医学影像技术的区域集群外文文献英文

Full length article Regional clustering of medical imaging technologies Cuma Son gur Mehmet Top Hacettepe University Faculty of Economics and Administrative Sciences Department of Health Care Management Turkey a r t i c l e i n f o Article history Received 5 October 2015 Received in revised form 15 March 2016 Accepted 16 March 2016 Available online 24 March 2016 Keywords Medical imaging technologies Ultrasonography Magnetic resonance imaging Computed tomography Turkish health system Cluster analysis a b s t r a c t The purpose of this study was to analyze clusters from 12 regions in Turkey in terms of medical imaging technologies capacity and use 12 statistical region units were determined by the Ministry of Develop ment and Turkish Statistical Institute clustered in terms of selected medical imaging indicators regarding capacity and use by using the hierarchical clustering method This study was based on the Ward s Method one of the hierarchical clustering methods and the distance matrix was created by using Euclidean distance measure analysis When the distance matrix which was created by using the squared Euclidean distance measure was analyzed it was found that the two regions most distant from each other were Southeast Anatolia and Western Anatolia Euclidean distance 13 69 two regions which have the least distance from each other were Mediterranean and Aegean regions Euclidean distance 0 99 for public university and private hospitals When we analyze the dendrogram which was created by using hierarchical clustering it was seen that the 12 statistical region units were gathered in four different clusters This article revealed that there were inequities in medical imaging technologies according to regions in Turkey and hospital ownerships 2016 Elsevier Ltd All rights reserved 1 Introduction Advanced diagnosis systems e g MRI scanner CT scanner and X Rays are much more complex and different in nature than other medical technologies As healthcare organizations are adopting new medical imaging technologies for better patient care under standing these technological environment users and their con cerns and perceptions toward these technologies is important for proposing solutions that are context specifi c to these organizations Rahman Ko Warren CIHI 2008 Medical imaging canplaya central role in the healthcare systems as it contributes to improved patient outcome and more cost effi cient healthcare in all major disease entities Cappellaro Ghislandi European Science Foundation 2007 Finoc chiaro et al 2014 Hillman Oh Imanaka Packer Simpson Silva especially in evidence based practice and medicine Although the roots of evidence based medicine are in fi elds other than radiology and medical imaging in recent years a number of radiologists and medical imaging professionals academicians have emerged to assume leadership roles in evidence based medicine Today medical imaging medicine has its own evidence based practice as in named evidence based medical imaging Medina physical examination and medicals test methods for diagnosis On the other hand medical tests which are one of the methods used in patient examination are conducted in three different methods including laboratory tests bioanalyses Electroencephalography EEG elec trocardiography EKG etc and medical imaging analyses MRI BT mammography etc Strzelecki 2013 Medical imaging covers many different imaging technologies and implications x ray based methods such as radiography and Computed Tomography CT Magnetic Resonance Imaging MRI ultrasound US nuclear medicine with Positron Emission Tomography PET and Single Photon Emission Computed Tomography SPECT and several methods in optical imaging European Science Foundation 2007 Rubenzer 2013 The history of medical imaging expands over more than two centuries Gonz alez Kuijpers 1995 Signifi cant advancements occurred in the analysis of the human body and structures disease after the discovery of X rays by W C Roentgen Diagnosis procedures turned into digitally taking fi lms of the human body using X rays Computed tomogra phy CT and magnetic resonance imaging MRI were discovered towards the end of the 20 Century Krupinski Pavli 2010 Additional key tools for medical imaging were developed during the last century including magnetic resonance MR due to Felix Bloch and Edward Purcell in 1946 jointly Nobel prize in 1952 nuclear magnetic resonance NMR developed for chemical and physical molecular analysis between 1950 and 1970 magnetic resonance imaging MRI developed by Raymonde Damadian 1974e1976 and Computed Tomography CT developed by British engineer Godfrey Hounsfi eld in 1972 Gonz alez especially since the 1950s Doi 2006 Medical imaging technologies began to be widely used in examination procedures due to the advancements in computer technology after the 1980s James Kuijpers 1995 In this respect it can be stated that the use of medical im aging technology increased incrementally in recent years For example in the USA while a total of 26 million computed tomog raphy CTs were taken in 1998 total number of CTs taken exceeded 70 million in 2008 Watson different medical imaging technologies can signifi cantly enhance quality patient safety effectiveness and performance in patient care and health services Azpiroz Leehan M endez Cappellaro et al 2011 Finocchiaro et al 2014 Hillman Pan Yang European Science Foundation 2007 Finocchiaro et al 2014 Hillman for example the number of organ transplantations went from 745 in 2002 to nearly 4000 in 2011 and diagnostic procedures such as the number of MRI exams doubled in all the Turkish hospital sectors OECD 2014 Recently created affi liated agencies e such as the Turkish Public Health Institution and the Turkish Public Hospital Institution e might be considered to position functions such as accreditation and health technology assessment more distant from the Ministry of Health as is the case in many other OECD countries This is espe cially the case when quality governance activities apply to public healthcare services as well as the private ones OECD 2014 The Turkish Medicines and Medical Devices Agency was set up in March 2012 to replace the General Directorate of Pharmaceuti cals and Pharmacy under the Ministry of Health The Agency is responsible for the regulation of four key areas pharmaceuticals licensing risk assessment pharmacovigilance pricing ensuring rational drug use and access to drugs medical devices cosmetics and laboratory services OECD 2014 There is a limited body of research on medical imaging tech nology in Turkey We found no study in the literature on the clas sifi cation and regional clustering of medical imaging technologies here Furthermore a review of the literature found no study analyzing the clustering of medical imaging technologies in terms of capacity and usage indicators at either country or international level In this respect this study is the fi rst study that analyzes the regional clustering of medical imaging technologies in terms of both capacity and use The aim of this study was to determine how public private and university hospitals in 12 statistical regions in Level 1 in Turkey are clustered separately and generally according to the medical imag ing technology capacity and use status and to make evaluations by making necessary analyses 12 statistical regional units determined by the Ministry of Health and Turkish Statistical Institute were clustered analyzed in terms of medical imaging and technology capacity and use indicators using hierarchical clustering method 3 Method Secondary data was used in this study This research did not involved human participants This study has not external sources of funding This study was funded by author s This study covered 12 statistical regions including Western Anatolia the Eastern Black Sea the Western Black Sea Northeastern Anatolia Central Anatolia Western Marmara Aegean Central Eastern Anatolia Eastern Mar mara the Mediterranean Istanbul and Southeastern Anatolia in the Ministry of Health Health 2013 Statistics Yearbook Medical imaging technologies capacityand use of data in these regions were used as the basic data set for the study The study and database is representative of Turkey in general The data for 12 statistical regions consisting of 12 different ob servations were obtained from the Healthcare Statistics Yearbook 2013 Ministry of Health document The obtained data was transferred to computer media and was analyzed using the SPSS 20 statistics program The variables between V1 and V6 were taken as those related to medical technology and service capacity while the variables be tween V7 and V12 were taken as those related to medical tech nology and service use status Table 1 Hierarchical methods form the backbone of cluster analysis in practice Miszczy nska 2013 Clustering analysis has been widely used in the fi eld of healthcare services and health care systems Chan et al 2006 Liu Miszczy nska 2013 Roy Pharm Gatignon 2010 The Ward s Method gave the most obvious re sults which is justifi ed because this method is considered to be the most effective Miszczy nska 2013 A hierarchical tree diagram called a dendrogram on SPSS can be produced to show the linkage points The clusters are linked at increasing levels of dissimilarity Burns Mediterranean e Western Black Sea 1 325 and the Istanbul Aegean Region 1 590 The longest distance between the regions was observed between Southeastern Anatolia WesternAnatolia regions 13 690 followed by the Istanbul Southeastern Anatolia 13 421 and Western Ana tolia e Northeastern Anatolia regions 12 744 0 5 10 15 20 25 values in the upper section of the graph regarding public hospitals in Fig 1 are Rescaled Distance Cluster Combines The closer the values are to 0 the lower the distance is between the regions observations The analysis conducted ac cording to the Euclidean distance measure of these units revealed that when compared to other regions the Southeastern Anatolia region had a lower distance from the Northeastern Anatolia and Central eastern Anatolia regions In this respect the Northeastern Anatolia Southeastern Anatolia and Central eastern Anatolia re gions are in the same cluster as presented in the following den dogram Fig 2 According to Fig 2 it can be stated that the public university and private hospitals in Turkey consist of four different clusters in terms of technology use capacity and use While the Aegean Mediterranean and Istanbul regions formed a cluster and the Eastern Black Sea Western Black Sea Western Marmara and Eastern Marmara formed the other cluster The central eastern Anatolia Southeastern Anatolia Northeastern Anatolia and Cen tral Anatolia Regions which are mostly located in the eastern part of Turkey were observed as forming the same cluster On the other hand the Western Anatolia region was not included in any of the clusters However it can be stated that it was close to the clusters including the Aegean and Eastern Marmara The data used to determine the medical technologycapacityand use ratios are presented in Table 3 These values are important in terms of providing information about the general status of medical technology use capacity and use The clustering analysis is con ducted after these data of variables take standardized Z values In this respect it could be misleading to conduct clustering analysis and to make defi nite implications on the clustering over the following values According to the medical technology capacity variables of the clusters it was observed that the regions in the Cluster 4 Central eastern Anatolia Southeastern Anatolia Northeastern Anatolia and Central Anatolia were lower than the general average in terms of the medical imaging technologycapacity and generallyhigher than the general average in terms of medical imaging technology use The Western Anatolia Region constituting the Cluster 3 was found to be higher than the general average in terms of medical imaging technology use On the other hand it was found that the mean values of the regions in Cluster 2 were lower than the general average while the Cluster 1 was closer to the general average in terms of medical imaging technology capacity and use status Table 3 4 1 1 Hierarchical clustering analysis of public hospitals in Turkey Similar to the public university and private hospitals in Turkey the Ward s Method and Squared Euclidean distance approach were used in the medical technologycapacity and use clustering analysis of the public hospitals inTurkey The regionaldistance matrixof the public hospitals formed by the analysis is presented in Table 4 According to the Squared Euclidean Distance values in Table 3 the lowest distance was between the Western Black Sea Eastern Black Sea 0 868 followed by the Western Marmara Central Ana tolia 1 070 and Mediterranean Eastern Marmara regions 1 261 The highest distance values between the regions were found to be between the Southeastern Anatolia Eastern Black Sea regions 9 853 followed by Istanbul Northeastern Anatolia 9 183 and the Southeastern Anatolia Western Black Sea 8 656 regions Table 4 Approximately 11e25 areas of the dendongram point to 2 clusters and 3e10 areas of the dendogram scale point to 5 clusters Fig 3 The number of clusters was decided to be taken as 5 to better determine the differences in the medical technology in dicators between the regions There was an attempt to keep the number of clusters optimal in order to better determine the dif ference in the regions in other dendograms It is understood from the graph that the public hospitals are divided into fi ve different groups according to the medical tech nology use capacity and use status The Eastern Black Sea and Western Black Sea constituted cluster 1 the Central Anatolia and Western Marmara regions constituted cluster 2 the Western Anatolia Aegean and Northeastern Anatolia regions constituted cluster 3 the Central eastern Anatolia and Southeastern Anatolia regions constituted cluster 4 and fi nally the Eastern Marmara Mediterranean and Istanbul regions constituted Cluster 5 As indicated in the table above all the means of the medical imaging technology capacities of the regions in Cluster 4 and Table 2 Region based squared euclidean distance matrix of public university and private hospitals in turkey according to standardized data RegionsWest Anatolia East Black Sea West Black Sea Northeast Anatolia Central Anatolia West Marmara Aegean Central east Anatolia D Marmara Mediterranean Istanbul Southeast Anatolia West Anatolia0 East Black Sea3 5420 West Black Sea5 9521 1470 Northeast Anatolia 12 7446 2554 3150 Central Anatolia 5 3752 9172 6953 0550 West Marmara7 9402 0201 6366 2514 0030 Aegean3 6832 2191 8097 4564 1092 7570 Central east Anatolia 9 2265 5893 5895 9306 3537 6615 8840 East Marmara7 9573 6452 1457 0914 9193 9093 8172 8480 Mediterranean4 8992 5441 3254 6362 8353 2250 9932 8632 1320 Istanbul3 2402 5193 29612 2947 1524 7431 5907 4774 8303 2830 Southeast Anatolia 13 6909 3636 6983 6016 68110 3479 5402 1426 8035 14613 4210 C Son gur M Top Computers in Human Behavior 61 2016 333e343336 Cluster 5 were below the general average while the mean values of the medical technology capacities in cluster 1 were above the general average On the other hand it was observed that unlike the medical imaging technology capacity mean values of the regions in cluster 5 the mean value of the medical technology use status were above the general average Cluster 3 was observed to be above the general average both in terms of medical imaging technology ca pacity and medical technology use status While the regions in the north of the country which constitute Cluster 2 were above the general average in terms of medical imaging technology capacities they were observed to take values that were mostly below or close to the general average in terms of medical imaging technology use Medical Diagnosis Consultation information obtained from patients and other physicians Physical examination isnspection auscultation measurements etc Medical tests Laboratory Analysis radiology biochemistry hematology pathology microbiology etc Biosignal analysis ECG EEG etc Image Analysis CT mamogarphy MRI ultrasonography anjiography etc Fig 1 Medical Diagnosis e methods to determine potential disease and ailments Strzelecki 2013 6 510 152025 East Black Sea West Black Sea Central Anatolia West Marmara Northeast Anatolia Centraleast Anatolia Southeast Anatolia West Anatolia Aegean East Marmara Mediterranean stanbul Fig 2 Dendrogram of public university and private hospitals Table 3 Clustering of public university and private hospitals according to regions ClustersRegionsMedical imaging technologies capacityMedical imaging technologies use Variables V1 V6 Variables V7 V12 V 1V 2V 3V 4V 5V 6V 7V 8V 9V 10V 11V 12 Cluster 1Aegean13 5012 6015 4063 2034 8018 906 6023 7028 2063 8019 7014 70 Mediterranean12 5010 4013 6062 4032 5017 705 7022 5028 6070 8022 5014 90 Istanbul14 9012 4016 0060 9041 0024 206 6026 8035 5056 9023 1013 10 Cluster 1 Mean13 6311 81562 1636 120 266 324 3330 7663 8321 7614 23 Cluster 2East Black Sea12 509 8014 9071 3042 3021 504 6025 7028 1053 1017 2014 50 West Black Sea11 6010 0013 8061 8031 6020 204 5024 5028 5053 1019 4015 30 West Marmara12 509 5016 2055 8034 8017 405 3020 7020 5046 5014 6012 30 East Marmara10 409 0011 0053 5036 7019 006 0021 7026 0055 3027 6012 70 Cluster 2 Mean11 759 57513 97560 636 3519 5255 123 1525 7755219 713 7 Cluster 3West Anatolia12 8011 3016 3077 2047 9026 907 4023 7026 9074 6019 0017 20 Cluster 3 Mean12 811 316 377 247 926 97 423 726 974 61917 2 Cluster 4Central east Anatolia9 509 0013 0053 8035 0018 902 9027 0029 1072 8031 0016 70 Southeast Anatolia7 207 5014 4055 0030 0015 402 0023 1032 0087 7025 9015 50 Northeast Anatolia7 707 7013 1067 5027 2013 603 3028 0033 5071 9012 6014 70 Central Anatolia9 808 8012 1068 4034 9021 205 2020 9024 5072 9010 9014 40 Cluster 4 Mean8 558 2513 1561 17531 77517 2753 3524