The Experts below are selected from a list of 18279 Experts worldwide ranked by ideXlab platform
Masaru Kitsuregawa - One of the best experts on this subject based on the ideXlab platform.
-
Enabling Patient Traceability Using Anonymized Personal Identifiers in Japanese Universal Health Insurance Claims Database.
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019Co-Authors: Jumpei Sato, Kazuo Goda, Masaru Kitsuregawa, Hiroyuki Yamada, Naohiro MitsutakeAbstract:Anonymization of medical data helps protect patient identities. However, with conventional anonymized personal identifiers it is difficult to trace patients, which hinders longitudinal analyses in Insurance Claim database. Herein, we describe the development of a method to identify unique patients by using partial equivalence relationships of multiple anonymized personal identifiers. By using two conventional anonymized personal identifiers, we have developed virtual patient identifiers (vPIDs) to indicate unique patients. To verify the effectiveness of the developed identifiers, we have applied vPIDs to a six-year dataset of national-level Japanese Insurance Claims dataset and a prefectural-level Insurance Claims dataset with enrollee master data. In addition, we have applied vPIDs to practical analyses of medical expenditures and doctor consultations. vPID has enabled the continued tracing of patients throughout the six-year study period, and demonstrated the validity of our method. Therefore, the proposed method can be used to improve patient traceability in Insurance Claims database.
-
a prescription trend analysis using medical Insurance Claim big data
International Conference on Data Engineering, 2019Co-Authors: Kazutoshi Umemoto, Kazuo Goda, Naohiro Mitsutake, Masaru KitsuregawaAbstract:Understanding the spread of diseases and the use of medicines is of practical importance for various organizations, such as medical providers, medical payers, and national governments. This study aims to detect the change in the prescription trends and to identify its cause through an analysis of Medical Insurance Claims (MICs), which comprise the specifications of medical fees charged to health insurers. Our approach is two-fold. (1) We propose a latent variable model that simulates the medication behavior of physicians to accurately reproduce monthly prescription time series from the MIC data, where prescription links between the diseases and medicines are missing. (2) We apply a state space model with intervention variables to decompose the monthly prescription time series into different components including seasonality and structural changes. Using a large dataset consisting of 3.5-year MIC records, we conduct experiments to evaluate our approach in terms of accuracy, usefulness, and efficiency. We also demonstrate three applications for our medical analysis.
Andrea Barron - One of the best experts on this subject based on the ideXlab platform.
-
the company car driver occupational stress as a predictor of motor vehicle accident involvement
Human Relations, 1996Co-Authors: Susan Cartwright, Cary L Cooper, Andrea BarronAbstract:Human factors play a significant role in accident causation. According to the Transport and Road Research Laboratory, human factors are responsible for 65% of all U.K. road accidents. In contrast, less than 6% of accidents can be accounted for exclusively by vehicle/road conditions. With an estimated 50 million workers within the European Community, traveling to work by car each day (Pickup & Di Martino, 1987), the frequency and cost of motor vehicle Claims has risen significantly during the last 10 years. In 1988, the total cost of road traffic accidents in the U.K. was estimated at £5,500M, with an average cost per accident of £17,760 (DTP, 1989). For every 100 company cars on U.K. roads, 44 are the subject of an Insurance Claim each year, with many fleet operators recording Claims frequencies in excess of 100 or 200% (Crighton, 1991). In 1989, there was one death or injury per 41 company cars.
Genevieve Grant - One of the best experts on this subject based on the ideXlab platform.
-
injured worker experiences of Insurance Claim processes and return to work a national cross sectional study
BMC Public Health, 2019Co-Authors: Alex Collie, Luke Sheehan, Tyler Lane, Shannon Gray, Genevieve GrantAbstract:Insurance Claims management practices may have a significant impact on the health and experiences of injured workers Claiming in workers’ compensation systems. There are few multi-jurisdictional studies of the way workers experience compensation processes, and limited data on the association between Claims experience and return to work outcomes. This study sought to identify worker, Claim and injury related factors associated with injured worker experiences of workers’ compensation Claims management processes, and to examine associations between Claims experience and return to work. A national, cross-sectional survey of injured workers involved in ten Australian workers’ compensation schemes. A total of 10,946 workers completed a telephone survey at 6 to 24 months post Claim acceptance. Predictors of positive or negative/neutral Claims experience were examined using logistic regression. Associations between Claims experience, return to work status and duration of time loss were examined using logistic regression. Nearly one-quarter (23.0%, n = 2515) of workers reported a negative or neutral Claims experience. Injury type, jurisdiction of Claim, and time to lodge Claim were most strongly associated with Claims experience. Having a positive Claims experience was strongly associated with having returned to work after accounting for injury, worker, Claim and employer factors. There is a strong positive association between worker experiences of the Insurance Claims process and self-reported return to work status. Revision and reform of workers’ compensation Claims management practices to enhance worker experience and the fairness of procedures may contribute to improved return to work outcomes.
Susan Cartwright - One of the best experts on this subject based on the ideXlab platform.
-
the company car driver occupational stress as a predictor of motor vehicle accident involvement
Human Relations, 1996Co-Authors: Susan Cartwright, Cary L Cooper, Andrea BarronAbstract:Human factors play a significant role in accident causation. According to the Transport and Road Research Laboratory, human factors are responsible for 65% of all U.K. road accidents. In contrast, less than 6% of accidents can be accounted for exclusively by vehicle/road conditions. With an estimated 50 million workers within the European Community, traveling to work by car each day (Pickup & Di Martino, 1987), the frequency and cost of motor vehicle Claims has risen significantly during the last 10 years. In 1988, the total cost of road traffic accidents in the U.K. was estimated at £5,500M, with an average cost per accident of £17,760 (DTP, 1989). For every 100 company cars on U.K. roads, 44 are the subject of an Insurance Claim each year, with many fleet operators recording Claims frequencies in excess of 100 or 200% (Crighton, 1991). In 1989, there was one death or injury per 41 company cars.
Kazutoshi Umemoto - One of the best experts on this subject based on the ideXlab platform.
-
a prescription trend analysis using medical Insurance Claim big data
International Conference on Data Engineering, 2019Co-Authors: Kazutoshi Umemoto, Kazuo Goda, Naohiro Mitsutake, Masaru KitsuregawaAbstract:Understanding the spread of diseases and the use of medicines is of practical importance for various organizations, such as medical providers, medical payers, and national governments. This study aims to detect the change in the prescription trends and to identify its cause through an analysis of Medical Insurance Claims (MICs), which comprise the specifications of medical fees charged to health insurers. Our approach is two-fold. (1) We propose a latent variable model that simulates the medication behavior of physicians to accurately reproduce monthly prescription time series from the MIC data, where prescription links between the diseases and medicines are missing. (2) We apply a state space model with intervention variables to decompose the monthly prescription time series into different components including seasonality and structural changes. Using a large dataset consisting of 3.5-year MIC records, we conduct experiments to evaluate our approach in terms of accuracy, usefulness, and efficiency. We also demonstrate three applications for our medical analysis.