Market Concentration in the Health Care Insurance Industry Has Adverse Repercussions on Patients and Physicians

2008 ◽  
Vol 121 (6) ◽  
pp. 435e-440e ◽  
Author(s):  
Ahmer K. Ghori ◽  
Kevin C. Chung
2012 ◽  
Vol 12 (1) ◽  
pp. 49-60 ◽  
Author(s):  
V. Sree Hari Rao ◽  
Murthy V. Jonnalagedda

Abstract Extraction of customer behavioral patterns is a complex task and widely studied for various industrial applications under different heading viz., customer retention management, business intelligence and data mining. In this paper, authors experimented to extract the behavioral patterns for customer retention in Health care insurance. Initially, the customers are classified into three general categories - stable, unstable and oscillatory. To extract the patterns the concept of Novel index tree (a variant of K-d tree) clubbed with K-Nearest Neighbor algorithm is proposed for efficient classification of data, as well as outliers and the concept of insurance dynamics is proposed for analyzing customer behavioral patterns


2021 ◽  
pp. 002073142098564
Author(s):  
John Geyman

The COVID-19 pandemic has exposed long-standing system problems of U. S. health care ranging from access barriers, uncontrolled prices and costs, unacceptable quality, widespread disparities and inequities, and marginalization of public health. All of these have been well documented by international comparisons. Our largely privatized market-based system and medical-industrial complex have been ill equipped to respond effectively to the pandemic. The accompanying economic downturn exacerbates these problems that further reveal the failures of our largely for-profit private health insurance industry, dependent as it is on continued government subsidies while it profiteers on the backs of vulnerable Americans. This article brings historical perspective to these problems, and provides markers of the extent of our unpreparedness and ineffective response to the pandemic. Coherent national health and public health policies are urgently needed based on evidence-based science, not political pressures. Financing reform is necessary, such as through single-payer Medicare for All. Eight takeaway lessons are summarized that can help to inform now best to rebuild U. S. health care and public health, an urgent task for the incoming Biden administration.


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.


2010 ◽  
Vol 5 (4) ◽  
pp. 459-479 ◽  
Author(s):  
Asako S. Moriya ◽  
William B. Vogt ◽  
Martin Gaynor

AbstractThere has been substantial consolidation among health insurers and hospitals, recently, raising questions about the effects of this consolidation on the exercise of market power. We analyze the relationship between insurer and hospital market concentration and the prices of hospital services. We use a national US dataset containing transaction prices for health care services for over 11 million privately insured Americans. Using three years of panel data, we estimate how insurer and hospital market concentration are related to hospital prices, while controlling for unobserved market effects. We find that increases in insurance market concentration are significantly associated with decreases in hospital prices, whereas increases in hospital concentration are non-significantly associated with increases in prices. A hypothetical merger between two of five equally sized insurers is estimated to decrease hospital prices by 6.7%.


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