Care Strategies for Reducing Hospital Readmissions Using Stochastic Programming
A hospital readmission occurs when a patient has an unplanned admission to a hospital within a specific time period of discharge from an earlier or initial hospital stay. Preventable readmissions have turned into a critical challenge for the healthcare system globally, and hospitals seek care strate...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/9/8/940 |
| _version_ | 1797523712459669504 |
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| author | Behshad Lahijanian Michelle Alvarado |
| author_facet | Behshad Lahijanian Michelle Alvarado |
| author_sort | Behshad Lahijanian |
| collection | DOAJ |
| description | A hospital readmission occurs when a patient has an unplanned admission to a hospital within a specific time period of discharge from an earlier or initial hospital stay. Preventable readmissions have turned into a critical challenge for the healthcare system globally, and hospitals seek care strategies that reduce the readmission burden. Some countries have developed hospital readmission reduction policies, and in some cases, these policies impose financial penalties for hospitals with high readmission rates. Decision models are needed to help hospitals identify care strategies that avoid financial penalties, yet maintain balance among quality of care, the cost of care, and the hospital’s readmission reduction goals. We develop a multi-condition care strategy model to help hospitals prioritize treatment plans and allocate resources. The stochastic programming model has probabilistic constraints to control the expected readmission probability for a set of patients. The model determines which care strategies will be the most cost-effective and the extent to which resources should be allocated to those initiatives to reach the desired readmission reduction targets and maintain high quality of care. A sensitivity analysis was conducted to explore the value of the model for low- and high-performing hospitals and multiple health conditions. Model outputs are valuable to hospitals as they examine the expected cost of hitting its target and the expected improvement to its readmission rates. |
| first_indexed | 2024-03-10T08:46:54Z |
| format | Article |
| id | doaj.art-7d63ac9a23fe4e759f8b9107c3df9356 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-10T08:46:54Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-7d63ac9a23fe4e759f8b9107c3df93562023-11-22T07:48:31ZengMDPI AGHealthcare2227-90322021-07-019894010.3390/healthcare9080940Care Strategies for Reducing Hospital Readmissions Using Stochastic ProgrammingBehshad Lahijanian0Michelle Alvarado1Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USADepartment of Industrial and Systems Engineering, University of Florida, Gainesville, FL 32611, USAA hospital readmission occurs when a patient has an unplanned admission to a hospital within a specific time period of discharge from an earlier or initial hospital stay. Preventable readmissions have turned into a critical challenge for the healthcare system globally, and hospitals seek care strategies that reduce the readmission burden. Some countries have developed hospital readmission reduction policies, and in some cases, these policies impose financial penalties for hospitals with high readmission rates. Decision models are needed to help hospitals identify care strategies that avoid financial penalties, yet maintain balance among quality of care, the cost of care, and the hospital’s readmission reduction goals. We develop a multi-condition care strategy model to help hospitals prioritize treatment plans and allocate resources. The stochastic programming model has probabilistic constraints to control the expected readmission probability for a set of patients. The model determines which care strategies will be the most cost-effective and the extent to which resources should be allocated to those initiatives to reach the desired readmission reduction targets and maintain high quality of care. A sensitivity analysis was conducted to explore the value of the model for low- and high-performing hospitals and multiple health conditions. Model outputs are valuable to hospitals as they examine the expected cost of hitting its target and the expected improvement to its readmission rates.https://www.mdpi.com/2227-9032/9/8/940OR in health serviceshospital readmissionscenario-based stochastic programmingprobabilistic constraintscare strategy |
| spellingShingle | Behshad Lahijanian Michelle Alvarado Care Strategies for Reducing Hospital Readmissions Using Stochastic Programming Healthcare OR in health services hospital readmission scenario-based stochastic programming probabilistic constraints care strategy |
| title | Care Strategies for Reducing Hospital Readmissions Using Stochastic Programming |
| title_full | Care Strategies for Reducing Hospital Readmissions Using Stochastic Programming |
| title_fullStr | Care Strategies for Reducing Hospital Readmissions Using Stochastic Programming |
| title_full_unstemmed | Care Strategies for Reducing Hospital Readmissions Using Stochastic Programming |
| title_short | Care Strategies for Reducing Hospital Readmissions Using Stochastic Programming |
| title_sort | care strategies for reducing hospital readmissions using stochastic programming |
| topic | OR in health services hospital readmission scenario-based stochastic programming probabilistic constraints care strategy |
| url | https://www.mdpi.com/2227-9032/9/8/940 |
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