Patient Group Care-Gap Analysis for Proactive Treatment Planning
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Solution Overview
Problem
Existing healthcare management systems are backward-looking and focused on past events, failing to identify common care gaps for patients or patient groups, limiting the ability to guide future healthcare actions.
Innovation Solution
A system and method that analyzes patient data to identify common care gaps by grouping patients based on shared values, determining care gaps, and generating reports to address these gaps, utilizing a processor and computer memory to receive, analyze, and transmit data to entities capable of providing treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If healthcare management systems focus on backward-looking analysis of past events, then historical treatment data can be observed and recorded, but the ability to identify common care gaps and guide future healthcare actions is limited
Solution Approach 1:
The system performs preliminary analysis of patient data to identify care gaps before they become critical issues. By analyzing historical data patterns and comparing against treatment guidelines, the system proactively identifies patients who may benefit from future interventions, enabling healthcare providers to take preventive actions rather than reacting to problems after they occur.
Solution Approach 2:
Instead of only analyzing what treatments were provided in the past, the system inverts the approach by analyzing what treatments should have been provided but were not. This inversion allows the system to identify care gaps by comparing actual treatment patterns against ideal care pathways, revealing missed opportunities for intervention.
2Reliability
If comprehensive patient data analysis is performed to identify care gaps, then actionable insights for future treatment can be obtained, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex task of care gap identification into manageable components: data collection from multiple sources, patient grouping based on characteristics, treatment pattern analysis, care gap detection, and reporting. This segmentation allows each component to be optimized independently and processed efficiently, reducing overall system complexity while maintaining comprehensive analysis capabilities.
Solution Approach 2:
The system employs universal algorithms and data structures that can handle multiple types of patient data and treatment scenarios through a single analytical framework. By creating a multi-functional analysis engine that can process different data formats and apply various treatment guidelines uniformly, the system reduces complexity compared to having separate specialized systems for each analysis type.
3Loss of information
If patient data is grouped and analyzed by common values in data fields, then common care gaps can be identified across patient groups, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary grouping of patients by common data field values (such as diagnosis codes, demographics, or provider identifiers) before conducting detailed care gap analysis. This pre-grouping organizes the data in a way that enables efficient batch processing and reduces the computational complexity of subsequent analysis, allowing the system to maintain both comprehensive group-level insights and fast processing speeds.
Data Source
AI summary
A system and method are described for analyzing electronic data records received from a patient database. Analyzing the electronic data records may include identifying one or more common care gaps shared between a group of patients having a common value in a data field of the patient's corresponding electronic data record. The identification of the common care gap can be used to identify treatments that are not being provided to the group of patients, but should be provided to the group of patients.