Multi-tenant Data Staging for Real-time Analytics
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Solution Overview
Problem
Conventional healthcare data management systems require extensive manual effort to extract useful information from transactional data, leading to inefficiencies in data analysis and reporting, as standard reports often lack user-specific data and are static, limiting user-friendly access to unique data.
Innovation Solution
A system architecture with multiple layers facilitates real-time source data availability, including a source layer for data collection, an integration layer for data routing, a staging database for real-time data collection, and a front-end service like Microsoft SharePoint for user-friendly access and analysis, enabling role-based access and customizable reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If transactional data is stored and made available record-by-record, then data availability is improved, but extracting useful information becomes time consuming
Solution Approach 1:
The system segments the data extraction and processing into separate functional layers: a data collection layer that automatically gathers transactional data, a data processing layer that extracts useful information, and a presentation layer that delivers customized reports. This segmentation allows automated data collection while separating the time-consuming extraction task into a dedicated processing stage that operates independently of data availability.
Solution Approach 2:
The system implements self-service through automated data collection mechanisms that continuously gather transactional data without manual intervention, and through standardized report templates that automatically process and present extracted information. The system serves itself by automatically extracting useful characteristics from transactional data and generating customized reports without requiring manual analysis for each request.
2Productivity
If standard reports are generated automatically, then ability to understand transaction data is improved, but user-specific customized data access is limited
Solution Approach 1:
The system achieves universality by creating a multi-functional platform that can both generate standardized reports and customize data presentations according to user-specific requirements. The data processing layer universally handles all transactional data, while the presentation layer adapts its functionality based on user roles and preferences, allowing the same system to serve both standardized reporting needs and customized analysis requirements.
Solution Approach 2:
The system implements dynamics through its adaptive presentation layer that adjusts report customization based on user interactions and requirements. The system transitions from static standard reports to dynamic, user-specific views where data presentation can be modified in real-time based on user roles, preferences, and specific analysis needs, while maintaining the automated processing backbone.
3Adaptability or versatility
If manual processing is used to extract information, then customized analysis is possible, but extensive manual effort is required
Solution Approach 1:
The system performs self-service by automatically extracting useful information from transactional data through dedicated processing algorithms, and by generating customized reports without requiring manual analysis. The data processing layer autonomously identifies and extracts relevant characteristics, while the presentation layer automatically formats and delivers customized reports, eliminating the need for extensive manual processing while maintaining customized analysis capabilities.
Solution Approach 2:
The system replaces manual mechanical processing with automated computational processes. Instead of manual data analysis and report generation, the system uses automated algorithms to extract information from transactional data, process it through defined logic, and generate customized reports. This substitution maintains the adaptability for customized analysis while eliminating extensive manual effort through automated mechanical processing.
Data Source
AI summary
An embodiment provides a method, including: a plurality of devices, each of the plurality of devices being associated with one of the plurality of tenants; an integration layer that routes transactional data corresponding to the plurality of tenants; a staging database associated with one of the plurality of tenants, wherein the staging database comprises a collection of transactional data identifying information related to statistics corresponding to the tenants; wherein the staging database comprises utilizing a drill down operation to implement real-time source data collection, wherein the drill down operation to implement real-time data source collection comprises linking raw data associates one of the plurality tenants by use of the drill down operation to reveal refined sourced data; and a front end program that displays on a graphical user interface the refined source data associated with each of the plurality of tenants. Other embodiments are described and claimed.