• Korea Journal of Hospital Management
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• v.15 no.1
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• pp.49-69
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• 2010

The major purpose of this study is to find out relevant factors affecting utilization of Long Term Care Hospitals since the Elderly Long Term Care Insurance System was adopted in Korea. The sample hospitals of this study are 5 long term care hospitals located in 4 big cities and 1 local area. The research data were collected with structured questionnaire from 247 patients and patients' protectors in 5 sample hospitals. Analyzing methods are descriptive statistics, factor analysis and multiple regression with SPSS(version 12.0). Major results of this study are as follows. 1) Utilization and recommendation of patients is affected significantly by the level of hospital facilities (0.043), fee level(0.026), level of staff (0.000), and discomfort of services(0.001). 2) Level of staff is very positively correlated with utilization and recommendation of patients. 3) Discomport of services is very negatively correlated with utilization and recommendation of patients. On the basis of results this study conclude that the management of Long Term Care Hospitals is required conclude to improve the level of staff and facilities and to solve discomport problems of services for patients' marketing. And also more in-depth study on the utilization factors of long term care hospital in Korea is required.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.1-7
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• 2020

As the demand for health screening increases, there is a need for efficient design of screening items. We build machine learning models for health screening and recommend screening items to provide personalized health care service. When offline, a synthetic data set is generated based on guidelines and clinical results from institutions, and a machine learning model for each screening item is generated. When online, the recommendation server provides a recommendation list of screening items in real time using the customer's health condition and machine learning models. As a result of the performance analysis, the accuracy of the learning model was close to 100%, and server response time was less than 1 second to serve 1,000 users simultaneously. This paper provides an adaptive and automatic recommendation in response to changes in the new screening environment.

• Health Policy and Management
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• v.29 no.3
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• pp.342-356
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• 2019

Background: The objectives of the study are to find out the effect of the implementing reform in three Central Asian countries, identify its impact on health status and health care delivery systems. This study address to identify strong and weak points of the health systems and provide a recommendation for further health care organization. Methods: A comparative analysis was conducted to evaluate the effects of implemented policy on health care system efficiency and equity. Secondary data were collected on selected health indicators using information from the World Health Organization Global Health Expenditure Database, European Health Information Platform, and World Bank Open Data. Results: In terms of population status, countries achieved relatively good results. Infant mortality and under-5 mortality rate decreased in all countries; also, life expectancy increased, and it was more than 70 years. Regulations of the health systems are still highly centralized, and the Ministry of Health is the main organ responsible for national health policy developing and implementation. Among the three countries, only Kyrgyzstan was successful in introducing a national health system. Distribution of health expenditure between public expenditure and out-of-pocket payments was decreased, and out-of-pocket payments were less the 50% of total health expenditure in all countries, in 2014. Conclusion: After independent, all three countries implemented a certain number of the policy reform, mostly it was directed to move away from the old the Soviet system. Subsequent reform should be focused on evidence-based decision making and strengthening of primary health care in terms of new public health concepts.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.693-706
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• 2018

It is very important to support the elderly with disability ageing in place. Assisting devices can help them to live independently in their community; however, they have to be used appropriately to meet care needs. This study develops an assisting device recommendation system for the beneficiaries of long-term care insurance that include algorithms to decide the most appropriate type of assisting device for beneficiaries. We used long-term care (LTC) insurance data for grade assessment including 8,084 beneficiaries from July 2015 to June 2016. In addition, we collected standard care plans for assisting devices, that power-assessors made, considering their performance and ability that could subsequently be matched with grade assessment data. We used a decision-tree model in data-mining to develop the model. Finally, we developed 15 algorithms for recommending assisting devices. The findings might be useful in evidence-based care planning for assisting devices and can contribute to enhancing independence and safety in LTC.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.316-318
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• 2019

Recently, healthy life has become an issue in an aging society, and the number of people who have been interested in continuous health care for better life is increasing. In this paper, we implemented a personalized recommendation systm to provide convenient healthcare management for user. The PHR (Personal Health Record) of user could be stored in the server along with health related information such as lifestyle, disease, and physical condition. The users could be classified into similar clusters according to the PHR profile in order to provide healthcare contents to the users who had similar PHR profile. K-Means clustering was applied to generate clusters based on PHR profile and ACDT(Ant Colony Decision Tree) algorithm was used to provide personalised recommendation of health information stored in knowledge base. The app system developed in this paper is useful for users to perform healthcare themselves by providing information on serious diseases and lifestyle habits to be improved according to the clusters classified by PHR profile.

• Journal of Biomedical Engineering Research
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• v.22 no.5
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• pp.469-477
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• 2001

In this Paper. we describe a home health care service using electronic health questionnaires and routine checkup of vital signs Including ECG (Electrocardiography) , blood pressure. and SpO$_2$ (Oxygen Saturation) . This system is for patients at home with chronic diseases, discharged Patients, or any normal people for the Prevention of disease The service requires a home health care terminal and a PC with Interned connection installed at Patient home. The distance health care management center is equipped with a vital-sign and questionnaire interpreter as well as database, Web, and notification servers with UMS (Unified Messaging System). Participating Physician can access the servers at the center using a Web browser running on a PC available to them at any time. These components are linked together through various kinds of data and voice communication channels including PSTN (Public Switched Telephone Network) . CATV(Community Antenna TV) . Interned. and mobile communication network. Following the Physician's direction given to a Patient. he or she uses the home health care terminal to collect vital signs and fill out the questionnaire. When the terminal automatically transmits these data to the management center. the data interpreter and servers at the center process the information fo1lowing the Protocol implemented on the system. Physicians can retrieve and review data corresponding to their Patients and send back their diagnostic reports to the center. UMS at the center delivers the physician 's recommendation to the corresponding patient through the notification server. Patients can also reprieve and review their own records as well as diagnostic reports from physicians. The system Provides a new way of collecting diagnostic information and delivering doctor's recommendation to patients at home for their health management. Future works are needed in the development of new technology for measurements and interpretations of various vital signs .

• Korean Journal of Health Education and Promotion
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• v.33 no.4
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• pp.67-77
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• 2016

Objectives: The study was to propose strategies and directions how to manage the hypertension and diabetes in communities. Methods: The survey data from 606 patients with hypertension or diabetes based on Community Health Survey, 2013 were analyzed and the hypertension and diabetes projects in communities for last 10 years were reviewed. Results: The patients visiting the primary clinics had statistically significant lower rates than those of teaching hospitals in physician's recommendation experience, perception level of attention from doctors, self-efficacy and health habit practice level. Since the Hypertension and diabetes registration and management system in 2007, there have been several trials for management of hypertension and diabetes such as Chronic diseases management system on the primary clinics, Community based primary medical care pilot projects, Post-national health screening management, and Pilot project on reimbursement for chronic diseases care services. Conclusions: The upmost urgent task might be to have a support system for patients' self care affiliated with primary clinics. To achieve it, it is necessary to expand the current Hypertension and diabetes registration and management system into nation and to find a way to attract the active participation from primary clinics.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2434-2441
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• 2015

This paper proposes an exercise recommendation system for treating obesity that provides systematic recommendations for exercise and diet. Five body indices are considered as indicators for recommend exercise and diet. The system also informs users of prohibited foods using health data including blood pressure, blood sugar, and total cholesterol. To maximize the utility of the system, it displays recommendations for both indoor and outdoor activities. The system is equipped with multimode sensors, including a three-axis accelerometer, a laser, a pressure sensor, and a wrist-mounted sensor. To demonstrate the effectiveness of the system, field tests are carried out with three participants over 20 days, which show that the proposed system is effective in treating obesity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.19-28
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• 2021

Nowadays, interest in health care is increasing due to Coronavirus (COVID-19), and a lot of people are doing home training as there are more difficulties in using fitness centers and public facilities that are used together. In this paper, we propose a personalized exercise recommendation algorithm using personalized propensity information to provide more accurate and meaningful exercise recommendation to home training users. Thus, we classify the data according to the criteria for obesity with a k-nearest neighbor algorithm using personal information that can represent individuals, such as eating habits information and physical conditions. Furthermore, we differentiate the exercise dataset by the level of exercise activities. Based on the neighborhood information of each dataset, we provide personalized exercise recommendations to users through a dimensionality reduction algorithm (SVD) among model-based collaborative filtering methods. Therefore, we can solve the problem of data sparsity and scalability of memory-based collaborative filtering recommendation techniques and we verify the accuracy and performance of the proposed algorithms.