Patient Document Alerting Using Learning-Based Necessity Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Medical institutions face challenges in generating necessary documents for patients due to prioritization of medical care, leading to potential administrative issues and financial risks.
Innovation Solution
A decision apparatus and method that calculates the necessity of document generation for a patient using a learning model, determining whether to output an alert based on state information and actual document generation status.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If medical care is prioritized over document generation, then quality of patient treatment is improved, but administrative compliance and financial stability deteriorate
Solution Approach 1:
The system implements feedback by continuously monitoring patient state information and document generation status, then providing alerts to medical professionals when documents are missing. This closed-loop feedback mechanism ensures that document generation requirements are tracked and addressed without requiring constant manual administrative oversight, thus maintaining treatment quality while improving compliance.
Solution Approach 2:
The system enables self-service by automatically calculating the necessity of document generation based on patient state information and learning model predictions. The alert notification system allows medical professionals to self-monitor and self-correct document generation status, reducing the burden on administrative staff while ensuring compliance with medical regulations.
2Productivity
If document generation is prioritized over medical care, then administrative compliance is improved, but time for patient treatment is reduced
Solution Approach 1:
The system performs preliminary action by pre-calculating and predicting which documents will be necessary based on patient state information before medical professionals complete their treatment tasks. The learning model identifies potential document requirements in advance, allowing medical professionals to generate necessary documents with minimal interruption to patient care activities.
3Productivity
If manual monitoring of document generation is implemented, then administrative compliance is improved, but workload on medical professionals increases
Solution Approach 1:
The system replaces the mechanical system of manual monitoring with an automated information processing system. The learning model and alert notification mechanism substitute for human administrative oversight, automatically analyzing patient state information and generating alerts when documents are missing, thereby reducing the operational complexity and workload on medical professionals.
4Productivity
If automated alert system is implemented, then document generation compliance is improved, but system complexity increases
Solution Approach 1:
The system achieves universality by designing a multi-functional platform that combines patient state information management, learning model-based necessity calculation, and alert notification capabilities in a single integrated system. This universal system serves multiple purposes: tracking patient information, predicting document requirements, and notifying relevant personnel, thereby managing system complexity through functional consolidation.
Data Source
AI summary
The decision apparatus according to one example embodiment of the present disclosure includes at least one memory configured to store an instruction, and at least one processor configured to execute the instruction. The processor executes the instruction, and thereby calculates a degree of necessity of generation of a document relating to a decision target person, by inputting state information of the decision target person with respect to a learning model being learned by using learning data including state information of a person and information indicating whether generation of the document relating to the person is necessary or unnecessary, and decides whether to output an alert related to document generation of the decision target person, by using the calculated degree of necessity and information indicating whether the document is actually generated for the decision target person.