Neural Network Visit Note Prediction Model
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Solution Overview
Problem
Generating visit notes manually is time-consuming and prone to errors, and existing tools for aiding this process, such as templates and auto-population, can be inaccurate and inefficient.
Innovation Solution
A computer-implemented method using a machine-learned note prediction model, specifically a neural network, to predict visit notes by processing attendee data from previous encounters, enabling the generation of accurate and efficient documentation for subsequent encounters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual note generation is used, then accuracy can be maintained through human judgment, but time consumption increases significantly
Solution Approach 1:
The patent introduces a machine-learned note prediction model as an intermediary between the encounter data and the final visit note. This model processes attendee data and generates predicted notes that assist providers in creating accurate documentation, thereby reducing time consumption while maintaining accuracy through human-in-the-loop verification.
Solution Approach 2:
The patent replaces the mechanical process of manual note writing with an automated machine-learned prediction system. The neural network model automatically processes encounter data and generates note predictions, substituting the manual mechanical typing process while maintaining quality through iterative refinement and provider review.
2Loss of time
If conventional auto-population techniques are used, then time can be saved, but accuracy and coherence deteriorate
Solution Approach 1:
The patent fundamentally changes the parameters of the auto-population process by transitioning from rule-based template filling to a machine-learned prediction model. The neural network learns from training data to generate coherent, contextually appropriate notes, improving accuracy while maintaining time efficiency through automated processing.
Solution Approach 2:
The machine-learned model performs self-service by automatically generating note predictions from encounter data without requiring manual template selection or data entry. The system autonomously processes information and produces coherent notes, reducing both time consumption and errors associated with manual auto-population.
3Ease of operation
If simple templates are used, then ease of operation improves, but manufacturing precision of note quality decreases
Solution Approach 1:
The patent introduces dynamics into the note generation process by using a machine-learned model that adapts to different encounter types, providers' writing styles, and specific patient contexts. The system dynamically generates notes rather than relying on static templates, improving quality consistency while maintaining ease of operation through automated adaptation.
Data Source
AI summary
Systems and methods of predicting documentation associated with an encounter between attendees are provided. For instance, attendee data indicative of one or more previous visit notes associated with a first attendee can be obtained. The attendee data can be inputted into a machine-learned note prediction model that includes a neural network. The neural network can generate one or more context vectors descriptive of the attendee data. Data indicative of a predicted visit note can be received as output of the machine-learned note prediction model based at least in part on the context vectors. The predicted visit note can include a set of predicted information expected to be included in a subsequently generated visit note associated with the first attendee.