In the recent years, especially post inception of the Covid-19 pandemic, there has been a proliferation of mental health apps available to smartphone users. One of the main reasons why such platforms are rapidly being adopted is because they offer a wealth of resources that make therapeutic techniques more accessible, portable, and cost-effective as compared to the traditional in-house therapy.
While the vast majority of these apps do not have peer-reviewed research to support their claims, health experts predict they will play an important role in the future of mental health care by providing innovative solutions for the self-management of mental health disorders. Some researchers are working on guidelines for mental health apps and in the meantime, the American Psychiatric Association has developed an app rating system to help psychiatrists, clinical psychologists, psychotherapists, and other mental health clinicians assess the efficacy and risks of mobile and online apps.
Thus, the Mental Healthcare Software industry is bound to get more accurate over time! Accurate measurement and improvement of population mental health requires the recording of indicators that capture the full spectrum of disease severity. These indicators can be recorded successfully with the help of these mental healthcare software solutions and analysed through specific Machine learning algorithms, thereby making the platform itself smarter!
Please find below one such architecture that we have created using the AWS services. The architecture records the behaviour of individuals and analyzes their mood to predict their mental health needs.
Please find below the brief working of each AWS service being utilized:
VPC: Amazon Virtual Private Cloud(Amazon VPC) is used for creating a virtual networking environment for resource placement, connectivity, and security.
Auto Scaling Group: An Auto Scaling group is used for collecting the Amazon EC2 instances that are treated as a logical grouping for the purposes of automatic scaling and management. As explained earlier, with time such solutions need will scale and need to be updated frequently, hence, the use of Auto Scaling group which also enables the use Amazon EC2 Auto Scaling features such as health check replacements and scaling policies.
EC2 Instance: Amazon Elastic Compute Cloud (Amazon EC2) is a web service that provides secure, resizable compute capacity in the cloud. It is used due to its elastic nature, thereby providing ease in scaling and storing the healthcare data.
Kinesis Data Firehose: Amazon Kinesis Data Firehose is an extract, transform, and load (ETL) service that is used for reliably capturing, and delivering streaming data to data lakes, data stores, and analytics services.
S3 Bucket(Standard): Amazon Simple Storage Service (Amazon S3) is an object storage service that provides scalability, data availability, security, and performance.
HealthLake: Amazon HealthLake is a HIPAA-eligible service offering a complete view of individual or patient population health data for query and analytics at scale. It is used for providing deep insights on population mental healthcare data.
Lambda: Serverless compute service that lets you run code without provisioning or managing servers, creating workload-aware cluster scaling logic, maintaining event integrations. It is used for maintaining the S3 events.