Acquisition Costs Research Articles

Budget impact analysis of subcutaneous infliximab (CT-P13 SC) for treating inflammatory bowel disease in Saudi Arabia: Analysis from payer perspective.

The Saudi Food and Drug Authority (SFDA) has approved the subcutaneous (SC) administration of infliximab, presenting a more convenient alternative with reduced outpatient visits and diminished expenses compared to the intravenous (IV) administration. However, the financial implications of this formulation have not been examined from the perspective of Saudi payers. A prevalence-based budget impact model was developed to evaluate the financial effects of introducing "environment without" versus "with infliximab SC." The model's time horizon spanned over 2 years (2021-2023), aligning with the biennial national pharmaceutical procurement cycle. The comparison focused on infliximab SC versus all available formulations of infliximab IV in the Saudi market for two inflammatory bowel diseases (IBD): Ulcerative Colitis (UC) and Crohn's Disease (CD). Treatment comparators' comparability and dose escalations were substantiated by published studies, utilizing dosing information from the summary of product characteristics. Drug acquisition costs were derived from SFDA registered prices, with IV formulation administration costs included. Scenario analysis assessed the budget impact of infliximab SC introduction at uptake rates ranging from 0% to 100%. Introducing infliximab SC demonstrated cost-saving potential in the treatment of IBD. At 100% uptake with UC patients for 2 years, infliximab SC resulted in savings of -SAR-31.9 million (-SAR29,145 per patient). Similarly, for CD, introducing infliximab SC at 100% uptake over 2 years yielded savings of -SAR106.2 million (-SAR36,585 per patient). This study reveals that infliximab SC is associated with cost-saving potential when compared to infliximab IV formulations available in Saudi Arabia. Future research should address uncertainties related to real-world comparative effectiveness, the convenience of administration, patient tolerability, and physician acceptance of the SC formulation of infliximab, alongside comparisons with other TNF-alpha inhibitors.

Cost-Effectiveness Analysis of the Use of V116, a 21-Valent Pneumococcal Conjugate Vaccine, in Vaccine-Naïve Adults Aged ≥ 65 Years in the United States.

Given the recent approval and recommendation of V116, a 21-valent pneumococcal conjugate vaccine (PCV), in the United States(US), we evaluated the cost-effectiveness of using V116 versus the 20-valent PCV(PCV20) or the 15-valent PCV(PCV15) in series with the 23-valent pneumococcal polysaccharide vaccine (PPSV23) among adults aged ≥ 65years in the US who had never received a PCV previously. A static multi-cohort state-transition Markov model was developed to estimate the lifetime incremental clinical and economic impact of V116 vs. PCV20 or PCV15 + PPSV23 from the societal perspective. All model inputs were based on published literature and publicly available databases and/or reports. Model outcomes included undiscounted clinical cases: invasive pneumococcal disease (IPD), inpatient and outpatient non-bacteremic pneumococcal pneumonia (NBPP), post-meningitis sequelae (PMS), deaths from IPD and inpatient NBPP, discounted quality-adjusted life years (QALYs) as well as the discounted total cost (in 2023 USD), which consisted of vaccine acquisition and administration costs, direct and indirect costs associated with the disease, and travel costs for vaccination. The final summary measure was the incremental cost-effectiveness ratio (ICER), reported as $/QALY gained. Three percent was used for the annual discounting rate. Based on the inputs and assumptions used, the results indicated that the V116 strategy prevented 27,766 and 32,387 disease cases/deaths and saved $239 million and $1.8 billion in total costs when compared to the PCV20andPCV15 + PPSV23 strategies, respectively, in vaccine-naïve adults aged ≥ 65years. The estimated ICERs were cost saving in both regimens (i.e., V116 vs. PCV20 or vs. PCV15 + PPSV23). The scenario analysis and deterministic and probabilistic sensitivity analyses also demonstrated the robustness of the qualitative results. These results demonstrated that using V116 in adults aged ≥ 65years in the US can prevent a substantial number of PD cases and deaths while remaining highly favorable economically over a wide range of inputs and scenarios.

Effect of solids concentration and operational variables on the performance of a geometrically optimized concentrator hydrocyclone employing a pseudoplastic fluid

The employment of hydrocyclones in thickening operations is an attractive option when compared to centrifuges and filters due to their low operating, maintenance, and acquisition costs. However, the performance of hydrocyclone separation is impaired as the concentration of solids and the viscosity of the suspension increases. Using geometric optimization techniques, the Federal University of Uberlândia developed one concentrator hydrocyclone named HC. When working with diluted and Newtonian slurries, the HC could generate a stream 45 times more concentrated than the one fed into it. In this study, the HC performance was evaluated when operating pseudoplastic fluids containing up to 10% solids by volume. The combination of the underflow diameter and vortex finder length with an adequate supply of pressure energy maintained the thickening potential of the HC even when the rheology of the fluid was changed. For different working suspensions, the HC hydrocyclone achieved a minimum water split to underflow of 5%, a maximum concentration ratio of 7.0, and a maximum efficiency of 49%. The encouraging results obtained by the HC validated the benefits of the geometric optimization, as they point to a significant advance in the thickening operation of non–Newtonian sludges with a flow behavior index greater than 0.5.

Digital tools in primary total knee arthroplasty-Prevalence in the German-speaking region

Digital tools are being increasingly used worldwide in primary knee arthroplasty. This study aimed to analyze the utilization density of digital tools, the preferred alignment strategies, and the obstacles and benefits of implementing these technologies in German-speaking countries. An online survey with 57questions about digital tools in primary knee arthroplasty and their usage was conducted among members of the Arthroplasty Working Group (AE). The survey included questions on navigation, robotics, patient-specific instruments, individualized implants, and augmented reality. The survey revealed that 18% of hospitals use navigation and 17% use robotic systems in primary total knee arthroplasty surgery. The main reasons for not implementing supportive technologies were high acquisition and ongoing costs, as well as longer surgical duration. Patient-specific instruments and individualized implants currently play aminor role. Patient-specific alignment strategies, such as kinematic (navigation: 35%; robotics: 44%) and functional alignment (navigation: 15%; robotics: 35%), are preferred in this context. With conventional instrumentation predominantly mechanical alignment was applied (79%). The results indicate arelatively high utilization density of digital tools, which are mainly used to perform personalized alignment strategies in primary knee arthroplasty in German-speaking countries. This was particularly evident in high-volume hospitals. Economic aspects were the main reasons for not using these technologies. Future developments should aim to simplify the systems and thus achieve improved cost efficiency.

The perverse valuation effect on mergers and acquisitions in Europe

We investigate the macroeconomic and financial determinants of mergers and acquisitions (M&As) in Europe and address the gap in understanding how financial market dynamics influence corporate decisions. While previous literature has examined macroeconomic factors affecting M&As, the impact of (10-year government) bond yields remains underexplored. We fill this gap using panel data of 21 European Union Countries over 2006:Q1–2022:Q2. Across different model specifications we find that bond yields and real gross domestic product growth are robust determinants of M&As, even after controlling for other determinants. A novelty of our study is that rising and higher bond yields are associated with a reduction in M&As activity because investors are shifting their portfolios out of bonds and into riskier assets like equities, thereby increasing acquisition costs. We denote this as a “perverse valuation effect”offering new insights into M&As dynamics.

Second-price auctions with information acquisition costs

Second-price auctions with information acquisition costs

Perhitungan Cadangan Premi Asuransi Jiwa Berjangka dengan Menggunakan Metode Zillmer dan Fackler

Human life is never free from risk. Along with the development of the era, humans began to realize the importance of protecting themselves in the event of a risk, including the risk of death. To overcome this, many individuals transfer the risk by registering themselves or their families with life insurance. The life insurance that is focused on is term life insurance, which is a form of protection with a certain period of time that has been set. In order to run its operations properly, insurance companies need to prepare premium reserves with accurate calculations. These calculations can be done through two actuarial methods, namely prospective and retrospective methods. This study uses the Zillmer method (prospective) and the Fackler method (retrospective) for calculating premium reserves. The purpose of this study was to determine the analysis of the results of calculating the premium reserve value from the two methods. The premium reserve values ​​calculated using the Zillmer and Fackler methods were different. At an interest rate of 6.25%, the Fackler method produces a higher premium reserve compared to the Zillmer method. This difference is because the Zillmer method includes other costs such as acquisition costs, administration, and agent commissions in calculating premium reserves. In contrast, the Fackler method does not take these costs into account in its calculations.

Evaluating the budget impact of Empagliflozin in managing heart failure with reduced ejection fraction: Proposing strategic policies for Malaysian public healthcare.

Sodium-glucose co-transporter-2 (SGLT2) inhibitors such as Empagliflozin, are increasingly recommended as part of guideline-directed medical therapy (GDMT) for heart failure with reduced ejection fraction (HFrEF) in many developed nations. This recommendation is based on robust clinical evidence showing that adding Empagliflozin to GDMT improves heart failure symptoms, clinical outcomes, functional status, and overall quality of life. In Malaysia, where healthcare is predominantly public and heavily subsidized, the introduction of new treatments can significantly impact costs, requiring detailed economic assessments. This study evaluates the budget impact of incorporating Empagliflozin into GDMT for HFrEF from the perspective of the public healthcare system. A five-year budget impact model was developed, integrating local data such as population, drug use, costs, clinical outcomes, and healthcare expenses. In the current scenario (GDMT alone), the projected five-year expenditure is MYR 6.12 billion (USD 3.92 billion). With Empagliflozin, the total cost rises by 0.71% to MYR 6.16 billion (USD 3.95 billion), driven by drug acquisition costs of MYR 160.12 million (USD 102.64 million) and adverse event costs of MYR 211,543 (USD 135,604). However, these costs are offset by savings from reduced HF hospitalizations, fewer cardiovascular deaths, and improved renal outcomes. Sensitivity analysis identified hospitalization costs, the price of Empagliflozin, and cardiovascular deaths in diabetic patients as key factors influencing the budget impact. Policymakers can improve the affordability of Empagliflozin through strategies like price negotiations, cost-sharing, and focusing on high-risk groups to optimize healthcare expenditure while ensuring effective treatment access.

Portable ultrasound devices for obstetric care in resource-constrained environments: mapping the landscape

Background The WHO’s recommendations on antenatal care underscore the need for ultrasound assessment during pregnancy. Given that maternal and perinatal mortality remains unacceptably high in underserved regions, these guidelines are imperative for achieving better outcomes. In recent years, portable ultrasound devices have become increasingly popular in resource-constrained environments due to their cost-effectiveness, useability, and adoptability in resource-constrained settings. This desk review presents the capabilities and costs of currently available portable ultrasound devices, and is meant to serve as a resource for clinicians and researchers in the imaging community. Methods A list of ideal technical features for portable ultrasound devices was developed in consultation with subject matter experts (SMEs). Features included image acquisition modes, cost, portability, compatibility, connectivity, data storage and security, and regulatory certification status. Information on each of the devices was collected from publicly available information, input from SMEs and/or discussions with company representatives. Results 14 devices were identified and included in this review. The output is meant to provide objective information on ideal technical features for available ultrasound systems to researchers and clinicians working in obstetric ultrasound in low-resource settings. No product endorsements are provided. Conclusions This desk review provides an overview of the landscape of low-cost portable ultrasound probes for use in obstetrics in resource-constrained environments, and provides a description of key capabilities and costs for each. Methods could be applied to mapping the landscape of portable ultrasound devices for other clinical applications, or may be extended to reviewing other types of healthcare technologies. Further studies are recommended to evaluate portable ultrasound devices for usability and durability in global field settings.

A cost-consequence analysis comparing three distinct cardiac ablation strategies for the treatment of paroxysmal atrial fibrillation

Abstract Background Cardiac ablation is a well-established treatment option for preventing atrial fibrillation (AF). While radiofrequency, cryo-energy, and laser energies are commonly employed in catheter ablation, they present the risk of potential damage to adjacent organs such as the oesophagus and phrenic nerve. To address these concerns, new technologies are being explored including pulsed field ablation (PFA), a non-thermal energy approach that employs high-voltage, short-duration pulses to selectively destroy target cells. Purpose The present analysis aims at: i) quantifying the short-term costs related to three ablation strategies for paroxysmal AF (PFA, radiofrequency ablation -RFA-, and cryo-balloon ablation -CRYO-), identify the cost drivers, and assess potential variations in resource consumption for each treatment alternative; ii) comparing the medium-term (1-year) economic implications of adopting the three alternatives, in terms of redo procedures (effectiveness) and complications (safety). Methods A cost-consequence model was developed to compare economic outcomes of the three ablation strategies for paroxysmal AF in the short- (during procedure) and medium-term (1-year). Resource consumption data during the procedure were collected by two European medical centres specialized in cardiac ablation (N=61 patients). For the medium-term analysis, costs were correlated to the effectiveness and safety of the procedures. The clinical data (i.e., redo and complication rates) considered in the model were retrieved from published literature. Costs and tariffs were collected from economic formularies of the respective hospitals. A scenario analysis was conducted to evaluate economic outcomes under various hypothetical difference in the prices of treatment kits. Results A significant difference was registered in terms of pre-procedural (mean±SD: 13.9±4.4 min for PFA, 20.1±7.6 min for CRYO and 20.5±7.7 min for RFA; ANOVA test p=0.003) and procedural time (skin-to-skin), favouring PFA over RFA and CRYO (53.6±26.8 min for PFA, 72.4±28.1 min for CRYO and 149.9±99.9 min for RFA; p<0.0001; Table 1). PFA was associated with lower procedural costs vs CRYO (-€400) and RFA (-€827). A similar economic advantage was observed in the medium-term (Table 2). The scenario analysis showed that PFA would be cost-saving vs CRYO (Δtotal costs ≤ 0) up to a price difference of +€881 (i.e., PFA kit is more expensive than CRYO kit), and similarly, up to a price difference of +€1,306 vs RFA. Above these thresholds, PFA would be considered cost-effective vs CRYO or RFA, due to its efficacy despite being more expensive. Conclusion In summary, PFA emerges as cost-effective or even cost-saving over CRYO and RFA, thanks to its faster execution, lower redo, and complications rate. Moreover, PFA time predictability would facilitate hospital planning. For a complete analysis, these benefits should be evaluated against the difference in acquisition costs among treatments.Procedural time by phase and techniqueCumulative savings per technique

On the Suitability of Process Mining for Enhancing Transparency of Blockchain Applications

AbstractBlockchain technology is known for its transparency properties due to its publicly available, immutable data. Yet, as data availability does not inherently ensure transparency, further analytical methods may be required for human interpretation of data traces. Process mining has emerged as a popular toolbox for understanding processes and how they are executed in practice. The paper studies process mining as a method to enhance the transparency of blockchain data. To this end, two popular Ethereum applications were analyzed using process mining: the prediction and betting marketplace Augur and the network marketing platform Forsage. Observations from the process-mining analyses are used to discuss if process mining can serve as a method to establish transparency of a blockchain. For both applications, new insights are generated for usage scenarios such as application redesign, security analysis, user behavior analysis, and revealing blind spots in Augur’s and Forsage’s documentation. The paper concludes that there is evidence that process mining can serve as a method to enhance transparency in blockchains at the cost of technical setup and knowledge acquisition.

Evaluation and fusion of multi-source sea ice thickness products with limited in-situ observations

Sea ice thickness (SIT) is a critical and sensitive parameter in the climate system, with its dynamic changes profoundly influencing global climate models, navigational routes, and the potential for Arctic resource development. Given the widespread application of current satellite remote sensing technology in monitoring SIT, significant uncertainties remain. This study first underscores the importance of in-situ observations as a direct measurement method for SIT. However, the limitations of in-situ data in terms of acquisition cost, spatiotemporal coverage continuity, and distribution uniformity significantly hinder the effective evaluation of multi-source SIT products. To address this, the study innovatively introduces the Triple Collocation (TC) method, which effectively mitigates the impact of errors from individual data sources on the overall evaluation results through a mutual validation mechanism among multiple satellite data sources. This allows for a scientific assessment of multi-source SIT products even in the context of scarce in-situ observations. The findings indicate that the TC method not only successfully resolves the challenges of multi-source data evaluation but also facilitates data integration among these products, significantly enhancing the overall accuracy and spatiotemporal consistency of SIT data.

A deep transfer learning model for online monitoring of surface roughness in milling with variable parameters

Surface roughness is crucial for the functional and aesthetic properties of mechanical components and must be carefully controlled during machining. However, predicting it under varying machining parameters is challenging due to limited experimental data and fluctuating factors like tool wear and vibration. This study develops a deep transfer learning model that incorporates the correlation alignment method and tool wear to enhance model generalization and reduce data acquisition costs. It utilizes multi-sensor data and the ResNet18 with a convolutional block attention module (CBAM-ResNet) to extract features with improved generalization and accuracy for monitoring milled surface roughness under varying conditions. The performance of the model is evaluated from different perspectives. First, the proposed model achieves high accuracy with fewer than 500 experimental samples from the target domain by using the CORAL module in the CBAM-ResNet model. This demonstrates the model's strong generalization capability by minimizing second-order statistical discrepancies between different datasets. Second, ablation experiments reveal a significant reduction in test error when incorporating CORAL and tool wear, highlighting their contributions to improved model generalization. Integrating tool wear information significantly reduces test errors across various transfer conditions, as it reflects changes in cutting force, vibration, and built-up edge formation. Third, comparisons with existing deep transfer models further emphasize the advantages of the proposed approach in improving model generalization. In summary, the proposed surface roughness model, which incorporates tool wear and multi-sensor signal features as inputs and employs feature transfer and CBAM-ResNet, demonstrates superior generalization and accuracy across various machining parameters.

Study on terrain acquisition and processing technology of BDS-3 auxiliary mountain highway

The operation mode of UAV equipped with LiDAR can significantly reduce the efficiency and cost of highway terrain acquisition. At present, due to the complicated terrain and weak signal of mountain road, it is difficult to collect terrain data. In view of the above problems, this paper puts forward the technology of BDS assisted terrain acquisition and processing on complex mountain roads. This paper describes in detail the new signal unfuzzy acquisition and tracking algorithm and multi-antenna positioning and attitude determination technology and equipment of BDS-3 mountain highway, through millimeter wave Lidar altimetry technology, imitation ground flight, air-ground multi-source data combined with air three-direction method and other technologies. To realize efficient 3D collection and 3D reconstruction of massive original images of terrain of complex mountain traffic lines in the range of 100 km. Finally, a case study of 100 km mountain road is taken to verify the effectiveness of the method.

Low‐cost animal tracking using Bluetooth low energy beacons on a crowd‐sourced network

Abstract Animal tracking has opened the door to address many fundamental questions in ecology and conservation. Whilst historically animals have been tracked as a means to understand their large‐scale movements, such as migration, there is now a greater focus on using tracking to study movements over smaller scales, individual variation in movement or how movements shape social network structure. With this shift in focus also comes different tracking needs, including the need to track larger numbers of individuals. Tracking studies all face some technological limitations. For example, GPS and other active tracking solutions can collect fine‐scale movement data, but have a high cost per tag, limiting the number of individuals that can be followed. They also have high low‐energy costs of data acquisition and download, limiting time periods over which data can be collected. Low‐energy passive (e.g. PIT) or active (e.g. reverse GPS) tags can overcome these limitations, but instead require animals to remain within a bounded study area or to come into close proximity to detectors. Here we describe one solution that can overcome many current limitations by employing the massive global network of personal mobile phones as gateways for tracking animals using Bluetooth low‐energy (BLE) beacons. In areas with medium to high density of people, these simple‐to‐make beacons can provide regular updates of position over long time periods (battery life 1–3 years). We describe how to use off‐the‐shelf components to produce BLE beacons that weigh c. 5–6 g and cost <$7USD. Using field‐testing, we then show that beacons are capable of producing high‐frequency tracking data that can be used to build home ranges or to detect spatiotemporal co‐occurrences among individuals. BLE beacons are a low cost, low‐energy solution for studying organisms (e.g. birds, mammals and reptiles) living and moving in urban landscapes. Their low weight and small size makes them particularly well‐suited for tracking smaller species. When combined with fixed gateways, their use can also be extended to non‐urban habitats. Their high accessibility is likely to make them an attractive solution for many research projects.

Data-Efficient Dimensionality Reduction and Surrogate Modeling of High-Dimensional Stress Fields

Abstract Tensor datatypes representing field variables like stress, displacement, velocity, etc., have increasingly become a common occurrence in data-driven modeling and analysis of simulations. Numerous methods [such as convolutional neural networks (CNNs)] exist to address the meta-modeling of field data from simulations. As the complexity of the simulation increases, so does the cost of acquisition, leading to limited data scenarios. Modeling of tensor datatypes under limited data scenarios remains a hindrance for engineering applications. In this article, we introduce a direct image-to-image modeling framework of convolutional autoencoders enhanced by information bottleneck loss function to tackle the tensor data types with limited data. The information bottleneck method penalizes the nuisance information in the latent space while maximizing relevant information making it robust for limited data scenarios. The entire neural network framework is further combined with robust hyperparameter optimization. We perform numerical studies to compare the predictive performance of the proposed method with a dimensionality reduction-based surrogate modeling framework on a representative linear elastic ellipsoidal void problem with uniaxial loading. The data structure focuses on the low-data regime (fewer than 100 data points) and includes the parameterized geometry of the ellipsoidal void as the input and the predicted stress field as the output. The results of the numerical studies show that the information bottleneck approach yields improved overall accuracy and more precise prediction of the extremes of the stress field. Additionally, an in-depth analysis is carried out to elucidate the information compression behavior of the proposed framework.

Part Feeding and Internal Transportation Decision Making for a Machinery Manufacturer

This research validates a mixed integer linear programming model that minimizes total feeding and acquisition costs by integrating assembly line feeding and vehicle type decisions in a machinery manufacturing company. The results show that the optimal assignment reduces costs by 56% compared with the company's current assignment.

Digital Innovations and Competitive Advantage of Commercial Banks in Kenya

Purpose: The purpose of the study was to ascertain the role of digital banking technological innovations in enhancing competitive advantage among commercial banks in Kenya. Specifically, the study sought to examine the influence of digital technological innovations for market, digital banking innovations for differentiation, digital innovations for customer satisfaction and the influence of ICT infrastructure on competitive advantage of commercial banks in Kenya Methodology: Descriptive research design was adopted for the present study. The study was carried out in Kenya, covering all the 39 solvent commercial banks in Kenya as of 31st March 2024. The study adopted the census data collection method from the headquarters of the 22 solvent commercial banks belonging to Tier 3. Four key officers including the sales manager, loan officer, relationship manager, and risk manager totaling to 88 were sampled to participate in the study. Findings: The findings indicate that there is a significant relationship between market positioning and competitive advantage (B = 0.292, p = 0.003). On the other hand, Differentiation is established to have a negligible influence on competitive advantage in this model (B = 0.027, p = 0.852). Customer Satisfaction comes out strongly, as a factor that defines competitive advantage (Coefficient = 0.247, P = 0.046). Unique Contribution to Theory, Policy and Practice: The study recommends that Kenyan commercial banks invest in continuous digital innovation, particularly in mobile and Internet banking, to strengthen market positioning, improve brand perception, and offer personalized services. Banks should also enhance their ICT frameworks for better service delivery, data protection, and adaptability to market changes. Emphasizing mobile banking innovations and staying updated with emerging digital trends will help banks reduce customer acquisition costs, expand their customer base, and maintain a competitive edge in a dynamic industry.

Tracking Blood Product Wastage over a 6-year Period in Virginia Commonwealth University Medical Center

Abstract Introduction/Objective Blood transfusions are a necessary practice for hospital emergency and surgical department. In emergent situations, blood components are in unmonitored settings during transit of critically ill patients. Any unused blood products are returned to the blood banks outside of a controlled temperature environment and are no longer viable to use. This study seeks to collect the average amount of blood products wasted in the Emergency and Surgery Department at Virginia Commonwealth University’s (VCU) Main Hospital. In addition, the study intends to highlight the budget that is impacted by these wasted blood units. Methods/Case Report Emergency Stock Records of VCU’s Hospital Emergency Department, Operating Room, Children’s Hospital of Richmond’s (CHoR) Emergency Department, and the CHoR’s Operating Room were collected and accumulated by the Transfusion Service’s Quality Assurance Coordinator. VCU’s Main Hospital Emergency Department monitors blood unit viability with a sensitive temperature indicator on each unit of blood stored in unmonitored refrigerators. Blood units above 10 degrees Celsius trigger the indicator and are required to be discarded. Our study collected a tally of these discarded units due to a triggered temperature indicator from 2018 to 2023. Acquisition cost per blood unit was estimated to be approximately $250 and overhead cost per unit was estimated to be $760. Results (if a Case Study enter NA) From 2018 to 2023, VCU’s health system wasted an average of 89.16 blood units per year due to the triggered temperature indicators, with a total of 535 units wasted over six years. The average annual cost of the wasted units totaled $90,058.33 per year ($22,291.67 and $67,766.67 in acquisition and overhead costs, respectively); cumulative cost over the 6 years amounted to $540,350 ($133,750 and $406,600 in acquisition and overhead costs, respectively). Conclusion With a notable number of resources and funding lost in the process due to blood unit wastage, an administrative discussion should focus on the use of high-level remote- controlled automated refrigerators in lieu of manual temperature indicator to reduce the expenditure and wastage of blood products.

Artificial intelligence in food and nutrition evidence: The challenges and opportunities based on a convening of content experts

Abstract Science-informed decisions are best guided by the objective synthesis of the totality of evidence around a particular question and assessing its trustworthiness through systematic processes. However, there are major barriers and challenges that limit science-informed food and nutrition policy, practice, and guidance. First, insufficient evidence, primarily due to acquisition cost of generating high-quality data, and the complexity of the diet-disease relationship. Furthermore, the sheer number of systematic reviews needed across the entire agriculture and food value chain, and the cost and time required to conduct them, can delay the translation of science to policy. Artificial intelligence (AI) offers the opportunity: 1) to better understand the complex etiology of diet-related chronic diseases; 2) to bring more precision to our understanding of the variation among individuals in the diet-chronic disease relationship; 3) to provide new types of computed data related to the efficacy and effectiveness of nutrition/food interventions in health promotion; and 4) to automate the generation of systematic reviews that support timely decisions. These advances include the acquisition and synthesis of heterogeneous and multimodal datasets. This perspective summarizes findings from a meeting of experts convened at the National Academy of Sciences, Engineering and Medicine. The purpose of the meeting was to examine the current state and future potential of AI in generating new types of computed data as well as automating the generation of systematic reviews to support evidence-based food and nutrition policy, practice and guidance.