Prev Mama Mia: A platform to connect patients with healthcare experts.

Category: Healthcare

Services: Healthcare Data Analytics, Patient Engagement Platforms, Medical Imaging Solutions, Healthcare Workflow Automation, Compliance and Security Solutions, Custom Healthcare Application Development.

Mama Mia Project
  • Improved security by 50% and achieved GDPR compliance
  • Achieved a 60% improvement in data monitoring efficiency
  • Reduced infrastructure costs by 30%
  • Achieved a 55% improvement in maintaining high availability

About Prev Mama Mia

Prev Mama Mia is a healthcare business facilitating consulting, communications, and outreach for women in Sweden to expert “midwives”(doctors).  The main objective of Prev Mama Mia was to build a platform that enables patients to connect with healthcare professionals


  • The most significant challenge for the client was providing patients with easy access to communication with different healthcare professionals.
  • Ensuring the security and compliance of sensitive patient data stored in the database and managing compliance with regulations like HIPAA was a challenge.
  • Setting up comprehensive data monitoring, managing regular system maintenance, and ensuring high availability of services was complex.
  • Managing the infrastructure costs associated with data storage and communication services was a significant challenge.
  • Developing effective communication features to connect patients with doctors, recording sessions, and ensuring patient data management needed effective solutions.

Proposed Solution & Architecture

  • Simform developed and implemented features to connect patients with doctors, record sessions, and manage patient data securely.
  • To ensure the security and compliance of patient data and regulatory adherence, Simform implemented robust access controls, encryption mechanisms, and regular security audits using AWS WAF.
  • Simform designed a comprehensive architecture leveraging containerization and enabled infrastructure management with multiple AWS services like Amazon ELB, Auto Scaling Groups, and Amazon ECS.
  • Our team integrated Amazon Chime, enabling effective communication between patients and healthcare professionals through chat, audio, and video calls.
  • We set up comprehensive data monitoring and alerting using AWS CloudWatch to manage regular system maintenance and maintain high service availability effectively. 
  • Our team of experts included prudent management of infrastructure costs associated with data storage and communication services by optimizing AWS services usage.
  • We implemented cost-effective storage strategies within Amazon S3 and Amazon RDS.
  • Our team used AWS CloudFront as a CDN to enable edge location caching for faster patient content delivery.

Metrics for success

  • We improved security by 50% and achieved GDPR compliance through access controls, encryption, and regular audits.
  • Using AWS CloudWatch, we achieved a 60% improvement in data monitoring and system maintenance efficiency
  • Our team reduced infrastructure costs by 30% through AWS optimization and cost-effective storage strategies.
  • Simform achieved a 55% improvement in maintaining high service availability by using Amazon ELB, Auto Scaling Groups, and Amazon ECS.

Architecture Diagram

Prev Mama Mia Arhitecture Diagram

AWS Services

  • AWS CloudWatch: We used AWS CloudWatch for log collection, alarms, and resource visualization for all AWS services on the account.
  • AWS Cloudfront: Our team leveraged AWS CloudFront as CDN to provide end users with edge location caching for low-latency content delivery.
  • AWS WAF: We used AWS WAF to protect the application from bot attacks, SQL Injection, DDoS attacks, Windows PowerShell & Linux LFI attacks.
  • Amazon Chime: With the Amazon Chime SDK, our experts added real-time voice, video, and messaging powered by machine learning into their applications.
  • Amazon Elastic load balancer: We leveraged Amazon ELB to enhance the availability and fault tolerance of the platform by distributing incoming traffic across multiple targets.
  • Amazon ECS: Our team used Amazon ECS to offer a highly scalable containerized environment to run applications.
  • Auto Scaling Group: We used Auto Scaling Group to manage the server scaling algorithms for the ECS cluster.
  • Amazon S3: We leveraged Amazon S3 for static data storage for application artifacts data.
  • Amazon Pinpoint: Our team of experts used Amazon Pinpoint to send end users push and in-app notifications.
  • Amazon RDS: We used Amazon RDS to support different SQL servers like MSSQL, MySQL, and PostgreSQL.
  • AWS Code pipeline: Our team leveraged the AWS Code pipeline to provide capabilities to create CICD pipelines to automate the application build and deployment.
  • AWS Codebuild: We used AWS Codebuild to create dockerized application builds for deployment in a containerized environment.
  • AWS Codedeploy: Our experts leveraged AWS Codedeploy to deploy the newly created application build to the ECS cluster without downtime.

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