Reverse supply chain gateway for medical equipment
The Reverse Supply Chain Gateway for Used Medical Equipment is a time, resource, and value optimizer that eliminates gaps faced in the disposition process for medical equipment. By making the economics and resource requirements to participate feasible for all parties, healthcare systems and/or asset owners see increased service, better compliance, reduced costs, less environmental waste generated, and more capital returned. Meanwhile the downstream supply chain for reconditioning medical devices and harvesting parts to reenter the market for productive healthcare provider use is strengthened and expanded through access to significantly more equipment, reduced capital requirements to participate, increased information available about medical device conditions to make more informed purchases, less landfill consumption, and more equipment reuse. The efficiency gains from the UME Gateway enable the solutions and services it provides to more than pay for itself for all active participants.
1 . A system for selling, processing, and/or refurbishing medical equipment, the system comprising one or more computer-readable storage media containing a set of instructions executable by one or more logic machines to perform the steps of:
collecting data on at least one piece of medical equipment;
evaluating the at least one piece of medical equipment;
using AI driven analytics, making a recommendation regarding how much investment to make in identifying additional details about the at least one piece of medical equipment, wherein the set of instructions encoded on the non-transitory computer-readable storage medium comprises an AI enhanced engine, wherein
the AI enhanced engine takes the step of making a recommendation regarding how much investment to make in identifying additional details about the at least one piece of medical equipment is executed iteratively and repeatedly, wherein the system can dynamically change its recommendations regarding the at least one piece of medical equipment based on new or changed information;
the AI enhanced engine uses econometric data to evaluate the at least one piece of medical equipment, wherein the econometric data comprises demand, supply, and transactional data from within and outside the system;
the AI enhanced engine normalizes transactional and demand data by weighting the transactional and demand data using available information on recency, seasonal timing, volumes, number of transactions, channels where the transaction occurs, and any other medical equipment purchased or quoted simultaneously;
the AI enhanced engine uses an acquisition and identification capture process, wherein incoming medical equipment are identified, photographed, and entered into the system;
the AI driven analytics used in making the recommendation regarding how much investment to make in identifying additional details about the at least one piece of medical equipment utilizes at least one of the following data sets in making the recommendation:
manufacturer, model, quantity, condition, specification data, accessories, transaction history, registered demand, terms, quotes, offers, market supply data, market pricing signals, available compatible inventory, lotting options, kitting options, refurbishment valuation predictions, refurbishment costs, parts harvest valuation predictions, and parts harvest costs;
the AI driven analytics used in making the recommendation regarding how much investment to make in identifying additional details about the at least one piece of medical equipment utilizes seller contract rules and constraints in making the recommendation, wherein
models used by the AI enhanced engine are continuously enhanced using transactional, demand, and supply data;
the system assesses potential benefit of making value added investments of labor, logistics, and accessories into the at least one piece of medical equipment;
models used by the AI enhanced engine identify gaps in data, metadata and master data and automatically fill them in or generate alerts for manual intervention;
models used by the AI enhanced engine perform prework in sourcing relevant market data and either process the relevant market data into a consumable format or present the relevant market data to a user;
using AI driven analytics, automatically making a recommendation regarding ideal pricing and escape path for at least one piece of medical equipment based on collected data on the at least one piece of medical equipment, wherein the set of instructions encoded on the non-transitory computer-readable storage medium comprises an AI enhanced engine, wherein
the escape path comprises:
a store path,
a redeploy path,
a recondition path,
a harvest path,
a multi-channel resale path,
a donate path, and
a remediate or recycle path;
the step of making a recommendation regarding ideal pricing and escape path for at least one piece of medical equipment based on collected data on the at least one piece of medical equipment is executed iteratively and repeatedly, wherein the system can dynamically change its
recommendations regarding the at least one piece of medical equipment based on new or changed information, wherein
the AI enhanced engine uses econometric data to evaluate the at least one piece of medical equipment, wherein the econometric data comprises demand, supply, and transactional data from within and outside the system;
models used by the AI enhanced engine normalizes data across a variety of factors to determine ultimate potential pocket margin returns from different medical equipment escape path alternatives along with assessing confidence in the recommendations, wherein the factors are at least one of the following:
demand, supply, manufacturer, model, quantity, condition, specification data, accessories, transaction history, registered demand, terms, quotes, offers, market supply data, market pricing signals, available compatible inventory, lotting options, kitting options, refurbishment valuation predictions, refurbishment costs, parts harvest valuation predictions, parts harvest costs, recency, seasonal timing, volumes, number of transactions, and channels where the transaction occurs;
the AI enhanced engine uses at least one of the following data sets to gain insight for recommending listing price points, anticipated results of negotiations, and potential guidance where quotes and offers may have been too high or too low:
quote history, offer history, current negotiations, and negotiation history;
the AI enhanced engine utilizes quality control methods to ensure that there are no anomalies in information used by the system to make the recommendation; and
the AI enhanced engine dynamically controls factors that the system uses to make the recommendation.