Evaluating Risk-Adjusted Hospital Performance Using Large-Scale Data on Mortality Rates of Patients in Intensive Care Units: A Flexible Semi-Nonparametric Modeling Approach
Background and objective: Significant variability in the quality of healthcare supplied by hospitals is drawing broad attention from the United States Centers for Medicare and Medicaid Services. The primary issue is to evaluate hospital performance based on patient outcomes. Generalized linear rando...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Translational Engineering in Health and Medicine |
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| Online Access: | https://ieeexplore.ieee.org/document/10068535/ |
| _version_ | 1797866197349302272 |
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| author | Yakun Liang Xuejun Jiang Bo Zhang |
| author_facet | Yakun Liang Xuejun Jiang Bo Zhang |
| author_sort | Yakun Liang |
| collection | DOAJ |
| description | Background and objective: Significant variability in the quality of healthcare supplied by hospitals is drawing broad attention from the United States Centers for Medicare and Medicaid Services. The primary issue is to evaluate hospital performance based on patient outcomes. Generalized linear random-effects models are a promising analytical tool for evaluating hospital performance. However, hospital compare data often violate the classical assumptions of normality on random effects and linearity representation on transformed conditional mean structures in these models. Methods: In this article, we proposed and tested the performance of a class of hospital compare models that embraces nonparametric mean structures with semi-nonparametric hospital random effects. Such models were further improved and integrated into a zero-inflated model. <inline-formula> <tex-math notation="LaTeX">$\mathtt {SAS}$ </tex-math></inline-formula> programs to implement these newly proposed hospital compare models were thoroughly developed. The <inline-formula> <tex-math notation="LaTeX">$\mathtt {SAS}$ </tex-math></inline-formula> programs are freely available via a GitHub (<uri>https:\\www.GitHub.com</uri>) repository. Results: We demonstrate the robustness of the proposed hospital compare models by conducting intensive empirical studies. Flexible semi-nonparametric random effects and functional fixed-effects mean structure were used to analyze patient mortality in a large-scale intensive care unit data set. After applying the proposed models to assess standardized modality rates and address patient-mix variability across hospitals, we detected those underperforming hospitals with higher mortality rates. Conclusions: Our research findings highlight how constructing advanced assessment tools for hospital performance could support better decision-making at the administrative and public levels. The proposed hospital compare models are comprehensive in their capacity to identify patterns of hospital random effects and to convey the variability in healthcare quality with powerful accuracy and interpretability. |
| first_indexed | 2024-04-09T23:21:13Z |
| format | Article |
| id | doaj.art-dfb2158d3f054f2b921950e367f4d5fa |
| institution | Directory Open Access Journal |
| issn | 2168-2372 |
| language | English |
| last_indexed | 2024-04-09T23:21:13Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Translational Engineering in Health and Medicine |
| spelling | doaj.art-dfb2158d3f054f2b921950e367f4d5fa2023-03-21T23:00:14ZengIEEEIEEE Journal of Translational Engineering in Health and Medicine2168-23722023-01-011123224610.1109/JTEHM.2023.325717910068535Evaluating Risk-Adjusted Hospital Performance Using Large-Scale Data on Mortality Rates of Patients in Intensive Care Units: A Flexible Semi-Nonparametric Modeling ApproachYakun Liang0https://orcid.org/0000-0002-5282-2511Xuejun Jiang1https://orcid.org/0000-0001-8966-8864Bo Zhang2Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, ChinaDepartment of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, ChinaDepartment of Neurology and ICCTR Biostatistics and Research Design Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USABackground and objective: Significant variability in the quality of healthcare supplied by hospitals is drawing broad attention from the United States Centers for Medicare and Medicaid Services. The primary issue is to evaluate hospital performance based on patient outcomes. Generalized linear random-effects models are a promising analytical tool for evaluating hospital performance. However, hospital compare data often violate the classical assumptions of normality on random effects and linearity representation on transformed conditional mean structures in these models. Methods: In this article, we proposed and tested the performance of a class of hospital compare models that embraces nonparametric mean structures with semi-nonparametric hospital random effects. Such models were further improved and integrated into a zero-inflated model. <inline-formula> <tex-math notation="LaTeX">$\mathtt {SAS}$ </tex-math></inline-formula> programs to implement these newly proposed hospital compare models were thoroughly developed. The <inline-formula> <tex-math notation="LaTeX">$\mathtt {SAS}$ </tex-math></inline-formula> programs are freely available via a GitHub (<uri>https:\\www.GitHub.com</uri>) repository. Results: We demonstrate the robustness of the proposed hospital compare models by conducting intensive empirical studies. Flexible semi-nonparametric random effects and functional fixed-effects mean structure were used to analyze patient mortality in a large-scale intensive care unit data set. After applying the proposed models to assess standardized modality rates and address patient-mix variability across hospitals, we detected those underperforming hospitals with higher mortality rates. Conclusions: Our research findings highlight how constructing advanced assessment tools for hospital performance could support better decision-making at the administrative and public levels. The proposed hospital compare models are comprehensive in their capacity to identify patterns of hospital random effects and to convey the variability in healthcare quality with powerful accuracy and interpretability.https://ieeexplore.ieee.org/document/10068535/Hospital random effectsICU mortalityrepeated measuresrisk adjustmentzero-inflated models |
| spellingShingle | Yakun Liang Xuejun Jiang Bo Zhang Evaluating Risk-Adjusted Hospital Performance Using Large-Scale Data on Mortality Rates of Patients in Intensive Care Units: A Flexible Semi-Nonparametric Modeling Approach IEEE Journal of Translational Engineering in Health and Medicine Hospital random effects ICU mortality repeated measures risk adjustment zero-inflated models |
| title | Evaluating Risk-Adjusted Hospital Performance Using Large-Scale Data on Mortality Rates of Patients in Intensive Care Units: A Flexible Semi-Nonparametric Modeling Approach |
| title_full | Evaluating Risk-Adjusted Hospital Performance Using Large-Scale Data on Mortality Rates of Patients in Intensive Care Units: A Flexible Semi-Nonparametric Modeling Approach |
| title_fullStr | Evaluating Risk-Adjusted Hospital Performance Using Large-Scale Data on Mortality Rates of Patients in Intensive Care Units: A Flexible Semi-Nonparametric Modeling Approach |
| title_full_unstemmed | Evaluating Risk-Adjusted Hospital Performance Using Large-Scale Data on Mortality Rates of Patients in Intensive Care Units: A Flexible Semi-Nonparametric Modeling Approach |
| title_short | Evaluating Risk-Adjusted Hospital Performance Using Large-Scale Data on Mortality Rates of Patients in Intensive Care Units: A Flexible Semi-Nonparametric Modeling Approach |
| title_sort | evaluating risk adjusted hospital performance using large scale data on mortality rates of patients in intensive care units a flexible semi nonparametric modeling approach |
| topic | Hospital random effects ICU mortality repeated measures risk adjustment zero-inflated models |
| url | https://ieeexplore.ieee.org/document/10068535/ |
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