Comprehensive Big Data Medical Record System
Solid, powerful, and secure software for next-gen healthcare provision.
25%
higher diagnostic accuracy
42%
rise in early detection and cure
34%
fewer readmissions
Challenge
Build a custom healthcare database powered by big data technologies.
Solution
Implementation of the most effective cutting-edge big data solutions and practices.
Tech stack
Hadoop, Apache Spark, MongoDB, Plotly, Cassandra.
Client
Our client is a European startup company that offers smart corporate solutions for various businesses in the western market. Over the recent years, the company has been actively growing and developing technological software for clients worldwide.
Challenge
The practical efficiency, potential of big data in medicine, and the growing demand for smarter healthcare decisions encouraged our partner to consider the development of a big data system capable of:
- anaging electronic health records and providing insightful analytics
- employing predictive analytics for early identification and prevention of disease development
- real-time monitoring of patient condition within a hospital and delivery of alert messages to medical staff
- strengthening patient data security used within the system
- collecting health data from a number of the clinic-owned wearables given to patients for at-home use
- accumulating data from various sources such as clinic test laboratory, ER visits, diagnostic facilities, etc.
- managing medications stock
The development of big data management systems that touch upon the whole healthcare institution's internal performance is a highly complex task that requires an extensive skillset and broad experience in implementing similar solutions to avoid numerous pitfalls and deliver the final product within the deadlines.
Team
1
Project manager
1
UI/UX designer
2
QA testers
3
Software engineers
1
Business analyst
3
Big Data engineers
1
Solution architect
1 Requirements processing
When it comes to big data projects, there can never be too many details. Our team carried out a series of consultations with the client to gather as much information as possible to ensure a smooth issue-free further development process. Requirement processing resulted in drafting a comprehensive document, outlining the big data system features and functionalities to be implemented.
2 Team assembly
For each of our projects, we select only relevant specialists whose skills and knowledge perfectly fit the client’s purpose. To deliver unmatched results to the client, our CTO preselected a team consisting of a project manager, a business analyst, a solution architect, three big data engineers, a UI/UX designer, four software engineers, and two QA testers. However, the final word about the team composition is for the client. This time they asked for partial verification of the team members’ experience and big data problem-solving skills as applied to healthcare.
3 Design
Building the software solution, we kept in mind that people working with the future big data system should be able to manage the data quickly and efficiently, as the healthcare industry is not deprived of high-stress levels which build up even more if the key data processing software looks and feels confusing and non-navigable. Our UI/UX designer made sure to build the application’s layout in a clear and user-friendly way.
Coding:
Our big data engineers involved in the project had been implementing respective technologies for 5+ years, so they had a clear understanding of the process in every detail and were able to suggest several app optimization solutions accepted by the client. During this stage, they employed the most reliable and robust technologies such as Hadoop, MongoDB, and Cassandra for building the storing component, Apache Spark for data mining, Plotly for data visualization, and others.
Testing:
In the pre-launch phase, big data products are prone to bugs related to data extraction. To ensure impeccable issue-free big data system performance our project QA engineers ran a series of automated tests, identified several imperfections, and fixed them timely.
Integration:
After a predefined period of time, we submitеd our work to the client, who checked the big data system and asked for a couple of minor UI/UX alterations. When the adjustments were put in place, the software was ready to be integrated into the clinic’s system.
Servicing:
Such type of software has to be supported and serviced in a regular manner to be able to stay secure, accurate, and efficient, which is crucial since the system we developed was dealing with health records and health state predictions. That is why several months after the launch we were asked to carry out the system diagnostic and make sure it operates properly.
Results:
As a result of our cooperation, the client received a highly functional big data management system with the integration of ML algorithms and predictive analytics to modernize the provision of healthcare services in their clinic.
25%
more accurate diagnosis making
42%
increase in early disease identification and cure
34%
decrease in readmissions