Huiwen Xu1,2, Orna Intrator3,4, Eva Culakova5, John R Bowblis6,7. 1. Department of Preventive Medicine and Population Health, University of Texas Medical Branch, Galveston, Texas, USA. 2. Sealy Center on Aging, University of Texas Medical Branch, Galveston, Texas, USA. 3. Department of Public Health Sciences, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA. 4. Geriatrics & Extended Care Data Analysis Center (GECDAC), Canandaigua VA Medical Center, Canandaigua, New York, USA. 5. Department of Surgery, Cancer Control, University of Rochester School of Medicine and Dentistry, Rochester, New York, USA. 6. Department of Economics, Farmer School of Business, Miami University, Oxford, Ohio, USA. 7. Scripps Gerontology Center, Miami University, Oxford, Ohio, USA.
Abstract
OBJECTIVE: Nursing homes (NHs) are serving an increasing proportion of residents with cognitive issues (e.g., dementia) and mental health conditions. This study aims to: (1) implement unsupervised machine learning to cluster NHs based on residents' dementia and mental health conditions; (2) examine NH staffing related to the clusters; and (3) investigate the association of staffing and NH quality (measured by the number of deficiencies and deficiency scores) in each cluster. DATA SOURCES: 2009-2017 Certification and Survey Provider Enhanced Reporting (CASPER) were merged with LTCFocUS.org data on NHs in the United States. STUDY DESIGN: Unsupervised machine learning algorithm (K-means) clustered NHs based on percent residents with dementia, depression, and serious mental illness (SMI, e.g., schizophrenia, anxiety). Panel fixed-effects regressions on deficiency outcomes with staffing-cluster interactions were conducted to examine the effects of staffing on deficiency outcomes in each cluster. DATA EXTRACTION METHODS: We identified 110,463 NH-year observations from 14,671 unique NHs using CASPER data. PRINCIPAL FINDINGS: Three clusters were identified: low dementia and mental illnesses (Postacute Cluster); high dementia and depression, but low SMI (Long-stay Cluster); and high dementia and mental illnesses (Cognitive-mental Cluster). From 2009 to 2017, the number of Postacute Cluster NHs increased from 3074 to 5719, while the number of Long-stay Cluster NHs decreased from 6745 to 3058. NHs in Long-stay/Cognitive-mental Clusters reported slightly lower nursing staff hours in 2017. Regressions suggested the effect of increasing staffing on reducing deficiencies is statistically similar across NH clusters. For example, 1 hour increase in registered nurse hours per resident day was associated with -0.67 (standard error [SE] = 0.11), -0.88 (SE = 0.12), and -0.97 (SE = 0.15) deficiencies in Postacute Cluster, Long-stay Cluster, and Cognitive-mental Cluster, respectively. CONCLUSIONS: Unsupervised machine learning detected a changing landscape of NH serving residents with dementia and mental illnesses, which requires assuring staffing levels and trainings are suited to residents' needs.
OBJECTIVE: Nursing homes (NHs) are serving an increasing proportion of residents with cognitive issues (e.g., dementia) and mental health conditions. This study aims to: (1) implement unsupervised machine learning to cluster NHs based on residents' dementia and mental health conditions; (2) examine NH staffing related to the clusters; and (3) investigate the association of staffing and NH quality (measured by the number of deficiencies and deficiency scores) in each cluster. DATA SOURCES: 2009-2017 Certification and Survey Provider Enhanced Reporting (CASPER) were merged with LTCFocUS.org data on NHs in the United States. STUDY DESIGN: Unsupervised machine learning algorithm (K-means) clustered NHs based on percent residents with dementia, depression, and serious mental illness (SMI, e.g., schizophrenia, anxiety). Panel fixed-effects regressions on deficiency outcomes with staffing-cluster interactions were conducted to examine the effects of staffing on deficiency outcomes in each cluster. DATA EXTRACTION METHODS: We identified 110,463 NH-year observations from 14,671 unique NHs using CASPER data. PRINCIPAL FINDINGS: Three clusters were identified: low dementia and mental illnesses (Postacute Cluster); high dementia and depression, but low SMI (Long-stay Cluster); and high dementia and mental illnesses (Cognitive-mental Cluster). From 2009 to 2017, the number of Postacute Cluster NHs increased from 3074 to 5719, while the number of Long-stay Cluster NHs decreased from 6745 to 3058. NHs in Long-stay/Cognitive-mental Clusters reported slightly lower nursing staff hours in 2017. Regressions suggested the effect of increasing staffing on reducing deficiencies is statistically similar across NH clusters. For example, 1 hour increase in registered nurse hours per resident day was associated with -0.67 (standard error [SE] = 0.11), -0.88 (SE = 0.12), and -0.97 (SE = 0.15) deficiencies in Postacute Cluster, Long-stay Cluster, and Cognitive-mental Cluster, respectively. CONCLUSIONS: Unsupervised machine learning detected a changing landscape of NH serving residents with dementia and mental illnesses, which requires assuring staffing levels and trainings are suited to residents' needs.
Authors: Meg Bourbonniere; Zhanlian Feng; Orna Intrator; Joseph Angelelli; Vincent Mor; Jacqueline S Zinn Journal: Med Care Res Rev Date: 2006-02 Impact factor: 3.929
Authors: Orna Intrator; Mark Schleinitz; David C Grabowski; Jacqueline Zinn; Vincent Mor Journal: Health Serv Res Date: 2008-09-08 Impact factor: 3.402