ADC Trip Identification for Scheduled and Unscheduled Pharmacy Runs
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Solution Overview
Problem
Current systems lack efficient methods to identify and manage medication delivery trips to automated dispensing cabinets, leading to inefficient, redundant, and unplanned trips, which can interfere with patient care and waste resources.
Innovation Solution
A method that analyzes transaction records to identify unique trips by comparing time and elapsed times against thresholds, predicts trip types using machine-learning models, and annotates records to distinguish between scheduled and unscheduled trips, allowing for improved scheduling and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If automated dispensing cabinets are used to provide convenient access to medication, then medication accessibility is improved, but trip management efficiency deteriorates due to lack of identification and management methods
Solution Approach 1:
The system implements feedback by continuously monitoring ADC inventory levels, trip histories, and transaction data to dynamically optimize future trip schedules. The analytics engine processes this feedback information to identify patterns, predict future needs, and adjust scheduling parameters, creating a closed-loop system that improves trip management efficiency while maintaining medication accessibility.
Solution Approach 2:
The system enables self-service by allowing ADCs to automatically generate trip requests based on their own inventory levels and usage patterns. The analytics engine processes these autonomous requests and integrates them into optimized schedules without requiring manual intervention from pharmacy personnel, thereby improving productivity while maintaining ease of operation.
2Reliability
If multiple trips are scheduled to perform maintenance transactions at ADCs, then medication supply maintenance is improved, but resource waste increases due to inefficient and redundant trips
Solution Approach 1:
The system merges multiple separate trips into consolidated routes by identifying trips that can be combined into single journeys. The analytics engine analyzes trip data to determine when multiple ADC visits can be grouped together, reducing the total number of trips required while ensuring all medication supply maintenance needs are met, thereby reducing resource waste without compromising reliability.
Solution Approach 2:
The system performs preliminary action by proactively identifying and scheduling trips before medications actually reach critical low levels. The analytics engine predicts future inventory needs based on historical usage patterns and schedules trips in advance, preventing the need for urgent, inefficient last-minute trips and reducing overall resource consumption while maintaining reliable medication supplies.
3Reliability
If unscheduled ad hoc trips are taken to respond to immediate needs, then medication availability is improved, but time consumption increases and other tasks are interfered with
Solution Approach 1:
The system performs preliminary action by proactively scheduling trips before medications reach critical levels. The analytics engine continuously monitors inventory and predicts future needs, arranging for deliveries in advance so that medications are available when needed without requiring time-consuming ad hoc trips, thus maintaining reliability while reducing time loss.
Solution Approach 2:
The system implements feedback by monitoring the effectiveness of scheduled trips and comparing actual medication availability outcomes against predictions. This feedback loop allows the analytics engine to refine its scheduling algorithms, improving the accuracy of predictive scheduling and reducing the frequency of unscheduled trips, thereby maintaining medication availability while minimizing time consumption.
4Ease of manufacture
If scheduled trips are used to deliver medications, then trip planning is improved, but inefficiency increases when medications do not need refilling or when routes are poorly planned
Solution Approach 1:
The system implements feedback by continuously monitoring actual trip outcomes, including whether medications were actually refilled, route efficiency metrics, and ADC inventory changes. This feedback information is fed back to the analytics engine, which uses it to refine scheduling decisions and route optimization algorithms, thereby improving trip efficiency while maintaining the benefits of structured trip planning.
Solution Approach 2:
The system applies dynamics by making trip schedules flexible and adaptive rather than static. The analytics engine continuously adjusts scheduled trips based on real-time inventory data, usage patterns, and predicted needs, allowing the system to optimize routes and timing dynamically. This ensures that scheduled trips remain efficient even as conditions change, preventing the inefficiency of rigid, poorly planned routes.
Data Source
AI summary
A method for managing trips from a pharmacy may include receiving a plurality of transaction records that, as received, represent an independent transaction of a plurality of transactions at an ADC. The plurality of transaction records may include: time information between respective transactions and an elapsed time for a portion of the plurality of transactions. The method may include identifying, based at least in part on: (i) a comparison of the time information between respective transactions and a threshold inter-transaction trip time; and (ii) a comparison of the elapsed time for the portion and a threshold elapsed trip time, a sequence of the portion as a unique trip from a pharmacy. The method may include annotating, with an identifier for the unique trip, a portion of the plurality of transaction records representing the portion of the plurality of transactions. Related methods and articles of manufacture are also disclosed.