Publication: Shelf arrangement via data mining techniques and a case study
Abstract
VERİ MADENCİLİĞİ TEKNİKLERİ İLE RAF DÜZENLEME VE BİR VAKA ÇALIŞMASI Sağlık sektörü dünyanın en büyük ve hızlı gelişen alanlarından biridir. Ayrıca sağlık sistemleri, karmaşık ve bileşeni çok fazla olan bir yapıya sahiptir. Sektördeki bu büyüme ve sistemin karmaşıklığı, veri tabanlarında depolanan ham verilerin miktarında ve çeşitliliğinde artışa neden olmaktadır. Veri madenciliği, büyük miktarlardaki ham verilerden gizli ve yararlı bilginin çıkartılmasında önemli bir araçtır. Veri madenciliği teknikleri sağlık sistemlerinde, dolandırıcılık tespiti, etkin tedavinin bulunması ve sağlık hizmetlerinin iyileştirilmesi gibi çeşitli amaçlar için kullanılabilir. Bu nedenle veri madenciliğinin sağlık sektörünün farklı zincirlerinde ve bileşenlerinde uygulanması çok önemlidir. Eczaneler sağlık sistemlerinin önemli halkalarından biridir.. Fakat, ilaçların eczanelerde yerleşimi genellikle karmaşıktır. Ayrıca, ilaç tür ve marka sayısındaki artış, hizmet sürelerinin uzaması ve ilaç hataları ile sonuçlanmaktadır. İlaç hataları, ters ilaç reaksiyonlarına ve kaynak israfına neden olmaktadır. Bu sebeple, ilaçların uygun raflarda sistematik düzenlenmesi hem eczane personeli hem de hastalar için önemlidir. Bu çalışmada, birlikte satın alınma eğiliminde olan ilaçlar arasındaki ilişkileri ortaya çıkarmak için bir eczanenin reçete verileri üzerinde, veri madenciliği tekniklerinden Birliktelik Kuralı yaklaşımı uygulanmıştır. Analiz, SPSS Clementine kullanılarak, birliktelik kuralının en iyi bilinen algoritmalarından biri olan Apriori algoritması uygulanarak gerçekleştirilmiştir. Bu araştırmada amaç, elde edilen kurallar ile yeni bir ilaç-raf düzeni önermektir. Yeni raf düzeni, eczane alanının etkin kullanılmasını, hastalara daha kısa sürede hizmet verilmesini ve olası ilaç hatalarında azalmayı sağlayabilir
SHELF ARRANGEMENT VIA DATA MINING TECHNIQUES AND A CASE STUDY The healthcare sector is one of the world’s largest and fast growing area. Furthermore, health systems have a structure which is complex and consisted of many components. This growth and complexity of the system in the sector cause increase in amount and diversity of raw data stored in databases. Data mining is an important tool to extract hidden and useful knowledge from huge volume of raw data. Data mining techniques can be used extensively in healthcare systems in various purposes such as fraud detection, identification of effective treatments and improvements of healthcare services. Therefore, application of data mining techniques on healthcare sector is crucial. The pharmacies are important parts of the healthcare systems. However, the placement of drugs in pharmacies are generally confusing. Furthermore, increasing number of drug types and brands results in longer service time and the drug errors. Drug errors cause adverse drug reactions and wasting resources. Hence, the convenient arrangement of drugs is significant for pharmacy staff and patient, as well. In this study, Association Rules approach of data mining techniques was applied on prescriptions data of a pharmacy to reveal the associations between drugs which tend to be purchased together. Analyses were performed by applying the most-known association rule mining algorithm, Apriori algorithm, using SPSS Clementine software. The main objective of this study is to propose a new drug-shelf arrangement with obtained rules. New shelf arrangement can provide efficient utilization of pharmacy area, lower service time, and reduction of possible drug errors.
SHELF ARRANGEMENT VIA DATA MINING TECHNIQUES AND A CASE STUDY The healthcare sector is one of the world’s largest and fast growing area. Furthermore, health systems have a structure which is complex and consisted of many components. This growth and complexity of the system in the sector cause increase in amount and diversity of raw data stored in databases. Data mining is an important tool to extract hidden and useful knowledge from huge volume of raw data. Data mining techniques can be used extensively in healthcare systems in various purposes such as fraud detection, identification of effective treatments and improvements of healthcare services. Therefore, application of data mining techniques on healthcare sector is crucial. The pharmacies are important parts of the healthcare systems. However, the placement of drugs in pharmacies are generally confusing. Furthermore, increasing number of drug types and brands results in longer service time and the drug errors. Drug errors cause adverse drug reactions and wasting resources. Hence, the convenient arrangement of drugs is significant for pharmacy staff and patient, as well. In this study, Association Rules approach of data mining techniques was applied on prescriptions data of a pharmacy to reveal the associations between drugs which tend to be purchased together. Analyses were performed by applying the most-known association rule mining algorithm, Apriori algorithm, using SPSS Clementine software. The main objective of this study is to propose a new drug-shelf arrangement with obtained rules. New shelf arrangement can provide efficient utilization of pharmacy area, lower service time, and reduction of possible drug errors.