Automated Pharmacy Dispensing System NDC Optimization
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Solution Overview
Problem
Pharmacies face challenges in maintaining automation efficiency due to frequent changes in the mix of National Drug Codes (NDCs) over time, leading to decreased automation throughput and requiring complex manual processes for optimization, which are often performed infrequently.
Innovation Solution
A system and method for regularly assessing pharmacy productivity by recording and comparing the dispensing data of automated and manual pharmaceuticals, identifying low and high performers, and recommending replacements to optimize automated dispensing, using an automated prescription monitoring unit and a controller to provide real-time feedback and improve efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the NDC mix in the automated machine is established at installation based on a snapshot of recent prescription history, then initial automation throughput is improved, but automation efficiency deteriorates over time due to frequent changes in pharmacy's NDC mix
Solution Approach 1:
The system dynamically adjusts the NDC mix in the automated machine based on real-time monitoring of prescription patterns. The controller continuously receives data about which NDCs are being dispensed manually versus automatically, and automatically updates the NDC assignment to dispensing cells to reflect current pharmacy needs, making the system adaptable to changing seasonal and market conditions
Solution Approach 2:
The system implements continuous feedback by monitoring all prescriptions filled through both automated and manual processes, comparing the performance of different NDCs, and using this information to automatically reconfigure the automated dispensing machine. This closed-loop feedback ensures the NDC mix remains optimized for current prescription volumes without requiring manual intervention
2Productivity
If manual optimization processes are performed to replace slow moving NDCs with fast moving NDCs, then automation efficiency is improved, but the complexity of the optimization process increases
Solution Approach 1:
The system performs self-optimization by automatically monitoring its own performance, identifying underperforming NDCs, and replacing them with better-performing alternatives without human intervention. The controller autonomously analyzes dispensing data, determines optimal NDC assignments, and reconfigures the automated machine, eliminating the need for complex manual optimization procedures
Solution Approach 2:
The patent replaces manual mechanical optimization processes with an automated electronic system. Instead of staff manually analyzing reports and physically reconfiguring the machine, an electronic controller continuously monitors performance data and automatically updates NDC assignments through software, substituting human labor and manual procedures with automated computational and control systems
3Productivity
If pharmacies perform optimization tasks 1-2 times per year, then some efficiency gains are achieved, but the frequency is insufficient to maximize efficiency due to the complex manual process
Solution Approach 1:
The system enables continuous optimization by operating 24/7 without interruption. The monitoring unit continuously tracks all prescriptions, and the controller continuously processes this data to maintain optimal NDC assignments. This continuous operation eliminates the gaps between annual or biannual manual optimizations, ensuring the system adapts immediately to changing prescription patterns throughout the year
Data Source
AI summary
A method of assessing the productivity of a pharmacy includes: (a) recording information regarding the identity and quantity of each pharmaceutical being automatically dispensed with an automated prescription monitoring unit; (b) with an overall pharmacy prescription monitoring unit, recording information regarding (i) dispensing of pharmaceuticals dispensed from the automated pharmacy machine and (ii) manual dispensing of pharmaceuticals; (c) identifying in step (a) low performing pharmaceuticals; (d) identifying in step (b) high performing pharmaceuticals dispensed manually as candidates for automated dispensing; (e) comparing low performing pharmaceuticals of step (c) with high performing pharmaceuticals of step (d) to determine whether replacement is recommended; (f) confirming whether high performing pharmaceuticals identified in step (d) are capable of automated dispensing; and (g) replacing in the automated pharmacy machine a low performing pharmaceutical identified in step (c) with a high performing pharmaceutical identified in step (d).