Can we optimize locations of hospitals by minimizing the number of patients at risk?
Abstract Background To reduce risk of death in acute ST-segment elevation myocardial infraction (STEMI), patients must reach a percutaneous coronary intervention (PCI) within 120 min from the start of symptoms. Current hospital locations represent choices made long since and may not provide the best...
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| Format: | Article |
| Language: | English |
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BMC
2023-04-01
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| Series: | BMC Health Services Research |
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| Online Access: | https://doi.org/10.1186/s12913-023-09375-x |
| _version_ | 1827956884863188992 |
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| author | Pasi Fränti Radu Mariescu-Istodor Awais Akram Markku Satokangas Eeva Reissell |
| author_facet | Pasi Fränti Radu Mariescu-Istodor Awais Akram Markku Satokangas Eeva Reissell |
| author_sort | Pasi Fränti |
| collection | DOAJ |
| description | Abstract Background To reduce risk of death in acute ST-segment elevation myocardial infraction (STEMI), patients must reach a percutaneous coronary intervention (PCI) within 120 min from the start of symptoms. Current hospital locations represent choices made long since and may not provide the best possibilities for optimal care of STEMI patients. Open questions are: (1) how the hospital locations could be better optimized to reduce the number of patients residing over 90 min from PCI capable hospitals, and (2) how this would affect other factors like average travel time. Methods We formulated the research question as a facility optimization problem, which was solved by clustering method using road network and efficient travel time estimation based on overhead graph. The method was implemented as an interactive web tool and tested using nationwide health care register data collected during 2015–2018 in Finland. Results The results show that the number of patients at risk for not receiving optimal care could theoretically be reduced significantly from 5 to 1%. However, this would be achieved at the cost of increasing average travel time from 35 to 49 min. By minimizing average travel time, the clustering would result in better locations leading to a slight decrease in travel time (34 min) with only 3% patients at risk. Conclusions The results showed that minimizing the number of patients at risk alone can significantly improve this single factor but, at the same time, increase the average burden of others. A more appropriate optimization should consider more factors. We also note that the hospitals serve also for other operators than STEMI patients. Although optimization of the entire health care system is a very complex optimization problems goal, it should be the aim of future research. |
| first_indexed | 2024-04-09T15:10:35Z |
| format | Article |
| id | doaj.art-4e2b49b2e70d4216a5b01ef82c5d21aa |
| institution | Directory Open Access Journal |
| issn | 1472-6963 |
| language | English |
| last_indexed | 2024-04-09T15:10:35Z |
| publishDate | 2023-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj.art-4e2b49b2e70d4216a5b01ef82c5d21aa2023-04-30T11:11:23ZengBMCBMC Health Services Research1472-69632023-04-0123111510.1186/s12913-023-09375-xCan we optimize locations of hospitals by minimizing the number of patients at risk?Pasi Fränti0Radu Mariescu-Istodor1Awais Akram2Markku Satokangas3Eeva Reissell4School of Computing, University of Eastern FinlandKarelia University of Applied SciencesSchool of Computing, University of Eastern FinlandFinnish Institute for Health and Welfare (THL)Finnish Institute for Health and Welfare (THL)Abstract Background To reduce risk of death in acute ST-segment elevation myocardial infraction (STEMI), patients must reach a percutaneous coronary intervention (PCI) within 120 min from the start of symptoms. Current hospital locations represent choices made long since and may not provide the best possibilities for optimal care of STEMI patients. Open questions are: (1) how the hospital locations could be better optimized to reduce the number of patients residing over 90 min from PCI capable hospitals, and (2) how this would affect other factors like average travel time. Methods We formulated the research question as a facility optimization problem, which was solved by clustering method using road network and efficient travel time estimation based on overhead graph. The method was implemented as an interactive web tool and tested using nationwide health care register data collected during 2015–2018 in Finland. Results The results show that the number of patients at risk for not receiving optimal care could theoretically be reduced significantly from 5 to 1%. However, this would be achieved at the cost of increasing average travel time from 35 to 49 min. By minimizing average travel time, the clustering would result in better locations leading to a slight decrease in travel time (34 min) with only 3% patients at risk. Conclusions The results showed that minimizing the number of patients at risk alone can significantly improve this single factor but, at the same time, increase the average burden of others. A more appropriate optimization should consider more factors. We also note that the hospitals serve also for other operators than STEMI patients. Although optimization of the entire health care system is a very complex optimization problems goal, it should be the aim of future research.https://doi.org/10.1186/s12913-023-09375-xHealth care information systemsFacility optimizationClusteringMyocardial infarction |
| spellingShingle | Pasi Fränti Radu Mariescu-Istodor Awais Akram Markku Satokangas Eeva Reissell Can we optimize locations of hospitals by minimizing the number of patients at risk? BMC Health Services Research Health care information systems Facility optimization Clustering Myocardial infarction |
| title | Can we optimize locations of hospitals by minimizing the number of patients at risk? |
| title_full | Can we optimize locations of hospitals by minimizing the number of patients at risk? |
| title_fullStr | Can we optimize locations of hospitals by minimizing the number of patients at risk? |
| title_full_unstemmed | Can we optimize locations of hospitals by minimizing the number of patients at risk? |
| title_short | Can we optimize locations of hospitals by minimizing the number of patients at risk? |
| title_sort | can we optimize locations of hospitals by minimizing the number of patients at risk |
| topic | Health care information systems Facility optimization Clustering Myocardial infarction |
| url | https://doi.org/10.1186/s12913-023-09375-x |
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