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1.
J Am Med Dir Assoc ; 23(10): 1653.e15-1653.e18, 2022 10.
Artigo em Inglês | MEDLINE | ID: mdl-35714700

RESUMO

OBJECTIVES: The objective of this study was to determine if providing home-based primary care (HBPC) to individuals with intellectual and/or developmental disabilities (IDD) was associated with a lower hospitalization rate than a control group receiving traditional primary care. DESIGN AND INTERVENTION: Individuals with IDD living in supported residential settings in Ohio were offered HBPC. Individuals electing HBPC made up the intervention group. Those who did not opt for HBPC continued to receive traditional primary care services and made up the control group. Hospitalizations were tracked in both groups. SETTING AND PARTICIPANTS: The 757 study participants had IDD diagnoses and received residential support services throughout the study period. METHODS: Annualized hospitalization rate was determined in both groups and was compared using generalized estimating equations while controlling for patients' age and hospitalization rate in the year prior to the study. RESULTS: The results showed that group membership had a significant effect on the hospitalization rate (Wald χ2 = 20.71, P < .01). Being in the control group was associated with a 2.12-fold increase in annual hospitalization rate for a given patient. The overall population hospitalization rate was 329 hospitalizations per 1000 per year in the HBPC-receiving individuals and 619 hospitalizations per 1000 per year in the control group. CONCLUSIONS AND IMPLICATIONS: We found that individuals with IDD receiving HBPC were hospitalized at a lower rate than a control group receiving traditional primary care. Expanding access to HBPC may be a worthwhile priority for organizations that support individuals with IDD.


Assuntos
Serviços de Assistência Domiciliar , Criança , Deficiências do Desenvolvimento/epidemiologia , Deficiências do Desenvolvimento/terapia , Hospitalização , Humanos , Ohio , Atenção Primária à Saúde
2.
Popul Health Manag ; 23(2): 107-114, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-31216255

RESUMO

In 2015, the Centers for Medicare & Medicaid Services (CMS) implemented a new benefit called chronic care management (CCM). A recent CMS-commissioned study of the program showed that CCM is effective in increasing advance care planning and decreasing overall costs. Despite positive effects on care planning, utilization, and cost, the CCM program remains underutilized. The authors sought to develop a platform to enable scale of the CCM program, and to report outcomes associated with its use. A technology and integrated clinical staff platform was built to enable a scalable, evidence-based implementation of the Medicare CCM program. The model created care management data elements that were used to flag clinical and utilization risks such as falls, mortality, hospitalization and polypharmacy. In 2018, CCM support was provided for 26,500 patients. Logistic regression analyses were used to identify risk factors associated with hospitalization. The cohort experienced 2679 hospitalizations (184 admissions per 1000 patient months per year). Among patients residing in non-nursing home settings, a higher Gagne mortality risk was associated with a 32 times greater chance of being hospitalized. Other positive predictors of hospitalization included being a nursing home resident and being ambulatory without assistance. Negative predictors of hospitalization included being flagged as having a high hospitalization risk, and scoring in the low-risk category for falls or polypharmacy. This CCM model is a scalable method of supporting care management for people with multiple chronic conditions, and can help identify risk factors for hospitalization.


Assuntos
Doença Crônica , Gerenciamento Clínico , Medicare , Modelos Organizacionais , Idoso , Idoso de 80 Anos ou mais , Comorbidade , Controle de Custos , Eficiência Organizacional , Medicina Baseada em Evidências , Feminino , Hospitalização/tendências , Humanos , Masculino , Avaliação de Programas e Projetos de Saúde , Análise de Regressão , Estados Unidos
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