Type
Healthcare Data Analytics Platform
Objective
The given United Kingdom healthcare organization needed to enhance the volume of patient data that they could analyze and visualize. Their core objective was to enhance
the accuracy of diagnoses, accelerate decision-making, and thus enhance patient care. They were experiencing significant challenges in converting gargantuan health care
data into actionable insights in terms of regulatory compliance and recommendation of personalized treatment strategies. They need a scalable solution that allows them to process, analyze, and represent data properly.
Technologies
- Java for backend operations and application logic.
- Ruby on Rails (RoR) for web development and rapid deployment.
- Big Data & Spark for large-scale data processing and real-time analytics.
- Tableau for the creation of intuitive, insightful data visualizations
Country
United Kingdom
Project Attributes
Engagement Model
Dedicated Team Engagement
Duration
Ongoing since 4 Years
App Users
Healthcare professionals, data analysts, and administrative staff.
Challenges
- Volume of Data: The health care group was dealing with enormous
patient-related files and diagnostic information. The existing system made it a
headache to process the large data volumes. - Integration Issues: For example, patient data was flowing in through various
sources, such as hospital records and wearable devices, creating difficulties in
extracting a single view. - Compliance with Regulatory Issues: All these were expected to be strictly
adherent to HIPAA, going into minute details. - Lack of Real-Time Analytics: The constraint of real-time data analytics was that
it could not provide instant, accurate diagnoses and subsequent treatment
programs.
Solutions
- Construction of Big Data Infrastructure: We developed a Big Data scalable
architecture using Apache Spark. Because of this, the system is now processing
large numbers of patient data with real-time analysis. Data was being pulled out
from a variety of health systems and later placed in a central repository. - Java & RoR Integration: Java at the back end ensured that data processing
was reliable in every sense, and Ruby on Rails allowed agile development of the
web application such that data access was made available to healthcare
professionals efficiently. - Complex Data Visualization with Tableau: It is through integration with Tableau
that there was intuitive and action-oriented visualization of the data. The
healthcare providers could analyze the trends in condition of patients, treatment
outcome, and operational efficiency in a highly user-friendly environment. - Real-Time Analytics: Through Spark, we presented real-time data insights to
the clinicians who could see the live patient data and then give the much-needed
treatment recommendations with more accuracy and also support from the
backend data.
Results:
- Improved Decision Making: Healthcare providers were able to improve their decisions through this portal as there was a 35% rise in proper diagnosis. Data Processing Time Reduced: Data of large volumes was processed in the shortest time possible, by 50%, thanks to Spark and Big Data architecture.
- Real-time insights: These insights helped diagnose patients quicker with more customized treatment plans, thus enhancing the outcome in terms of patient care by 30%.
- Operational Efficiency: Massed data from multiple sources were integrated and streamlined effectively so that their overall operational efficiency at the health institution improved by 40%.
Conclusion:
Adorebits’s approach addressed the needs of data processing for the client while revolutionizing their approach toward patient care and giving them a competitive edge in healthcare analytics.