Chronic care model and cost reduction in initial health: a new approach for satisfaction and improvement of chronicity

Marcelli, Stefano; Gatti, Chiara; Rocchi, Renato; Troiani, Silvano; Tuccio, Sandro Di; Giuli, Cinzia; Postacchini, Demetrio; Santarelli, Adoriano

doi:10.4081/gc.2017.7066

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…The chronic care model has been devised to transform the service delivery from an "outdated care" to an "effective care" model (see Fig. 2), with the following key shifts (adapted from Marcelli et al 2017). Health policy makers should consider following it more closely:…”

Section: Service Delivery Policy Prioritiesmentioning

confidence: 99%

Priorities for Arab Health Policy Makers

El-Saharty¹,

Liu²

2021

Handbook of Healthcare in the Arab World

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Section: Service Delivery Policy Prioritiesmentioning

confidence: 99%

Priorities for Arab Health Policy Makers

El-Saharty¹,

Liu²

2021

Handbook of Healthcare in the Arab World

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“…These data do not only have population significance, but also have economic and health consequences since 35% of men and 65% of women over 65 years old have some type of disability, which represents 52% of the health care costs in specialized care for people over 65, and 40% of the total health expenditure in those over 70. Therefore, both in the present and in the future, it is a priority to effectively and efficiently address the expected changes in both the healthcare system (treatment of multiple diseases and chronicity) and in the social system (social structure) to respond to the current growing demands [3,4].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Risk Factors in Dementia Through Machine Learning

Balea-Fernández

Martinez-Vega

Ortega

et al. 2021

JAD

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Background: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer’s disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold: on the one hand, non-modifiable factors and on the other, modifiable. Objective: This study aims to develop a processing framework based on machine learning (ML) and optimization algorithms to study sociodemographic, clinical, and analytical variables, selecting the best combination among them for an accurate discrimination between controls and subjects with major neurocognitive disorder (MNCD). Methods: This research is based on an observational-analytical design. Two research groups were established: MNCD group (n = 46) and control group (n = 38). ML and optimization algorithms were employed to automatically diagnose MNCD. Results: Twelve out of 37 variables were identified in the validation set as the most relevant for MNCD diagnosis. Sensitivity of 100%and specificity of 71%were achieved using a Random Forest classifier. Conclusion: ML is a potential tool for automatic prediction of MNCD which can be applied to relatively small preclinical and clinical data sets. These results can be interpreted to support the influence of the environment on the development of AD.

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