Developing a standardized healthcare cost data warehouse
Abstract Background Research addressing value in healthcare requires a measure of cost. While there are many sources and types of cost data, each has strengths and weaknesses. Many researchers appear to create study-specific cost datasets, but the explanations of their costing methodologies are not...
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| Format: | Article |
| Language: | English |
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BMC
2017-06-01
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| Series: | BMC Health Services Research |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12913-017-2327-8 |
| _version_ | 1828792060219490304 |
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| author | Sue L. Visscher James M. Naessens Barbara P. Yawn Megan S. Reinalda Stephanie S. Anderson Bijan J. Borah |
| author_facet | Sue L. Visscher James M. Naessens Barbara P. Yawn Megan S. Reinalda Stephanie S. Anderson Bijan J. Borah |
| author_sort | Sue L. Visscher |
| collection | DOAJ |
| description | Abstract Background Research addressing value in healthcare requires a measure of cost. While there are many sources and types of cost data, each has strengths and weaknesses. Many researchers appear to create study-specific cost datasets, but the explanations of their costing methodologies are not always clear, causing their results to be difficult to interpret. Our solution, described in this paper, was to use widely accepted costing methodologies to create a service-level, standardized healthcare cost data warehouse from an institutional perspective that includes all professional and hospital-billed services for our patients. Methods The warehouse is based on a National Institutes of Research–funded research infrastructure containing the linked health records and medical care administrative data of two healthcare providers and their affiliated hospitals. Since all patients are identified in the data warehouse, their costs can be linked to other systems and databases, such as electronic health records, tumor registries, and disease or treatment registries. Results We describe the two institutions’ administrative source data; the reference files, which include Medicare fee schedules and cost reports; the process of creating standardized costs; and the warehouse structure. The costing algorithm can create inflation-adjusted standardized costs at the service line level for defined study cohorts on request. Conclusion The resulting standardized costs contained in the data warehouse can be used to create detailed, bottom-up analyses of professional and facility costs of procedures, medical conditions, and patient care cycles without revealing business-sensitive information. After its creation, a standardized cost data warehouse is relatively easy to maintain and can be expanded to include data from other providers. Individual investigators who may not have sufficient knowledge about administrative data do not have to try to create their own standardized costs on a project-by-project basis because our data warehouse generates standardized costs for defined cohorts upon request. |
| first_indexed | 2024-12-12T03:01:39Z |
| format | Article |
| id | doaj.art-bc0eb2240659416780a8746b7b45747d |
| institution | Directory Open Access Journal |
| issn | 1472-6963 |
| language | English |
| last_indexed | 2024-12-12T03:01:39Z |
| publishDate | 2017-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj.art-bc0eb2240659416780a8746b7b45747d2022-12-22T00:40:37ZengBMCBMC Health Services Research1472-69632017-06-0117111110.1186/s12913-017-2327-8Developing a standardized healthcare cost data warehouseSue L. Visscher0James M. Naessens1Barbara P. Yawn2Megan S. Reinalda3Stephanie S. Anderson4Bijan J. Borah5Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo ClinicRobert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo ClinicDepartment of Research, Olmsted Medical CenterDivision of Biomedical Statistics and Informatics, Mayo ClinicDivision of Biomedical Statistics and Informatics, Mayo ClinicRobert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo ClinicAbstract Background Research addressing value in healthcare requires a measure of cost. While there are many sources and types of cost data, each has strengths and weaknesses. Many researchers appear to create study-specific cost datasets, but the explanations of their costing methodologies are not always clear, causing their results to be difficult to interpret. Our solution, described in this paper, was to use widely accepted costing methodologies to create a service-level, standardized healthcare cost data warehouse from an institutional perspective that includes all professional and hospital-billed services for our patients. Methods The warehouse is based on a National Institutes of Research–funded research infrastructure containing the linked health records and medical care administrative data of two healthcare providers and their affiliated hospitals. Since all patients are identified in the data warehouse, their costs can be linked to other systems and databases, such as electronic health records, tumor registries, and disease or treatment registries. Results We describe the two institutions’ administrative source data; the reference files, which include Medicare fee schedules and cost reports; the process of creating standardized costs; and the warehouse structure. The costing algorithm can create inflation-adjusted standardized costs at the service line level for defined study cohorts on request. Conclusion The resulting standardized costs contained in the data warehouse can be used to create detailed, bottom-up analyses of professional and facility costs of procedures, medical conditions, and patient care cycles without revealing business-sensitive information. After its creation, a standardized cost data warehouse is relatively easy to maintain and can be expanded to include data from other providers. Individual investigators who may not have sufficient knowledge about administrative data do not have to try to create their own standardized costs on a project-by-project basis because our data warehouse generates standardized costs for defined cohorts upon request.http://link.springer.com/article/10.1186/s12913-017-2327-8Cost data warehouseStandardized healthcare costsMicrocostingOlmsted County Healthcare Expenditure and Utilization Database (OCHEUD)Rochester Epidemiology Project (REP) |
| spellingShingle | Sue L. Visscher James M. Naessens Barbara P. Yawn Megan S. Reinalda Stephanie S. Anderson Bijan J. Borah Developing a standardized healthcare cost data warehouse BMC Health Services Research Cost data warehouse Standardized healthcare costs Microcosting Olmsted County Healthcare Expenditure and Utilization Database (OCHEUD) Rochester Epidemiology Project (REP) |
| title | Developing a standardized healthcare cost data warehouse |
| title_full | Developing a standardized healthcare cost data warehouse |
| title_fullStr | Developing a standardized healthcare cost data warehouse |
| title_full_unstemmed | Developing a standardized healthcare cost data warehouse |
| title_short | Developing a standardized healthcare cost data warehouse |
| title_sort | developing a standardized healthcare cost data warehouse |
| topic | Cost data warehouse Standardized healthcare costs Microcosting Olmsted County Healthcare Expenditure and Utilization Database (OCHEUD) Rochester Epidemiology Project (REP) |
| url | http://link.springer.com/article/10.1186/s12913-017-2327-8 |
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