Veri Madenciliği Kullanarak Tıbbi Hataların Azaltılması için Yeni Bir İlaç-Raf Düzeni: Bir Vaka Analizi

Tıbbi hatalar yaygın, ölümcül ve maliyetli ama önlenebilirlerdir. İlaçların raflardaki konumu ve reçetelerdeki yanlış ilaç isimleri, ilaçların dağıtım sürecindeki hataları arttırabilir. Bu nedenle, eczanelerde iyi bir ilaç-raf düzenleme sistemi tıbbi hataların önlenmesi, hasta güvenliğinin arttırılması, eczane performansının değerlendirilmesi ve hasta geri-dönüşlerinin iyileştirilmesi açısından hayati önem taşımaktadır. Bu çalışmanın temel amacı, eczacı tarafından raflardan yanlış ilaç seçimini önlemek üzere eczane için yeni bir ilaç-raf düzeni önermektir. Çalışma, 3-aşamalı veri madenciliği metodu ile veri tabanındaki hasta reçete kayıtlarını kullanarak bir entegre yapı önermektedir. İlk aşamada, reçetelerdeki ilaçlar, ilaç kullanımları arasındaki ilişkileri belirlemek üzere Anatomik Terapötik Kimyasal (ATK) sınıflandırma sistemine göre sınıflandırıldı. İkinci aşamada, iyi bilinen bir veri madenciliği tekniği olan Birliktelik Kural Madenciliği (BKM), birlikte satın alınma eğilimindeki ilaçlar arasındaki sık rastlanan birliktelik kurallarını elde etmek için uygulandı. Üçüncü aşamada, BKM ile üretilen kurallar, eczane raflarında ilaçların göreceli olarak yerlerini gösteren haritayı oluşturmak için Çok Boyutlu Ölçekleme (ÇBÖ) analizinde kullanıldı. Çalışmanın sonuçları, veri madenciliğinin hasta güvenliğinin arttırılması üzerine gelecekte yapılacak araştırmalar için temel oluşturan değerli ve çok verimli bir araç olduğunu göstermiştir.

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

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), wellknown 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.

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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.
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.
SPSS Clementine 11.1. User’s Guide http://home.kku.ac.th/wichuda/DMining/Clem entineUsersGuide_11.1.pdf
Turkish Medicines and Medical Device Agency. E-prescription drug list. TMMDA. 2014. http://www.titck.gov.tr/DisplayDynamicModule .aspx?mId=a/0Tp/ovYIU.
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.
Kruskal J.B. 1964. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29, 1–27.
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.
Çil I. 2012. Consumption universes based supermarket layout through association rule mining and multidimensional scaling. Expert Systems with Applications, 39(10), 8611–8625.
Borg I., Groenen P. 2005. Modern Multidimensional scaling theory and applications. Berlin: Springer.
Blattberg R.C., Kim B.D., Neslin, S.A. 2008 Database Marketing: Analyzing and Managing Customers. New York, Springer.
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.
Feng F., Cho J., Pedrycz W., Fuzita H., Herawan T. 2016. Soft set based association rule mining, Knowledge-Based Systems, Volume 111, 268- 282.
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.
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.
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.
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.
Linoff G.S., Berry M.J. 2011. Data mining techniques: For marketing, sales and customer relationship management (3rd ed.) Indianapolis, Wiley Publishing Inc.
Rønnig M. 2001. Coding and classification in drug statistics—From national to global application. Nor J Epidemiol, 11, 37–40.
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
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.
Kim J.W. 2017. Construction and evaluation of structured association map for visual exploration of association rules. Expert systems with applications, 74, 70-81.
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.
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.
Agrawal R., Srikant R. 1994. Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB Conference, Santiago, Chile.
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.
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.
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.
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.
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.
Hamuro Y., Katoh N., Matsuda Y., Yada K. 1998. Mining pharmacy data helps to make profits. Data Mining and Knowledge Discovery, 2, 391– 398.
Koh H.C., Tan G. 2005. Data mining applications in healthcare. Journal of Healthcare Information Management, 19(2), 64-72.
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.
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.
Han J., Kamber M., Pei J. 2012. Data Mining Concepts and Techniques (3rd ed.) USA, Morgan Kaufmann Publishers.
Tan P.N., Steinbach M., Kumar V. 2006. Introduction to data mining. Boston, Pearson Education.
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.
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/C ommunityPharmacy.pdf.
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.
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.
Taylor J., Gaucher M. 1986. Medication selection errors made by pharmacy technicians in filling unit dose orders. Can J Hosp Pharm., 39, 9–12.
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.
Kenagy J.W., Stein G.C. 2001. Naming, labeling, and packaging of pharmaceuticals. Am J HealthSyst Pharm., 58(21), 2033-41.
Hoffman J.M., Proulx S.M. 2003. Medication errors caused by confusion of drug names. Drug Saf., 26(7), 445–452.
Sunny Downstate Medical, Department of Pharmacy Service. Top 10 Sound-Alike & LookAlike. http://www.downstate.edu/patientsafety/Look _alike_Sound_alike_drug_list.pdf
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.
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/.
Joint Commission on Accreditation of Healthcare Organizations. Look-alike, sound-alike drug names. JCAHO. 2001. http://www.jointcommission.org/assets/1/18/S EA_19.pdf.
Ciociano N., Bagnasco L. 2014. Look alike/sound alike drugs: a literature review on causes and solutions. Int J Clin Pharm., 36, 233–242.
Emmerton L.M., Rizk M.F. 2012. Look-alike and sound-alike medicines: risks and ‘solutions’. Int J Clin Pharm., 34(1), 4–8.
Food and Drug Administration. FDA 101: Medication Errors. FDA. 2009. http://www.fda.gov/downloads/ForConsumers/ ConsumerUpdates/UCM143038.pdf.
World Health Organization. Drug and therapeutics committees - A practical guide. WHO. 2003. http://apps.who.int/medicinedocs/en/d/Js4882 e/4.html.
National Coordinating Council for Medication Error Reporting and Prevention. What is a medication error? http://www.nccmerp.org/about-medicationerrors
World Health Organization. Patient Safety Curriculum Guide Multi Professional Edition. WHO. 2011. http://caipe.org.uk/silo/files/multiprofessional-patient-safety-curriculum-guide.pdf