A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study

Zeynep Ceylan; Seniye Ümit Fırat

doi:10.19113/sdufbed.14205

A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study

Abstract

Medication errors are common, fatal, costly but preventable. Location of drugs on the shelves and wrong drug names in prescriptions can cause errors during dispensing process. Therefore, a good drug-shelf arrangement system in pharmacies is crucial for preventing medication errors, increasing patient’s safety, evaluating pharmacy performance, and improving patient outcomes. The main purpose of this study to suggest a new drug-shelf arrangement for the pharmacy to prevent wrong drug selection from shelves by the pharmacist. The study proposes an integrated structure with three-stage data mining method using patient prescription records in database. In the first stage, drugs on prescriptions were clustered depending on the Anatomical Therapeutic Chemical (ATC) classification system to determine associations of drug utilizations. In the second stage association rule mining (ARM), well-known data mining technique, was applied to obtain frequent association rules between drugs which tend to be purchased together. In the third stage, the generated rules from ARM were used in multidimensional scaling (MDS) analysis to create a map displaying the relative location of drug groups on pharmacy shelves. The results of study showed that data mining is a valuable and very efficient tool which provides a basis for potential future investigation to enhance patient safety.

Keywords

Association rules,Data mining; Drug-shelf arrangement; Medication errors; Multi-dimensional scaling

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Details

Primary Language

Turkish

Subjects

-

Journal Section

-

Authors

Zeynep Ceylan This is me

Seniye Ümit Fırat
Türkiye

Publication Date

September 19, 2017

Submission Date

October 25, 2016

Acceptance Date

-

Published in Issue

Year 2017 Volume: 21 Number: 3

DOI

https://doi.org/10.19113/sdufbed.14205

IZ

https://izlik.org/JA78CC38CL

Cite

RIS / Bibtex

APA

Ceylan, Z., & Fırat, S. Ü. (2017). A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 21(3), 774-781. https://doi.org/10.19113/sdufbed.14205

AMA

1.Ceylan Z, Fırat SÜ. A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study. J. Nat. Appl. Sci. 2017;21(3):774-781. doi:10.19113/sdufbed.14205

Chicago

Ceylan, Zeynep, and Seniye Ümit Fırat. 2017. “A New Drug-Shelf Arrangement for Reducing Medication Errors Using Data Mining: A Case Study”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 21 (3): 774-81. https://doi.org/10.19113/sdufbed.14205.

EndNote

Ceylan Z, Fırat SÜ (December 1, 2017) A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 21 3 774–781.

IEEE

[1]Z. Ceylan and S. Ü. Fırat, “A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study”, J. Nat. Appl. Sci., vol. 21, no. 3, pp. 774–781, Dec. 2017, doi: 10.19113/sdufbed.14205.

ISNAD

Ceylan, Zeynep - Fırat, Seniye Ümit. “A New Drug-Shelf Arrangement for Reducing Medication Errors Using Data Mining: A Case Study”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 21/3 (December 1, 2017): 774-781. https://doi.org/10.19113/sdufbed.14205.

JAMA

1.Ceylan Z, Fırat SÜ. A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study. J. Nat. Appl. Sci. 2017;21:774–781.

MLA

Ceylan, Zeynep, and Seniye Ümit Fırat. “A New Drug-Shelf Arrangement for Reducing Medication Errors Using Data Mining: A Case Study”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 21, no. 3, Dec. 2017, pp. 774-81, doi:10.19113/sdufbed.14205.

Vancouver

1.Zeynep Ceylan, Seniye Ümit Fırat. A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study. J. Nat. Appl. Sci. 2017 Dec. 1;21(3):774-81. doi:10.19113/sdufbed.14205

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International Journal of Data Science and Analytics

https://doi.org/10.1007/s41060-025-00756-5