System Dynamics Modeling Can be Leveraged to Predict Critical Care Pathways and Costs for End Stage Renal Disease: US Population to 2020
**Background:** End Stage Renal Disease (ESRD) accounts for 9% of Medicare spending, with the beneficiaries suffering from ESRD costing 7-9 times more than the average. This population is expected to continue to grow as a portion of Medicare beneficiaries. To provide clinicians and administrators wi...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Columbia Data Analytics, LLC
2015-07-01
|
| Series: | Journal of Health Economics and Outcomes Research |
| Online Access: | https://doi.org/10.36469/9839 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823860372977745920 |
|---|---|
| author | Luca Fernandez Christopher Koliba Asim Zia Katharine Cheung Richard Solomon Christopher Jones |
| author_facet | Luca Fernandez Christopher Koliba Asim Zia Katharine Cheung Richard Solomon Christopher Jones |
| author_sort | Luca Fernandez |
| collection | DOAJ |
| description | **Background:** End Stage Renal Disease (ESRD) accounts for 9% of Medicare spending, with the beneficiaries suffering from ESRD costing 7-9 times more than the average. This population is expected to continue to grow as a portion of Medicare beneficiaries. To provide clinicians and administrators with a greater understanding of the combined costs associated with the multiple critical care pathways for End Stage Renal Disease we have developed a model to predict ESRD populations through 2020.
**Methods:** A system dynamics model was designed to project the prevalence and total costs of ESRD treatment for the United States through 2020. Incidence, transplant and mortality rates were modeled for 35 age and primary diagnosis subgroups coursing through different ESRD critical care pathways. Using a web interface that allows users to alter certain combinations of parameters, several demonstration analysis were run to predict the impact of three policy interventions on the future of ESRD care
**Results:** The model was successfully calibrated against the output of United States Renal Data System’s (USRDS) prior predictions and tested by comparing the output to historical data. Our model predicts that the ESRD patient population will continue to rise, with total prevalence increasing to 829,000 by 2020. This would be a 30% increase from the reported 2010 prevalence.
**Conclusions:** Findings suggest that clinical care and policy changes can be leveraged to more effectively and efficiently manage the inevitable growth of ESRD patient populations. Patients can be shifted to more effective treatments, while planning integrating systems thinking can save Medicare’s ESRD program billions over the next decade. |
| format | Article |
| id | doaj-art-c136155179cb4bb3b014b5febb32b030 |
| institution | Kabale University |
| issn | 2327-2236 |
| language | English |
| publishDate | 2015-07-01 |
| publisher | Columbia Data Analytics, LLC |
| record_format | Article |
| series | Journal of Health Economics and Outcomes Research |
| spelling | doaj-art-c136155179cb4bb3b014b5febb32b0302025-02-10T16:12:40ZengColumbia Data Analytics, LLCJournal of Health Economics and Outcomes Research2327-22362015-07-0131System Dynamics Modeling Can be Leveraged to Predict Critical Care Pathways and Costs for End Stage Renal Disease: US Population to 2020Luca FernandezChristopher KolibaAsim ZiaKatharine CheungRichard SolomonChristopher Jones**Background:** End Stage Renal Disease (ESRD) accounts for 9% of Medicare spending, with the beneficiaries suffering from ESRD costing 7-9 times more than the average. This population is expected to continue to grow as a portion of Medicare beneficiaries. To provide clinicians and administrators with a greater understanding of the combined costs associated with the multiple critical care pathways for End Stage Renal Disease we have developed a model to predict ESRD populations through 2020. **Methods:** A system dynamics model was designed to project the prevalence and total costs of ESRD treatment for the United States through 2020. Incidence, transplant and mortality rates were modeled for 35 age and primary diagnosis subgroups coursing through different ESRD critical care pathways. Using a web interface that allows users to alter certain combinations of parameters, several demonstration analysis were run to predict the impact of three policy interventions on the future of ESRD care **Results:** The model was successfully calibrated against the output of United States Renal Data System’s (USRDS) prior predictions and tested by comparing the output to historical data. Our model predicts that the ESRD patient population will continue to rise, with total prevalence increasing to 829,000 by 2020. This would be a 30% increase from the reported 2010 prevalence. **Conclusions:** Findings suggest that clinical care and policy changes can be leveraged to more effectively and efficiently manage the inevitable growth of ESRD patient populations. Patients can be shifted to more effective treatments, while planning integrating systems thinking can save Medicare’s ESRD program billions over the next decade.https://doi.org/10.36469/9839 |
| spellingShingle | Luca Fernandez Christopher Koliba Asim Zia Katharine Cheung Richard Solomon Christopher Jones System Dynamics Modeling Can be Leveraged to Predict Critical Care Pathways and Costs for End Stage Renal Disease: US Population to 2020 Journal of Health Economics and Outcomes Research |
| title | System Dynamics Modeling Can be Leveraged to Predict Critical Care Pathways and Costs for End Stage Renal Disease: US Population to 2020 |
| title_full | System Dynamics Modeling Can be Leveraged to Predict Critical Care Pathways and Costs for End Stage Renal Disease: US Population to 2020 |
| title_fullStr | System Dynamics Modeling Can be Leveraged to Predict Critical Care Pathways and Costs for End Stage Renal Disease: US Population to 2020 |
| title_full_unstemmed | System Dynamics Modeling Can be Leveraged to Predict Critical Care Pathways and Costs for End Stage Renal Disease: US Population to 2020 |
| title_short | System Dynamics Modeling Can be Leveraged to Predict Critical Care Pathways and Costs for End Stage Renal Disease: US Population to 2020 |
| title_sort | system dynamics modeling can be leveraged to predict critical care pathways and costs for end stage renal disease us population to 2020 |
| url | https://doi.org/10.36469/9839 |
| work_keys_str_mv | AT lucafernandez systemdynamicsmodelingcanbeleveragedtopredictcriticalcarepathwaysandcostsforendstagerenaldiseaseuspopulationto2020 AT christopherkoliba systemdynamicsmodelingcanbeleveragedtopredictcriticalcarepathwaysandcostsforendstagerenaldiseaseuspopulationto2020 AT asimzia systemdynamicsmodelingcanbeleveragedtopredictcriticalcarepathwaysandcostsforendstagerenaldiseaseuspopulationto2020 AT katharinecheung systemdynamicsmodelingcanbeleveragedtopredictcriticalcarepathwaysandcostsforendstagerenaldiseaseuspopulationto2020 AT richardsolomon systemdynamicsmodelingcanbeleveragedtopredictcriticalcarepathwaysandcostsforendstagerenaldiseaseuspopulationto2020 AT christopherjones systemdynamicsmodelingcanbeleveragedtopredictcriticalcarepathwaysandcostsforendstagerenaldiseaseuspopulationto2020 |