e-Health Technological Ecosystems: Advanced Solutions to Support Informal Caregivers and Vulnerable Populations During the COVID-19 Outbreak.

Introduction: This study highlights the importance of technological ecosystems in supporting informal caregivers and vulnerable populations in coping with the ongoing coronavirus disease 2019 (COVID-19) pandemic. Methods: This study integrates the available literature on internet of things (IoT) e-health ecosystem and informal care. Results: In the first part of this article, we describe the health consequences of quarantine and isolation and outline the potential role of informal care in containing the risk of spreading the infection and reducing the burden on the health care system. Then, we present an overview of the characteristics of emerging technological ecosystems in health care and how they can be adopted as a strategic option to achieve different goals: (1) support informal carers to help vulnerable populations during quarantine and isolation and facilitate the recovery process; (2) promote the adoption of e-health and telemedicine resources to reduce the well-documented burden experienced by caregivers; and (3) lessen the various forms of digital disadvantage among vulnerable individuals, who are at more risk to be digitally excluded. In the last part of this work, we introduce solutions to overcome potential challenges related to the development and adoption of advanced technological ecosystems and propose a reflection on the legacy of COVID-19 on telemedicine. Conclusions: Thanks to the disruptive potential of IoT for health and wellness promotion, technological ecosystems emerge as a valuable resource to support both informal carers and vulnerable populations. The main factors that represent a strategic advantage of a technological ecosystem are affordability, regulatory, and availability. A high degree of interconnection between all the stakeholders emerges as a key element for the provision of intergenerational care. The most important technical challenges of IoT e-health require to optimize privacy, security, and user-friendliness of IoT e-health.

A Predictive Model for Estimating Risk of Harm and Aggression in Inpatient Mental Health Clinics.

Serious mental illness is a major risk factor for aggression and violence. The present study aimed to develop and test an algorithm to predict inpatient aggressions that involve a risk of harm to self or others. This work is based on a retrospective study aimed to investigate the prediction of risk of harm and aggressions at St. Joseph's Healthcare Hamilton, between 2016 and 2017. An analysis of the risk factors most strongly associated with harmful incidents is, followed by the description of the process involved in the development of a predictive model which estimates the risk of harm. The efficiency of the model developed is finally evaluated, showing an overall accuracy of 75%: the specificity to identify episodes considered not at risk of harm is equal to 91.85%, whereas the sensitivity to identify episodes considered harmful is equal to 28.57%. The model proposed can be seen as a seminal project towards the development of a more comprehensive, precise and effective tool capable to predict the risk of harm in the inpatient setting.

An Examination of the Performance of the interRAI Risk of Harm to Others Clinical Assessment Protocol (RHO CAP) in Inpatient Mental Health Settings.

We study violence risk prediction at St. Joseph's Healthcare Hamilton. Data from January 2016 to December 2017 have been anonymized and collected, for a total of 870 episodes of inpatient aggressions perpetrated by 337 patients. We examine the predictive performance of a clinical indicator embedded in a mandatory assessment tool for psychiatric facilities in Ontario, the Resident Assessment Instrument for Mental Health (RAI-MH): the Risk of Harm to Others Clinical Assessment Protocol (RHO CAP). The RHO CAP's performance is studied among two groups of patients. Moreover, an analysis of the most important risk factors associated with harmful incidents is presented. The RHO CAP has demonstrated a better performance in discriminating which patients were more at risk to commit some type of aggression than at identifying the risk of harm among those who will commit aggression.