. This was an AI-based platform to predict the success chances of an IVF treatment.

Healthcare

Industry

In-Vitro Fertility

Domain

Europe

Location

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How we helped?

An Automated mechanism was set up using AWS lambda which automated the data transformation and exchange from EHR/EMR systems to the AI platform. The prediction & evaluation of the platform is fed back to the EHR/EMR system for clinical records.

Increase first-cycle pregnancy rate

Save administrative and embryo evaluation time

Manage multiple EHR/EMR Data

The Solution

Do you recall when patient information was trapped in separate EHR/EMR systems, isolating it from the rest of the healthcare network? In the current data-driven environment, this division impedes comprehensive analysis and well-informed decision-making. We’ve tackled this challenge head-on for our client, empowering healthcare organizations with seamless EHR/EMR integration into their proprietary systems.

Below are some key highlights of the solution implementation:

Integration Layer Setup:

✅ Built an integration layer within the AWS cloud using AWS Lambda functions.

✅ Extensible to multiple EHR/EMR systems.

✅ When APIs are unavailable, healthcare professionals upload data files to the AWS S3 bucket.

✅ Lambda functions capture and transform data into a suitable format.

Data Conversion:

✅ Lambda functions convert raw data into the proprietary format of the client’s system.

✅ Ensures compatibility for AI/ML analysis and embryo ranking.

AI-Based Ranking and Prediction:

✅ Transformed data, including images and morphokinetics, is analyzed by AI models.

✅ AI system ranks embryos based on clinical pregnancy potential.

✅ Recommendations are sent back to EHR/EMR systems for IVF treatment administration.

Extendible Integrations:

✅ Architecture supports integration with multiple EHR/EMR systems.

✅ Orchestration layer accommodates diverse EHR/EMR ecosystems.

Automated Data Pipeline:

✅ Facilitates communication between EHR/EMR systems and the client’s SaaS platform.

✅ Handles systems hosted on-premises or in the cloud.

Below is the integrated process flow:

EMR EHR Integration

Beyond the Integration:

✅ Ongoing support and maintenance to ensure data pipeline robustness and quality.

✅ Integration of new EHR/EMR systems as needed, maintaining system integrity.

The Final Verse:

Our EHR/EMR integration approach harmonizes healthcare data, enhances patient care, and streamlines operations. Connect with us to rediscover data-driven healthcare harmony.

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