Alternate Dose Regimen Modeling for Lower Drug Vial Waste
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Solution Overview
Problem
Existing medication regimens result in drug waste and increased costs due to the inefficiency in selecting drug vial combinations, leading to overage in vials that cannot be stored and used with the next dose, which is not optimized by prescribers or pharmacists.
Innovation Solution
A predictive model is implemented to identify and recommend alternate dose regimens that minimize drug waste and cost by optimizing drug vial combinations while maintaining treatment efficacy, using machine learning techniques to analyze prescription data and generate visual interfaces for selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If predetermined drug quantities are delivered through vials, then the prescribed dose can be fulfilled, but drug waste increases due to overage in vials that cannot be stored
Solution Approach 1:
The system segments the drug delivery process by analyzing individual vial combinations and their corresponding doses. It breaks down the prescribing process into discrete vial selection units, allowing optimization of each segment to minimize waste while ensuring the prescribed total dose is fulfilled through careful combination of multiple vials.
Solution Approach 2:
The system performs preliminary calculation and analysis of optimal vial combinations before actual drug dispensing. By pre-determining the best vial combinations that minimize waste while meeting the prescribed dose, the system enables informed decision-making and reduces drug waste at the source rather than addressing waste after it occurs.
2Ease of operation
If vials with predetermined drug quantities are used, then medication dispensing can be simplified, but cost increases due to drug waste
Solution Approach 1:
The system incorporates feedback mechanisms by analyzing the relationship between vial combinations, prescribed doses, and resulting waste. It provides insights and recommendations that feed back into the dispensing decision-making process, enabling pharmacists and providers to select optimal vial combinations that reduce both complexity and cost.
Solution Approach 2:
The system changes the parameters of vial selection by considering multiple factors such as vial size, concentration, prescribed dose, and waste implications. It optimizes the combination of vials by adjusting these parameters to achieve the prescribed dose while minimizing both operational complexity and associated costs.
3Reliability
If prescribers or pharmacists manually select vial combinations, then treatment efficacy can be maintained, but drug waste is not optimized
Solution Approach 1:
The system acts as an intermediary between the prescribed treatment requirements and the actual vial selection process. It mediates by providing automated analysis and recommendations that bridge the gap between maintaining treatment efficacy and optimizing waste reduction, enabling informed vial combination decisions without compromising therapeutic outcomes.
Solution Approach 2:
The system replaces manual mechanical selection processes with automated computational analysis. Instead of relying solely on human calculation and experience for vial combination selection, it uses algorithms and data processing to optimize waste reduction while ensuring treatment efficacy is maintained through systematic evaluation of multiple vial options.
Data Source
AI summary
Methods and systems for performing dose regimen modification are provided. The methods and systems perform operations comprising: receiving prescription related data for treating a patient with an expected level of efficacy, the prescription related data comprising medication regimen information including dose and interval; determining, using a model, a first amount of drug waste based on the prescription related data; comparing the first amount of drug waste to a threshold value; and in response to determining that the first amount of drug waste transgresses the threshold value, identifying an alternate medication regimen that is associated with a treatment having a given level of efficacy corresponding to the expected level of efficacy, the alternate medication regimen being associated with a second amount of drug waste that is lower than the first amount of drug waste; and triggering a notification associated with the alternate medication regimen.