Generative Medical Order Drafting From Real-Time Patient Data
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Solution Overview
Problem
Existing computerized patient order entry systems lack efficiency and accuracy in generating and managing medical orders, leading to potential errors and inconsistencies.
Innovation Solution
A generative model utilizing machine learning techniques to analyze patient data in real-time, suggest orders, identify missing elements, and optimize the ordering process through a practice management system with components like data processing, communication, AI-based chatbots, and order engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual order entry is used by clinicians, then system complexity is reduced, but productivity and accuracy of medical orders decrease
Solution Approach 1:
The system automatically generates medical orders by analyzing patient data, clinical notes, and provider preferences without requiring manual clinician input. The AI model self-services the order creation process, extracting relevant information and drafting orders that are then presented to the provider for review and signature.
Solution Approach 2:
The system performs preliminary analysis of patient data, lab results, and clinical documentation before the provider needs to finalize orders. The AI model pre-generates order drafts based on available information, allowing providers to review and approve rather than create orders from scratch.
2Manufacturing precision
If standardized templates and order sets are used, then manufacturing precision of orders is improved, but adaptability to individual patient needs deteriorates
Solution Approach 1:
The system dynamically adapts order generation to individual patient needs by analyzing specific patient data, clinical context, and provider preferences. The AI model adjusts order recommendations in real-time based on the unique characteristics of each patient and clinical situation, rather than applying static templates.
Solution Approach 2:
The system applies different levels of standardization to different aspects of order creation. Common elements like formatting, safety checks, and standard order structures are standardized, while patient-specific details, clinical judgments, and customized treatment decisions are tailored to individual needs.
3Reliability
If real-time analysis of patient data is performed, then reliability of order suggestions is improved, but use of energy and processing resources increases
Solution Approach 1:
The system performs real-time analysis of only the most relevant patient data elements needed for order generation, rather than processing all available data. The AI model focuses on critical clinical information, lab results, and order history while filtering out unnecessary data, achieving high reliability with optimized resource usage.
4Reliability
If multiple validation checks are implemented, then reliability of medical orders is improved, but device complexity and time for order completion increase
Solution Approach 1:
The system performs validation checks automatically and in parallel during the order generation process, rather than sequentially after order creation. Safety checks, conflict detection, and completeness verification are built into the AI model's order drafting process, reducing errors without adding significant time to provider workflow.
Data Source
AI summary
Techniques for using machine learning models to create and present medical orders for patients are disclosed. These techniques facilitate the identification, selection, and fulfillment of an order, e.g., prescription or treatment, in response to updates to patient data for the patient, e.g., reporting of test results, receipt of messages or referrals, and addition of discussions. The system monitors, in real time, updates to the patient data. The patient data may be part of an EHR. When the system determines that content of an update satisfies a trigger for generating an order, the system applies a machine learning model to the patient data to determine an order corresponding to the patient data. The machine learning model generates the order for the patient and presents the order to medical professionals for review.