Exploring the Association Between Patient Waiting Time, No-Shows and Overbooking Strategy to Improve Efficiency in Health Care

Many primary care clinics are using overbooking as a strategy to mitigate the negative impacts on operations and performance caused by patient nonattendance of appointments, also known as “no-shows”. However, overbooking tends to increase patient waiting time and worker overtime. It is also acknowledged that patient waiting time is associated with no-show behavior, yet there is a lack of observational study to quantify the relationship. The overall goal of this research is to explore the relationships between patient waiting time, no-show behavior and overbooking strategy in terms of clinic performance. Arena® simulation software is used to create a discrete-event simulation model that represents daily processes of a standard primary care clinic. The model is used to test the three variables by varying (1) the amount increase in no-show probability by tolerance group, (2) waiting time tolerance threshold, and (3) overbooking strategy. We observe from the results that the three features (waiting time, no-show behavior and overbooking strategy) are interrelated because higher no-show probability leads to higher number of no-shows, which suggests overbooking more patients, and eventually leads to longer waiting time, resulting in an increase in the patient’s no show probability. However, as limited by the size of the clinic case, we were not able to see a clear cut-off of average waiting tolerance for making overbooking decisions that are not only based on the prediction of patient no-shows, but also consider the impact on patient waiting time and its association with no-show behavior. Nevertheless, by having the waiting time as one of the constraint variables, we were able to see the trade-off of choosing a certain overbooking decision and its impact on no-shows. To fully understand the impact of the relationship between the three variables, we recommend that more observational studies should be conducted as pertaining to the desired clinic environment

Features and applications of the Groove Analysis Program (GAP)

An IBM Personal Computer (PC) version of the Groove Analysis program (GAP) was developed to predict the steady state heat transport capability of an axially grooved heat pipe for a specified groove geometry and working fluid. In the model, the capillary limit is determined by the numerical solution of the differential equation for momentum conservation with the appropriate boundary conditions. This governing equation accounts for the hydrodynamic losses due to friction in liquid and vapor flows and due to liquid/vapor shear interaction. Back-pumping in both 0-g and 1-g is accounted for in the boundary condition at the condenser end. Slug formation in 0-g and puddle flow in 1-g are also considered in the model. At the user's discretion, the code will perform the analysis for various fluid inventories (undercharge, nominal charge, overcharge, or a fixed fluid charge) and heat pipe elevations. GAP will also calculate the minimum required heat pipe wall thickness for pressure containment at design temperatures that are greater than or lower than the critical temperature of the working fluid. This paper discusses the theory behind the development of the GAP model. It also presents the many useful and powerful capabilities of the model. Furthermore, a correlation of flight test performance data and the predictions using GAP are presented and discussed

Suprachoroidal and Subretinal Injections of AAV Using Transscleral Microneedles for Retinal Gene Delivery in Nonhuman Primates.

Retinal gene therapy using adeno-associated viruses (AAVs) is constrained by the mode of viral vector delivery. Intravitreal AAV injections are impeded by the internal limiting membrane barrier, while subretinal injections require invasive surgery and produce a limited region of therapeutic effect. In this study, we introduce a novel mode of ocular gene delivery in rhesus macaques using transscleral microneedles to inject AAV8 into the subretinal or suprachoroidal space, a potential space between the choroid and scleral wall of the eye. Using in vivo imaging, we found that suprachoroidal AAV8 produces diffuse, peripheral expression in retinal pigment epithelial (RPE) cells, but it elicited local infiltration of inflammatory cells. Transscleral subretinal injection of AAV8 using microneedles leads to focal gene expression with transduction of RPE and photoreceptors, and minimal intraocular inflammation. In comparison, intravitreal AAV8 shows minimal transduction of retinal cells, but elicits greater systemic humoral immune responses. Our study introduces a novel mode of transscleral viral delivery that can be performed without vitreoretinal surgery, with focal or diffuse transgene expression patterns suitable for different applications. The decoupling of local and systemic immune responses reveals important insights into the immunological consequences of AAV delivery to different ocular compartments surrounding the blood-retinal barrier

The Relationship between Uterine, Fecal, Bedding, and Airborne Dust Microbiota from Dairy Cows and Their Environment: A Pilot Study

Simple Summary After calving, dairy cows face the risk of negative energy balance, inflammation, and immunosuppression, which may result in bacterial infection and disruption of the normal microbiota, thus encouraging the development of metritis and endometritis. This study characterized uterine, fecal, bedding, and airborne dust microbiota from postpartum dairy cows and their environment during summer and winter. The results clarify the importance of microbiota in cowshed environments, i.e., bedding and airborne dust, in understanding the postpartum uterine microbiota of dairy cows. Abstract The aim of this study was to characterize uterine, fecal, bedding, and airborne dust microbiota from postpartum dairy cows and their environment. The cows were managed by the free-stall housing system, and samples for microbiota and serum metabolite assessment were collected during summer and winter when the cows were at one and two months postpartum. Uterine microbiota varied between seasons; the five most prevalent taxa were Enterobacteriaceae, Moraxellaceae, Ruminococcaceae, Staphylococcaceae, and Lactobacillaceae during summer, and Ruminococcaceae, Lachnospiraceae, Bacteroidaceae, Moraxellaceae, and Clostridiaceae during winter. Although Actinomycetaceae and Mycoplasmataceae were detected at high abundance in several uterine samples, the relationship between the uterine microbiota and serum metabolite concentrations was unclear. The fecal microbiota was stable regardless of the season, whereas bedding and airborne dust microbiota varied between summer and winter. With regards to uterine, bedding, and airborne dust microbiota, Enterobacteriaceae, Moraxellaceae, Staphylococcaceae, and Lactobacillaceae were more abundant during summer, and Ruminococcaceae, Lachnospiraceae, Bacteroidaceae, and Clostridiaceae were more abundant during winter. Canonical analysis of principal coordinates confirmed the relationship between uterine and cowshed microbiota. These results indicated that the uterine microbiota may vary when the microbiota in cowshed environments changes

Digital transient torque measurement for rotating or linear AC machines (real time measurement)

The digital transient torque measuring device presented in this paper is able to make a "real time" calculation in steady-state and transient conditions of the electromagnetic torque or force of an extremely wide range of ac electrical machines supplied with sinusoidal voltages or through frequency converters with frequencies up to 30 kHz. The device needs no shaft position or speed sensors, it uses exclusively the acquisition of the stator currents and voltages. The main fields of applications are: test shop, control, measurement, integration in a control or regulation unit, element of a monitoring system

Decreased STARD10 expression is associated with defective insulin secretion in humans and mice

Genetic variants near ARAP1 (CENTD2) and STARD10 influence type 2 diabetes (T2D) risk. The risk alleles impair glucose-induced insulin secretion and, paradoxically but characteristically, are associated with decreased proinsulin:insulin ratios, indicating improved proinsulin conversion. Neither the identity of the causal variants nor the gene(s) through which risk is conferred have been firmly established. Whereas ARAP1 encodes a GTPase activating protein, STARD10 is a member of the steroidogenic acute regulatory protein (StAR)-related lipid transfer protein family. By integrating genetic fine-mapping and epigenomic annotation data and performing promoter-reporter and chromatin conformational capture (3C) studies in β cell lines, we localize the causal variant(s) at this locus to a 5 kb region that overlaps a stretch-enhancer active in islets. This region contains several highly correlated T2D-risk variants, including the rs140130268 indel. Expression QTL analysis of islet transcriptomes from three independent subject groups demonstrated that T2D-risk allele carriers displayed reduced levels of STARD10 mRNA, with no concomitant change in ARAP1 mRNA levels. Correspondingly, β-cell-selective deletion of StarD10 in mice led to impaired glucose-stimulated Ca2+ dynamics and insulin secretion and recapitulated the pattern of improved proinsulin processing observed at the human GWAS signal. Conversely, overexpression of StarD10 in the adult β cell improved glucose tolerance in high fat-fed animals. In contrast, manipulation of Arap1 in β cells had no impact on insulin secretion or proinsulin conversion in mice. This convergence of human and murine data provides compelling evidence that the T2D risk associated with variation at this locus is mediated through reduction in STARD10 expression in the β cell

Modeling of Cloud-Based Digital Twins for Smart Manufacturing with MT Connect

The common modeling of digital twins uses an information model to describe the physical machines. The integration of digital twins into productive cyber-physical cloud manufacturing (CPCM) systems imposes strong demands such as reducing overhead and saving resources. In this paper, we develop and investigate a new method for building cloud-based digital twins (CBDT), which can be adapted to the CPCM platform. Our method helps reduce computing resources in the information processing center for efficient interactions between human users and physical machines. We introduce a knowledge resource center (KRC) built on a cloud server for information intensive applications. An information model for one type of 3D printers is designed and integrated into the core of the KRC as a shared resource. Several experiments are conducted and the results show that the CBDT has an excellent performance compared to existing methods