A systematic literature review of predicting patient discharges using statistical methods and machine learning.

Discharge planning is integral to patient flow as delays can lead to hospital-wide congestion. Because a structured discharge plan can reduce hospital length of stay while enhancing patient satisfaction, this topic has caught the interest of many healthcare professionals and researchers. Predicting discharge outcomes, such as destination and time, is crucial in discharge planning by helping healthcare providers anticipate patient needs and resource requirements. This article examines the literature on the prediction of various discharge outcomes. Our review discovered papers that explore the use of prediction models to forecast the time, volume, and destination of discharged patients. Of the 101 reviewed papers, 49.5% looked at the prediction with machine learning tools, and 50.5% focused on prediction with statistical methods. The fact that knowing discharge outcomes in advance affects operational, tactical, medical, and administrative aspects is a frequent theme in the papers studied. Furthermore, conducting system-wide optimization, predicting the time and destination of patients after discharge, and addressing the primary causes of discharge delay in the process are among the recommendations for further research in this field.

Closed-loop MOEMS accelerometer.

In this paper, a closed-loop micro-opto-electro-mechanical system (MOEMS) accelerometer based on the Fabry-Pérot (FP) interferometer is presented. The FP cavity is formed between the end of a cleaved single-mode optical fiber and the cross-section of a proof mass (PM) which is suspended by four U-shaped springs. The applied acceleration tends to move the PM in the opposite direction. The arrays of fixed and movable comb fingers produce an electrostatic force which keeps the PM in its resting position. The voltage that can provide this electrostatic force is considered as the output of the sensor. Using a closed-loop detection method it is possible to increase the measurement range without losing the resolution. The proposed sensor is fabricated on a silicon-on-insulator wafer using the bulk micromachining method. The results of the sensor characterization show that the accelerometer has a linear response in the range of ±5 g. In the closed-loop mode, the sensitivity and bias instability of the sensor are 1.16 V/g and 40 µg, respectively.

Coarse-to-fine optical MEMS accelerometer design and simulation.

A coarse-to-fine optical microelectromechanical systems (MEMS) accelerometer based on the Fabry-Pérot (FP) interferometer is proposed. The mechanical structure consists of a proof mass that is suspended by four L-shaped springs. The deflection of the proof mass due to the applied acceleration is detected using two FP cavities that comprise the system's optical system. Using coarse-to-fine measurement and the dual wavelength method simultaneously increases the sensitivity of the accelerometer as well as the linear measurement range. The optical simulation shows that the sensitivity of the proposed device is 10 times as high as that of a similar optical MEMS accelerometer with one FP cavity. In addition, the proposed optical system is insensitive to the displacements of the proof mass in the orthogonal directions, which considerably reduced the cross-axis sensitivity. The minimum feature size of the structure is 15 µm and the optical signal is conducted completely through the optical fibers, facilitating the device fabrication. Here are the results of the simulation: mechanical sensitivity of 190 nm/g, optical sensitivity of 8 nm/g, linear measurement of ±5g, and first resonance frequency of 1141 Hz.

A Narrative Literature Review on Human Resource Planning for Palliative Care Personnel.

A literature search was started with the objective of finding works pertaining to the use of operations research techniques in planning for human resources in palliative care. Since the search indicated that there is no such work, in this paper, we report on the literature on workforce planning and human resource planning for palliative care personnel. Using our findings, we discuss the factors that influence the supply and demand for the palliative care workforce. Our results show that the enhancement of efficiency, training more primary caregivers to deliver palliative care, and allowing for mid-career specialist training are practical ways to compensate for the gap between the supply and demand in the palliative care workforce.

Fabrication, characterization, and simulation of a cantilever-based airflow sensor integrated with optical fiber.

In this paper, we present the fabrication and packaging of a cantilever-based airflow sensor integrated with optical fiber. The sensor consists of a micro Fabry-Perot (FP) cavity including a fiber and a micro cantilever that is fabricated using the photolithography method. Airflow causes a small deflection of the micro cantilever and changes the cavity length of the FP, which makes the fringe shift. The pressure distribution and velocity streamlines across the cantilever resulted from the airflow in the channel have been simulated by the finite element method. The experimental results demonstrate that the sensor has a linear sensitivity of 190 [fringe shift (pm)] per (l/min) and a minimum detectable airflow change of 0.05 (l/min).

Machine learning to identify attributes that predict patients who leave without being seen in a pediatric emergency department.

PURPOSE: To characterize patients who left without being seen (LWBS) from a Canadian pediatric Emergency Department (ED) and create predictive models using machine learning to identify key attributes associated with LWBS. METHODS: We analyzed administrative ED data from April 1, 2017, to March 31, 2020, from IWK Health ED in Halifax, NS. Variables included: visit disposition; Canadian Triage Acuity Scale (CTAS); triage month, week, day, hour, minute, and day of the week; sex; age; postal code; access to primary care provider; visit payor; referral source; arrival by ambulance; main problem (ICD10); length of stay in minutes; driving distance in minutes; and ED patient load. The data were randomly divided into training (80%) and test datasets (20%). Five supervised machine learning binary classification algorithms were implemented to train models to predict LWBS patients. We balanced the dataset using Synthetic Minority Oversampling Technique (SMOTE) and used grid search for hyperparameter tuning of our models. Model evaluation was made using sensitivity and recall on the test dataset. RESULTS: The dataset included 101,266 ED visits where 2009 (2%) records were excluded and 5800 LWBS (5.7%). The highest-performing machine learning model with 16 patient attributes was XGBoost which was able to identify LWBS patients with 95% recall and 87% sensitivity. The most influential attributes in this model were ED patient load, triage hour, driving minutes from home address to ED, length of stay (minutes since triage), and age. CONCLUSION: Our analysis showed that machine learning models can be used on administrative data to predict patients who LWBS in a Canadian pediatric ED. From 16 variables, we identified the five most influential model attributes. System-level interventions to improve patient flow have shown promise for reducing LWBS in some centres. Predicting patients likely to LWBS raises the possibility of individual patient-level interventions to mitigate LWBS.

RéSUMé: BUT: Caractériser les patients qui sont partis sans être vus (left without being seen LWBS) d'un service d'urgence (SU) pédiatrique canadien et créer des modèles prédictifs utilisant l'apprentissage automatique pour identifier les attributs clés associés au LWBS. MéTHODES: Nous avons analysé les données administratives de SU du 1er avril 2017 au 31 mars 2020 provenant de l'urgence de IWK Health à Halifax, en Nouvelle-Écosse. Les variables comprenaient: disposition de la visite; l'échelle canadienne de triage de la gravité (ETG); mois, semaine, jour, heure, minute et jour de la semaine; sexe; âge; code postal; accès au fournisseur de soins primaires; payeur de la visite; source de l'aiguillage; arrivée par ambulance; principal problème (CIM10); durée du séjour en minutes; distance de conduite en minutes; et la charge de patients de l'urgence. Les données ont été divisées de manière aléatoire en ensembles de données de formation (80%) et de test (20%). Cinq algorithmes de classification binaire d'apprentissage automatique supervisés ont été mis en œuvre pour former des modèles de prévision des patients atteints de LWBS. Nous avons équilibré l'ensemble de données à l'aide de la technique de suréchantillonnage synthétique des minorités (SMOTE) et utilisé la recherche de grille pour le réglage des hyperparamètres de nos modèles. L'évaluation du modèle a été faite en utilisant la sensibilité et le rappel sur l'ensemble de données d'essai. RéSULTATS: L'ensemble de données comprenait 101266 visites aux urgences où les enregistrements de 2009 (2%) ont été exclus et 5800 LWBS (5,7%). Le modèle d'apprentissage automatique le plus performant avec 16 attributs de patient était XGBoost, qui a été en mesure d'identifier les patients LWBS avec 95% de rappel et 87% de sensibilité. Les attributs les plus influents dans ce modèle étaient la charge de patients à l'urgence, l'heure de triage, les minutes de conduite entre l'adresse du domicile et l'urgence, la durée du séjour (minutes depuis le triage) et l'âge. CONCLUSION: Notre analyse a montré que les modèles d'apprentissage automatique peuvent être utilisés sur des données administratives pour prédire les patients qui sont partis sans être vus dans un service d'urgence pédiatrique canadien. À partir de 16 variables, nous avons identifié les cinq attributs de modèle les plus influents. Les interventions au niveau du système visant à améliorer le flux de patients se sont révélées prometteuses pour réduire les LWBS dans certains centres. La prévision des patients susceptibles de LWBS soulève la possibilité d'interventions individuelles au niveau des patients pour atténuer le LWBS.

Liquid-amplified zipping actuators for micro-air vehicles with transmission-free flapping.

Flapping micro-air vehicles (MAVs) can access a wide range of locations, including confined spaces such as the inside of industrial plants and collapsed buildings, and offer high maneuverability and tolerance to disturbances. However, current flapping MAVs require transmission systems between their actuators and wings, which introduce energetic losses and additional mass, hindering performance. Here, we introduce a high-performance electrostatic flapping actuation system, the liquid-amplified zipping actuator (LAZA), which induces wing movement by direct application of liquid-amplified electrostatic forces at the wing root, eliminating the requirement of any transmission system and their associated downsides. The LAZA allows for accurate control of flapping frequency and amplitude, exhibits no variation in performance over more than 1 million actuation cycles, and delivers peak and average specific powers of 200 and 124 watts per kilogram, respectively, exceeding mammalian and insect flight muscle and on par with modern flapping MAV actuation systems. The inclusion of 50-millimeter-long passively pitching wings in a dragonfly-sized LAZA flapping system allowed the rectification of net directional thrust up to 5.73 millinewtons. This thrust was achieved while consuming only 243 milliwatts of electrical power, implying a thrust-to-power ratio of 23.6 newtons per kilowatt, similar to state-of-the-art flapping MAVs, helicopter rotors, and commercial drone motors. Last, a horizontally moving LAZA flapping system supported by a taut nylon wire was able to accelerate from at-rest and travel at speeds up to 0.71 meters per second. The LAZA enables lightweight, high-performance transmission-free flapping MAVs for long-term remote exploration and search-and-rescue missions.

Characteristic Analysis and Design Optimization of Bubble Artificial Muscles.

Soft robotics requires new actuators and artificial muscles that are lighter, less expensive, and more effective than current technologies. Recently developed bubble artificial muscles (BAMs) are lightweight, flexible, inexpensive, pneumatic actuators with the capability of being scalable, contracting at a low pressure, and generating sufficient tension and contraction for assisting human mobility. The BAMs are simply fabricated by using a commercial plastic tubing with retaining rings, forming a "bubble" shape and creating a series of contractile units to attain a desired stroke. They can deliver high contraction through optimization of actuator length and radius, or high tension by strengthening their materials to operate at high pressure. Here, we present a detailed analysis of BAMs, define a model for their actuation, and verify the model through a series of experiments with fabricated BAM actuators. In tests, a maximum contraction of 43.1% and a maximum stress of 0.894 MPa were achieved, corresponding to the BAM lifting a load 1000 times its own weight (5.39 g). The BAM model was built to predict experimental performance, for example, the relationship between tension and contraction at various applied pressures, and between contraction and pressure. Characteristic analysis and design optimization of the BAM are presented as an approach to design and manufacture the ideal "bubble" actuator at any required dimensions. A BAM orthosis is demonstrated as assisting a sit-to-stand transition on a leg mechanism, constructed to match the scale of a human's lower limb. Guidelines for further improvement of the BAM are also included.

FleXert: A Soft, Actuatable Multiwell Plate Insert for Cell Culture under Stretch.

Porous multiwell plate inserts are widely used in biomedical research to study transport processes or to culture cells/tissues at the air-liquid interface. These inserts are made of rigid materials and used under static culture conditions, which are unrepresentative of biological microenvironments. Here, we present FleXert, a soft, actuatable cell culture insert that interfaces with six-well plates. It is made of polydimethylsiloxane (PDMS) and comprises a porous PDMS membrane as cell/tissue support. FleXerts can be pneumatically actuated using a standard syringe pump, imparting tensile strains of up to 30%. A wide range of actuation patterns can be achieved by varying the air pressure and pumping rate. Facile surface functionalization of FleXert's porous PDMS membrane with fibronectin enables adhesion of human dermal fibroblasts and strains developing on FleXert's membrane are successfully transduced to the cell layer. 3D tissue models, such as fibroblast-laden collagen gels, can also be anchored to PDMS following polydopamine coating. Furthermore, collagen-coated FleXert membranes support the establishment of a human skin model, demonstrating the material's excellent biocompatibility required for tissue engineering. In contrast to existing technologies, FleXerts do not require costly fabrication equipment or custom-built culture chambers, making them a versatile and low-cost solution for tissue engineering and biological barrier penetration studies under physiological strain. This paper is an extensive toolkit for multidisciplinary mechanobiology studies, including detailed instructions for a wide variety of methods such as device fabrication, theoretical modeling, cell culture, and image analysis techniques.

Workforce Planning for Community-Based Palliative Care Specialist Teams Using Operations Research.

CONTEXT: Many countries have aging populations. Thus, the need for palliative care will increase. However, the methods to estimate optimal staffing for specialist palliative care teams are rudimentary as yet. OBJECTIVES: To develop a population-need workforce planning model for community-based palliative care specialist teams and to apply the model to forecast the staff needed to care for all patients with terminal illness, organ failure, and frailty during the next 20 years, with and without the expansion of primary palliative care. METHODS: We used operations research (linear programming) to model the problem. We used the framework of the Canadian Society of Palliative Care Physicians and the Nova Scotia palliative care strategy to apply the model. RESULTS: To meet the palliative care needs for persons dying across Nova Scotia in 2019, the model generated an estimate of 70.8 nurses, 23.6 physicians, and 11.9 social workers, a total of 106.3 staff. Thereby, the model indicated that a 64% increase in specialist palliative care staff was needed immediately, and a further 13.1% increase would be needed during the next 20 years. Trained primary palliative care providers currently meet 3.7% of need, and with their expansion are expected to meet 20.3% by 2038. CONCLUSION: Historical, current, and projected data can be used with operations research to forecast staffing levels for specialist palliative care teams under various scenarios. The forecast can be updated as new data emerge, applied to other populations, and used to test alternative delivery models.

Twisted Rubber Variable-Stiffness Artificial Muscles.

Variable-stiffness artificial muscles are important in many applications including running and hopping robots, human-robot interaction, and active suspension systems. Previously used technologies include pneumatic muscles, layer and granular jamming, series elastic actuators, and shape memory polymers. All these are limited in terms of cost, complexity, the need for fluid power supplies, or controllability. In this article, we present a new concept for variable-stiffness artificial muscles (the twisted rubber artificial muscle, TRAM) made from twisted rubber cord that overcomes these limitations. Rubber cord is inexpensive, readily available, and inherently compliant. When an extended piece of rubber cord is twisted, the tensile force it exerts is reduced and its stiffness is altered. This behavior makes twisted rubber ideal for use as an artificial muscle, because its output force and natural stiffness are both controllable by varying twist angle. We investigate the behavior of four types of rubber cord and evaluate which type of rubber allows for the greatest reversible reduction in average stiffness (fluoroelastomer [FKM standard] rubber, 56.42% reduction) and initial stiffness (silicone rubber, 92.62%). Tensile force and stiffness can be further altered by increasing the twist angle of the artificial muscle beyond a threshold angle, which initiates nonlinear buckling behavior. This, however, can cause plastic deformation of the artificial muscle. Using a single TRAM, we show how the equilibrium position and natural frequency of a system can be simultaneously altered by controlling twist angle. We further demonstrate independent position and stiffness control of a functional robotic arm system using an antagonistic pair of TRAMs. TRAMs are ready for immediate inclusion in a wide range of robotic systems.

Closed-Loop Control of Electro-Ribbon Actuators.

Electro-ribbon actuators are lightweight, flexible, high-performance actuators for next generation soft robotics. When electrically charged, electrostatic forces cause the electrode ribbons to progressively zip together through a process called dielectrophoretic liquid zipping (DLZ), delivering contractions of more than 99% of their length. Electro-ribbon actuators exhibit pull-in instability, and this phenomenon makes them challenging to control: below the pull-in voltage threshold, actuator contraction is small, while above this threshold, increasing electrostatic forces cause the actuator to completely contract, providing a narrow contraction range for feedforward control. We show that application of a time-varying voltage profile that starts above pull-in threshold, but subsequently reduces, allows access to intermediate steady-states not accessible using traditional feed-forward control. A modified proportional-integral closed-loop controller is proposed (Boost-PI), which incorporates a variable boost voltage to temporarily elevate actuation close to, but not exceeding, the pull-in voltage threshold. This primes the actuator for zipping and drastically reduces rise time compared with a traditional PI controller. A multi-objective parameter-space approach was implemented to choose appropriate controller gains by assessing the metrics of rise time, overshoot, steady-state error, and settle time. This proposed control method addresses a key limitation of the electro-ribbon actuators, allowing the actuator to perform staircase and oscillatory control tasks. This significantly increases the range of applications which can exploit this new DLZ actuation technology.

Electroactive Textile Actuators for Breathability Control and Thermal Regulation Devices.

Smart fabrics offer the potential for a new generation of soft robotics and wearable technologies through the fusion of smart materials, textiles and electrical circuitries. Conductive and stretchable textiles have inherent compliance and low resistance that are suitable for driving artificial muscle actuators and are potentially safer electrode materials for soft actuation technologies. We demonstrate how soft electroactive actuating structures can be designed and fabricated from conducting textiles. We first quantitatively analyse a range of stretchable conductive textiles for dielectric elastomer actuators (DEAs). We found that conductive-knit textiles are more suitable for unidirectional DEA applications due to the largest difference (150%) in principle strain axes, whereas isotropic textiles are more suited to bidirectional DEA applications due to the smallest (11.1%) principle strain difference. Finally, we demonstrate controllable breathability through a planar e-textile DEA-driven skin and show thermal regulation in a wearable prototype that exploits soft actuation and kirigami.

Utilizing polarization-selective mode shaping by chalcogenide thin film to enhance the performance of graphene-based integrated optical devices.

High refractive index (RI) thin films are capable of pulling waveguide mode profiles towards themselves. In this study, it is shown that by applying high RI coatings with specific thicknesses on the side of optical waveguides, significantly different mode profiles for orthogonal polarizations can be achieved. This phenomenon, that we call it polarization-selective mode shaping, can be extensively used in the enhancement of polarization-dependent integrated optical devices. As an illustrating application, a tri-layer structure consisting of poly(methyl methacrylate)/graphene/chalcogenide on a side-polished fiber is designed to realize an extremely high extinction ratio polarizer. This structure changes the mode profiles in a way that the attenuation of TE mode is maximized, while the power carried by the TM mode remains relatively constant. Simulations and experimental characterizations confirm that polarization-selective mode shaping coordinates four loss mechanisms to maximize the extinction ratio and minimize the insertion loss of the polarizer. The fabricated polarizer is examined in the O, C, and L telecommunication frequency bands. This configuration achieves the high extinction ratio of 51.3 dB and its maximum insertion loss in the tested wavelengths is 1.79 dB. The proposed polarizer has been compared with other state-of-the-art polarizers in the conclusion section which shows its superiority.

Electro-ribbon actuators and electro-origami robots.

Origami has inspired novel solutions across myriad fields from DNA synthesis to robotics. Even wider impact can be achieved by active origami, which can move and change shape independently. However, current active origami and the materials that power it are both limited in terms of strength, speed, and strain. Here, we introduce an electrostatic active origami concept, electro-origami, that overcomes these limitations and allows for simple, inexpensive, lightweight, efficient, powerful, and scalable electronic actuators and lightweight and thin robots. The simplest embodiment of electro-origami, electro-ribbon actuators, can be easily fabricated from any combination of conducting and insulating material. We present electro-ribbon actuators that can lift 1000 times their own weight, contract by 99.8% of their length, and deliver specific energy and specific power equivalent to muscle. We demonstrate their versatility in high-stroke and high-force morphologies, multiactuator lattices, 3D-printed and paper actuators, self-twisting spirals, and tensile elements inspired by spider silk. More complex electro-origami devices include solenoids, adaptive grippers, robotic cilia, locomoting robots, self-packing deployable structures, origami artificial muscles, and dynamic origami art.

Synthesizing tubular and trapezoidal shaped ZnO nanowires by an aqueous solution method.

In this paper we present a novel bottom-up method suitable for developing vertically aligned hollow ZnO nanowires, ZnO nanotubes as well as longitudinally half ZnO nanowires. The procedures used for synthesizing such crystals combine chemical and electrochemical growth processes in aqueous solution at low temperatures (<90 °C), with a growth block process. A thin layer of gold, deposited when the nanowire growth process is at half way, has the crucial role of blocking the growth along the intended directions. The possibility of fabricating highly aligned crystals on a wide range of polymeric substrates, including flexible or transparent ones, is also illustrated. Our proposed methods hold potential for new developments in piezotronics and piezophotonics by allowing fabrication of nanodevices in the inner region of the hollow nanowires and nanotubes.