Cargando…

Developing a standardized healthcare cost data warehouse

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 c...

Descripción completa

Detalles Bibliográficos
Autores principales: Visscher, Sue L., Naessens, James M., Yawn, Barbara P., Reinalda, Megan S., Anderson, Stephanie S., Borah, Bijan J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5469019/
https://www.ncbi.nlm.nih.gov/pubmed/28606088
http://dx.doi.org/10.1186/s12913-017-2327-8
_version_ 1783243504232693760
author Visscher, Sue L.
Naessens, James M.
Yawn, Barbara P.
Reinalda, Megan S.
Anderson, Stephanie S.
Borah, Bijan J.
author_facet Visscher, Sue L.
Naessens, James M.
Yawn, Barbara P.
Reinalda, Megan S.
Anderson, Stephanie S.
Borah, Bijan J.
author_sort Visscher, Sue L.
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12913-017-2327-8) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-5469019
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-54690192017-06-14 Developing a standardized healthcare cost data warehouse Visscher, Sue L. Naessens, James M. Yawn, Barbara P. Reinalda, Megan S. Anderson, Stephanie S. Borah, Bijan J. BMC Health Serv Res Technical Advance 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12913-017-2327-8) contains supplementary material, which is available to authorized users. BioMed Central 2017-06-12 /pmc/articles/PMC5469019/ /pubmed/28606088 http://dx.doi.org/10.1186/s12913-017-2327-8 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Technical Advance
Visscher, Sue L.
Naessens, James M.
Yawn, Barbara P.
Reinalda, Megan S.
Anderson, Stephanie S.
Borah, Bijan J.
Developing a standardized healthcare cost data warehouse
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 Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5469019/
https://www.ncbi.nlm.nih.gov/pubmed/28606088
http://dx.doi.org/10.1186/s12913-017-2327-8
work_keys_str_mv AT visschersuel developingastandardizedhealthcarecostdatawarehouse
AT naessensjamesm developingastandardizedhealthcarecostdatawarehouse
AT yawnbarbarap developingastandardizedhealthcarecostdatawarehouse
AT reinaldamegans developingastandardizedhealthcarecostdatawarehouse
AT andersonstephanies developingastandardizedhealthcarecostdatawarehouse
AT borahbijanj developingastandardizedhealthcarecostdatawarehouse