Health care insurance policies When the provider and patient may collude

2020 ◽  
Author(s):  
Yaping Wu ◽  
David Bardey ◽  
Yijuan Chen ◽  
Sanxi Li
Author(s):  
Sujeet K Sinha ◽  
Reena Kumar

ABSTRACT Introduction Health insurance is emerging fast as an important mechanism to finance health care needs of the people. Complexity of the health insurance industry has been much talked about and less understood in the Indian scenario. Hence, it is imperative to assess the level of awareness that the population has with respect to health insurance policies. Materials and methods Cross-sectional prospective study conducted over a period of 6 months, at the third-party administrator (TPA) desk of the hospital. The data was collected using a preformed close-ended questionnaire after obtaining consent from all the participants. Only patients admitted in the hospital availing cashless hospitalization were included in the study. The study was undertaken with the objective to determine the level of awareness about insurance policies and procedures among those insured and identify the problems faced by those insured when availing cashless treatment. Responses to the variables in the questionnaire were compiled and tabulated using Excel 2010. Results Response rate of 76% was observed. 56% of the study population were planned admissions and 44% were admitted through emergency department. The study showed that about 56% of the principal policy holders were between 30 and 50 years of age. The awareness regarding the terms and conditions of the health care insurance policy and the servicing TPA was found to be 70%. However, on interacting with patients it came to light that despite being appraised by their insurance agent, they faced challenges while availing health care benefits under health care insurance and were ignorant about the procedure involved. For the current admission, in 78% of the cases, the TPA responded within 24 hours of intimation; however, in 22% cases there were delays in response from the TPA mostly attributed to communication gap between the Insurance Company and the TPA. Preexisting disease was not covered in 14% cases. 82% cases had to wait for more than 2 hours for the final clearance from the TPA. Over the years, as ascertained in 2016 also, the scenario of insurance has not undergone significant change. Conclusion Strategies to optimize claims by bringing about a uniformity in the rates being charged by the hospitals for different procedures are needed to increase coverage. How to cite this article Jain K, Sinha SK, Jain D, Kumar R. Does Health Insurance give Us an Assurance? A Study on the Extent of Coverage of Health Insurance at a Tertiary Care Hospital in North India. Int J Res Foundation Hospc Health Adm 2016;4(1):25-30.


2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Ryoya Tsunoda ◽  
Hirayasu Kai ◽  
Masahide Kondo ◽  
Naohiro Mitsutake ◽  
Kunihiro Yamagata

Abstract Background and Aims Although knowing the accurate number of patients of hemodialysis important, data collection is a hard task. Establishing a simplified and prompt method of data collection for perspective hemodialysis is strongly needed. In Japan, there is a universal health care insurance system that covers almost all population. This study aimed to know a seasonal variation of hemodialysis patients using the big database of medical bills in Japan. Method Japanese Ministry of Health, Labour and Welfare established a big database named National Database (NDB), that consists of medical bills data in Japan. All bills data were sent to the data server from The Examination and Payment Agency, the organization that receives all medical bills from each medical institution and judge validity for payment. Each record of the database consists of bill data of one patient of a month for each medical institution. All data were anonymized before saved in the server and gave virtual patient identification number (VPID) that is unique for each patient. VPID is a hash value calculated by patient’s individual data such as name, date of birth, so that the value cannot be duplicate. Calculation of VPID is executed by an irreversible way to make it difficult to decrypt VPID into patient’s individual data. This database includes all information about medical care of whole population in Japan except for patients not under the insurance system (patients under public assistance system, victims of the war, or any other specified people under the public medical expense). Using this database, we investigated monthly number of patients who were recorded to be undergone hemodialysis (HD, includes hemodiafiltration). We searched chronic HD patients who have undergone HD on the month and continued it for 3 months, and acute HD patients who have discontinued HD within 3 months. Results In NDB, the number of chronic HD patients under public insurance system who confirmed to have undergone HD in December 2014 was 284 433. In contrast, the number of HD patients identified from the year-end survey by Japanese Society of Dialysis Therapy in the same year was of 311 193, but this number includes patients not under insurance system. Incidence rate of acute HD in Japan was persisted at 30-39 per million per month. There is a reproducible seasonal variation in number of acute HD patients, that increases in every winter and decreasing in every summer. The significantly highest frequency was observed in February(38.5/million/month) compared with September(30.6/million/month), the lowest month of the year (p<0.01). Conclusion We could show the trend in number of HD patients using nationwide bills data. Seasonality in some clinical factors in patients under chronic hemodialysis such as blood pressure, intradialytic body weight gain, morbidity of congestive heart failure, and, mortality, has been reported in many observational studies. Also, there are a few former reports about seasonality in AKI. However, a report about acute RRT is few. From our knowledge, this is the first report that revealed monthly dynamics of HD in a whole nation and rising risk of acute HD in winter. The true mechanism of this seasonality remains unclear. We have to establish a method to collect clinical data such as prevalence of CKD, causative diseases of AKI, kinds of precedent operations, and medications in connection with billing data.


2021 ◽  
Author(s):  
Mihajlo RABRENOVIC ◽  
◽  
Usman IQBAL ◽  

Big data is a complex noun that marks sets of data in various formats. Th ere are a lot of challenges in dealing with them, including how to store, search, analyze and share them. In this paper, co-authors deal with relation of big data and artifi cial intelligence and eff ective healthcare insurance plans. In the analysis is taken into account that insurance as a business activity is critically connected to managing risk. In the paper is tested hypothesis: the quality of understanding risks in health care insurance is directly connected to the quality of information. Th is subject requires multidisciplinary approach that includes: informatics, legal and organizational science as well as insurance in health care.


PEDIATRICS ◽  
1980 ◽  
Vol 65 (1) ◽  
pp. 168-170
Author(s):  
Stephen M. Davidson ◽  
John P. Connelly ◽  
R. Don Blim ◽  
James E. Strain ◽  
H. Doyl Taylor

The National Commission on the Cost of Medical Care1 states in part (Recommendation 2) that "insurance policies should include provisions through which the consumer shares in the cost of care received, at the time of service, for selected benefits and for selected groups...." These cost-sharing provisions are expected to reduce national medical care expenditures by encouraging consumers to reduce their use of services in order to avoid paying additional money out of their own pockets. They will thus moderate the demand-inducing tendency of insurance, leading the rational consumer to seek only necessary services and to forego those services contributing to what is believed to be over-utilization. As the Commission states in its supporting statement:


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