Study on Blockchain-Based Healthcare Insurance Claim System

Author(s):  
Anuj S S Mishra
2009 ◽  
Vol 40 (1) ◽  
pp. 32
Author(s):  
JOSEPH S. EASTERN
Keyword(s):  

Author(s):  
Divyang Oza ◽  
Dhruv Padhiyar ◽  
Viraj Doshi ◽  
Sunita Patil
Keyword(s):  

2017 ◽  
Vol 30 (7) ◽  
pp. 991-1000 ◽  
Author(s):  
Miharu Nakanishi ◽  
Yasuyuki Okumura ◽  
Asao Ogawa

ABSTRACTBackground:In April 2016, the Japanese government introduced an additional benefit for dementia care in acute care hospitals (dementia care benefit) into the universal benefit schedule of public healthcare insurance program. The benefit includes a financial disincentive to use physical restraint. The present study investigated the association between the dementia care benefit and the use of physical restraint among inpatients with dementia in general acute care settings.Methods:A national cross-sectional study design was used. Eight types of care units from acute care hospitals under the public healthcare insurance program were invited to participate in this study. A total of 23,539 inpatients with dementia from 2,355 care units in 937 hospitals were included for the analysis. Dementia diagnosis or symptoms included any signs of cognitive impairment. The primary outcome measure was “use of physical restraint.”Results:Among patients, the point prevalence of physical restraint was 44.5% (n= 10,480). Controlling for patient, unit, and hospital characteristics, patients in units with dementia care benefit had significantly lower percentage of physical restraint than those in any other units (42.0% vs. 47.1%; adjusted odds ratio, 0.76; 95% confident interval [0.63, 0.92]).Conclusions:The financial incentive may have reduced the risk of physical restraint among patients with dementia in acute care hospitals. However, use of physical restraint was still common among patients with dementia in units with the dementia care benefit. An educational package to guide dementia care approach including the avoidance of physical restraint by healthcare professionals in acute care hospitals is recommended.


2021 ◽  
pp. 1-17
Author(s):  
Sen Hu ◽  
T. Brendan Murphy ◽  
Adrian O’Hagan

Abstract The mvClaim package in R provides flexible modelling frameworks for multivariate insurance claim severity modelling. The current version of the package implements a parsimonious mixture of experts (MoE) model family with bivariate gamma distributions, as introduced in Hu et al., and a finite mixture of copula regressions within the MoE framework as in Hu & O’Hagan. This paper presents the modelling approach theory briefly and the usage of the models in the package in detail. This package is hosted on GitHub at https://github.com/senhu/.


2001 ◽  
Vol 28 (5) ◽  
pp. 804-812 ◽  
Author(s):  
Paul de Leur ◽  
Tarek Sayed

Road safety analysis is typically undertaken using traffic collision data. However, the collision data often suffer from quality and reliability problems. These problems can inhibit the ability of road safety engineers to evaluate and analyze road safety performance. An alternate source of data that characterize the events of a traffic collision is the records that become available from an auto insurance claim. In settling an auto insurance claim, a claim adjuster must make an assessment and determination of the circumstances of the event, recording important contributing factors that led to the crash occurrence. As such, there is an opportunity to access and use the claims data in road safety engineering analysis. This paper presents the results of an initial attempt to use auto insurance claims records in road safety evaluation by developing and applying a claim prediction model. The prediction model will provide an estimate of the number of auto insurance claims that can be expected at signalized intersections in the Vancouver area of British Columbia, Canada. A discussion of the usefulness and application of the claim prediction model will be provided together with a recommendation on how the claims data could be utilized in the future.Key words: road safety improvement programs, auto insurance claims, road safety analysis, prediction models.


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