• The Korean Journal of Health Service Management
• /
• v.12 no.4
• /
• pp.203-217
• /
• 2018

Objectives : This study aimed to investigate the clinical practice ability and satisfaction of clinical training of health-medical information management major students. Methods : The data were collected from 68 persons from students finished clinical training at medical record (information) team using self administered questionnaires. The data were analyzed using t-test, ANOVA and correlation with SPSS 22.0 version. Results: Performance of data collection, data management, and data analysis were analyzed in three areas of the job area. In terms of academic characteristics and correlation, they were not related to the level of satisfaction with the practical experience. Conclusions : Research on a virtuous cycle clinical practice program that analyzes the factors by assessing the satisfaction level of clinical practice in each area of health care information management will be conducted continuously.

• Journal of Internet Computing and Services
• /
• v.13 no.1
• /
• pp.45-55
• /
• 2012

Electronic data management is getting important to reduce overall cost and run-time of clinical data management with the enhancement of data quality. It also critically needs to meet regulated guidelines for the overall quality and safety of electronic clinical trials. The purpose of this paper is to develop the performance evaluation framework in electronic clinical data management. Four key metrics in the area of infrastructure, intellectual preparation, study implementation and study completion covering major aspects of clinical trial processes are proposed. The performance measures evaluate the extent of regulation compliance, data quality, cost and efficiency of electronic data management process. They also provide measurement indicators for each evaluation items. Based on the key metrics, the performance evaluation framework is developed in three major areas involved in clinical data management - clinical site, monitoring and data coordinating center. As of the initial attempt how to evaluate the extent of electronic data management in clinical trials by Delphi survey, further empirical studies are planned and recommended.

• The Journal of the Korea Contents Association
• /
• v.11 no.1
• /
• pp.350-356
• /
• 2011

The purpose of this study is to empirically examine how much the electronic clinical trial data management system actually enhances its efficiency. While the development of clinical trial markets highlights the significance of data management with increasing rate of adoption of electronic systems, its effects have not been fully supported with rigorous evidences. Particularly, the adoption rate of electronic clinical trial systems is low in domestic clinical trials markets. This study attempts to analyze the effect of the systems for reminding the importance of e-data management in clinical trials. The measurement indicator is experimented with the time related data collected from a multi-center clinical trial case. The result showed that the speed of the electronic clinical trial processes can be improved. The implication of this study lies in its first attempt to empirically analyze the effect of electronic clinical data management systems. Furthermore, application of the indicator in conjunction with electronic clinical trial processes is expected to facilitate strategic data management.

• Korea Journal of Hospital Management
• /
• v.28 no.3
• /
• pp.1-14
• /
• 2023

Purposes: It is very important to establish a clinical data warehouse based on a common data model to offset the different data characteristics of each medical institution and for drug surveillance. This study attempted to establish a clinical data warehouse for Dankook university hospital for drug surveillance, and to derive the main items necessary for development. Methodology/Approach: This study extracted the electronic medical record data of Dankook university hospital tracked for 9 years from 2013 (2013.01.01. to 2021.12.31) to build a clinical data warehouse. The extracted data was converted into the Observational Medical Outcomes Partnership Common Data Model (Version 5.4). Data term mapping was performed using the electronic medical record data of Dankook university hospital and the standard term mapping guide. To verify the clinical data warehouse, the use of angiotensin receptor blockers and the incidence of liver toxicity were analyzed, and the results were compared with the analysis of hospital raw data. Findings: This study used a total of 670,933 data from electronic medical records for the Dankook university clinical data warehouse. Excluding the number of overlapping cases among the total number of cases, the target data was mapped into standard terms. Diagnosis (100% of total cases), drug (92.1%), and measurement (94.5%) were standardized. For treatment and surgery, the insurance EDI (electronic data interchange) code was used as it is. Extraction, conversion and loading were completed. R language-based conversion and loading software for the process was developed, and clinical data warehouse construction was completed through data verification. Practical Implications: In this study, a clinical data warehouse for Dankook university hospitals based on a common data model supporting drug surveillance research was established and verified. The results of this study provide guidelines for institutions that want to build a clinical data warehouse in the future by deriving key points necessary for building a clinical data warehouse.

• The Journal of the Korea Contents Association
• /
• v.13 no.4
• /
• pp.281-289
• /
• 2013

As the number of clinical trials conducted in Korea increases, the need of the Electronic Data Capture (EDC) system for effective clinical data management is also increased. Recently, the Korea Food and Drug Association published 'Guideline for the Electronic Clinical Trial Data Management and Processing' and it would be the foundation for establishing regulation of electronic clinical data management. In this research, we conducted the survey regarding adoption rate of EDC system in clinical trials in hospitals, Contract Research Organizations (CRO), and pharmaceutical companies. And the perceived importance and the ease of application for the Guideline were investigated. The adoption rates of EDC system was 77.6% but it mostly applied to less than five trials. Also EDC system was mostly used in phase I and phase II trials and the utilization rate of CRO was the highest. The perceived importance for the Guideline was high among all three organizations but, in case of the perceived ease of its application, CRO was the highest. Also, the perceived importance of the clinical data standard was high and the standard for data collection was mostly required. However, the comprehension for the global standard of the electronic data was relatively low, so that education is required. This result would be the foundation to increase the electronic clinical trials and develop proper regulation and principles for clinical data standards in Korea.

• Journal of Korean Society for Quality Management
• /
• v.42 no.1
• /
• pp.81-89
• /
• 2014

Purpose: The purpose of this study is to propose Clinical Practice Guideline(CPG) model and Clinical Index(CI) for implementing CDSS in digital hospitals. Methods: This study uses EMR data at department of family practice in A hospital; 636 patients, 570 diseases (based on ICD 10-CM criteria), and 37,000 data related with labs and treatments. This study focuses on disease J342 which is the most high rate of incidence. Results: Using the suggested model, this study calculates frequency matrix and probability matrix to find out the correlation of diseases and labs. This study indicates the lab sets of Disease (J342) as CI for CPG. Conclusion: This study suggests CPG model including Lab-based, Disease-Based and Case-based modules. Through 6 level cased-based CPG model, especially, this study develops Clinical Index(CI) such as the Incidence Rate, Lab Rate, Disease Lab Rate, Disease confirmed by Lab.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.6
• /
• pp.2935-2937
• /
• 2014

Studies of the causes of cancer, early detection, prevention or treatment need accurate, comprehensive, and timely cancer data. The clinical laboratory provides important cancer information needed for physicians which influence clinical decisions regarding treatment, diagnosis and patient monitoring. Poor communication between health care providers and clinical laboratory personnel can lead to medical errors and wrong decisions in providing cancer care. Because of the key impact of laboratory information on cancer diagnosis and treatment the quality of the tests, lab reports, and appropriate lab management are very important. A laboratory information management system (LIMS) can have an important role in diagnosis, fast and effective access to cancer data, decrease redundancy and costs, and facilitate the integration and collection of data from different types of instruments and systems. In spite of significant advantages LIMS is limited by factors such as problems in adaption to new instruments that may change existing work processes. Applications of intelligent software simultaneously with existing information systems, in addition to remove these restrictions, have important benefits including adding additional non-laboratory-generated information to the reports, facilitating decision making, and improving quality and productivity of cancer care services. Laboratory systems must have flexibility to change and have the capability to develop and benefit from intelligent devices. Intelligent laboratory information management systems need to benefit from informatics tools and latest technologies like open sources. The aim of this commentary is to survey application, opportunities and necessity of intelligent clinical laboratory as a tool to increase cancer care management productivity.

• 한국데이터정보과학회:학술대회논문집
• /
• 2005.04a
• /
• pp.200-201
• /
• 2005

• Journal of Korean Academic Society of Home Health Care Nursing
• /
• v.15 no.1
• /
• pp.22-28
• /
• 2008

Purpose: The aim of this study was to analyze the effects of time management on the clinical nurse's organizational commitment and job satisfaction. Methods: Subjects were recruited in two general hospitals in Seoul and Incheon. Data collection was done using a self-report questionnaire. Time management was measured using the questionnaire developed by Han (1992). Organizational commitment and job satisfaction were measured using the questionnaire developed by Yoon (2000), based on Mowday et al. (1979) and Stamps et al. (1978). The data were analyzed using the SAS statistical package program, version 10.0. Specifically, descriptive statistics and stepwise multiple regression were performed. Results: The predictive time management factors for organizational commitment included deadline decision, simplification, and goal-setting. The predictive time management factors for job satisfaction included planning/making the priority order, deadline decision, simplification, asking for help, and responsibility reduction. Conclusion: Time management factors are highly correlated with organizational commitment and job satisfaction in clinical nurses. Deadline decision and simplification are common predictive factors for organizational commitment and job satisfaction. These results can be used to develop more effective time management strategies for increasing organizational effectiveness in clinical nurses.

• The Journal of the Convergence on Culture Technology
• /
• v.4 no.4
• /
• pp.387-392
• /
• 2018

Clinical trials are an important part of developing new medical devices or developing new drugs. However, it takes many time and expense to conduct clinical trials. In order to solve this problem, it define the standard format of the data collected in clinical trials in major countries and in Korea. Clinical trials are also underway in korean medicine to develop new medical devices. If it define the standards for the data we collect in clinical trials, there are likely to be a variety of ways to use them. If it collect and manage various clinical trial data as one standardized data, you will be able to draw various results. Based on these findings, we established a system for collecting and managing integrated data on clinical trials. And let's look at how to use it.