Our customer is a European healthcare data management platform vendor operating numerous healthcare centers across the EU.
Detailed information about the client cannot be disclosed under the provisions of the NDA.
The client came up with the need for improving the existing data management processes. They needed a platform that could be seamlessly integrated into the business processes and would allow easy access to the patient’s records. Since the data is very sensitive, the platform had to be compliant with all the data protection regulations.
To create such a platform, Innowise Group’s software engineers needed to:
Our software engineers have designed the solution’s architecture and data flow that allow the client’s employees to collect and analyze patient data as well as utilize it for various purposes such as estimating the treatment effectiveness or providing insurance companies with needed information.
Amazon Web Services
The solution is based on Amazon Web Services due to its security, flexibility, scalability, and cost-effectiveness.
The client’s employees insert patient data in their personal files in various forms like pictures, videos, text, etc. This data is uploaded to AWS and stored in a data lake. Such data includes the results of medical examinations, time and date of appeals, media files from MRIs, CTs, ultrasounds, etc.
Our data engineers have designed and implemented ETL pipelines to automatically gather data chunks from the client’s employees into the cloud storage.
Data lake and data warehouse
All the data collected through ETL pipelines is transferred by Apache Airflow to the data lake for the refinement process. After the data is refined, it is sent to the data warehouse and can be used for various purposes, such as
The platform gives users access to sensitive data that must be protected from unauthorized use. For these purposes, Innowise Group’s software engineers have implemented an access distribution and management system.
This module drags information about the employees’ statuses from the client’s database and checks it with the data of a particular patient. Access to private data is granted only to those specialists that work with the patient. The only possible exclusion is the substitution in case the healthcare worker is on vacation or sick leave. In this case, the platform shares access to the patient’s data with the substitute employee and takes it away as soon as the substitution period ends.
When there’s a need for sharing patient data with colleagues or third parties (i.e. a consilium in case of a serious disease or a request from an insurance firm), the employee can request permission to do so. If permission is granted, the platform automatically fills in the form with the patient’s data and shares it with the employee so that no extra data could be included either accidentally or on purpose.
Taking into consideration all the project requirements and specifics, we have selected Scrum as a software development methodology, conducting bi-weekly sprints and sprint overviews to demonstrate the progress. We used Jira and Confluence and held the meetings and overall communication with the client in Microsoft Teams.
Our team developed the requirements in accordance with the client’s vision of the solution and documented them. During the development process, we were constantly analyzing, refining, and decomposing the requirements into tasks and subtasks for easier progress tracking. After several tasks were completed, Innowise Group’s quality assurance engineers checked whether the solution was compliant with the outlined requirements, was bug-free, and our team was on the same page with the client’s vision and desired outcomes.
Innowise Group has built the secure platform that allows healthcare workers to collect, store, and manage patient data. This information can be used for various purposes and won’t be leaked due to strong security measures and strict access and operation management.
Our software engineers have automated many processes that were previously managed manually and designed the data flow to make the solution as efficient as possible.
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