• Title/Summary/Keyword: Service pattern

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The Changes in Patients and Medical Services by Separation of Prescribing and Dispensing Practice in Health Center (의약분업 실시 전후 보건소 내소환자 진료내용 변화)

  • Chun, Jae-Kyung;Kam, Sin;Han, Chang-Hyun
    • Journal of agricultural medicine and community health
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    • v.27 no.2
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    • pp.75-86
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    • 2002
  • This study was conducted to investigate the changes in patients and medical services before and after the Separation of Prescription and Dispensing in Health Center. For the purpose of this study, prescription data of 5,890 prescribed patients in March 2000(before the Separation of Prescription and Dispensing) and 3,496 prescribed patients in March 2001(after the Separation) in 4 Health Centers located in Gyeongsangbuk-do and Gyeongsangnam-do were collected. For investigation of the change of character of prescribed patients and the disease, sex, age, chief diagnosis, the hind of medical insurance, days of visit, days of prescription were investigated by using National Health Insurance claim data. And for investigation of change of prescription, prescribed drugs per each claim, the use rate of antibiotics, injection, and high-price antiphlogistic drug were investigated for acute respiratory disease and musculoskeletal disease. The major results were as follows: For the changes of prescribed patients of each disease, patients with acute respiratory disease were decreased by 49.7% after the Separation of Prescription and Dispensing than before the Separation of Prescription and Dispensing and patients with hypertension(18.1%), patients with musculoskeletal disease(70.5%), patients with diabetes(8.5%), patients with digestive organ disease(71.2%), patients with chronic respiratory disease(76.4%) were decreased. But patients with urethritis were increased by 66.7%. The mean Health Center visited days of prescribed patients decreased significantly after the Separation of Prescription and Dispensing than before in both male and female(p<0.01) and in health insurance patients(p<0.01). For the each of the disease, hypertension, diabetes, musculoskeletal disease decreased. The mean prescribed days increased after the Separation of Prescription and Dispensing than before(p<0.01). According to the kine of disease, the mean prescribed days increased after the Separation of Prescription and Dispensing than before in all the diseases except the urethritis(p<0.01). For acute respiratory diseases, number of prescribed drugs per each claim decreased significantly after the Separation of Prescription and Dispensing(4.7 drugs) than before(4.9 drugs) and the prescription rate of injection decreased significantly from 63.8% to 7.70%, and the prescription rate of antibiotics decreased significantly from 337% to 19.1%(p<0.01). For musculoskeletal diseases before and after Separation of Prescription and Dispensing, number of prescribed drugs per each claim decreased significantly from 3.7 to 3.2 and the prescription rate of injection decreased significantly from 64.9% to 1.7%, and the prescription rate of high-price antiphlogistic drugs increased significantly from 29.1% to 397%(p<0.01). In consideration of above findings, the mean visited days decreased and on the contrary, the mean prescribed days per each prescription increased after Separation of Prescription and Dispensing than before in health centers. For the prescription pattern of physicians, number of prescribed drugs and the prescription rates of injection and antibiotics per each claim decreased, but the prescription rate of high-price antiphlogistic drugs increased after Separation of Prescription and Dispensing.

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Case Analysis of the Promotion Methodologies in the Smart Exhibition Environment (스마트 전시 환경에서 프로모션 적용 사례 및 분석)

  • Moon, Hyun Sil;Kim, Nam Hee;Kim, Jae Kyeong
    • Journal of Intelligence and Information Systems
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    • v.18 no.3
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    • pp.171-183
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    • 2012
  • In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.

Structural features and Diffusion Patterns of Gartner Hype Cycle for Artificial Intelligence using Social Network analysis (인공지능 기술에 관한 가트너 하이프사이클의 네트워크 집단구조 특성 및 확산패턴에 관한 연구)

  • Shin, Sunah;Kang, Juyoung
    • Journal of Intelligence and Information Systems
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    • v.28 no.1
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    • pp.107-129
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    • 2022
  • It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.