The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns
Abstract Background High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this stu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2019-12-01
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| Series: | BMC Health Services Research |
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| Online Access: | https://doi.org/10.1186/s12913-019-4769-7 |
| _version_ | 1818432742354845696 |
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| author | Shawn Choon Wee Ng Yu Heng Kwan Shi Yan Chuen Seng Tan Lian Leng Low |
| author_facet | Shawn Choon Wee Ng Yu Heng Kwan Shi Yan Chuen Seng Tan Lian Leng Low |
| author_sort | Shawn Choon Wee Ng |
| collection | DOAJ |
| description | Abstract Background High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. Methods We extracted data from our transitional care program (TCP), a Hospital-to-Home program launched by the Singapore Ministry of Health, from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. Results Among 752 patients, a 3-class best fit model was selected: Class 1 “Frail, cognitively impaired and physically dependent”, Class 2 “Pre-frail, but largely physically independent” and Class 3 “Physically independent”. The 3 classes have distinct demographics, medical and socioeconomic characteristics (p < 0.05), 30- and 90-day readmission (p < 0.05) and mortality (p < 0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR = 2.04, 95%CI: 1.21–3.46, p = 0.008), 30- (OR = 6.92, 95%CI: 1.76–27.21, p = 0.006) and 90-day mortality (OR = 11.51, 95%CI: 4.57–29.02, p < 0.001). Conclusions We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort. |
| first_indexed | 2024-12-14T16:10:02Z |
| format | Article |
| id | doaj.art-39423f48aa9a442b84c486dee7a4fc29 |
| institution | Directory Open Access Journal |
| issn | 1472-6963 |
| language | English |
| last_indexed | 2024-12-14T16:10:02Z |
| publishDate | 2019-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj.art-39423f48aa9a442b84c486dee7a4fc292022-12-21T22:55:01ZengBMCBMC Health Services Research1472-69632019-12-011911810.1186/s12913-019-4769-7The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patternsShawn Choon Wee Ng0Yu Heng Kwan1Shi Yan2Chuen Seng Tan3Lian Leng Low4Duke-NUS Medical SchoolDuke-NUS Medical SchoolDuke-NUS Medical SchoolSaw Swee Hock School of Public Health, National University of SingaporeSingHealth Regional Health System, Singapore Health ServicesAbstract Background High-risk patients are most vulnerable during transitions of care. Due to the high burden of resource allocation for such patients, we propose that segmentation of this heterogeneous population into distinct subgroups will enable improved healthcare resource planning. In this study, we segmented a high-risk population with the aim to identify and characterize a patient subgroup with the highest 30-day and 90-day hospital readmission and mortality. Methods We extracted data from our transitional care program (TCP), a Hospital-to-Home program launched by the Singapore Ministry of Health, from June to November 2018. Latent class analysis (LCA) was used to determine the optimal number and characteristics of latent subgroups, assessed based on model fit and clinical interpretability. Regression analysis was performed to assess the association of class membership on 30- and 90-day all-cause readmission and mortality. Results Among 752 patients, a 3-class best fit model was selected: Class 1 “Frail, cognitively impaired and physically dependent”, Class 2 “Pre-frail, but largely physically independent” and Class 3 “Physically independent”. The 3 classes have distinct demographics, medical and socioeconomic characteristics (p < 0.05), 30- and 90-day readmission (p < 0.05) and mortality (p < 0.01). Class 1 patients have the highest age-adjusted 90-day readmission (OR = 2.04, 95%CI: 1.21–3.46, p = 0.008), 30- (OR = 6.92, 95%CI: 1.76–27.21, p = 0.006) and 90-day mortality (OR = 11.51, 95%CI: 4.57–29.02, p < 0.001). Conclusions We identified a subgroup with the highest readmission and mortality risk amongst high-risk patients. We also found a lack of interventions in our TCP that specifically addresses increased frailty and poor cognition, which are prominent features in this subgroup. These findings will help to inform future program modifications and strengthen existing transitional healthcare structures currently utilized in this patient cohort.https://doi.org/10.1186/s12913-019-4769-7High-risk healthcare utilizersIntegrated careTransitional care programLatent class analysisHospital readmissionsMortality |
| spellingShingle | Shawn Choon Wee Ng Yu Heng Kwan Shi Yan Chuen Seng Tan Lian Leng Low The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns BMC Health Services Research High-risk healthcare utilizers Integrated care Transitional care program Latent class analysis Hospital readmissions Mortality |
| title | The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns |
| title_full | The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns |
| title_fullStr | The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns |
| title_full_unstemmed | The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns |
| title_short | The heterogeneous health state profiles of high-risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns |
| title_sort | heterogeneous health state profiles of high risk healthcare utilizers and their longitudinal hospital readmission and mortality patterns |
| topic | High-risk healthcare utilizers Integrated care Transitional care program Latent class analysis Hospital readmissions Mortality |
| url | https://doi.org/10.1186/s12913-019-4769-7 |
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