ABSTRACT
In France, the supply of healthcare services remains insufficient to meet the growing needs of the population, which has prompted the legislator to authorize direct access to certain professionals, in particular advanced practice nurses (APNs) working in coordinated practice structures. This article details an innovative multi-professional pathway in a nursing home, where the APN takes direct charge of people without a general practitioner, with the aim of improving their access to care and reintegrating them into the healthcare system.
Subject(s)
Advanced Practice Nursing , Primary Health Care , Humans , Advanced Practice Nursing/legislation & jurisprudence , France , Primary Health Care/organization & administration , Nursing Homes/organization & administration , Health Services Accessibility , Delivery of Health Care/organization & administrationABSTRACT
Real-world sensory-processing applications require compact, low-latency, and low-power computing systems. Enabled by their in-memory event-driven computing abilities, hybrid memristive-Complementary Metal-Oxide Semiconductor neuromorphic architectures provide an ideal hardware substrate for such tasks. To demonstrate the full potential of such systems, we propose and experimentally demonstrate an end-to-end sensory processing solution for a real-world object localization application. Drawing inspiration from the barn owl's neuroanatomy, we developed a bio-inspired, event-driven object localization system that couples state-of-the-art piezoelectric micromachined ultrasound transducer sensors to a neuromorphic resistive memories-based computational map. We present measurement results from the fabricated system comprising resistive memories-based coincidence detectors, delay line circuits, and a full-custom ultrasound sensor. We use these experimental results to calibrate our system-level simulations. These simulations are then used to estimate the angular resolution and energy efficiency of the object localization model. The results reveal the potential of our approach, evaluated in orders of magnitude greater energy efficiency than a microcontroller performing the same task.
