Neighborhood-Level Disparities in Resuscitation and the Potential of Connected Health

Merchant, Raina M.; Groeneveld, Peter W.

doi:10.1001/jamacardio.2017.2763

Cited by 4 publications

(2 citation statements)

References 10 publications

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“…The integration of unmanned aerial vehicles (UAVs), also known as drones, into health systems represents an area of massive potential [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Half the world population lives in rural areas, defined in diverse ways across countries, but characterized by non-urban density population [19,20].…”

Section: Introductionmentioning

confidence: 99%

“…In Canada, Drone Delivery Canada has partnered with Moose Cree First Nation communities living in remote areas of Northern Ontario to deliver goods, such as medical supplies [4]. Studies have shown that automatic external defibrillator (AED) delivered by drones can reach individuals in cardiac arrest approximately 19 min faster than the emergency medical services (EMS), improving patient survival and recovery rates [2,3,10,12,18]. Drones are being used to expedite the process of getting biological samples for diagnostic purposes to laboratories, reducing risks of biological samples becoming non-viable in the process of transportation [8,11,14,23,24], and there are many potential uses of for drones for telemedicine are in expansion [1,25].…”

Section: Introductionmentioning

confidence: 99%

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Context-Specific Challenges, Opportunities, and Ethics of Drones for Healthcare Delivery in the Eyes of Program Managers and Field Staff: A Multi-Site Qualitative Study

Jeyabalan

Nouvet

Meier³

et al. 2020

Drones

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Unmanned aerial vehicles (UAVs), also known as drones, have significant potential in the healthcare field. Ethical and practical concerns, challenges, and complexities of using drones for specific and diverse healthcare purposes have been minimally explored to date. This paper aims to document and advance awareness of diverse context-specific concerns, challenges, and complexities encountered by individuals working on the front lines of drones for health. It draws on original qualitative research and data from semi-structured interviews (N = 16) with drones for health program managers and field staff in nine countries. Directed thematic analysis was used to analyze interviews and identify key ethical and practical concerns, challenges, and complexities experienced by participants in their work with drones for health projects. While some concerns, challenges, and complexities described by study participants were more technical in nature, for example, those related to drone technology and approval processes, the majority were not. The bulk of context-specific concerns and challenges identified by participants, we propose, could be mitigated through community engagement initiatives.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Context-Specific Challenges, Opportunities, and Ethics of Drones for Healthcare Delivery in the Eyes of Program Managers and Field Staff: A Multi-Site Qualitative Study

Jeyabalan

Nouvet

Meier³

et al. 2020

Drones

View full text Add to dashboard Cite

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Racial differences in overdose events and polydrug detection in Indianapolis, Indiana

Ray

Lowder

Bailey

et al. 2020

Drug and Alcohol Dependence

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Influence of Mild Cognitive Impairment, Depression, and Anxiety on the Quality of Life of Patients with Parkinson Disease

Wiesli

Meyer

Fuhr

et al. 2017

Dement Geriatr Cogn Disord Extra

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Background: Neuropsychiatric symptoms (NPS) and mild cognitive impairment (MCI) are common in Parkinson disease (PD) and have a negative impact on the patient’s quality of life (QoL). We aim to describe the effect of NPS and MCI on each other and on QoL. Methods: Sixteen MCI and 37 non-MCI PD patients completed different self-assessment questionnaires including the Parkinson’s Disease Questionnaire (PDQ-39), the Beck Depression Inventory (BD), the Beck Anxiety Inventory (BAI), and the Self-Report Manic Inventory (SRMI). Linear regression with stepwise elimination was used to select the significant predictors of QoL and to control for confounding factors. Results: The significant linear regression model (R² = 0.68, p ≤ 0.01) revealed significant effects of MCI (p = 0.03), BDI (p ≤ 0.01), BAI (p ≤ 0.01), age (p = 0.03), a trend of SRMI (p = 0.06), and disease duration (p = 0.08) on QoL. Conclusions: MCI, anxiety, depression, and age decrease QoL in patients with PD. NPS has the main influence in the prediction of QoL in patients with PD, whereas MCI is only of minor importance.

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Neighborhood-Level Disparities in Resuscitation and the Potential of Connected Health

Cited by 4 publications

References 10 publications

Context-Specific Challenges, Opportunities, and Ethics of Drones for Healthcare Delivery in the Eyes of Program Managers and Field Staff: A Multi-Site Qualitative Study

Context-Specific Challenges, Opportunities, and Ethics of Drones for Healthcare Delivery in the Eyes of Program Managers and Field Staff: A Multi-Site Qualitative Study

Racial differences in overdose events and polydrug detection in Indianapolis, Indiana

Influence of Mild Cognitive Impairment, Depression, and Anxiety on the Quality of Life of Patients with Parkinson Disease

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