Automated Educational Coupon Generation for Prescription Adherence
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Solution Overview
Problem
Current methods for increasing patient adherence to prescribed medications are inefficient, as healthcare providers lack real-time awareness of patient adherence and struggle to effectively distribute educational materials and incentives at the point-of-care, while pharmaceutical companies face restrictions in targeting incentives to patients who will benefit from them.
Innovation Solution
A system that generates a combined educational coupon by receiving electronic prescription data, determining relevant educational and coupon data, and creating a single data file, allowing healthcare providers to easily distribute personalized educational materials and incentives to patients through a networked computer system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If healthcare providers manually distribute educational materials and coupons to patients, then patient adherence may improve, but the complexity and burden on providers increases significantly
Solution Approach 1:
The system enables self-service by automatically determining eligibility, selecting appropriate materials, and distributing them to patients without requiring provider intervention. The computer apparatus autonomously processes prescription data, matches patients with relevant educational materials and coupons, and handles the entire distribution workflow, freeing providers from manual adherence management tasks.
Solution Approach 2:
The patent replaces the mechanical/manual system of physical hand-delivering brochures and coupons with an automated electronic system. The computer apparatus electronically processes prescriptions, retrieves digital educational materials and coupon data, and transmits them to patients through electronic channels, eliminating the need for providers to physically manage and distribute these materials.
2Quantity of substance
If pharmaceutical companies distribute coupons to all patients, then incentive coverage increases, but cost efficiency decreases due to wasted resources on patients who won't use them
Solution Approach 1:
The system applies local quality by tailoring coupon and educational material distribution to specific patient characteristics, diagnoses, and prescription patterns. Rather than uniform distribution, the computer apparatus analyzes individual patient data to determine which patients are most likely to benefit from each incentive, creating personalized distribution strategies that maximize return on investment.
Solution Approach 2:
The system incorporates feedback mechanisms where the computer apparatus monitors and analyzes patient responses to previous coupon and educational material distributions. This feedback data is used to refine future distribution algorithms, improving the accuracy of predicting which patients will engage with incentives and adjusting distribution strategies accordingly to reduce waste.
3Reliability
If providers have access to extensive educational materials and coupons for each patient condition, then patient adherence improves, but the time required to prepare and distribute these materials increases
Solution Approach 1:
The system performs preliminary action by pre-processing and organizing extensive educational materials and coupon databases before they are needed at the point of care. The computer apparatus pre-categorizes materials by diagnosis, prescription type, and patient characteristics, and pre-loads relevant content into the system, so that when a provider enters a prescription, the appropriate materials are already ready for immediate electronic distribution without requiring on-site preparation.
Solution Approach 2:
The patent uses copying by creating digital copies of educational materials and coupons that can be instantly replicated and transmitted to multiple patients simultaneously. Instead of physically preparing unique materials for each patient, the system creates and distributes digital copies through electronic channels, eliminating the time-consuming process of custom printing, copying, and manually delivering physical materials to each patient.
Data Source
AI summary
A computer system identifies supplemental materials most effective at increasing adherence for each of a plurality of different medications and provides the materials at an optimal point in time. An example method generates a first user interface for receiving an electronic prescription for a patient for a prescribed substance. Responsive to receiving the electronic prescription, the method includes obtaining adherence data for the patient, identifying supplemental programs associated with the prescribed substance from a database of supplemental programs, generating a second user interface that presents the supplemental programs for selection by the health care provider, and responsive to receiving selection of at least one of the supplemental programs in the second user interface, providing the supplemental programs to the patient. The supplemental programs identified from the database are associated with at least one rule relating to adherence data that is met by the adherence data for the patient.