Granular Analysis Research Articles

Improved assessment of donor liver steatosis using Banff consensus recommendations and deep learning algorithms

The Banff Liver Working Group recently published consensus recommendations for steatosis assessment in donor liver biopsy, but few studies reported their use and no automated deep-learning algorithms based on the proposed criteria have been developed so far. We evaluated Banff recommendations on a large monocentric series of donor liver needle biopsies by comparing pathologists' scores with those generated by convolutional neural networks (CNNs) we specifically developed for automated steatosis assessment. We retrospectively retrieved 292 allograft liver needle biopsies collected between January 2016 and January 2020 and performed steatosis assessment using a former intra-institution method (pre-Banff method) and the newly introduced Banff recommendations. Scores provided by pathologists and CNN models were then compared, and the degree of agreement was measured with the intraclass correlation coefficient (ICC). Regarding the pre-Banff method, poor agreement was observed between the pathologist and CNN models for small droplet macrovesicular steatosis (ICC: 0.38), large droplet macrovesicular steatosis (ICC: 0.08), and the final combined score (ICC: 0.16) evaluation, but none of these reached statistically significance. Interestingly, significantly improved agreement was observed using the Banff approach: ICC was 0.93 for the low-power score (p <0.001), 0.89 for the high-power score (p <0.001), and 0.93 for the final score (p <0.001). Comparing the pre-Banff method with the Banff approach on the same biopsy, pathologist and CNN model assessment showed a mean (±SD) percentage of discrepancy of 26.89 (±22.16) and 1.20 (±5.58), respectively. Our findings support the use of Banff recommendations in daily practice and highlight the need for a granular analysis of their effect on liver transplantation outcomes. We developed and validated the first automated deep-learning algorithms for standardized steatosis assessment based on the Banff Liver Working Group consensus recommendations. Our algorithm provides an unbiased automated evaluation of steatosis, which will lay the groundwork for granular analysis of steatosis's short- and long-term effects on organ viability, enabling the identification of clinically relevant steatosis cut-offs for donor organ acceptance. Implementing our algorithm in daily clinical practice will allow for a more efficient and safe allocation of donor organs, improving the post-transplant outcomes of patients.

PiGateway: Real-time granular analysis of smart home network traffic using P4

The Internet of Things (IoT) makes the home smarter by real-time sensing of the situation and transmission of data to the cloud. As IoT devices continue to expand rapidly, a critical need arises for effective device-type identification methods. Accurate identification of IoT device types is essential for various applications, including network management, security, etc. To identify the type of IoT devices, existing works have proposed various Media Access Control (MAC)-based and Machine Learning (ML) approaches. These solutions lack real-time granular analysis and In-network classification of IoT traffic. In this work, we propose PiGateway: a P4-based gateway to classify the type of IoT devices at a line rate and prioritize the device type. Initially, PiGateway develops a Decision Tree (DT) model and generates flow rules from the model. The developed model is deployed in the PiGateway using two mechanisms. One mechanism is to convert the DT model into Match-Action Tables (MATs), where the tables store the flow rules. Another approach developed an optimized model by deploying the DT model into PiGateway as a mathematical expression. Further, to achieve the In-network classification, the gateway calculates flow-level features dynamically from incoming IoT traffic flow. The feature values are given as input to the deployed DT model for classifying the flow into a specific device type. Subsequently, the flow is prioritized based on device type and Type of Service (ToS) field value. Additionally, our proposed solution is designed and tested in a real-time environment. Through implementation results, we show that the PiGateway can classify the type of IoT devices with 98.61% accuracy and an average CPU usage of 10.68%. Also, the classification time in PiGateway is 0.098ms for 8000 flows using an optimized model. Finally, the proposed system serves as a gateway in a smart home IoT network.

Comparing the contribution of formal and local seed systems to household seed security in eastern Zimbabwe

IntroductionInterventions aimed at improving the seed security of smallholder farmers do not always yield positive results. Governments, donors, and other actors have neglected local seed systems as they are assumed to be incapable of addressing farmers’ seed challenges. Instead, external actors use seed aid and formal seed provisioning outlets, such as agro-input dealers, to channel seed to farmers. This paper compares the “formal” seed systems, mainly comprising certified seed obtained from government and non-governmental organisations and agro-input dealers, with local seed systems that include farm-saved seed, local informal markets, and social networks.MethodsA seed security assessment was used to determine the contributions of seed systems to household-level seed security. A stratified sample was conducted of 227 randomly selected smallholder farming households from the Chimanimani district, eastern Zimbabwe, complemented by group discussions and individual life histories.ResultsWe show the superiority of local seed systems in ensuring greater access to affordable and timely seed at household level, in comparison to formal sources. Cluster analysis enabled determination of the seed security status of farming households, providing a more granular analysis beyond the standard seed security assessments that are applied to wider geographical locations. Farmers assessed the quality of locally sourced seed favourably when compared to seed obtained from formal sources.DiscussionWe show that local seed systems play a critical role in contributing to household seed security for resource-constrained households, and in supporting the use of diverse crop species. However, such systems have not been fully drawn upon by government and development agencies in seed security endeavours. More efforts are needed to understand how different seed systems interact in contributing to the seed security of smallholder farming households.

Granular flow in novel Octet shape-based lattice frame material

Abstract Moving packed-bed heat exchanger in concentrated solar power (CSP) plants involves heat transfer between heated falling particles and supercritical carbon dioxide. The overall effective thermal conductivity of the moving packed bed and particle-side channel contact resistances are still the bottlenecks in achieving the desirable thermal transport levels. To this end, a novel moving packed bed heat exchanger consisting of Octet lattice packed between the walls of the particle-side channel is proposed in this study. Granular flow analysis in Octet lattice moving-packed bed heat exchanger (OLHX) was conducted through experiments and DEM-based numerical simulations. The experimental images clearly demonstrated stagnation regions upstream of lattice fibers, void regions downstream of the fiber junctions and wavy type unobstructed flow on lateral sides of the fibers. DEM simulations were successful in capturing all these critical flow phenomena. Larger flow velocities were observed on the lateral sides of the fibers in the simulations. Also, when the particles in the silo were emptied, the final images showed accumulation of particles on the inter-fiber as well as fiber-channel wall junctions. Moreover, the fiber connections resulted in some regions devoid of the particle contact on the channel endwall which means that these regions would suffer from poor thermal exchange. The overall mass flow rate increased with increasing porosity for a fixed particle diameter.

Abstract 14177: Alarming Burden and Disparities of MACCE in Postmenopausal Women of a Contemporary Nationwide Cohort, 2020

Background: Cardiovascular disease is the leading cause of death worldwide, with a notable impact on postmenopausal women. However, contemporary large-scale population-based data for the burden and disparities of Major Adverse Cardiovascular and Cerebrovascular Events (MACCE) in this population remains limited. Methods: The National Inpatient Sample (2020, ICD10 codes, age&gt;55 years) was queried to identify postmenopausal women's hospitalizations. Socio-demographics, racial subgroups, comorbidities, and MACCE rates were retrieved with a more granular comparative analysis in postmenopausal women. A p&lt;0.05 was considered statistically significant. Results: Of the total 8435195 postmenopausal women hospitalizations (median age 75 years), 1021375 (12.1%) were identified with MACCE. MACCE cohort, in comparison to non-MACCE cohort, had significantly higher prevalence of hypertension (77.1% vs 71.3), diabetes (39.8% vs 34.0%), hyperlipidemia (53.7% vs 48.4%), PVD (9.8% vs 7.1%), prior MI (8.3 vs 5.7%), prior TIA/stroke (10.5 vs 9%) and CKD (29.4 vs 23.4%). Significant clinical predictors for all-cause mortality were found to be race Asian/PI vs white ( 1.31 [1.09-1.18]), median household income national quartile (1.16 [1.13-1.20]), hypertension (1.18[1.16-1.20]) diabetes (1.20 [1.19-1.22]), hyperlipidemia (1.12[1.11-1.14]), prior MI (1.37[1.34-1.40]) and prior TIA stroke (1.0 5 [1.03-1.07] (all p&lt;0.05). Conclusion: Among hospitalizations, Asian/Pacific Islanders, patients from the lowermost income quartile with co-existent hypertension, diabetes, hyperlipidemia, CKD, and cardiovascular comorbidities independently revealed the highest MACCE rates. Special emphasis needs to be laid on socio-demographics, comorbidities, and racial differences for CVD disease prevention, and risk factor modification in postmenopausal women.

Abstract 18125: Disparities in HFrEF Patients Excluded From Guideline Directed Medical Therapy at an NYC Safety Net Hospital

Introduction: Prescription of GDMT in heart failure with HFrEF patients is an important performance measure. However, little has been explored into the populations that are purposely excluded from the prescription of GDMT because of medical contraindications, patient refusal, cost, etc. We explored the disparities for the non-prescription of GDMT at an NYC hospital. This is essential to understand the gaps in the treatment of HFrEF patients. Hypothesis: No clinical or demographic disparities exist in the exclusion of patients from the prescription of GDMT. Methods: Data were collected retrospectively through the Get With The Guidelines™- Heart Failure registry for HFrEF (LVEF =&amp;lt;40%) patients admitted at NYC Health+Hospital/Jacobi Medical Center from 1/1/2017-5/31/2022. Only records without missing data for each medication class were included, resulting in 869 records for ACEI/ARB/ARNI, 373 for SGLT2i, 830 for beta-blocker, and 865 for MRA. Logistic regression including both demographic and clinical factors was used to analyze the exclusion outcome from each GDMT. Results were presented as adjusted odds ratios at 0.05 level of significance. Results: 50.29% had documented exclusion (e.g. contraindication, patient refusal, etc.) from the prescription of ACEI/ARB/ARNI, 46.65% from SGLT2i, 7.5% from beta-blockers, 42% from MRAs. Of these patients, 56.85%, 48.39%, 53.33% and 41.76% of patients had a “patient reason” for not being prescribed ACEI/ARB/ARNI, beta-blockers, SGLT2i, and MRAs at discharge, respectively. Results are summarized in the Table. Conclusions: Both clinical and demographic factors had significant associations with the nonprescription of GDMT. There is a sizable population of patients with non-medical reasons for nonprescription of GDMT. A more granular analysis of patient-related reasons (such as cost, patient education, etc.) should be explored to improve the prognosis of this vulnerable population with HFrEF.

Blood's Hidden Price Tag: A Comprehensive Analysis of Transfusion Economics and the Inherent Valuation Challenges with Blood

Introduction: In the juxtaposition of a bustling hospital and a serene blood donation center, the duality of blood is unveiled. For the donor, it may symbolize an act of selfless giving, a priceless offering freely bestowed. For the patient in dire need, it assumes the status of a vital life force, with a value transcending conventional calculation akin to the rarest of treasures. This dichotomy, where blood stands at the crossroads of human generosity and life's essential currency, opens a pathway for an intricate and profound investigation into both the economic and philosophical appraisals of this indispensable resource. Blood is an essential life-sustaining resource transcending conventional commodity status. Donated voluntarily, its valuation cannot merely adhere to supply and demand principles but must align with ethical and social considerations. The notion of compensation for blood donation harbors inherent contradictions and forms an ongoing international debate. In some jurisdictions, monetary incentives for blood donations are strictly prohibited and illegal, such as Brazil and Australia. In others, like the United States (with the exception of California) payment for blood is not illegal, however many hospitals chose not to use paid donor's blood due to safety concerns. Major regulatory bodies like The World Health Organization (WHO) and the Food and Drug Administration (FDA) firmly discourage payment for blood donations. This study dissects the multifaceted financial dimensions of blood transfusions, establishing the groundwork for assessing a potential compensation program for blood donors and delineating the numerous stakeholders whose interests intertwine with the precise monetary valuation of blood. Methods: We undertook a meticulous analysis of publicly available financial expenditures associated with blood transfusion, drawing from data provided by insurance entities, hospitals, blood banks, and donation programs. Through a visually illustrative chart and graph, we mapped out the complexities inherent in the financial breakdown. We delineated the many variables, and further researched into current compensation or incentives provided by blood drives, investigating how these align with our complex financial findings. Results: Our granular financial analysis revealed that costs were allocated across collection fees (7.1%), processing (14.3%), testing (21.4%), storage (10.7%), distribution (14.3%), and transfusion (32.2%). A notable disparity emerged between urban and rural costs, with the latter displaying up to 20% lower expenses. Insurance claim markups ranged from 25% to 50%. On a philosophical level, tensions between blood commodification and social good perspectives surfaced. We found that most U.S. blood drives abstained from financial incentives, instead offering non-monetary rewards. The variability in cost and ethical considerations were surmised to contribute to this trend. Conclusions: Our research underscores the rationale behind WHO and FDA advisements against compensation for blood donation, citing safety, ethics, and complexity. As our visual tools exhibit, blood transfusions are financially multifaceted, interweaving variables like market demands, transportation, storage, and hospital fees. The entanglement of financial complexity with profound ethical and social dilemmas creates an unparalleled challenge in attributing a precise monetary value to blood. Such an endeavor would necessitate an unprecedented level of accuracy to preclude potential exploitation by stakeholders. The observed variability in transfusion costs alone substantiates the near-impossibility of monetizing blood, reaffirming the necessity for ongoing dialogue and exploration if we are to ever truly reveal blood's hidden price tag.

A novel online survey approach designed to measure consumer sunscreen application thickness-implications for estimating environmental emissions.

The effects of ultraviolet (UV) filters in the aquatic environment have been well studied, but environmental exposures remain unclear and understudied. Consumer usage directly influences the amount of sunscreen products, and subsequently UV filters, potentially released into the environment. To conduct a literature review of previous research into sunscreen application thickness, develop a questionnaire protocol designed to semi-quantify sunscreen usage by US consumers, and conduct a large-scale survey to determine a sunscreen application thickness (to face and body) that is more refined than conservative defaults. The United States Food & Drug Administration (US FDA) recommends a sunscreen application rate of 2 mg/cm2. This value is typically used as a worst-case assumption in environmental exposure assessments of UV filters. Designed a novel approach to estimate lotion sunscreen application thickness using an online questionnaire protocol employing visual references and self-reported height and weight of the respondents. A literature review was also conducted to collect historical sunscreen usage. Over 9000 people were surveyed in the US, and after the dataset was refined, their sunscreen application thickness was estimated based on calculated body surface area and reported sunscreen amounts. The mean and median values for survey respondents are 3.00 and 1.78 mg/cm2, respectively, for facial application thickness and 1.52 and 1.35 mg/cm2, respectively, for body application thickness. Earlier research from 1985-2020 reported 36 of the 38 values are below the US FDA's recommended application thickness of 2 mg/cm2 (range 0.2-5 mg/cm2). This web-based surveyis the first of its kind, designed specifically to quantify sunscreen application in a large and diverse set of consumers. This method provides a greater reach to larger populations thus enabling more granular data analysis and understanding. Exposure assessments of sunscreen ingredients typically use conservative parameters. These data can refine those assessments and allow for more informed and science-based risk management decisions.

Tourism forecasting with granular sentiment analysis

Generic sentiment calculations cannot fully reflect tourists' preferences, whereas fine-grained sentiment analysis identifies tourists' precise attitudes. This study forecasted visitor arrivals at two tourist attractions in China using Internet data from multiple sources. Empirical results indicate that 1) fine-grained sentiment analysis of online review data can substantially improve tourism demand models' forecasting performance; 2) combining multidimensional sentiment analysis–based online review data with search engine data outperforms search engine data in tourism demand prediction; and 3) fine-grained sentiment analysis–based online review data and search engine data maintain stable predictive power during times of uncertainty.

Application of Cross model for granular flow and impact analysis using three-dimensional B-spline material point method

Granular flow and impact, such as landslides and debris flows, are typical problems involving large deformations of non-Newtonian free surfaces, which are of great interest in geological engineering and also pose challenges for traditional mesh-based numerical methods. As an enhancement of the traditional Material Point Method (MPM), the B-Spline Material Point Method (BSMPM) can not only effectively reduce mesh-crossing and quadrature errors, but also facilitate the tracking and capturing of free surfaces and contact interfaces. In this paper, the non-Newtonian general Cross model, the rheological parameters of which are determined by the Bingham model and the Mohr-Coulomb yield criterion, is introduced into the three-dimensional BSMPM (3D BSMPM) for simulating granular flow and impact problems. Interparticle effects of granular materials are represented by artificial damping forces. Dynamic simulations are carried out on the collapse of sand column and glass beads, the flow of bentonite suspensions, and the impact between granular mass and elastic obstacles. The overall evolution of granular flow and impact behavior are presented and compared with existing experimental and/or original MPM results. It is found that the proposed method well reproduces the overall process of granular flow and impact behavior, including its various stages of formation, acceleration, deceleration, and deposition, as well as the impact pressure. The results show that the granular flow and impact behavior can be well represented by the proposed 3D BSMPM with the Cross rheological model, demonstrating the effectiveness of the proposed method in simulating complicated environmental fluid dynamics.

An Interval Type 2 Fuzzy Decision-Making Framework for Exploring Critical Issues for the Sustenance of the Tea Industry

The purpose of the present study is to propose an interval-valued type 2 fuzzy set (IT2FS)-based analytic hierarchy process (AHP) framework to unfold the critical challenging factors influencing the sustenance and growth of the Indian tea industry. The current work follows an expert opinion-based group decision-making approach. The challenging factors have been identified through a literature review and finalized after a pilot study based on the opinions of professionals, consumers, and experts. Finally, the critical challenging factors and sub-factors have been figured out through analysis of the responses of the experts. To offset the subjective bias, an IT2FS-based granular analysis has been carried out. The findings reveal that market diversification and productivity are the central issues. Additionally, it is important to give attention to improving the quality of the products, increasing the use of modern technology and organic farming, and developing a variety of products. The result shows a considerable level of consistency in the group decision-making (CR &lt; 0.1) for all pairwise comparisons. The present work shall be of use to formulate appropriate strategies and policy decisions. It shows a robust application of IT2FS-AHP for complex decision-making in real life.

Don’t Look Up: House Prices in Emerging Europe

Abstract This paper investigates how housing prices respond to economic, financial and demographic conditions in emerging markets in Europe. We use quarterly data covering 10 countries over the period 1998–2022 and implement a panel quantile regression approach to obtain a granular analysis of real estate markets. Overall, economic, financial and demographic developments explain real house price growth in emerging Europe, with income growth having the most significant impact. Quantile regression estimations show that income growth matters more for higher quantiles of the property market. We also find that an increase in short-term or long-term interest rates has a price-dampening impact, indicating that a higher cost of borrowing is associated with lower real house price growth. These results indicate that the downturn in house prices could deepen with the looming economic recession and soaring interest rates.

Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis

This paper presents multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between 25 May 2020 and 31 January 2023. First, the analysis shows that the average number of Tweets per month wherein individuals self-reported Long COVID on Twitter was considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings from sentiment analysis using VADER show that the percentages of Tweets with positive, negative, and neutral sentiments were 43.1%, 42.7%, and 14.2%, respectively. To add to this, most of the Tweets with a positive sentiment, as well as most of the Tweets with a negative sentiment, were not highly polarized. Third, the result of tokenization indicates that the tweeting patterns (in terms of the number of tokens used) were similar for the positive and negative Tweets. Analysis of these results also shows that there was no direct relationship between the number of tokens used and the intensity of the sentiment expressed in these Tweets. Finally, a granular analysis of the sentiments showed that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively.

High linearity temperature-compensated SPR fiber sensor for the detection of glucose solution concentrations

In the realm of optical fiber sensing, conventional surface plasmon resonance (SPR) sensors often face challenges due to their low linearity, broad resonance, and susceptibility to external temperature changes. To that end, we evaluate a groundbreaking approach with a temperature-compensating SPR biosensor based on the Mach-Zehnder interferometer (MZI), featuring a novel tapered multimode fiber-single mode fiber-multimode fiber (MMF-SMF-MMF) architecture. Specifically, the SPR is excited on a silver film modified by reduced graphene oxide (rGO) and π-π stacked pyrene-1-boric acid providing specific glucose binding ability. The MZI effect compensating for ambient temperature influence on the SPR is realized with the MMF-SMF-MMF structure. The study also explores the use of Fast Fourier Transform (FFT) for separate, granular analysis of the MZI and SPR signals and improves the linearity of the sensor using a novel centroid-fitting technique. This method utilizes the shift of calculated centroid coordinates rather than the single dip resonance wavelength from SPR, enhancing the overall performance of the sensor. The experimental results demonstrate excellent glucose sensitivity of 2.819 nm/mM, a linear range of 0–10 mmol/L, a LOD of 0.12 mM/L and concurrent temperature compensation. Compact and easy to fabricate, the proposed SPR sensor provides a novel solution for accurate glucose concentration detection in human blood.

Revolutions in the Twenty-First Century.

Review of ‘Handbook of Revolutions in the 21st Century: The New Waves of Revolutions, and the Causes and Effects of Disrup-tive Political Change’. Edited by Jack A. Goldstone, Leonid Grinin, and Andrey Korotayev. Springer, 2022. ISBN 978-3-030-86467-5.This is a big book in more ways than one. In its 41 chapters, readers will find much to keep them occupied. The volume’s range is vast, covering several hundred years of history and an equally expan-sive geography, offering grand theorising alongside granular analysis, and providing insights ranging from the descriptive to the predictive. Within this panorama, I would highlight four general themes that strike me as particularly significant: first, the book’s focus on waves; second, its insistence on the back-and-forth between history and theo-ry; third, its global frame of reference; and fourth, its attentiveness to non-progressive revolutionary currents, most notably religious revolutionary forces.

Persistently Present, Yet Invisible? Exploring the Experiences of High-Achieving Black Students in the Greater Toronto Area

Through employing critical race theory, seen-invisibility, and circuits of dispossession as theoretical frames, this article complicates discourses around equity and Black student achievement by examining the underexplored experiences of high-achieving Black Canadian students in the Greater Toronto Area (GTA). Drawing on focus group data with four adolescent participants, the study finds that they experienced violent forms of racialization in their educational environments through a lack of physical, social, and intellectual space to exist as both Black and high-achieving. This rendered them persistently present due to their race, yet invisible in the perceptions of their intellect. Central to this article is an articulation, unpacking, and thus granular analysis of the particular ways that racialization can operate within education systems to still marginalize Black students and erect complex barriers––even when they demonstrate strong academic performance. These emerging insights inform a need for a broader and more holistic understanding of Black Canadian student experiences and a rethinking of intervention and resistance strategies.

Management Zones Delineation, Correct and Incorrect Application Analysis in a Coriander Field Using Precision Agriculture, Soil Chemical, Granular and Hydraulic Analyses, Fuzzy k-Means Zoning, Factor Analysis and Geostatistics

The objective of our investigation was to study the various effects of correct and incorrect application of fuzziness exponent, initial parameterization and fuzzy classification algorithms modeling on homogeneous management zones (MZs) delineation of a Coriandrum sativum L. field by using precision agriculture, soil chemical, granular and hydraulic analyses, fuzzy k-means zoning algorithms with statistical measures like the introduced Percentage of Management Zones Spatial Agreement (PoMZSA) (%), factor and principal components analysis (PCA) and geostatistical nutrients GIS mapping. Results of the exploratory fuzzy analysis showed how different fuzziness exponents applied to different soil parameter groups can reveal better insights for determining whether a fuzzy classification is a correct or incorrect application for delineating fuzzy MZs. In all cases, the best results were achieved by using the optimal fuzziness exponent with the full number of parameters of each soil chemical, granular and hydraulic parameter group or the maximum extracted PCAs. In each case study where the factor analysis and PCA showed optimal MZs &gt; 2, the results of the fuzzy PoMZSA clustering metric revealed low, medium and medium to high spatial agreement, which presented a statistically significant difference between the soil parameter datasets when an arbitrary or commonly used fuzziness exponent was used (e.g., φ = 1.30 or φ = 1.50). Soil sampling and laboratory analysis are tools of major significance for performing exploratory fuzzy analysis, and in addition, the FkM Xie and Benny’s index and the introduced fuzzy PoMZSA clustering metric are valuable tools for correctly delineating management zones.

Benefits, barriers, and facilitators of developing B2B mobile applications

PurposeWhile few studies have examined business-to-business (B2B) mobile application (app) usage, none have examined the challenges in developing these technological assets. This study aims to examine B2B marketing executives’ perceptions regarding benefits, barriers and facilitators in app development.Design/methodology/approachA survey of 311 B2B marketing executives at selling firms in the USA was conducted to identify key themes related to the benefits, barriers and facilitators in developing B2B apps. The research featured “open-ended” questions exclusively, and advanced textual and thematic analysis of executives’ responses produced several key themes.FindingsResults show that the perceived benefit of lowering customer servicing and costs drives development more so than trying to realize new revenue opportunities (e.g. “saving” vs. “making” money). Achieving internal buy-in/participation was perceived as a larger barrier than the commitment of financial resources. Additionally, training and education were viewed as the strongest facilitators of an app’s success over its design and functionality. Implications for B2B firms are discussed.Research limitations/implicationsThe open-ended format of this research captures a greater breadth of perspectives at the expense of more granular analysis of any particular issue.Originality/valueThe themes generated from the responses offer novel insights into the benefits sought in developing an app, as well as the technological, organizational and environmental factors that act as barriers and facilitators. The open-ended format of this research captures a greater breadth of perspectives at the expense of a more granular analysis of any particular issue.

Integration of the social determinants of health into quality indicators for colorectal cancer surgery: a scoping review protocol

IntroductionQuality monitoring is a critical component of high-performing cancer care systems. Quality indicators (QIs) are standardised, evidence-based measures of healthcare quality that allow healthcare systems to track performance, identify gaps...

A comparison of financial and operational efficiency between smart and traditional insufflation for laparoscopic surgery: A granular analysis

ObjectiveSmart insufflation (SI) techniques relying on valve and membrane-free insufflation are increasing in usage. Although considerable literature exists demonstrating the benefits of SI on procedural ease and patient outcomes, there remains a paucity describing the financial impact of these devices. The purpose of this study was to determine the financial and efficiency impact of these devices on the operating room and inpatient wards of a hospital. MethodsA discrete event simulation model representing a typical mid-sized North American hospital comparing SI to traditional insufflation (TI) was generated. The National Surgical Quality Improvement database from 2015 to 2019 was used to populate the model with data supplemented from the literature. Outcomes included length of stay (LOS), duration of surgery (DOS), annual procedure volume, profit, return on investment (ROI), and gross profit margin (GPM). From the literature review, DOS savings were 10–32 minutes/case, while LOS savings were 0–3 days/case. ResultsImplementation of an SI led to an increase in annual throughput of 42–346 (4.4%–36.6%) cases for all procedures and 38 to 297 (4.3%–33.3%) cases for complex procedures. LOS was found to be decreased by 175–614 (18.3%–64.2%) days for all procedures and 231 to 614 (35.6%–77.9%) cases for complex procedures with the implementation of an SI. Together, this resulted in an increase in net profit of $104,685 per annum. The ROI of SI over the TI device was >1000%, and the GPM for the TI was 90.0%, while the GPM for the SI was 71.7%. ConclusionDespite the initial financial investment being greater, the implementation of SI offsets these expenses and yields significant financial benefits. Our study demonstrates the financial benefits of SI over TI and illustrates how granular operational and financial analyses of technologies are essential to aid in sound healthcare procurement decision making.