Patient Data Framework for PGHD and Clinical Data Integration
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Solution Overview
Problem
The integration of patient-generated health data (PGHD) with clinical data is fragmented and lacks interoperability, making it difficult for clinicians to draw meaningful conclusions, and existing digital health applications are not normalized across devices or manufacturers, overwhelming healthcare providers with unmanaged and unstructured data.
Innovation Solution
A framework system integrates PGHD and clinical data into a patient data model, using artificial intelligence to generate biomarkers and provide a user interface for both patients and physicians, with data compression and normalization to facilitate understanding and actionable insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If patient-generated health data from multiple devices and sources is collected and integrated, then the quantity and comprehensiveness of health information increases, but the complexity of data management and interoperability increases
Solution Approach 1:
The patent segments health data into distinct categories (clinical data, patient-generated health data, wellness data) and processes each type through appropriate formatting and integration methods. Different data sources are handled separately before unification, reducing the complexity of direct integration of all data types simultaneously.
Solution Approach 2:
The patent introduces an intermediary processing layer that formats and standardizes data from multiple sources before integration. This intermediary system handles the complexity of interoperability between different devices and data formats, shielding the core integration system from direct complexity exposure.
2Loss of information
If raw patient-generated health data is provided to clinicians without processing, then data completeness is maintained, but clinician workload and difficulty of interpretation increase
Solution Approach 1:
The patent performs preliminary formatting, validation, and integration of health data before presenting it to clinicians. Data is pre-processed to establish consistency and remove obvious errors, reducing the burden on clinicians while preserving essential information through structured presentation.
Solution Approach 2:
The patent transforms raw health data into standardized parameters and formats that are more suitable for clinical interpretation. Data is converted from diverse device-specific formats into unified clinical parameters, maintaining information integrity while improving ease of analysis.
3Adaptability or versatility
If detailed patient-generated health data is made accessible through user interfaces, then patient engagement and self-monitoring improve, but information overload and difficulty in drawing conclusions increase
Solution Approach 1:
The patent presents different levels of data detail to different users based on their needs. Patients can access detailed data for self-monitoring, while clinicians receive processed summaries with highlighted actionable insights. This localized quality approach ensures each user gets appropriately formatted information without overwhelming them.
Solution Approach 2:
The patent implements partial processing by providing both raw data access and processed summaries. Users can choose to view detailed data when needed while relying on processed interpretations for routine monitoring, balancing comprehensive information access with ease of conclusion drawing.
Data Source
AI summary
Health information is integrated (106) into a framework system (400). PGHD and clinical data are integrated (106) into a patient data model (240). The PGHD data as integrated may be compressed or otherwise processed (104) to reduce the volume and/or frequency of the data for greater ease in understanding the PGHD data. A user interface (220) for this data model (240) allows for access to both types of data (PGHD and clinical data) by a patient or a physician. Artificial intelligence may be used to further consolidate the data by providing one or more biomarkers (120) estimated from both types of data, allowing for patient and/or physician goal, treatment success, and/or adverse event monitoring.