scholarly journals Data Privacy Compliant Validation of Health Insurance Claims Data: the IDOMENEO Approach

2019 ◽  
Vol 82 (S 02) ◽  
pp. S94-S100 ◽  
Author(s):  
Christian-Alexander Behrendt ◽  
Thea Schwaneberg ◽  
Sandra Hischke ◽  
Tobias Müller ◽  
Tom Petersen ◽  
...  

AbstractRecently, health insurance claims have regained the attention of the scientific community as a source of real-world evidence in health care research and quality improvement. To date, very few studies are available which investigate the validity of health insurance claims; these may be affected by bias from several sources, such as possible upcoding of co-morbidities and complications for reimbursement advantages. The IDOMENEO study investigates the inpatient treatment of peripheral arterial disease (PAD) comprehensively using various data sources with a consortium involving experts from health care research and data privacy, a large health insurance fund, biostatisticians, jurists, and computer scientists. Prospective registry data were collected from 30–40 vascular centres in Germany using the GermanVasc registry. In addition, health insurance claims data were prospectively collected from BARMER, the second largest health insurance fund in Germany. The consortium is currently developing a data privacy compliant method of health insurance claims data validation, the methodological foundations of which are described here.

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Michael Stucki ◽  
Janina Nemitz ◽  
Maria Trottmann ◽  
Simon Wieser

Abstract Background Decomposing health care spending by disease, type of care, age, and sex can lead to a better understanding of the drivers of health care spending. But the lack of diagnostic coding in outpatient care often precludes a decomposition by disease. Yet, health insurance claims data hold a variety of diagnostic clues that may be used to identify diseases. Methods In this study, we decompose total outpatient care spending in Switzerland by age, sex, service type, and 42 exhaustive and mutually exclusive diseases according to the Global Burden of Disease classification. Using data of a large health insurance provider, we identify diseases based on diagnostic clues. These clues include type of medication, inpatient treatment, physician specialization, and disease specific outpatient treatments and examinations. We determine disease-specific spending by direct (clues-based) and indirect (regression-based) spending assignment. Results Our results suggest a high precision of disease identification for many diseases. Overall, 81% of outpatient spending can be assigned to diseases, mostly based on indirect assignment using regression. Outpatient spending is highest for musculoskeletal disorders (19.2%), followed by mental and substance use disorders (12.0%), sense organ diseases (8.7%) and cardiovascular diseases (8.6%). Neoplasms account for 7.3% of outpatient spending. Conclusions Our study shows the potential of health insurance claims data in identifying diseases when no diagnostic coding is available. These disease-specific spending estimates may inform Swiss health policies in cost containment and priority setting.


2019 ◽  
Vol 51 (2) ◽  
pp. 327-334 ◽  
Author(s):  
Chirag M. Lakhani ◽  
Braden T. Tierney ◽  
Arjun K. Manrai ◽  
Jian Yang ◽  
Peter M. Visscher ◽  
...  

2020 ◽  
Vol Volume 12 ◽  
pp. 1129-1138
Author(s):  
Amir Sarayani ◽  
Xi Wang ◽  
Thuy Nhu Thai ◽  
Yasser Albogami ◽  
Nakyung Jeon ◽  
...  

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