Healthcare Cloud Platform for Medical Data Reconciliation
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Solution Overview
Problem
Clinicians face challenges in accessing comprehensive and up-to-date patient health information due to fragmented electronic medical records (EMRs) across multiple sources, leading to an incomplete picture of a patient's health history.
Innovation Solution
A healthcare cloud computing platform that aggregates and normalizes medical data from multiple sources into a single longitudinal record, using state machine module for data transformation, deduplication module to remove duplicates, and ranking engine to prioritize data, allowing seamless reconciliation and presentation of accurate, non-duplicative patient data to clinicians within their normal workflow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If medical data is maintained in separate, disparate databases and electronic health records across multiple sources, then data storage and management can be distributed, but clinicians are unable to access comprehensive information and have an incomplete picture of patient health history
Solution Approach 1:
The patent merges data from multiple disparate sources including electronic health records, third-party databases, and other venues of care into a single unified longitudinal patient record. This consolidation allows clinicians to access comprehensive patient information across all healthcare sources through one integrated system, resolving the contradiction between distributed storage and comprehensive accessibility.
Solution Approach 2:
The unified longitudinal record system serves multiple functions simultaneously: it aggregates data from diverse sources, normalizes different data formats, deduplicates information, ranks data by relevance, and presents a comprehensive view to clinicians. This multi-functional approach enables a single system to handle various data integration tasks that previously required separate systems.
2Loss of information
If medical data from multiple sources is collected and integrated manually, then comprehensive patient information can be assembled, but it takes days and weeks to collect and input data into the patient's primary record
Solution Approach 1:
The system automatically performs data aggregation, normalization, deduplication, and integration without requiring manual clinician intervention. The automated processes continuously pull data from multiple sources, process it through normalization and deduplication algorithms, and update the unified longitudinal record in real-time, eliminating the time-consuming manual data collection process while maintaining complete patient information.
Solution Approach 2:
The system performs preliminary data processing including normalization and deduplication before data is needed for clinical decision-making. By pre-aggregating and pre-processing data from multiple sources continuously, the system ensures comprehensive patient information is ready immediately when clinicians need it, eliminating delays in data availability.
3Loss of information
If duplicate data is retained from multiple sources, then all available information is preserved, but data redundancy increases storage requirements and complicates data review
Solution Approach 1:
The deduplication module extracts and removes duplicate data entries from the aggregated information while preserving unique and relevant patient data. The system identifies redundant records across multiple sources and eliminates them, maintaining only the essential non-duplicative information needed for comprehensive patient care, thus reducing redundancy while preserving data completeness.
Solution Approach 2:
The system applies data normalization that standardizes the format, structure, and representation of patient data from different sources. By transforming diverse data formats into a unified standard, the system makes data consistent and comparable across sources, enabling effective deduplication while preserving the essential information content regardless of its original format.
4Loss of information
If all medical data from various sources is presented to clinicians without organization, then complete information is available, but it increases the time spent by clinicians reviewing information
Solution Approach 1:
The unified longitudinal record segments and organizes aggregated patient data into structured categories and clinically relevant sections. By dividing the comprehensive data set into organized segments such as demographics, medical history, current conditions, and treatment information, the system makes all available information easily navigable and reduces the time clinicians need to review data by presenting it in a logical, structured format.
Solution Approach 2:
The system performs preliminary organization and structuring of data before presentation to clinicians. Data is pre-sorted, pre-categorized, and pre-formatted into clinically relevant sections based on the unified data model, so that when clinicians access the record, the information is already optimized for efficient review and decision-making.
Data Source
AI summary
Systems, methods, and storage media useful in a healthcare cloud computing platform to transform, deduplicate and store medical data from third-party databases to a patient's primary medical record in the healthcare cloud computing platform. Exemplary implementations may: load and read data from third-party databases, and determine if it is duplicative of what is in the patient's primary record. Other embodiments, provide a method for ranking medical data from two different third-party databases to determine which medical data should be written to the patient's primary medical record in the healthcare computing platform.