Utilizing data and artificial intelligence to optimize treatment room scheduling and staffing

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Background: Tennessee Oncology is a large community oncology practice with over 30 clinics providing 89,000 treatments per year across Tennessee and northern Georgia. Tennessee Oncology’s scheduling application was unable to optimally schedule treatment appointments. This scheduling gap was causing frequent patient delays and employee extended hours. Tennessee Oncology partnered with Smirta, Inc., to develop a data and artificial intelligence (AI) driven scheduling overlay platform that would optimize and simplify cancer treatment scheduling as well as predict scheduling patterns and resource needs. Methods: Named OncoSmart, the scheduling optimization platform ingests historic scheduling data, detailed clinic configuration data including provider and nursing schedules, and available resource data such as treatment room chairs. Utilizing AI, the platform generates optimal scheduling recommendations matching the specific set of services that need to be scheduled. The platform overlays the current scheduling app and provides dynamic, real-time recommendations based on current resource (treatment room, provider, etc.) schedule availabilities and bookings. Tennessee Oncology piloted the scheduling optimization platform at 1 clinic and has currently expanded the pilot to 12 additional clinics. Results: After various ranges of clinic pilot times (6 months to 2 years), Tennessee Oncology treatment volumes have increased by 7%. In parallel to this increase, the optimization platform has helped decrease extended hours by over 32%. The original pilot site has shown major improvement in all 4 primary key performance indicators (KPI): treatment volume +12%; Chair utilization +12%; treatment delay -9%; extended hours -82%. Additionally, using the platform’s predictive analytics capabilities, analyses have been completed to generate optimal treatment scheduling patterns as well as optimal treatment nursing staffing models. Conclusions: Within a short period after deployment, Smirta Inc’s OncoSmart has helped Tennessee Oncology identify better treatment scheduling options for these 13sites. The scheduling optimization platform has proven to be very effective in identifying optimal treatment scheduling strategies and in identifying critical resource bottlenecks. The platform’s clinic management, optimization, nurse assignment, business intelligence, and resource management modules has empowered Tennessee Oncology to better manage critical clinical resources and reduce staff overtime during a period of growth.

Characteristics of lead ion-stimulated phosphorylation of Electrophorus electricus electroplax (Na+ + K+)-adenosine triphosphatase and inhibition of ATP-ADP exchange

Pb2+-stimulated phosphorylation of Electrophorus electricus electroplax (Na+ + K+)-adenosine triphosphatase is prevented by stoichiometric quantities of 2,3-dimercaptopropanol. The chelator in the same low concentrations does not block Na+-dependent phosphorylation. Both Pb2+-and Na+-dependent phosphorylation reactions show the same dependence on MgCl2. Phosphorylation in the presence of both Na+ and Pb2+ is cumulative suggesting that Pb2+ and Na+ bind at separate, independent sites. The enthalpy change due to binding of Pb2+ is about -1.76 kcal/mol. 32P-phosphopeptides obtained from pronase or pepsin digests of Pb2+-and Na+-dependent phosphoproteins are electrophoretically identical. Pb2+ does not stimulate but does inhibit ATP-ADP exchange activity under the conditions in which this activity is stimulated by Na+. Since the phosphorylation sites are identical, it is concluded that the differences in reactivity of the Na+- and Pb2+-phosphoenzymes are due to different conformational changes produced by binding of Na+ and Pb2+. The Pb2+-sensitive conformation is critical for Na+ specificity of phosphorylation, reversibility of phosphorylation, and for phosphatase activity but not for acceptor site phosphorylation by ATP. These findings have implications for enzyme reaction models.

BMY-14802 antagonizes harmaline- and D-serine-induced increases in mouse cerebellar cyclic GMP: neurochemical evidence for a sigma receptor-mediated functional modulation of responses mediated by the N-methyl-D-aspartate receptor complex in vivo

BMY-14802 [alpha-(4-flurophenyl)-4-(5-fluoro-pyramidinyl)-1-piperazine butanol], a potent sigma ligand with poor affinity for dopamine and phencyclidine receptors in vitro, attenuated parenteral harmaline- and direct intracerebellar D-serine-induced increases in mouse cerebellar cGMP. Intracerebroventricularly injected BMY-14802 also antagonized the effects of intracerebellar D-serine, indicating a central mechanism. However, direct co-injection of BMY-14802 into the cerebellum failed to antagonize the D-serine-induced increases in cGMP, indicating a locus of action outside the cerebellum. In contrast, quisqualate-induced cGMP increases were not attenuated by BMY-14802. These results indicate a functional modulation of the N-methyl-D-aspartate/glycine/phencyclidine/ion channel complex-mediated events by BMY-14802, possibly through a transsynaptic mechanism, thus representing the first in vivo demonstration of a sigma ligand modulation of a response mediated through the N-methyl-D-aspartate receptor complex.

The relationship between the size and asymmetry of the lateral ventricles and cortical myelin content in individuals with mood disorders

Background

Although enlargement of the lateral ventricles was previously observed in individuals with mood disorders, the link between ventricular size and asymmetry with other indices of brain structure remains underexplored. In this study, we examined the association of lateral ventricular size and asymmetry with cortical myelin content in individuals with bipolar (BD) and depressive (DD) disorders compared to healthy controls (HC).

Methods

Magnetic resonance imaging (MRI) was used to obtain T1w and T2w images from 149 individuals (age=27.7 (SD=6.1) years, 78% female, BD=38, DD=57, HC=54). Cortical myelin content was calculated using the T1w/T2w ratio. Elastic net regularized regression identified brain regions whose myelin content was associated with ventricular size and asymmetry. A post-hoc linear regression examined how participants' diagnosis, illness duration, and current level of depression moderated the relationship between the size and asymmetry of the lateral ventricles and levels of cortical myelin in the selected brain regions.

Results

Individuals with mood disorders had larger lateral ventricles than HC. Larger ventricles and lower asymmetry were observed in individuals with BD who had longer lifetime illness duration and more severe current depressive symptoms. A greater left asymmetry was observed in participants with DD than in those with BD (p<0.01). Elastic net revealed that both ventricular enlargement and asymmetry were associated with altered myelin content in cingulate, frontal, and sensorimotor cortices. In BD, but not other groups, ventricular enlargement was related to altered myelin content in the right insular regions.

Conclusions

Lateral ventricular enlargement and asymmetry are linked to myelin content imbalance, thus, potentially leading to emotional and cognitive dysfunction in mood disorders.

Quantifying Distances Between Non-Elliptical Clusters to Enhance the Identification of Meaningful Emotional Reactivity Subtypes

Coordinated emotional responses across psychophysiological and subjective indices is a cornerstone of adaptive emotional functioning. Using clustering to identify cross-diagnostic subgroups with similar emotion response profiles may suggest novel underlying mechanisms and treatments.However, many psychophysiological measures are non-normal even in homogenous samples, and over-reliance on traditional elliptical clustering approaches may inhibit the identification of meaningful subgroups. Finite mixture models that allow for non-elliptical cluster distributions is an emerging methodological field that may overcome this hurdle. Furthermore, succinctly quantifying pairwise cluster separation could enhance the clinical utility of the clustering solutions. However, a comprehensive examination of distance measures in the context of elliptical and non-elliptical model-based clustering is needed to provide practical guidance on the computation, benefits, and disadvantages of existing measures. We summarize several measures that can quantify the multivariate distance between two clusters and suggest practical computational tools. Through a simulation study, we evaluate the measures across three scenarios that allow for clusters to differ in location, scale, skewness, and rotation. We then demonstrate our approaches using psychophysiological and subjective responses to emotional imagery captured through the Transdiagnostic Anxiety Study. Finally, we synthesize findings to provide guidance on how to use distance measures in clustering applications.

Mycotic aneurysm presenting as fever of unknown origin

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The interaction between depression diagnosis and BMI is related to altered activation pattern in the right inferior frontal gyrus and anterior cingulate cortex during food anticipation

BACKGROUND

Depression and overweight/obesity often cooccur but the underlying neural mechanisms for this bidirectional link are not well understood.

METHODS

In this functional magnetic resonance imaging study, we scanned 54 individuals diagnosed with depressive disorders (DD) and 48 healthy controls (HC) to examine how diagnostic status moderates the relationship between body mass index (BMI) and brain activation during anticipation and pleasantness rating of food versus nonfood stimuli.

RESULTS

We found a significant BMI-by-diagnosis interaction effect on activation in the right inferior frontal gyrus (RIFG) and anterior cingulate cortex (ACC) during food versus nonfood anticipation (p < .0125). Brain activation in these regions was greater in HC with higher BMI than in HC with lower BMI. Individuals with DD showed an opposite pattern of activation. Structural equation modeling revealed that the relationship between BMI, activation in the RIFG and ACC, and participants' desire to eat food items shown in the experiment depended on the diagnostic status.

CONCLUSIONS

Considering that food anticipation is an important component of appetitive behavior and that the RIFG and ACC are involved in emotion regulation, response inhibition and conflict monitoring necessary to control this behavior, we propose that future clinical trials targeting weight loss in DD should investigate whether adequate mental preparation positively affects subsequent food consumption behaviors in these individuals.