scholarly journals Association of the Magnitude of Nurses With the Use of Health Information Exchanges: Analyzing the National Health Insurance Claim Data of Hospitals and Clinics in Korea

2021 ◽  
Vol 58 ◽  
pp. 004695802110607
Author(s):  
Young-Taek Park ◽  
Yeon Sook Kim ◽  
Yun-Jung Heo ◽  
Jae-Ho Lee ◽  
Hyejung Chang
Keyword(s):  
Health Insurance ◽  
Health Information ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Organizational Factors ◽  
Claim Data ◽  
Insurance Claim ◽  
General Hospitals ◽  
Policy Makers ◽  
Insurance Claim Data

Background Many features of health care organizations (HCOs) have been identified to be associated with health information exchange (HIE), but subcategories of organizational factors focusing on nurse workforces still need to be identified. The objective of this study is to investigate the association of number of nurses with HIE use in Korea. Methods This study had a retrospective study design and used health insurance claim data from June 1, 2016 to June 30, 2018. The unit of analysis was the HCO, and any health insurance claims having HIE were counted by HCO. There were a total of 1490 HCOs having any HIE and 24 026 HCOs not having HIE. For statistical analysis, two-part model was used: logistic regression for HIE participation and the generalized linear model for the volume of HIE use. Results HIE was used by 44.6% of general hospitals, and 8.6% and 5.3% of small hospitals and clinics, respectively. Both HIE use and its volume were significantly positively associated with nurse variables. The use of HIE was significantly positively associated with nurse-to-bed ratio in general hospitals (OR 1.028; 1.016 to 1.041) and in small hospitals (OR 1.021; 1.016 to 1.027), and with the number of nurses (OR 1.041; 1.028 to 1.054) in clinics (P<.001). The volume of HIE use was also positively associated with nurse-to-bed ratio in general hospitals (OR 1.010; 1.004 to 1.017) and in small hospitals (OR 1.014; 1.006 to 1.022), and with the number of nurses (OR 1.055; 1.037 to 1.073) in clinics (P<.01). Conclusion This study found that there was a low rate of HIE use in small hospitals and clinics. The number of nurses was critically associated with the use of HIE and the volume of HIE claims. HIE policy makers need to be aware of this factor in seeking to accelerate HIE.

Therapeutic duplicate prescribing in Korean ambulatory care settings using the National Health Insurance claim data

2014 ◽  
Vol 37 (1) ◽  
pp. 76-85 ◽  
Author(s):  
Dong-Sook Kim ◽  
Nam Kyung Je ◽  
Grace Juyun Kim ◽  
Hena Kang ◽  
Yoon Jin Kim ◽  
...  
Keyword(s):  
Health Insurance ◽  
Ambulatory Care ◽  
National Health Insurance ◽  
National Health ◽  
Claim Data ◽  
Insurance Claim ◽  
Insurance Claim Data

An International Perspective on Health Information Exchange: Adoption in OECD Countries With Different Health Care System Configurations

2019 ◽  
Vol 77 (4) ◽  
pp. 299-311 ◽  
Author(s):  
Claudia Guerrazzi
Keyword(s):  
Health Care ◽  
Health Insurance ◽  
Health Information ◽  
Health Systems ◽  
Information Exchange ◽  
Health Information Exchange ◽  
Essential Feature ◽  
Health Care Systems ◽  
Social Health Insurance ◽  
Care Providers

The sharing of information among various care providers is becoming an essential feature of health care systems, and many countries are now adopting policies to foster health information exchange, defined as the electronic transfer of data or information among health care organizations involved in the delivery of care. Given the increasing adoption of this type of policy in several Organization for Economic Cooperation and Development countries, it is important to compare experiences from different countries, because policy adoption in one country can be explained more comprehensively and coherently through comparison with similar policies adopted in other nations. To make a more meaningful cross-country comparison, this article identifies a taxonomy of health systems, and it analyzes institutional and resource-based factors related to health information exchange adoption and how they differ in three main types of health systems: the National Health Service, social health insurance, and private health insurance.

scholarly journals Estimation of the incidence of MRSA patients: evaluation of a surveillance system using health insurance claim data

2016 ◽  
Vol 144 (11) ◽  
pp. 2260-2267 ◽  
Author(s):  
S. TANIHARA ◽  
S. SUZUKI
Keyword(s):  
Health Insurance ◽  
Surveillance System ◽  
Age Groups ◽  
Claim Data ◽  
Sentinel Surveillance ◽  
Surveillance Systems ◽  
Insurance Claim ◽  
Medical Facilities ◽  
Insurance Claim Data ◽  
Using Data

SUMMARYBecause sentinel surveillance systems cannot obtain information about patients who visit non-sentinel medical facilities, the characteristics of patients identified by these systems may be biased. In this study, we evaluated the representativeness of a methicillin-resistant Staphylococcus aureus (MRSA) surveillance system using health insurance claim (HIC) data, which does not depend on physician notification. We calculated the age-specific incidence of MRSA patients using data from the Japan Nosocomial Infections Surveillance (JANIS) programme, which is based on sentinel surveillance systems, and inpatient HICs submitted to employee health insurance organizations in 2011, and then computed age-specific incidence ratios between the HIC and JANIS data. Age-specific MRSA incidence in both datasets followed J-shaped curves with similar shapes. For all age groups, the ratios between HIC and JANIS data were around 10. These findings indicate that JANIS notification of MRSA cases was not affected by patients’ age.

scholarly journals Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ezekiel N. N. Nortey ◽  
Reuben Pometsey ◽  
Louis Asiedu ◽  
Samuel Iddi ◽  
Felix O. Mettle
Keyword(s):  
Health Insurance ◽  
Anomaly Detection ◽  
Quantile Regression ◽  
Claim Data ◽  
Sub Saharan Africa ◽  
Insurance Claims ◽  
Insurance Claim ◽  
Bayesian Quantile Regression ◽  
Health Insurance Claims ◽  
Insurance Claim Data

Research has shown that current health expenditure in most countries, especially in sub-Saharan Africa, is inadequate and unsustainable. Yet, fraud, abuse, and waste in health insurance claims by service providers and subscribers threaten the delivery of quality healthcare. It is therefore imperative to analyze health insurance claim data to identify potentially suspicious claims. Typically, anomaly detection can be posited as a classification problem that requires the use of statistical methods such as mixture models and machine learning approaches to classify data points as either normal or anomalous. Additionally, health insurance claim data are mostly associated with problems of sparsity, heteroscedasticity, multicollinearity, and the presence of missing values. The analyses of such data are best addressed by adopting more robust statistical techniques. In this paper, we utilized the Bayesian quantile regression model to establish the relations between claim outcome of interest and subject-level features and further classify claims as either normal or anomalous. An estimated model component is assumed to inherently capture the behaviors of the response variable. A Bayesian mixture model, assuming a normal mixture of two components, is used to label claims as either normal or anomalous. The model was applied to health insurance data captured on 115 people suffering from various cardiovascular diseases across different states in the USA. Results show that 25 out of 115 claims (21.7%) were potentially suspicious. The overall accuracy of the fitted model was assessed to be 92%. Through the methodological approach and empirical application, we demonstrated that the Bayesian quantile regression is a viable model for anomaly detection.

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