How to Improve Clinical Trial Using AWS

 

Amazon Web Services or AWS is a cloud infrastructure-as-a-Service platform which has enabled helped organization from the burden of setting up infrastructure for any application as it provides services for various applications whose price is minimal and could be paid only for the specific services delivered. Moreover, robust, scalable and secure services are provided by the AWS which are much better than any website which a company hosts. Its data centers all over the world ensure no data is lost.

There are various instances of AWS which contributes to its workflow such as the EC2 instance, the Elastic Compute Cloud, Elastic Load Balancer, Amazon Cloud Front and so on. The description of all these components is beyond the scope of this blog. Amazon Web Services has reduced the management, maintenance overhead. The resources of AWS are reliable and available all over. The right tools could improve productivity and scalability as well.

One of the key areas where Amazon Web Services has impacted is healthcare. In this article, we would learn AWS has helped to improve clinical trials and the process that’s being followed under the hood.

Improving the Clinical Trials

In the present world, incurable diseases are being cured by personalized medicines while those are made healthier using digital medicines. There is a constant study about the eradication of the cancer cells and the functioning of our immune system. The reported study has declined since the last few years and projected to go down even further.

Thus it is necessary to work securely with the clinical trial data and maintain the compliance. AI and Machine Learning are also playing their part in this regard. To modernize the clinical trials, the AWS customers could use some common architectural patterns which would ensure cost reduction, better generation of evidence, more personalized medicines and so on.

 

 

There is pressure within the pharma organizations for efficient use of the resources and streamline the process while reducing the costs. Thus gathering data from non-traditional sources such as mobile, IoT, clinical devices, and so on has become a necessity now. To improve accuracy, reduce costs, and speed up outcomes, mobile technologies could be used as it provides data ingestion supplemented with Machine Learning and Artificial Intelligence Technologies.

The traditional clinical trial process could not meet the need of emerging industries. Because of this, in case of evolving the clinical trial operations, pharmaceutical companies need assistance. To improve outcome as well as reduce the costs, clinical trials could deploy traditional technologies as well the mobile technologies. The various technologies could be integrated for some of the below use cases –

• In clinical trials, the participants could be identified and tracked and recruit them. Also, patients could be educated. The trial participants could receive the associated information with the help of the standardized protocols. The adverse events could be tracked as well.
• To identify novel biomarkers, the genomic and phenotypic data could be integrated.
• To manage the clinical trial better, mobile data could be integrated into clinical trials.
• Based on the historical data, a patient-control arm could be created.
• Based on the registry, claims data sets, the cohorts are stratified.
• To share data and create knowledge, an interoperable and collaborative network is built.
• To manage the clinical trial, compliance-ready infrastructure could be built.

 

Using site monitoring, smart analytics, the patients are found and recruited. Along with it, the trial outcomes are accelerated and adverse events are detected. To manage clinical trial using mobile technologies, the below architecture is used. It ensures processing the real-time data captured through mobile devices.

 

 

Steps Which the Architecture Follows –

1. Data Collection –

To generate real-time data for activity tracking, monitoring, and so on, the clinical trials and various pharmaceutical companies use personal wearables, mobile devices extensively. Devices like a dialysis machine, infusion pumps have remote setting management which is a major use case. A lot of telemetry data which are emitted by mobile devices requires data cleansing, transformation. 

A sufficient computing resource is provided by the connection of these devices to the edge node which allows to stream data to the AWS IoT Core that in real time would write data to the Amazon Kinesis Data Firehouse. Cost-Efficient, flexible storage is provided by the Amazon S3 which allows replicating data on the three availability zones. Electronic medical records, case report forms, and other kinds of data could also be captured. A voluminous data could be ingested with velocity as AWS securely connects to these data sources with the help of multiple tools and services.

2. Data Storing –

On the Amazon S3, the Kinesis Data Firehouse stores the data after it’s ingested in the clinical trial from various devices and wearables. To predict pattern, and historical analysis, a raw copy of the stored data is used. To optimize the costs, the data could be periodically moved to Amazon S3 Glacier which is reduced cost storage.

 When the pattern of the data access changes, the costs could be automatically optimized using the Amazon S3 Intelligent Tiering. Various encryption options are also available to encrypt the data. The data processing, backup, and other tasks have been simplified by the Amazon S3 data storage infrastructure which is durable and high availability.

3. Data Processing –

The events are published to the AWS Lambda and a lambda function is invoked which extracts the key performance indicators such as treatment schedule, medication adherence, etc., from the data. The KPI’s could be processed and stored in the Amazon DynamoDB with encryption. In real-time, the clinical trial coordinators are alerted to ensure appropriate measures are taken.

 A machine learning model could be trained and implemented using a full of a medical records data warehouse which would give an idea about the patients with the chances of switching medications. These findings would ensure coordinators of the clinical trials to take those patients matter into serious consideration.

The data could be processed in batches as well. An ETL process is triggered using the AWS Glue which helps in loading the data to perform analytics. The historical data could be mined and actionable insights could be derived. The data is loaded on to the Amazon Redshift after it is stored on Amazon S3. The machine learning models are trained to identify patterns of the adherence challenges risk. The co-ordinators of the clinical trials could reinforce support and patient education henceforth.

4. Data Visualization –

The Amazon QuickSight service which is serverless is used after the data is processed. Other third-party reporting tools like PostgreSQL could also be used. The costs could be optimized using the Pay-per session pricing model of the Amazon QuickSight.

 Real-Time feedback is sent to the patients using the Amazon Simple Notification Service. A fully managed pub/sub messaging is provided by the Amazon SNS which ensures many-to-many messaging and high throughput.

Amazon Web Services ensures the trust of the customer as their topmost priority. Over 190 countries, millions of customers, organizations, and so on are provided with active services and helps to protect sensitive information. The Amazon Web Services Identity and Access Management service could also be used which maintains access control and secures end user’s mobile applications.

A layer of security is also provided by the Amazon Web Services on the data. Encryption is provided for several services by the AWS. The ownership of the data could be maintained by the customer and for processing and hosting of the content, the AWS services could be selected. For marketing or any other purpose, the customer’s data is not used by AWS.

Numerous aspects of the clinical trials could be improved by the mobile devices and sensors in this ever growing technological advances in the medical devices. Activities such as patient counseling, recruitment, and so on could be helped by it. Alerting the patients, medication adherence, etc., could also be improved. In conducting clinical trials, the smart devices and robust interconnecting systems are in the heart of it.

To achieve trail performance and maintain the consistency of the data, a conundrum is faced even by the biopharma organizations. The collection, storage and the usage of data for clinical trials have been given a new dimension by the Amazon Web Services. All the conundrums are addressed and a new reality has been put into place. The technical challenges of establishing IT infrastructure, scaling, and so on has been abstracted away by the Amazon Web Services cloud. The ultimate mission is to improve the patient lives and the bio-pharma organizations have achieved just that with the development of ground-breaking and effective treatments.

Conclusion –

It is necessary to work securely with clinical trial data and maintain compliance. AI and Machine Learning are also playing their part in this regard. To modernize the clinical trials, the AWS customers could use some common architectural patterns which would ensure cost reduction, better generation of evidence, more personalized medicines and so on.

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