scholarly journals Real-time irrigation: Cost-effectiveness and benefits for water use and productivity of strawberries

2018 ◽  
Vol 240 ◽  
pp. 468-477 ◽  
Author(s):  
Laurence Gendron ◽  
Guillaume Létourneau ◽  
Lelia Anderson ◽  
Guillaume Sauvageau ◽  
Claire Depardieu ◽  
...  
Keyword(s):  
Cost Effectiveness ◽  
Real Time ◽  
Water Use ◽  
Irrigation Cost

scholarly journals Watson on the Farm: Using Cloud-Based Artificial Intelligence to Identify Early Indicators of Water Stress

2019 ◽  
Vol 11 (22) ◽  
pp. 2645 ◽  
Author(s):  
Daniel Freeman ◽  
Shaurya Gupta ◽  
D. Hudson Smith ◽  
Joe Mari Maja ◽  
James Robbins ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Water Stress ◽  
Real Time ◽  
Water Use ◽  
Visual Recognition ◽  
Small Sample ◽  
Unmanned Aircraft ◽  
Minimal Amount ◽  
Sustainable Water Use ◽  
Early Indicators

As demand for freshwater increases while supply remains stagnant, the critical need for sustainable water use in agriculture has led the EPA Strategic Plan to call for new technologies that can optimize water allocation in real-time. This work assesses the use of cloud-based artificial intelligence to detect early indicators of water stress across six container-grown ornamental shrub species. Near-infrared images were previously collected with modified Canon and MAPIR Survey II cameras deployed via a small unmanned aircraft system (sUAS) at an altitude of 30 meters. Cropped images of plants in no, low-, and high-water stress conditions were split into four-fold cross-validation sets and used to train models through IBM Watson’s Visual Recognition service. Despite constraints such as small sample size (36 plants, 150 images) and low image resolution (150 pixels by 150 pixels per plant), Watson generated models were able to detect indicators of stress after 48 hours of water deprivation with a significant to marginally significant degree of separation in four out of five species tested (p < 0.10). Two models were also able to detect indicators of water stress after only 24 hours, with models trained on images of as few as eight water-stressed Buddleia plants achieving an average area under the curve (AUC) of 0.9884 across four folds. Ease of pre-processing, minimal amount of training data required, and outsourced computation make cloud-based artificial intelligence services such as IBM Watson Visual Recognition an attractive tool for agriculture analytics. Cloud-based artificial intelligence can be combined with technologies such as sUAS and spectral imaging to help crop producers identify deficient irrigation strategies and intervene before crop value is diminished. When brought to scale, frameworks such as these can drive responsive irrigation systems that monitor crop status in real-time and maximize sustainable water use.

scholarly journals Conditions of Cost-Effectiveness for the Use of Real-Time Location Systems (RTLS) in Medical Facilities and the Benefits of Effective Implementation Process

2020 ◽  
Vol 58 (4) ◽  
Author(s):  
Tomasz Rostkowski ◽  
Damian Banat
Keyword(s):  
Cost Effectiveness ◽  
Real Time ◽  
Healthcare System ◽  
Implementation Process ◽  
Effective Implementation ◽  
Starting Point ◽  
Medical Facilities ◽  
The Cost ◽  
System Today ◽  
Real Time Location System

The healthcare system today faces numerous challenges due to lack of visibility of resources and low utilisation, excessive rental as well as purchase of medical equipment. The article attempts to identify the possibilities of implementing a system of identification and location of assets in healthcare system institutions. This research presents market analysis in the context of available technological solutions. The implementation of real-time location system (RTLS) would enable not only tracking of equipment and inventory in medical facilities, but also increase visibility of inventory and manage the entire supply chain. Presentation of the proposed solution made it possible to assess costs, potential savings and the implementation process. This article is a starting point for a discussion on the cost-effectiveness of using RTLS in the healthcare system.

scholarly journals Periodic Versus Real-time Adjustment of a Leaching Fraction-based Microirrigation Schedule for Container-grown Plants

HortScience ◽  
2020 ◽  
Vol 55 (1) ◽  
pp. 83-88 ◽  
Author(s):  
Jeff B. Million ◽  
Thomas H. Yeager
Keyword(s):  
Plant Growth ◽  
Real Time ◽  
Water Use ◽  
Water Conservation ◽  
Irrigation System ◽  
Irrigation Schedule ◽  
Irrigation Scheduling ◽  
Leaching Fraction ◽  
Reduced Water ◽  
Irrigation Practices

Two experiments were conducted to determine if a leaching fraction (LF)-guided irrigation practice with fixed irrigation run times between LF tests (LF_FX) could be improved by making additional adjustments to irrigation run times based on real-time weather information, including rain, using an evapotranspiration-based irrigation scheduling program for container production (LF_ET). The effect of the two irrigation practices on plant growth and water use was tested at three target LF values (10%, 20%, and 40%). For both Viburnum odoratissimum (Expt. 1) and Podocarpus macrophyllus (Expt. 2) grown in 36-cm-diameter containers with spray-stake microirrigation, the change in plant size was unaffected by irrigation treatments. LF_ET reduced water use by 10% compared with LF_FX in Expt. 2 but had no effect (P < 0.05) on water use in Expt. 1. Decreasing the target LF from 40% to 20% reduced water use 28% in both experiments and this effect was similar for both irrigation practices. For the irrigation system and irrigation schedule used in these experiments, we concluded that an LF-guided irrigation schedule with a target LF of 10% resulted in plant growth similar to one with a target LF of 40% and that the addition of a real-time weather adjustment to irrigation run times provided little or no improvement in water conservation compared with a periodic adjustment based solely on LF testing.

scholarly journals Long-term Cost-Effectiveness of Dexcom G6 Real-Time Continuous Glucose Monitoring Versus Self-Monitoring of Blood Glucose in Patients With Type 1 Diabetes in the U.K.

2020 ◽  
Author(s):  
Stéphane Roze ◽  
John Isitt ◽  
Jayne Smith-Palmer ◽  
Mehdi Javanbakht ◽  
Peter Lynch
Keyword(s):  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Cost Effectiveness ◽  
Real Time ◽  
Glucose Monitoring ◽  
Baseline Hba1c ◽  
Self Monitoring ◽  
Quality Adjusted Life

<b>Objective</b> <p>A long-term health economic analysis was performed to establish the cost-effectiveness of real-time continuous glucose monitoring (RT-CGM) (Dexcom G6) versus self-monitoring of blood glucose (SMBG) alone in UK-based patients with type 1 diabetes. </p> <p><b>Methods</b></p> <p>The analysis utilized the IQVIA CORE Diabetes Model. Clinical input data were sourced from the DIAMOND trial in adults with type 1 diabetes; simulations were performed separately in the overall population of patients with baseline HbA1c ≥7.5% (58 mmol/mol); and a secondary analysis was performed in patients with baseline HbA1c ≥8.5% (69 mmol/mol). The analysis was performed from the NHS healthcare payer perspective over a lifetime time horizon. </p> <p><b>Results</b></p> <p>In the overall population, G6 RT-CGM was associated with a mean incremental gain in quality-adjusted life expectancy of 1.49 quality-adjusted life years (QALYs) versus SMBG (mean [standard deviation; SD] 11.47 [2.04] QALYs versus 9.99 [1.84] QALYs). Total mean (SD) lifetime costs were also GBP 14,234 higher with RT-CGM (GBP 102,468 [35,681] versus GBP 88,234 [39,027]) resulting in an ICER of GBP 9,558 per QALY gained. Sensitivity analyses revealed that the findings were sensitive to changes in the quality of life benefit associated with reduced fear of hypoglycemia and avoidance of fingerstick testing as well as the HbA1c benefit associated with RT-CGM use. </p> <p><b>Conclusions</b></p> <p>For UK-based type 1 diabetes patients, the G6 RT-CGM device is associated with significant improvements in clinical outcomes and, over patient lifetimes, is a cost-effective disease management option relative to SMBG, based on a willingness-to-pay threshold of GBP 20,000 per QALY gained. </p>

Cost-effectiveness analysis of workplace-based distributed cardiopulmonary resuscitation training versus conventional annual basic life support training

2020 ◽  
pp. bmjstel-2020-000709
Author(s):  
Yiqun Lin ◽  
Kent Hecker ◽  
Adam Cheng ◽  
Vincent J Grant ◽  
Gillian Currie
Keyword(s):  
Sensitivity Analysis ◽  
Cost Effectiveness ◽  
Cardiopulmonary Resuscitation ◽  
Real Time ◽  
Care Providers ◽  
Training Time ◽  
Distributed Training ◽  
Cpr Quality ◽  
The Cost ◽  
Cpr Training

ContextAlthough distributed cardiopulmonary resuscitation (CPR) practice has been shown to improve learning outcomes, little is known about the cost-effectiveness of this training strategy. This study assesses the cost-effectiveness of workplace-based distributed CPR practice with real-time feedback when compared with conventional annual CPR training.MethodsWe measured educational resource use, costs, and outcomes of both conventional training and distributed training groups in a prospective-randomised trial conducted with paediatric acute care providers over 12 months. Costs were calculated and reported from the perspective of the health institution. Incremental costs and effectiveness of distributed CPR training relative to conventional training were presented. Cost-effectiveness was expressed as an incremental cost-effectiveness ratio (ICER) if appropriate. One-way sensitivity analyses and probabilistic sensitivity analysis were conducted.ResultsA total of 87 of 101 enrolled participants completed the training (46/53 in intervention and 41/48 in the control). Compared with conventional training, the distributed CPR training group had a higher proportion of participants achieving CPR excellence, defined as over 90% guideline compliant for chest compression depth, rate and recoil (control: 0.146 (6/41) vs intervention 0.543 (25/46), incremental effectiveness: +0.397) with decreased costs (control: $C266.50 vs intervention $C224.88 per trainee, incremental costs: −$C41.62). The sensitivity analysis showed that when the institution does not pay for the training time, distributed CPR training results in an ICER of $C147.05 per extra excellent CPR provider.ConclusionWorkplace-based distributed CPR training with real-time feedback resulted in improved CPR quality by paediatric healthcare providers and decreased training costs, when training time is paid by the institution. If the institution does not pay for training time, implementing distributed training resulted in better CPR quality and increased costs, compared with conventional training. These findings contribute further evidence to the decision-making processes as to whether institutions/programmes should financially adopt these training programmes.

scholarly journals The potential of real-time analytics to improve care for mechanically ventilated patients in the intensive care unit: an early economic evaluation

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Lytske Bakker ◽  
Katerina Vaporidi ◽  
Jos Aarts ◽  
William Redekop
Keyword(s):  
Intensive Care ◽  
Cost Effectiveness ◽  
Real Time ◽  
Cost Savings ◽  
Cost Effective ◽  
Potential Cost ◽  
Lifetime Horizon ◽  
Scenario Analyses ◽  
Life Years ◽  
The Impact

Abstract Background Mechanical ventilation services are an important driver of the high costs of intensive care. An optimal interaction between a patient and a ventilator is therefore paramount. Suboptimal interaction is present when patients repeatedly demand, but do not receive, breathing support from a mechanical ventilator (> 30 times in 3 min), also known as an ineffective effort event (IEEV). IEEVs are associated with increased hospital mortality prolonged intensive care stay, and prolonged time on ventilation and thus development of real-time analytics that identify IEEVs is essential. To assist decision-making about further development we estimate the potential cost-effectiveness of real-time analytics that identify ineffective effort events. Methods We developed a cost-effectiveness model combining a decision tree and Markov model for long-term outcomes with data on current care from a Greek hospital and literature. A lifetime horizon and a healthcare payer perspective were used. Uncertainty about the results was assessed using sensitivity and scenario analyses to examine the impact of varying parameters like the intensive care costs per day and the effectiveness of treatment of IEEVs. Results Use of the analytics could lead to reduced mortality (3% absolute reduction), increased quality adjusted life years (0.21 per patient) and cost-savings (€264 per patient) compared to current care. Moreover, cost-savings for hospitals and health improvements can be incurred even if the treatment’s effectiveness is reduced from 30 to 10%. The estimated savings increase to €1,155 per patient in countries where costs of an intensive care day are high (e.g. the Netherlands). There is considerable headroom for development and the analytics generate savings when the price of the analytics per bed per year is below €7,307. Furthermore, even when the treatment’s effectiveness is 10%, the probability that the analytics are cost-effective exceeds 90%. Conclusions Implementing real-time analytics to identify ineffective effort events can lead to health and financial benefits. Therefore, it will be worthwhile to continue assessment of the effectiveness of the analytics in clinical practice and validate our findings. Eventually, their adoption in settings where costs of an intensive care day are high and ineffective efforts are frequent could yield a high return on investment.

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