Prescription Drug Food Safety Traceability System Based on

Prescription Drug & Food Safety Traceability System Based on GS1 EPCIS

Objective and Problem Statement:

In recent years, food safety issues have drawn growing concerns for federal authorities and hence collectively the focus was to adapt and leverage technologies to efficiently detect and prevent food safety problems and trace the accountability, building a reliable traceability system is indispensable.

Traceability ensures accurate recording, sharing, and tracing of the specific data within the whole food supply chain which includes the process of production, processing, warehousing, transportation, and retail.

The traditional traceability systems have issues, such as data invisibility, tampering, and sensitive information disclosure. The blockchain was a promising technology for adaption in the food safety traceability system because of the characteristics, such as the irreversible time vector, smart contract, and consensus algorithm.

The Key Performance Indicators (KPIs) is the adaption of the information retrieval techniques being used for optimal, smarter way to analyze the data to trace decision making parameters. Most of the process solutions focus on building up the massive repository by using formulated patterns and nomenclature viz; GS1 barcodes GTINs specification.

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Traceability systems have become a dominant management information system within the production and marketing companies as they can effectively control the supply chain, by minimizing the risks of the production process and helping to enhance the consumer/customer reliability on products. There may have some limitations of using available traceability systems with small and medium scale industries as there may have design-reality gap of such systems.

Solution Approach

The limitation of the current papers titled “Food Safety Traceability System Based on Blockchain and EPCIS” focuses on how the implementation moved from a paper-based approach to leveraging blockchain technology to transcend on the EPCIS XML format.

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But it takes into account and goes with the assumption that the traceable data would be always be structured.

Having been involved in this Pharma drug traceability segment for over last 10 plus years with GS1 US and compliance agencies like FDA (US), MHRA(UK), DGFT(INDIA) I can share some of the challenges that were faced in implementation of the various regulations.

Although the original paper focuses on food safety, I am suggesting improvements combining both the food and Pharma as when it comes to safety, there is a zero tolerance as it involves the safety of humans.

Through this paper, we propose to extend the process to cater to any traceability centric vertical process. With respect to this approach, we like to integrate Food and Prescription Drugs as a core region on which this paper would focus on. We will share the various extensions to the traceability approach in this paper. We will analyse the current practices and specification and share amendments to the designs that would enable optimal traceability objective is met.

We will also analyze the effectiveness, limitations and practical problems of the available traceability systems for supply chain management. Design gap between the real system and proposed model, high cost, accuracy and lack of technically qualified staff are some main problems of such systems. Then we introduced a novel system, “Total Traceability System”, to overcome such limitations and problems by minimizing the gap between design and on-the ground reality (minimize the design reality gap).

For Food or prescription Drug Traceability, various federal government agencies, standardization and harmonization agencies have come up with guidelines and best practices for implementations.

“Distributed traceability system, such as the EPCIS-based distributed traceability system, can facilitate the creation and sharing of visibility event data concerning physical or digital objects both within and across enterprises. The EPCIS specification defines four different events namely ObjectEvent, AggregationEvent, QuantityEvent, TransactionEvent, which is good for the scalability of traceability system, but data tampering and information disclosure issues remain to be solved in the EPCIS-based system”.

Although the information is tagged as per the specification, the usual ad-hoc queries against the vast data which caters to over 20 years of retention period as required by local governing laws, the need for retrieval system based on relevance of the information request is fundamentally necessary.

Implementation:

The implementation of the FOUR modules of enterprise-user server is the key to the whole system implementation. The details are as follows:

Traceability Information Capture Module: Actual Primary barcoded Packs are scanned on the packaging line and captured into the TrackNTrace Application system (“electronically stored information”.

Event Information Database: Various compliance attributes are verified checks are involved to ensure compliance with the packaging regime. Generally the serial number to be used on the packs needs to be commissioned before the pack can be aggregated. The events can include the commission date/time, aggregation date/time and also establish a parent child relationship.

Consignment pedigree XML Generation Module: Once the batch/lot number packaging activity is closed, the In-process quality team needs to follow couple of checklist and generate the shipping and consignment data before releasing the consignment from their warehouse. The format of the data exchange is usually XML EPCIS format.

Information Extraction (Production) Module: The recipient who are usually the MAH, CMOs, 3PLs (logistics partners), wholesalers, distributors will need to extract the information of the pack data to establish the traceability with manufacturer, lot numbers, expiration dates along with the SKU unique serial numbers.

In this paper, I would like to focus my attention on the unranked retrieval sets and evaluate the same. In the traceability application, which needs to cater to millions of transactions every instance based on unstructured data sources which is fed into by various agencies in their own custom format. Usually an Operating Data Sources (ODS) is setup which acts as the central system and individual data is fed and mapped to the ODS data structure. This approach has limitations as there is a lot of preprocessing that is required and also predefined specification that needs to be followed.

Our approach, would be to produce every content in a XML file format and have them in various distributed data sources which is typical of how the various regulatory agency follow today.

We are all aware that the most frequent and basic measures of information retrieval effectiveness are PRECISION and RECALL.

The first step is retrieve the set of documents based on a query, (refer to search form below which invokes the database on the package label that is scanned).

In this paper, I would like to focus my attention on the unranked retrieval sets and evaluate the same. In the traceability application, which needs to cater to millions of transactions every instance based on unstructured data sources which is fed into by various agencies in their own custom format. Usually an Operating Data Sources (ODS) is setup which acts as the central system and individual data is fed and mapped to the ODS data structure. This approach has limitations as there is a lot of preprocessing that is required and also predefined specification that needs to be followed.

Our approach, would be to produce every content in a XML file format and have them in various distributed data sources which is typical of how the various regulatory agency follow today.

We are all aware that the most frequent and basic measures of information retrieval effectiveness are PRECISION and RECALL.

The first step is retrieve the set of documents based on a query, (refer to search form below which invokes the database on the package label that is scanned).

To understand how the evaluation of ranked retrieval results is implemented, we need to understand precision, recall and F measure.

Precision (P) = (Number of Relevant items retrieved) / (Number of retrieved items). In short, it is the fraction of retrieved documents that are relevant.

Recall (R) = (Number of items retrieved) / (Number of Relevant Items). In short the fraction of relevant documents that are retrieved.

Relevant Non-Relevant

Retrieved True positive (tp) False positive (fp)

Non Retrieved False negatives (fn) True negative (tn)

Hence,

Precision = tp/ (tp + fp)

Recall = tp/ (tp+fn)

F-Measure is a single measure that trades off precision versus recall.

The precision, recall and the F measures are set based measures. They are computed using unordered sets of documents. In a ranked retrieval context appropriate sets of retrieved documents are naturally given by the top K retrieved documents.

The goal of review for responsiveness is to produce a set of relevant documents from the collection or corpus in the producer’s possession. The e?ectiveness of the production can, therefore, be directly measured using set-based metrics.

Many statistical text analysis tools can also rank the documents by estimated responsiveness. Indeed, internally, they may work by ranking the documents ?rst, then automatically selecting a cuto? point; or the ranking itself might be generated and reviewed by the producing party to manually select the cuto? point. It can also be useful, therefore, to evaluate the e?ectiveness of such a ranking using rank metrics. While most evaluation to date has assumed binary relevance, there has been some work with graded relevance assessments.

Rank-Sensitive Metrics

Production in e-discovery is a set-based, binary process; a document either is produced, or it is not. However, many statistical classi?cation techniques independently generate a degree of match (or probability of relevance) for each document, by which the documents can be ranked.

In ranked retrieval, the extensible top of this ranking can be returned to the searcher. For set-based retrieval, a threshold is then selected, either implicitly by the system itself, or based on sampling and human review, and all documents ranked above this threshold are returned. The quality of the ranking that a system produces can usefully be evaluated in either case. If a system directly estimates probabilities of relevance, then the accuracy of those estimates can be directly measured, and indeed that has been tried. Most statistical classi?cation methods, however, produce document scores that can be interpreted only as ordinal, and not as interval or ratio values. In other words, scores produced by such systems can be useful for comparing degrees (or probability) of relevance in a relative sense, but we may not be able to easily make strong claims about the actual degree or probability of relevance of any speci?c document.

Approaches:

· One approach to assess the ranking quality is to select the cuto? point k in the ranking that would give the optimal score under the set metric of interest, such as F ; this has been referred to as hypothetical F1

· Another approach to extending a set-based metric to ranked evaluation is to calculate the set-based metric at di?erent ranking depths, and then to either graph or summarize the results

Where two metrics form a complementary pair, a common approach is to graph one metric at each value of the other. Recall and precision form one such natural pair, while sensitivity and speci?city form another

Example calculation of a hypothetical F1 score.

A system has returned a ranking over an eight-document collection; the relevance of the document returned at each rank is shown in the second column. The third through sixth columns show the counts of true positives, false positives, false negatives, and true negatives if the ranking were to be converted into a set retrieval by cutting it o? at that depth. The ?nal three columns show the precision, recall, and F1 scores corresponding to the set retrievals at that rank. Note that recall invariably increases with rank, and precision generally decreases. The maximum F1 score of 0.67, occurring at depth 3, is the hypothetical F1 score for this ranking.

Example precision-recall curve, with and without interpolation. The ranking being scored is the one shown below:

Section B: Graded Relevance

The evaluation methods described above assume that there are no degrees of relevance, that a document is either wholly relevant or wholly irrelevant. Some documents, however, while technically relevant, will play no part in case development, while others may be crucial to the case and perhaps even will be submitted as evidence. Although review for responsiveness is a set-based task, that does not mean that errors on di?erent relevance classes are equally problematic. Low recall would be less worrying if all the important documents were produced, while high recall could be insu?cient if crucial items were missed. For this reason, an evaluation methodology that rests on the assumption that documents are either relevant or not will at best be an imperfect model of reality.

  • Database Architecture of a Traceability System
  • Actual XML EPCIS Disposition File generated using sample data from our proposed solution.
  • Process Illustrations have been submitted in the Annexure Section of this document
  • Please Note: All the works demonstrated in this section are actual work executed by me and my team.

Results

We have extended our architecture to incorporate the support for un structured documents and move away from the traditional ODS approach thereby enabling implementation flexibility with individual stakeholders. With single window approach, we are now enabling three major customers who have to be compliant with the USDSCSA regulation, FMD of UK regulation and DAVA of India. Now we do not have to support or upgrade the individual schemas rather focus on providing a collections of various EPCIS information.

This solution approach have been already implemented on a trials basis and we are learning from our user experiences and focusing on providing the relevant information they are seeking.

Conclusion

The advantages of system evaluation is that we have a fixed setting in which we can vary information retrieval (IR) systems and system parameters to carry out comparative experiments.

With the development of new technology, most of the supply chains and production systems have become complex and a finished product is assembly of several parts or sub-products that are produced in another subsidiary companies or factories.

It would be our endeavor that the main production company as well as related companies could be achieved some benefits such as reduce the cost, efficiency and accuracy of production. If a product has several sub-processing steps; there should have an efficient system to integrate whole production process and to trace each steps in order to maintain the quality and to minimize the above mentioned problems and risks. The tracing methods may change with the type, expected risks, value of the products. While food and prescription drugs products may want a continuous traceability system to enhance the reliability and trust on products. Hence, today the traceability systems have become an integral part of the supply chain and services.

Annexures:

  • Illustration 1: Below is a dashboard representation of various KPI’s we measure. (Source: The work is part of the PharmaMax CRM developed by our firm SolutionsMax Technology services which implemented traceability by using E-Discovery Ranking)
  • Illustration 2: Traceability supply chain practice (Source: GS1.org)
  • Illustration 3: Recommend Standardization of Master Information that will be part of the data collection process. (Source: GS1.org)
  • Illustration 4: Segmentation of Global Trade Identification Number (Source: GS1.org)
  • Illustration 5: EPCIS Document Structure (Source: GS1.org)

References:

1. EPCIS and CBV Implementation Guideline, Using EPCIS and CBV standards to gain visibility of business processes, Release 1.2., Ratified, Feb 2017

2. Implementation Guideline: Applying GS1 Standards for DSCSA and Traceability, Release 1.2, Nov 07 2016

Cite this page

Prescription Drug Food Safety Traceability System Based on. (2019, Dec 14). Retrieved from http://studymoose.com/prescription-drug-food-safety-traceability-system-based-on-example-essay

Prescription Drug Food Safety Traceability System Based on

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