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Delirium is a significant geriatric condition associated with adverse clinical and economic outcomes. The cause of delirium is usually multifactorial, and person‐centered multicomponent approaches for proper delirium management are required. In 2017, the John A. Hartford Foundation and the Institute for Healthcare Improvement (IHI) launched a national initiative, Age‐Friendly Health System (AFHS), promoting the use of a framework called 4Ms (what matters, medication, mentation, and mobility). The 4Ms framework's primary goal is to provide comprehensive and practical person‐centered care for older adults and it aligns with the core concepts of optimal delirium management. In this special article, we demonstrate how a traditional delirium prevention and management model can be assessed from the perspective of AFHS. An example is the crosswalk with the Hospital Elder Life Program (HELP) Core Interventions and the 4MS, which demonstrates alignment in delirium management. We also introduce useful tools to create an AFHS environment in delirium management. Although much has been written about delirium management, there is a need to identify the critical steps in advancing the overall delirium care in the context of the AFHS. In this article, we suggest future directions, including the need for more prospective and comprehensive research to assess the impact of AFHS on delirium care, the need for more innovative and sustainable education platforms, fundamental changes in the healthcare payment system for proper adoption of AFHS in any healthcare setting, and application of AFHS in the community for continuity of care for older adults with delirium. [ABSTRACT FROM AUTHOR]

This article discusses the Actions for Delirium Assessment, Prevention and Treatment (ADAPT) intiatives introduced by the Hartford Hospital in Connecticut in August 2011. It is comprised of clinical groups such as nursing, medicine, surgery, pharmacy, social work, informatics and case-coordination and includes five strategies to save the brain. The article also details the etiology and risk factors, clinical manifestation, evaluation and management of delirium in hospitalized patients.

Delirium is a serious medical complication associated with poor outcomes. Given the complexity of the syndrome, prevention and early detection are critical in mitigating its effects. We used Confusion Assessment Method (CAM) screening and Electronic Health Record (EHR) data for 64,038 inpatient visits to train and test a model predicting delirium arising in hospital. Incident delirium was defined as the first instance of a positive CAM occurring at least 48 h into a hospital stay. A Random Forest machine learning algorithm was used with demographic data, comorbidities, medications, procedures, and physiological measures. The data set was randomly partitioned 80% / 20% for training and validating the predictive model, respectively. Of the 51,240 patients in the training set, 2774 (5.4%) experienced delirium during their hospital stay; and of the 12,798 patients in the validation set, 701 (5.5%) experienced delirium. Under-sampling of the delirium negative population was used to address the class imbalance. The Random Forest predictive model yielded an area under the receiver operating characteristic curve (ROC AUC) of 0.909 (95% CI 0.898 to 0.921). Important variables in the model included previously identified predisposing and precipitating risk factors. This machine learning approach displayed a high degree of accuracy and has the potential to provide a clinically useful predictive model for earlier intervention in those patients at greatest risk of developing delirium. [ABSTRACT FROM AUTHOR]

The article focuses on Confusion Assessment Method (CAM). Approximately 15-60 % of elderly patients experience a delirium prior to or during a hospitalization but the diagnosis is missed in up to 70% of cases. Delirium is associated with poor outcomes such as prolonged hospitalization, functional decline, and increased use of chemical and physical restraints. CAM includes two parts. Part one is an assessment instrument that screens for overall cognitive impairment. Part two includes only those four features that were found to have the greatest ability to distinguish delirium or reversible confusion from other types of cognitive impairment.

With approximately 15 to 60 % of elderly patients experience a delirium prior to or during a hospitalization but the diagnosis is missed in up to 70% of cases. Delirium is associated with poor outcomes such as prolonged hospitalization, functional decline, and increased use of chemical and physical restraints. The best solution to this problem is the Confusion Assessment Method, which includes two parts. Part one is an assessment instrument that screens for overall cognitive impairment. Part two includes only those four features that were found to have the greatest ability to distinguish delirium or reversible confusion from other types of cognitive impairment.