Yıl 2017, Cilt 21, Sayı 3, Sayfalar 774 - 781 2017-09-19

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

Zeynep CEYLAN [1] , Seniye Ümit OKTAY FIRAT [2]

770 218

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.
Association rules, Data mining; Drug-shelf arrangement; Medication errors; Multi-dimensional scaling
  • [1] World Health Organization. Patient Safety Curriculum Guide Multi Professional Edition. WHO. 2011. http://caipe.org.uk/silo/files/multi-professional-patient-safety-curriculum-guide.pdf
  • [2] National Coordinating Council for Medication Error Reporting and Prevention. What is a medication error? http://www.nccmerp.org/about-medication-errors
  • [3] World Health Organization. Drug and therapeutics committees - A practical guide. WHO. 2003. http://apps.who.int/medicinedocs/en/d/Js4882e/4.html.
  • [4] Food and Drug Administration. FDA 101: Medication Errors. FDA. 2009. http://www.fda.gov/downloads/ForConsumers/ConsumerUpdates/UCM143038.pdf.
  • [5] Emmerton L.M., Rizk M.F. 2012. Look-alike and sound-alike medicines: risks and ‘solutions’. Int J Clin Pharm., 34(1), 4–8.
  • [6] Ciociano N., Bagnasco L. 2014. Look alike/sound alike drugs: a literature review on causes and solutions. Int J Clin Pharm., 36, 233–242.
  • [7] Joint Commission on Accreditation of Healthcare Organizations. Look-alike, sound-alike drug names. JCAHO. 2001. http://www.jointcommission.org/assets/1/18/SEA_19.pdf.
  • [8] Institute for Safe Medication Practices. Improving Medication Safety in Community Pharmacy: Assessing Risk and Opportunities for change ISMP. 2009. http://www.ismp.org/communityRx/aroc/.
  • [9] Oh H.C., Wong J.A., Tan M.C. 2014. Enhancement of patient and staff experience at outpatient pharmacy via optimization of drug–shelf reallocation. Operations Research for Health Care, 3(1), 15–21.
  • [10] Sunny Downstate Medical, Department of Pharmacy Service. Top 10 Sound-Alike & Look-Alike. http://www.downstate.edu/patientsafety/Look_alike_Sound_alike_drug_list.pdf
  • [11] Hoffman J.M., Proulx S.M. 2003. Medication errors caused by confusion of drug names. Drug Saf., 26(7), 445–452.
  • [12] Kenagy J.W., Stein G.C. 2001. Naming, labeling, and packaging of pharmaceuticals. Am J Health-Syst Pharm., 58(21), 2033-41.
  • [13] Bohand X., Aupee O., Le Garlantezec P., Mullot H., Lefeuvre L., Simon L. 2009. Medication dispensing errors in a French military hospital pharmacy. Pharm World Sci., 31, 432-438.
  • [14] Taylor J., Gaucher M. 1986. Medication selection errors made by pharmacy technicians in filling unit dose orders. Can J Hosp Pharm., 39, 9–12.
  • [15] Cina J.L., Gandhi T.K., Churchill W., Fanikos J., McCrea M., Mitton P., et al. 2006. How many hospital pharmacy medication dispensing errors go undetected? Jt Comm J Qual Patient Saf., 32, 73-80.
  • [16] Klein E.G., Santora J.A., Pascale P.M., Kitrenos J.G. 1994. Medication cart filling time accuracy, and cost with an automated dispensing system. Am J Hosp Pharm., 51, 1193–6.
  • [17] Institute for Safe Medication Practices. A Call to Action: Protecting U.S. Citizens from Inappropriate Medication Use. ISMP. 2007. http://www.ismp.org/pressroom/viewpoints/CommunityPharmacy.pdf.
  • [18] Samaranayake N.R., Cheung S.T.D, Chui W.C.M., Cheung B.M.Y. 2013. The pattern of the discovery of medication errors in a tertiary hospital in Hong Kong. Int J Clin Pharm., 2013; 35(3), 432–438.
  • [19] Tan P.N., Steinbach M., Kumar V. 2006. Introduction to data mining. Boston, Pearson Education.
  • [20] Han J., Kamber M., Pei J. 2012. Data Mining Concepts and Techniques (3rd ed.) USA, Morgan Kaufmann Publishers.
  • [21] Orozova-Bekkevold I., Jensen H., Stensballe L., Olsen J. 2007. Maternal vaccination and preterm birth: Using data mining as a screening tool. Pharm World Sci., 29, 205–212.
  • [22] Patadia V.K., Schuemie M.J., Coloma P., Herings R., et al. 2015. Evaluating performance of electronic healthcare records and spontaneous reporting data in drug safety signal detection. Int J Clin Pharm., 37(1), 94-104.
  • [23] Koh H.C., Tan G. 2005. Data mining applications in healthcare. Journal of Healthcare Information Management, 19(2), 64-72.
  • [24] Hamuro Y., Katoh N., Matsuda Y., Yada K. 1998. Mining pharmacy data helps to make profits. Data Mining and Knowledge Discovery, 2, 391–398.
  • [25] Jensen P.B., Jensen L.J., Brunak S. 2012. Mining electronic health records: towards better research applications and clinical care. Nat Genet., 13, 395–405.
  • [26] Bereznicki B.J., Peterson G.M., Jackson S.L., Walters H., Fitzmaurice K., Gee P. 2008. Pharmacist-initiated general practitioner referral of patients with suboptimal asthma management. Pharmacy World & Science, 30: 869–875.
  • [27] Khader N., Lashier A., Yoon S.W. 2016. Pharmacy robotic dispensing and planogram analysis using association rule mining with prescription data, Expert Systems with Applications, 57, 296-310.
  • [28] Chapman P., Clinton J., Kerber R., Khabaza T., Reinartz T., Shearer C., Wirth R. CRISP-DM 1.0 Step-by-step data mining guide. CRISP-DM Consortium. 2000. http://the-modeling-agency.com/crisp-dm.pdf.
  • [29] Agrawal R., Imielinski T., Swami A. 1993. Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD international conference on management of data, Washington, DC.
  • [30] Agrawal R., Srikant R. 1994. Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB Conference, Santiago, Chile.
  • [31] Doddi S., Marathe A., Ravi S.S., Torney D.C. 2001. Discovery of association rules in medical data. Med Inform Internet Med., 26, 25–33.
  • [32] Nguyen P.A., Syed-Abdul S., Iqbal U., Hsu M.H., Huang C.L., Li H.C., Clinciu D.L., Jian W.S., Li Y.C. 2013. A probabilistic model for reducing medication errors. PLoS One, 8(12), e8240.
  • [33] Kim J.W. 2017. Construction and evaluation of structured association map for visual exploration of association rules. Expert systems with applications, 74, 70-81.
  • [34] Huang Y., Britton J., Hubbard R., Lewis S. 2013. Who receives prescriptions for smoking cessation medications? An association rule mining analysis using a large primary care database. Tob Control. 22(4), 274-279.
  • [35] World Health Organization Collaborating Center for Drug Statistics Methodology. Guidelines for ATC classification and DDD assignment. WHOCC. 2013. http://www.whocc.no/filearchive/publications/1_2013guidelines.pdf
  • [36] Rønnig M. 2001. Coding and classification in drug statistics—From national to global application. Nor J Epidemiol, 11, 37–40.
  • [37] Linoff G.S., Berry M.J. 2011. Data mining techniques: For marketing, sales and customer relationship management (3rd ed.) Indianapolis, Wiley Publishing Inc.
  • [38] Kalaichelvi A., Malini P.H., 2011. Application of fuzzy soft sets to investment decision making problem. Internal Journal of Mathematical Sciences and Applications, 1(3), 1583-1586.
  • [39] Yuksel S., Dizman T., Yildizdan G., Sert U. 2013. Application of soft sets to diagnose the prostate cancer risk. Journal of Inequalities and Applications, 229.
  • [40] Özgür N.Y., Taş N. 2015. A Note On "Application of Fuzzy Soft Sets to Investment Decision Making Problem". Journal of New Theory, 1(7), 1-10.
  • [41] Dash S.R., Dehuri S., Sahoo U.K. 2013. Interactions and Applications of Fuzzy, Rough, and Soft Set in Data Mining, International Journal of Fuzzy System Applications, 3(3), 37-50.
  • [42] Feng F., Cho J., Pedrycz W., Fuzita H., Herawan T. 2016. Soft set based association rule mining, Knowledge-Based Systems, Volume 111, 268-282.
  • [43] Kavakiotis I., Tsave O., Salifoglou A., Maglaveras N., Vlahavas I., Chouvarda I. 2017. Machine learning and data mining methods in diabetes research. Computational and Structural Biotechnology Journal, 15, 104-116.
  • [44] Blattberg R.C., Kim B.D., Neslin, S.A. 2008 Database Marketing: Analyzing and Managing Customers. New York, Springer.
  • [45] Borg I., Groenen P. 2005. Modern Multi-dimensional scaling theory and applications. Berlin: Springer.
  • [46] Çil I. 2012. Consumption universes based supermarket layout through association rule mining and multidimensional scaling. Expert Systems with Applications, 39(10), 8611–8625.
  • [47] Jaworska N., Chupetlovska-Anastasova A. 2009. A review of multidimentional scaling (MDS) and its utility in various psychological domains. Tutorials in Quantitative Methods for Psychology, 5(1), 1–10.
  • [48] Kruskal J.B. 1964. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29, 1–27.
  • [49] Kruskal J.B., Wish M. Multidimensional scaling. Sage University Paper series on Quantitative Applications in the Social Sciences, number 07-011. Newbury Park, CA: Sage Publications; 1978.
  • [50] Turkish Medicines and Medical Device Agency. E-prescription drug list. TMMDA. 2014. http://www.titck.gov.tr/DisplayDynamicModule.aspx?mId=a/0Tp/ovYIU.
  • [51] SPSS Clementine 11.1. User’s Guide http://home.kku.ac.th/wichuda/DMining/ClementineUsersGuide_11.1.pdf
  • [52] Borges A. 2003. Toward a new supermarket layout: from industrial categories to one stop shopping organization through a data mining approach. In: Proceedings of the 2003 society for marketing advances annual symposium on retail patronage and strategy, Montreal, November, 4–5.
  • [53] Mollahaliloglu S., Alkan A., Donertas B., Ozgulcu S, Akıcı A. 2013. Prescribing Practices of Physicians at Different Health Care Institutions. Saudi Pharmaceutical Journal, 21(3), 281-291.
Dergi Bölümü Makaleler

Yazar: Zeynep CEYLAN

Yazar: Seniye Ümit OKTAY FIRAT

Bibtex @ { sdufenbed382216, journal = {Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi}, issn = {}, eissn = {1308-6529}, address = {Süleyman Demirel Üniversitesi}, year = {2017}, volume = {21}, pages = {774 - 781}, doi = {10.19113/sdufbed.14205}, title = {A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study}, key = {cite}, author = {CEYLAN, Zeynep and OKTAY FIRAT, Seniye Ümit} }
APA CEYLAN, Z , OKTAY FIRAT, S . (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. Retrieved from http://dergipark.gov.tr/sdufenbed/issue/34610/382216
MLA CEYLAN, Z , OKTAY FIRAT, S . "A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study". Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 21 (2017): 774-781 <http://dergipark.gov.tr/sdufenbed/issue/34610/382216>
Chicago CEYLAN, Z , OKTAY FIRAT, S . "A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study". Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 21 (2017): 774-781
RIS TY - JOUR T1 - A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study AU - Zeynep CEYLAN , Seniye Ümit OKTAY FIRAT Y1 - 2017 PY - 2017 N1 - DO - T2 - Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi JF - Journal JO - JOR SP - 774 EP - 781 VL - 21 IS - 3 SN - -1308-6529 M3 - UR - Y2 - 2018 ER -
EndNote %0 Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study %A Zeynep CEYLAN , Seniye Ümit OKTAY FIRAT %T A New Drug-Shelf Arrangement for Reducing Medication Errors using Data Mining: A Case Study %D 2017 %J Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi %P -1308-6529 %V 21 %N 3 %R %U
ISNAD CEYLAN, Zeynep , OKTAY FIRAT, 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 (Eylül 2017): 774-781.