| dc.contributor.author | Bravo, Fernanda | |
| dc.contributor.author | Braun, Marcus | |
| dc.contributor.author | Farias, Vivek | |
| dc.contributor.author | Levi, Retsef | |
| dc.contributor.author | Lynch, Christine | |
| dc.contributor.author | Tumolo, John | |
| dc.contributor.author | Whyte, Richard | |
| dc.date.accessioned | 2022-01-24T15:07:00Z | |
| dc.date.available | 2021-10-27T16:37:27Z | |
| dc.date.available | 2022-01-24T15:07:00Z | |
| dc.date.issued | 2021-05 | |
| dc.date.submitted | 2020-01 | |
| dc.identifier.issn | 1572-9389 | |
| dc.identifier.issn | 1386-9620 | |
| dc.identifier.uri | https://hdl.handle.net/1721.1/133156.2 | |
| dc.description.abstract | Abstract
In the last several decades, the U.S. Health care industry has undergone a massive consolidation process that has resulted in the formation of large delivery networks. However, the integration of these networks into a unified operational system faces several challenges. Strategic problems, such as ensuring access, allocating resources and capacity efficiently, and defining case-mix in a multi-site network, require the correct modeling of network costs, network trade-offs, and operational constraints. Unfortunately, traditional practices related to cost accounting, specifically the allocation of overhead and labor cost to activities as a way to account for the consumption of resources, are not suitable for addressing these challenges; they confound resource allocation and network building capacity decisions. We develop a general methodological optimization-driven framework based on linear programming that allows us to better understand network costs and provide strategic solutions to the aforementioned problems. We work in collaboration with a network of hospitals to demonstrate our framework applicability and important insights derived from it. | en_US |
| dc.publisher | Springer US | en_US |
| dc.relation.isversionof | https://doi.org/10.1007/s10729-021-09565-1 | en_US |
| dc.rights | Creative Commons Attribution | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.source | Springer US | en_US |
| dc.title | Optimization-driven framework to understand health care network costs and resource allocation | en_US |
| dc.type | Article | en_US |
| dc.identifier.citation | Bravo, Fernanda, Braun, Marcus, Farias, Vivek, Levi, Retsef, Lynch, Christine et al. 2021. "Optimization-driven framework to understand health care network costs and resource allocation." | en_US |
| dc.contributor.department | Sloan School of Management | |
| dc.relation.journal | Health Care Management Science | en_US |
| dc.eprint.version | Final published version | en_US |
| dc.type.uri | http://purl.org/eprint/type/JournalArticle | en_US |
| eprint.status | http://purl.org/eprint/status/PeerReviewed | en_US |
| dc.date.updated | 2021-05-09T03:11:16Z | |
| dc.language.rfc3066 | en | |
| dc.rights.holder | The Author(s) | |
| dspace.embargo.terms | N | |
| dspace.date.submission | 2021-05-09T03:11:16Z | |
| mit.journal.volume | 24 | en_US |
| mit.license | PUBLISHER_CC | |
| mit.metadata.status | Authority Work Needed | en_US |