31 research outputs found

    An Empirical Analysis Based on the U.S. Pharmaceutical Industry

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    ν•™μœ„λ…Όλ¬Έ(석사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : κ³΅κ³ΌλŒ€ν•™ ν˜‘λ™κ³Όμ • κΈ°μˆ κ²½μ˜Β·κ²½μ œΒ·μ •μ±…μ „κ³΅, 2021.8. 이정동.Exaptation, a series of patterns that an emergent trait or function is coopted for a current usage which is completely different from the original usage, is a crucial component of novelty generation in innovation but has been underexplored so far. A few previous studies have scrutinized and analyzed the role of exaptation and its implications qualitatively, but most merely focus on an academic debate, which leaves a research gap in the practical field. The study here thus analyzes the pattern of exaptation by measuring its frequency in the pharmaceutical industry through which ultimately captures its core driving forces as an emergent mechanism of opportunity discovery in the real world. This process allows us to quantify the overall frequency of exaptation and demonstrate its origin with regards to the dual side of spaces of the possible: applicant-oriented and artifact-oriented. We observed that about 59% of emergent functions derived from the existing drugs stem from exaptation and about 30% of FDA-approved NMEs have an inherent exaptive nature. Furthermore, we found that firms’ open innovation adoption and portfolio diversification strategy stand spaces of the possible as a powerful inducer of exaptation along with the popularity of drug classes and the initial versatility of drugs which compose the other side of spaces of the possible. Based on its unique locus regarding the emergence of innovation, we propose that exaptation is one of the undeniable key attributes in innovation that can be structurally fostered, not via serendipity itself, which leads to innovation as an ex-post way of the exploration process.기쑴의 κ²ƒκ³ΌλŠ” μ „ν˜€ λ‹€λ₯Έ μš©λ„ 및 κΈ°λŠ₯으둜 μ§„ν™”λœ ν˜•μ§ˆμ΄ ν˜„μž¬μ˜ μ˜μ—­μ—μ„œ ν™œμš©λ˜λŠ” 일련의 νŒ¨ν„΄μ„ λœ»ν•˜λŠ” κ΅΄μ ˆμ μ‘ (Exaptation)은 μƒˆλ‘œμ›€μ˜ 창발 (Novelty generation)둜 말미암아 ν˜μ‹ μ„ κ²¬μΈν•˜λŠ” ν•΅μ‹¬μ›λ¦¬μž„μ—λ„ λΆˆκ΅¬ν•˜κ³  여지껏 그에 μƒμ‘ν•˜λŠ” μΆ©λΆ„ν•œ 연ꡬ가 λΆ€μž¬ν•œ 싀정이닀. 비둝 λͺ‡ 가지 선행연ꡬλ₯Ό 톡해 κ΅΄μ ˆμ μ‘μ˜ μ—­ν• κ³Ό ν•¨μ˜μ— λŒ€ν•œ λ©΄λ°€ν•œ 쑰사 및 뢄석이 μ΄λ£¨μ–΄μ‘Œμ§€λ§Œ, λŒ€λΆ€λΆ„μ˜ 경우 ν˜„μƒμ˜ μ‹€μ¦λ³΄λ‹€λŠ” κ°œλ…μ΄ κ°–λŠ” ν•™μˆ μ  λ…Όμ˜μ— 였둯이 κ·Έ μ΄ˆμ μ„ μ§‘μ€‘ν•œλ‹€λŠ” μ μ—μ„œ λΆ„λͺ…ν•œ ν•œκ³„λ₯Ό 보여주고 μžˆλ‹€. λ”λΆˆμ–΄ ν•΄λ‹Ή ν˜„μƒμ΄ κ°–λŠ” 사후성에 μž…κ°ν•˜μ—¬ λ‹€μ†Œ 편ν–₯된 μ‹œκ°μ—μ„œ κ΅΄μ ˆμ μ‘μ˜ λ°œμƒμ„ λ‹¨μˆœνžˆ μš°μ—°ν•œ 기회 (Serendipity)둜 말미암아 μ™Έμƒμ μœΌλ‘œ μ£Όμ–΄μ§€λŠ” κ²ƒμœΌλ‘œ λ°”λΌλ³΄μ•˜λ‹€. λ”°λΌμ„œ λ³Έ μ—°κ΅¬μ—μ„œλŠ” 기쑴의 편ν–₯된 톡념과 ν•œκ³„μ μ—μ„œ λ²—μ–΄λ‚˜ 보닀 κ· ν˜•μž‘νžŒ μ‹œκ°μ—μ„œ κ΅΄μ ˆμ μ‘μ„ 재쑰λͺ…ν•˜κ³ μž ν•œλ‹€. 이에 μ œμ•½μ‚°μ—…μ„ ν™œμš©ν•˜μ—¬ κ΅΄μ ˆμ μ‘μ˜ λΉˆλ„λ₯Ό μΈ‘μ •ν•˜κ³  κ·Έ 동인을 μ •λŸ‰μ μœΌλ‘œ λΆ„μ„ν•¨μœΌλ‘œμ¨, ν˜„μ‹€μ„Έκ³„μ—μ„œ ν˜μ‹  창발의 ν•΅μ‹¬μ›λ¦¬λ‘œμ„œ κ΅΄μ ˆμ μ‘μ΄ κ°–λŠ” 영ν–₯λ ₯을 μ‹€μ¦ν•˜μ˜€λ‹€. λ”λΆˆμ–΄ κ°€λŠ₯μ„±μ˜ 곡간 (Spaces of the possible)을 μ΄λ£¨λŠ” 두 가지 ν•΅μ‹¬μš”μ†Œμ— μ£Όλͺ©ν•˜μ—¬ κ΅΄μ ˆμ μ‘μ˜ 동인을 κΈ°μ—…κ³Ό 인곡물둜 각각 μ΄λΆ„ν™”ν•¨μœΌλ‘œμ¨ κ·Έ λ°œμƒμ„ ꡬ쑰적으둜 견인할 수 μžˆλŠ” ν˜„μ‹€μ μΈ λŒ€μ•ˆμ„ μ œμ‹œν•˜μ˜€λ‹€. 뢄석 κ²°κ³Ό FDA 승인 이후 신약이 νšλ“ν•œ μƒˆλ‘œμš΄ μš©λ„ (Off-label usage) 쀑 μ•½ 59%κ°€ κ΅΄μ ˆμ μ‘μœΌλ‘œλΆ€ν„° κΈ°μΈν•œ κ²ƒμœΌλ‘œ λ‚˜νƒ€λ‚¬κ³ , 전체 μ‹ μ•½μ˜ 30%μ—μ„œ κ΅΄μ ˆμ μ‘μ΄ λ°œμƒν•œ 것을 ν™•μΈν•˜μ˜€λ‹€. 덧뢙여 κΈ°μ—…μ˜ κ°œλ°©ν˜• ν˜μ‹  μ „λž΅κ³Ό 포트폴리였 닀각화 μ „λž΅μ„ 톡해 κ΅΄μ ˆμ μ‘μ˜ λ°œμƒμ„ ꡬ쑰적으둜 μœ λ„ν•  수 μžˆμŒμ„ ν™•μΈν•˜μ˜€λ‹€.Chapter 1. Introduction 1 1.1 Background 1 Chapter 2. Literature Review 5 2.1 Exaptation 5 2.2 Spaces of the possible 9 2.3 Open Innovation 13 2.4 Research Question 17 Chapter 3. Measuring the Frequency of Exaptation 19 3.1 The Pharmaceutical Industry 19 3.2 Empirical Setting 20 3.2.1 Sample 20 3.2.2 Measurement 21 3.3 Results 26 Chapter 4. Identifying the Origin of Exaptation and Capturing its Driving Force 34 4.1 Hypothesis 34 4.1.1 Applicant-oriented Exaptation 34 4.1.2 Artifact-oriented Exaptation 38 4.2 Empirical Setting 40 4.2.1 Variables 40 4.2.2 Methodology 43 4.2.3 Correlations 44 4.3 Results 46 4.3.1 Applicant-oriented Exaptation 48 4.3.2 Artifact-oriented Exaptation 50 4.4 Robustness for Model 53 Chapter 5. Conclusions 54 5.1 Summary 54 5.2 Implication 55 5.2.1 Managerial Implication 56 5.3 Limitation & Future Study 57 Bibliography 58 Appendix 71 Abstract (Korean) 91석

    신체 μž„λ² λ”©μ„ ν™œμš©ν•œ μ˜€ν† μΈμ½”λ” 기반 컴퓨터 λΉ„μ „ λͺ¨ν˜•μ˜ μ„±λŠ₯ κ°œμ„ 

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    ν•™μœ„λ…Όλ¬Έ(박사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : κ³΅κ³ΌλŒ€ν•™ 산업곡학과, 2021.8. λ°•μ’…ν—Œ.Deep learning models have dominated the field of computer vision, achieving state-of-the-art performance in various tasks. In particular, with recent increases in images and videos of people being posted on social media, research on computer vision tasks for analyzing human visual information is being used in various ways. This thesis addresses classifying fashion styles and measuring motion similarity as two computer vision tasks related to humans. In real-world fashion style classification problems, the number of samples collected for each style class varies according to the fashion trend at the time of data collection, resulting in class imbalance. In this thesis, to cope with this class imbalance problem, generalized few-shot learning, in which both minority classes and majority classes are used for learning and evaluation, is employed. Additionally, the modalities of the foreground images, cropped to show only the body and fashion item parts, and the fashion attribute information are reflected in the fashion image embedding through a variational autoencoder. The K-fashion dataset collected from a Korean fashion shopping mall is used for the model training and evaluation. Motion similarity measurement is used as a sub-module in various tasks such as action recognition, anomaly detection, and person re-identification; however, it has attracted less attention than the other tasks because the same motion can be represented differently depending on the performer's body structure and camera angle. The lack of public datasets for model training and evaluation also makes research challenging. Therefore, we propose an artificial dataset for model training, with motion embeddings separated from the body structure and camera angle attributes for training using an autoencoder architecture. The autoencoder is designed to generate motion embeddings for each body part to measure motion similarity by body part. Furthermore, motion speed is synchronized by matching patches performing similar motions using dynamic time warping. The similarity score dataset for evaluation was collected through a crowdsourcing platform utilizing videos of NTU RGB+D 120, a dataset for action recognition. When the proposed models were verified with each evaluation dataset, both outperformed the baselines. In the fashion style classification problem, the proposed model showed the most balanced performance, without bias toward either the minority classes or the majority classes, among all the models. In addition, In the motion similarity measurement experiments, the correlation coefficient of the proposed model to the human-measured similarity score was higher than that of the baselines.컴퓨터 비전은 λ”₯λŸ¬λ‹ ν•™μŠ΅ 방법둠이 강점을 λ³΄μ΄λŠ” λΆ„μ•Όλ‘œ, λ‹€μ–‘ν•œ νƒœμŠ€ν¬μ—μ„œ μš°μˆ˜ν•œ μ„±λŠ₯을 보이고 μžˆλ‹€. 특히, μ‚¬λžŒμ΄ ν¬ν•¨λœ μ΄λ―Έμ§€λ‚˜ λ™μ˜μƒμ„ λ”₯λŸ¬λ‹μ„ 톡해 λΆ„μ„ν•˜λŠ” νƒœμŠ€ν¬μ˜ 경우, 졜근 μ†Œμ…œ 미디어에 μ‚¬λžŒμ΄ ν¬ν•¨λœ 이미지 λ˜λŠ” λ™μ˜μƒ κ²Œμ‹œλ¬Όμ΄ λŠ˜μ–΄λ‚˜λ©΄μ„œ κ·Έ ν™œμš© κ°€μΉ˜κ°€ 높아지고 μžˆλ‹€. λ³Έ λ…Όλ¬Έμ—μ„œλŠ” μ‚¬λžŒκ³Ό κ΄€λ ¨λœ 컴퓨터 λΉ„μ „ νƒœμŠ€ν¬ 쀑 νŒ¨μ…˜ μŠ€νƒ€μΌ λΆ„λ₯˜ λ¬Έμ œμ™€ λ™μž‘ μœ μ‚¬λ„ 츑정에 λŒ€ν•΄ 닀룬닀. νŒ¨μ…˜ μŠ€νƒ€μΌ λΆ„λ₯˜ 문제의 경우, 데이터 μˆ˜μ§‘ μ‹œμ μ˜ νŒ¨μ…˜ μœ ν–‰μ— 따라 μŠ€νƒ€μΌ ν΄λž˜μŠ€λ³„ μˆ˜μ§‘λ˜λŠ” μƒ˜ν”Œμ˜ 양이 달라지기 λ•Œλ¬Έμ— μ΄λ‘œλΆ€ν„° 클래슀 λΆˆκ· ν˜•μ΄ λ°œμƒν•œλ‹€. λ³Έ λ…Όλ¬Έμ—μ„œλŠ” μ΄λŸ¬ν•œ 클래슀 λΆˆκ· ν˜• λ¬Έμ œμ— λŒ€μ²˜ν•˜κΈ° μœ„ν•˜μ—¬, μ†Œμˆ˜ μƒ˜ν”Œ ν΄λž˜μŠ€μ™€ λ‹€μˆ˜ μƒ˜ν”Œ 클래슀λ₯Ό ν•™μŠ΅ 및 평가에 λͺ¨λ‘ μ‚¬μš©ν•˜λŠ” μΌλ°˜ν™”λœ ν“¨μƒ·λŸ¬λ‹μœΌλ‘œ νŒ¨μ…˜ μŠ€νƒ€μΌ λΆ„λ₯˜ 문제λ₯Ό μ„€μ •ν•˜μ˜€λ‹€. λ˜ν•œ λ³€λΆ„ μ˜€ν† μΈμ½”λ” 기반의 λͺ¨λΈμ„ 톡해, 신체 및 νŒ¨μ…˜ μ•„μ΄ν…œ λΆ€λΆ„λ§Œ μž˜λΌλ‚Έ μ „κ²½ 이미지 λͺ¨λ‹¬λ¦¬ν‹°μ™€ νŒ¨μ…˜ 속성 정보 λͺ¨λ‹¬λ¦¬ν‹°κ°€ νŒ¨μ…˜ μ΄λ―Έμ§€μ˜ μž„λ² λ”© ν•™μŠ΅μ— λ°˜μ˜λ˜λ„λ‘ ν•˜μ˜€λ‹€. ν•™μŠ΅ 및 평가λ₯Ό μœ„ν•œ λ°μ΄ν„°μ…‹μœΌλ‘œλŠ” ν•œκ΅­ νŒ¨μ…˜ μ‡Όν•‘λͺ°μ—μ„œ μˆ˜μ§‘λœ K-fashion 데이터셋을 μ‚¬μš©ν•˜μ˜€λ‹€. ν•œνŽΈ, λ™μž‘ μœ μ‚¬λ„ 츑정은 ν–‰μœ„ 인식, 이상 λ™μž‘ 감지, μ‚¬λžŒ μž¬μΈμ‹ 같은 λ‹€μ–‘ν•œ λΆ„μ•Όμ˜ ν•˜μœ„ λͺ¨λ“ˆλ‘œ ν™œμš©λ˜κ³  μžˆμ§€λ§Œ κ·Έ μžμ²΄κ°€ μ—°κ΅¬λœ 적은 λ§Žμ§€ μ•Šμ€λ°, μ΄λŠ” 같은 λ™μž‘μ„ μˆ˜ν–‰ν•˜λ”λΌλ„ 신체 ꡬ쑰 및 카메라 각도에 따라 λ‹€λ₯΄κ²Œ ν‘œν˜„λ  수 μžˆλ‹€λŠ” 점으둜 λΆ€ν„° κΈ°μΈν•œλ‹€. ν•™μŠ΅ 및 평가λ₯Ό μœ„ν•œ 곡개 데이터셋이 λ§Žμ§€ μ•Šλ‹€λŠ” 점 λ˜ν•œ 연ꡬλ₯Ό μ–΄λ ΅κ²Œ ν•˜λŠ” μš”μΈμ΄λ‹€. λ”°λΌμ„œ λ³Έ λ…Όλ¬Έμ—μ„œλŠ” ν•™μŠ΅μ„ μœ„ν•œ 인곡 데이터셋을 μˆ˜μ§‘ν•˜μ—¬ μ˜€ν† μΈμ½”λ” ꡬ쑰λ₯Ό 톡해 신체 ꡬ쑰 및 카메라 각도 μš”μ†Œκ°€ λΆ„λ¦¬λœ λ™μž‘ μž„λ² λ”©μ„ ν•™μŠ΅ν•˜μ˜€λ‹€. μ΄λ•Œ, 각 신체 λΆ€μœ„λ³„λ‘œ λ™μž‘ μž„λ² λ”©μ„ 생성할 수 μžˆλ„λ‘ν•˜μ—¬ 신체 λΆ€μœ„λ³„λ‘œ λ™μž‘ μœ μ‚¬λ„ 츑정이 κ°€λŠ₯ν•˜λ„λ‘ ν•˜μ˜€λ‹€. 두 λ™μž‘ μ‚¬μ΄μ˜ μœ μ‚¬λ„λ₯Ό μΈ‘μ •ν•  λ•Œμ—λŠ” 동적 μ‹œκ°„ μ›Œν•‘ 기법을 μ‚¬μš©, λΉ„μŠ·ν•œ λ™μž‘μ„ μˆ˜ν–‰ν•˜λŠ” ꡬ간끼리 μ •λ ¬μ‹œμΌœ μœ μ‚¬λ„λ₯Ό μΈ‘μ •ν•˜λ„λ‘ ν•¨μœΌλ‘œμ¨, λ™μž‘ μˆ˜ν–‰ μ†λ„μ˜ 차이λ₯Ό λ³΄μ •ν•˜μ˜€λ‹€. 평가λ₯Ό μœ„ν•œ μœ μ‚¬λ„ 점수 데이터셋은 ν–‰μœ„ 인식 데이터셋인 NTU-RGB+D 120의 μ˜μƒμ„ ν™œμš©ν•˜μ—¬ ν¬λΌμš°λ“œ μ†Œμ‹± ν”Œλž«νΌμ„ 톡해 μˆ˜μ§‘λ˜μ—ˆλ‹€. 두 가지 νƒœμŠ€ν¬μ˜ μ œμ•ˆ λͺ¨λΈμ„ 각각의 평가 λ°μ΄ν„°μ…‹μœΌλ‘œ κ²€μ¦ν•œ κ²°κ³Ό, λͺ¨λ‘ 비ꡐ λͺ¨λΈ λŒ€λΉ„ μš°μˆ˜ν•œ μ„±λŠ₯을 κΈ°λ‘ν•˜μ˜€λ‹€. νŒ¨μ…˜ μŠ€νƒ€μΌ λΆ„λ₯˜ 문제의 경우, λͺ¨λ“  λΉ„κ΅κ΅°μ—μ„œ μ†Œμˆ˜ μƒ˜ν”Œ ν΄λž˜μŠ€μ™€ λ‹€μˆ˜ μƒ˜ν”Œ 클래슀 쀑 ν•œ μͺ½μœΌλ‘œ μΉ˜μš°μΉ˜μ§€ μ•ŠλŠ” κ°€μž₯ κ· ν˜•μž‘νžŒ μΆ”λ‘  μ„±λŠ₯을 λ³΄μ—¬μ£Όμ—ˆκ³ , λ™μž‘ μœ μ‚¬λ„ μΈ‘μ •μ˜ 경우 μ‚¬λžŒμ΄ μΈ‘μ •ν•œ μœ μ‚¬λ„ μ μˆ˜μ™€ μƒκ΄€κ³„μˆ˜μ—μ„œ 비ꡐ λͺ¨λΈ λŒ€λΉ„ 더 높은 수치λ₯Ό λ‚˜νƒ€λ‚΄μ—ˆλ‹€.Chapter 1 Introduction 1 1.1 Background and motivation 1 1.2 Research contribution 5 1.2.1 Fashion style classication 5 1.2.2 Human motion similarity 9 1.2.3 Summary of the contributions 11 1.3 Thesis outline 13 Chapter 2 Literature Review 14 2.1 Fashion style classication 14 2.1.1 Machine learning and deep learning-based approaches 14 2.1.2 Class imbalance 15 2.1.3 Variational autoencoder 17 2.2 Human motion similarity 19 2.2.1 Measuring the similarity between two people 19 2.2.2 Human body embedding 20 2.2.3 Datasets for measuring the similarity 20 2.2.4 Triplet and quadruplet losses 21 2.2.5 Dynamic time warping 22 Chapter 3 Fashion Style Classication 24 3.1 Dataset for fashion style classication: K-fashion 24 3.2 Multimodal variational inference for fashion style classication 28 3.2.1 CADA-VAE 31 3.2.2 Generating multimodal features 33 3.2.3 Classier training with cyclic oversampling 36 3.3 Experimental results for fashion style classication 38 3.3.1 Implementation details 38 3.3.2 Settings for experiments 42 3.3.3 Experimental results on K-fashion 44 3.3.4 Qualitative analysis 48 3.3.5 Eectiveness of the cyclic oversampling 50 Chapter 4 Motion Similarity Measurement 53 4.1 Datasets for motion similarity 53 4.1.1 Synthetic motion dataset: SARA dataset 53 4.1.2 NTU RGB+D 120 similarity annotations 55 4.2 Framework for measuring motion similarity 58 4.2.1 Body part embedding model 58 4.2.2 Measuring motion similarity 67 4.3 Experimental results for measuring motion similarity 68 4.3.1 Implementation details 68 4.3.2 Experimental results on NTU RGB+D 120 similarity annotations 72 4.3.3 Visualization of motion latent clusters 78 4.4 Application 81 4.4.1 Real-world application with dancing videos 81 4.4.2 Tuning similarity scores to match human perception 87 Chapter 5 Conclusions 89 5.1 Summary and contributions 89 5.2 Limitations and future research 91 Appendices 93 Chapter A NTU RGB+D 120 Similarity Annotations 94 A.1 Data collection 94 A.2 AMT score analysis 95 Chapter B Data Cleansing of NTU RGB+D 120 Skeletal Data 100 Chapter C Motion Sequence Generation Using Mixamo 102 Bibliography 104 ꡭ문초둝 123λ°•

    Normal-tension glaucoma in high myopia is associated with the presence of posterior staphyloma and subfoveal scleral thinning

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    Dept. of Medicine/석사Purpose: To evaluate the ocular biometry in patients with normal-tension glaucoma (NTG) and highly myopic eyes and to identify the ocular parameters significantly associated with the biomechanical changes of high myopia and glaucomatous optic neuropathy.Materials and Methods: The study included 45 patients with NTG and 38 controls with highly myopic eyes (≀ -6 diopters (D) or axial length β‰₯ 26.0 mm). The subfoveal retinal, choroidal, scleral thickness and the posterior staphyloma heights were examined from enhanced depth imaging spectral-domain optical coherence tomography (EDI-OCT). Scleral thickness and posterior staphyloma height in patients with highly myopic NTG was compared with those in highly myopic, non-glaucomatous eyes. A Pearson correlation was calculated to assess the relationships of scleral thickness and posterior staphyloma height with ocular parameters. Multiple regression analysis was performed to identify the ocular parameters significantly associated with the changes of scleral thickness and posterior staphyloma height. Results: Subfoveal scleral thickness could be measured in 32(71.1%) and 24(63.2%) of highly myopic NTG and highly myopic eyes, respectively. Highly myopic NTG eyes had thinner subfoveal scleral thickness (473.03 Β± 43.75 versus(vs). 579.46 Β± 75.87 γŽ›, P < .001) and higher posterior staphyloma (97.80 Β± 70.19 vs. 62.83 Β± 32.01 γŽ›, P = .027) than highly myopic, non-glaucomatous eyes. Subfoveal scleral thickness was significantly correlated with age (r = -0.453, P =.014), axial length (r = -0.343, P=.040), corneal hysteresis (CH) (r = 0.460, P = .010), and the posterior staphyloma height of superior quadrant (r = -0.538, P = .003), nasal quadrant (r = -0.519, P = .004) and the sum of four quadrants (r = -0.424, P = .022) in highly myopic NTG eyes. Corneal hysteresis (B = 2.694, P = .015), corneal resistance factor (B=-2.916, P = .010) and the posterior staphyloma height of nasal quadrant (B = -0.463, P = .017) were most significantly associated with the subfoveal scleral thickness in highly myopic NTG eyes.Conclusion: Subfoveal scleral thinning and ununiform posterior staphyloma formation were closely related in highly myopic NTG eyes. CH may be a clinically useful parameter to demonstrate the biomechanical material properties such as scleral stiffness.ope

    A Study on the Effect of National Health Insurance Non-payment Information Disclosure Policy on the Change of Medical Service Costs

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For this purpose, a total of 79,476 cases of 4,272 hospitals in non-payment 21 items were selected for the subjects of this study, which were continuously disclosed for six years from 2013 to 2018, when non-payment information was disclosed. Multiple regression analysis was used to analyze the changes in the price of non-payment medical services according to the year (period), legislation, number of doctors, and number of hospital beds. As a result of this study, it was confirmed that legislation on the mandatory disclosure of information had the effect of lowering the price of non-payment(-). On the other hand, as the information disclosure policy time (year) passed, the medical cost of non-payment was increased(+). This shows that the information disclosure policy was effective before and after the legislation, but it did not have a great effect during the six-year continuous information disclosure period. In addition, in the case of hospital with a large number of doctors, it was to increase the non-payment price(+) regardless of the information disclosure policy to maximize their profit. Lastly, hospital with large beds were expected to increase non-payment prices regardless of information disclosure policy to increase profits and maintain facilities. However, contrary to expectations, they reduced non-payment costs(-). The unexpected results were obtained through the analysis of the factors of non-payment price information change and the effectiveness of information disclosure, and several policy implications were drawn. First, the effectiveness of information disclosure must be enforced through legislation. It is proved through this study that 'disclosure of information forced through legislation has its policy effect'. Second, the information disclosure policy is effective when mixed with other policy means. As confirmed in this study, the long-term information disclosure policy is less effective and it is necessary to think about the direction and method of information disclosure policy. Third, in order to increase the utilization of non-payment information, it is necessary to provide the medical service salary and non-payment information together. Generally, medical services are used in combination of salary and non-payment. If only non-payment information is provided, it can make the choice of patients who are medical consumers more difficult. It is necessary to collect and manage medical cost information systematically and integratedly according to the demand of medical consumers. This study is meaningful in that it examined the factors and effectiveness of non-payment information disclosure policy that was not presented in the previous studies on the price information change of hospitals. The non-payment information disclosure policy should be improved so that it can take a step forward as a more effective policy means to achieve policy goals by eliminating the information imbalance problem and strengthening consumer rights. The social demand for the management mechanism and system of the rapidly increasing non-payment price has been rapidly increasing recently. In order to successfully establish the non-payment information disclosure policy, more comprehensive research should be conducted to prepare the non-payment management system in the future.제 1 μž₯ μ„œ λ‘  1 제 1 절 연ꡬ배경 및 ν•„μš”μ„± 1 제 2 절 연ꡬλͺ©μ  4 제 2 μž₯ 이둠적 λ°°κ²½ 및 선행연ꡬ κ²€ν†  6 제 1 절 이둠적 λ°°κ²½ 6 1. κ³΅κΈ‰μž μœ μΈμˆ˜μš”(SID, Supplier-Induced Demand) 6 2. μ •μ±…μˆ˜λ‹¨μ˜ λͺ©μ κ³Ό μ’…λ₯˜ 7 3. μ •μ±…κ³΅κ°œμ˜ νŠΉμ„±κ³Ό μ’…λ₯˜ 8 4. μ •μ±…μˆ˜λ‹¨μœΌλ‘œμ„œ μ •μ±…κ³΅κ°œμ˜ νš¨κ³Όμ„± 10 제 2 절 선행연ꡬ κ²€ν†  12 1. λΉ„κΈ‰μ—¬μ˜ μ •μ˜μ™€ μ’…λ₯˜, νŠΉμ„± 12 2. λΉ„κΈ‰μ—¬ μ •λ³΄κ³΅κ°œ μ œλ„(λΉ„κΈ‰μ—¬ μ§„λ£ŒλΉ„μš© μ •λ³΄κ³΅κ°œ 사업) 14 3. μ˜λ£ŒλΆ„μ•Όμ— μžˆμ–΄ μ •λ³΄κ³΅κ°œ μ œλ„ 17 4. μ˜λ£ŒλΆ„μ•Ό λΉ„μš©μ •λ³΄ 곡개 νš¨κ³Όμ„± 18 제 3 μž₯ 연ꡬ섀계 및 방법 21 제 1 절 μ—°κ΅¬λ²”μœ„μ™€ λŒ€μƒ 21 제 2 절 연ꡬ가섀 및 λͺ¨ν˜• 23 제 3 절 λ³€μˆ˜μ„ μ • 및 μ‘°μž‘μ  μ •μ˜ 26 1. λ…λ¦½λ³€μˆ˜ 26 2. μ’…μ†λ³€μˆ˜ 26 3. ν†΅μ œλ³€μˆ˜ 27 제 4 절 뢄석방법 29 제 4 μž₯ 연ꡬ결과 30 제 1 절 일반적 ν˜„ν™© 30 1. 일반 ν˜„ν™© 30 2. κΈ°μˆ ν†΅κ³„λŸ‰ 33 제 2 절 λΉ„κΈ‰μ—¬ μ •λ³΄κ³΅κ°œ 효과 싀증뢄석 41 1. λΉ„κΈ‰μ—¬ μ •λ³΄κ³΅κ°œ μ •μ±… μž…λ²• μ „ν›„ 평균 비ꡐ(T-test) 41 2. λΉ„κΈ‰μ—¬ μ •λ³΄κ³΅κ°œ μ •μ±… 효과 뢄석 43 제 5 μž₯ κ²° λ‘  47 제 1 절 μ—°κ΅¬λ‚΄μš© μš”μ•½ 47 제 2 절 정책적 μ‹œμ‚¬μ  50 제 3 절 μ—°κ΅¬μ˜ ν•œκ³„ 53 μ°Έκ³ λ¬Έν—Œ 55 Abstract 59Maste

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