Marked Point Process Secretory Events Statistically Characterize Leptin Pulsatile Dynamics.

Recent studies have highlighted leptin, a key hormone that regulates energy intake and induces satiety, due to the worldwide prevalence of obesity. In this study, we analyzed plasma leptin measurements from 18 women with premenopausal obesity before and after bromocriptine treatment. By using underlying pulses recovered through deconvolution, we modeled the leptin secretory pulses as marked point processes and applied statistical distributions to evaluate the dynamics of leptin, including the interpulse intervals and amplitudes of the secretion. We fit the generalized inverse Gaussian and lognormal distributions to the intervals and the Gaussian, lognormal, and gamma distributions to the amplitudes of pulses. We evaluated the models' goodness of fit using statistical metrics including Akaike's information criterion, Kolmogorov-Smirnov plots, and quantile-quantile plots. Our evaluation results revealed the effectiveness of these statistical distributions in modeling leptin secretion. Although the lognormal and gamma distributions performed the best based on the metrics, we found all distributions capable of accurately modeling the timing of secretory events, leading us to a better understanding of the physiology of leptin secretion and providing a basis for leptin monitoring. In terms of pulse amplitude, the evaluation metrics indicated the gamma distribution as the most accurate statistical representation. We found no statistically significant effect of bromocriptine intake on the model parameters except for one distribution model.

Eye tracking is more sensitive than skin conductance response in detecting mild environmental stimuli.

The skin conductance (SC) and eye tracking data are two potential arousal-related psychophysiological signals that can serve as the interoceptive unconditioned response to aversive stimuli (e.g. electric shocks). The current research investigates the sensitivity of these signals in detecting mild electric shock by decoding the hidden arousal and interoceptive awareness (IA) states. While well-established frameworks exist to decode the arousal state from the SC signal, there is a lack of a systematic approach that decodes the IA state from pupillometry and eye gaze measurements. We extract the physiological-based features from eye tracking data to recover the IA-related neural activity. Employing a Bayesian filtering framework, we decode the IA state in fear conditioning and extinction experiments where mild electric shock is used. We independently decode the underlying arousal state using binary and marked point process (MPP) observations derived from concurrently collected SC data. Eight of 11 subjects present a significantly (P-value < 0.001 ) higher IA state in trials that were always accompanied by electric shock ( CS + US + ) compared to trials that were never accompanied by electric shock ( CS - ). According to the decoded SC-based arousal state, only five (binary observation) and four (MPP observation) subjects present a significantly higher arousal state in CS + US + trials than CS - trials. In conclusion, the decoded hidden brain state from eye tracking data better agrees with the presented mild stimuli. Tracking IA state from eye tracking data can lead to the development of contactless monitors for neuropsychiatric and neurodegenerative disorders.

Corrections to "Sparse Multichannel Decomposition of Electrodermal Activity With Physiological Priors".

[This corrects the article DOI: 10.1109/OJEMB.2023.3332839.].

Comparing development-matched and age-matched play targets: A replication and extension.

Previous research has supported selecting development-matched targets rather than age-matched targets to teach play skills to children with autism spectrum disorder. However, few studies have been conducted, and replications and extensions of this research are needed. The current study replicated Pane et al. (2022) by comparing the acquisition of development-matched and age-matched play targets when teaching play skills to four children with autism. No contrived prompts or consequences were used to teach play skills in either condition. Extensions included identifying targets via a newer version of the Developmental Play Assessment, targeting different play categories, assessing additional imitation skills, and conducting a caregiver assessment to identify socially valid toys, play actions, and vocalizations based on each participant's common experiences and preferences as well as their caregiver's values and preferences. As in Pane et al., participants demonstrated a higher level of scripted play actions in the development-matched condition.

Bayesian Inference of Hidden Cognitive Performance and Arousal States in Presence of Music.

Goal: Poor arousal management may lead to reduced cognitive performance. Specifying a model and decoder to infer the cognitive arousal and performance contributes to arousal regulation via non-invasive actuators such as music. Methods: We employ a Bayesian filtering approach within an expectation-maximization framework to track the hidden states during the [Formula: see text]-back task in the presence of calming and exciting music. We decode the arousal and performance states from the skin conductance and behavioral signals, respectively. We derive an arousal-performance model based on the Yerkes-Dodson law. We design a performance-based arousal decoder by considering the corresponding performance and skin conductance as the observation. Results: The quantified arousal and performance are presented. The existence of Yerkes-Dodson law can be interpreted from the arousal-performance relationship. Findings display higher matrices of performance within the exciting music. Conclusions: The performance-based arousal decoder has a better agreement with the Yerkes-Dodson law. Our study can be implemented in designing non-invasive closed-loop systems.

Diagnostic challenges and outcome of fatty acid oxidation defects in a tertiary care center in Lebanon.

BACKGROUND: Fatty acid oxidation defects are rare autosomal recessive disorders with variable clinical manifestations and outcome. Early detection by systematic neonatal screening may improve their prognosis. Long-term outcome studies of these disorders in the Middle East and North Africa region are limited. The purpose of this study is to report the diagnostic challenges and outcome of fatty acid oxidation defects in a major tertiary care center in Lebanon, a resource-constrained country in the Middle East. METHODS: A retrospective review of charts of all fatty acid oxidation defects sequential patients diagnosed and followed at our center was conducted. Collected data included: parental consanguinity, age at diagnosis, clinical presentation, biochemical profile, confirmatory diagnosis, treatment and outcome. A genotype-phenotype correlation was also performed, when available. RESULTS: Seven types of fatty acid oxidation defects were identified in a total of 34 patients from 21 families. Most families (79%) were consanguineous (first-degree cousins). The majority were diagnosed when clinically symptomatic (78%), at various ages between 10 days and 19 years (average: 2 years). Follow-up duration spanned between 2 months and 15 years (average: 5 years). The remainder of the patients were detected while still asymptomatic by systematic neonatal screening (9%) or due to positive family history (9%). The most common defect was carnitine transporter deficiency (50%) with an exclusive cardiac presentation related to a founder variant c.981C > T, (p.Arg254*) in the SLC22A5 gene. Medium chain acyl-CoA dehydrogenase deficiency was found in 13% only, which could be explained by the absence of systematic neonatal screening. Rare gene variants were detected in very long chain and multiple acyl-CoA dehydrogenase deficiency. The worse prognosis was observed in very long chain acyl-CoA dehydrogenase deficiency. The overall survival at last follow-up reached 75% with a complete reversal of symptoms with treatment in most patients (63%), despite their late diagnosis. CONCLUSIONS: Our experience highlights the diagnostic challenges and outcome of fatty acid oxidation defects in a resource-constrained country with high consanguinity rates. Physicians' awareness and systematic neonatal screening are key for diagnosis. Larger genotype-phenotype studies are still needed to understand the natural history of these rare diseases and possibly improve their outcome.

A multimodal dataset for investigating working memory in presence of music: a pilot study.

Introduction: Decoding an individual's hidden brain states in responses to musical stimuli under various cognitive loads can unleash the potential of developing a non-invasive closed-loop brain-machine interface (CLBMI). To perform a pilot study and investigate the brain response in the context of CLBMI, we collect multimodal physiological signals and behavioral data within the working memory experiment in the presence of personalized musical stimuli. Methods: Participants perform a working memory experiment called the n-back task in the presence of calming music and exciting music. Utilizing the skin conductance signal and behavioral data, we decode the brain's cognitive arousal and performance states, respectively. We determine the association of oxygenated hemoglobin (HbO) data with performance state. Furthermore, we evaluate the total hemoglobin (HbT) signal energy over each music session. Results: A relatively low arousal variation was observed with respect to task difficulty, while the arousal baseline changes considerably with respect to the type of music. Overall, the performance index is enhanced within the exciting session. The highest positive correlation between the HbO concentration and performance was observed within the higher cognitive loads (3-back task) for all of the participants. Also, the HbT signal energy peak occurs within the exciting session. Discussion: Findings may underline the potential of using music as an intervention to regulate the brain cognitive states. Additionally, the experiment provides a diverse array of data encompassing multiple physiological signals that can be used in the brain state decoder paradigm to shed light on the human-in-the-loop experiments and understand the network-level mechanisms of auditory stimulation.

Dynamic Changes in Circulating Tumor Fraction as a Predictor of Real-World Clinical Outcomes in Solid Tumor Malignancy Patients Treated with Immunotherapy.

INTRODUCTION: A dynamic molecular biomarker that can identify early efficacy of immune checkpoint inhibitor (ICI) therapy remains an unmet clinical need. Here we evaluate if a novel circulating tumor DNA (ctDNA) assay, xM, used for treatment response monitoring (TRM), that quantifies changes in ctDNA tumor fraction (TF), can predict outcome benefits in patients treated with ICI alone or in combination with chemotherapy in a real-world (RW) cohort. METHODS: This retrospective study consisted of patients with advanced cancer from the Tempus de-identified clinical genomic database who received longitudinal liquid-based next-generation sequencing. Eligible patients had a blood sample ≤ 40 days prior to the start of ICI initiation and an on-treatment blood sample 15-180 days post ICI initiation. TF was calculated via an ensemble algorithm that utilizes TF estimates derived from variants and copy number information. Patients with molecular response (MR) were defined as patients with a ≥ 50% decrease in TF between tests. In the subset of patients with rw-imaging data between 2 and 18 weeks of ICI initiation, the predictive value of MR in addition to rw-imaging was compared to a model of rw-imaging alone. RESULTS: The evaluable cohort (N = 86) was composed of 14 solid cancer types. Patients received either ICI monotherapy (38.4%, N = 33) or ICI in combination with chemotherapy (61.6%, N = 53). Patients with MR had significantly longer rw-overall survival (rwOS) (hazard ratio (HR) 0.4, P = 0.004) and rw-progression free survival (rwPFS) (HR 0.4, P = 0.005) than patients with molecular non-response (nMR). Similar results were seen in the ICI monotherapy subcohort; HR 0.2, P = 0.02 for rwOS and HR 0.2, P = 0.01 for rwPFS. In the subset of patients with matched rw-imaging data (N = 51), a model incorporating both MR and rw-imaging was superior in predicting rwOS than rw-imaging alone (P = 0.02). CONCLUSIONS: xM used for TRM is a novel serial quantitative TF algorithm that can be used clinically to evaluate ICI therapy efficacy.

Recovery of arterial blood pressure after chest compression pauses in patients with out-of-hospital cardiac arrest.

BACKGROUND AND AIMS: Chest compressions generating good perfusion during cardiopulmonary resuscitation (CPR) in cardiac arrest patients are critical for positive patient outcomes. Conventional wisdom advises minimizing compression pauses because several compressions are required to recover arterial blood pressure (ABP) back to pre-pause values. Our study examines how compression pauses influence ABP recovery post-pause in out-of-hospital cardiac arrest. METHODS: We analyzed data from a subset of a prospective, randomized LUCAS 2 Active Decompression trial. Patients were treated by an anesthesiologist-staffed rapid response car program in Oslo, Norway (2015-2017) with mechanical chest compressions using the LUCAS device at 102 compressions/min. Patients with an ABP signal during CPR and at least one compression pause >2 sec were included. Arterial cannulation, compression pauses, and ECG during the pause were verified by physician review of patient records and physiological signals. Pauses were excluded if return of spontaneous circulation occurred during the pause (pressure pulses associated with ECG complexes). Compression, mean, and decompression ABP for 10 compressions before/after each pause and the mean ABP during the pause were measured with custom MATLAB code. The relationship between pause duration and ABP recovery was investigated using linear regression. RESULTS: We included 56 patients with a total of 271 pauses (pause duration: median = 11 sec, Q1 = 7 sec, Q3 = 18 sec). Mean ABP dropped from 53 ± 10 mmHg for the last pre-pause compression to 33 ± 7 mmHg during the pause. Compression and mean ABP recovered to >90% of pre-pause pressure within 2 compressions, or 1.7 sec. Pause duration did not affect the recovery of ABP post-pause (R2: 0.05, 0.03, 0.01 for compression, mean, and decompression ABP, respectively). CONCLUSIONS: ABP generated by mechanical CPR recovered quickly after pauses. Recovery of ABP after a pause was independent of pause duration.

Hyperornithinemia-hyperammonemia-homocitrullinuria: a rare neurometabolic disorder in two siblings.

Hyperornithinemia-hyperammonemia-homocitrullinuria syndrome is an extremely rare disorder of urea cycle, with few patients reported worldwide. Despite hyperammonemia control, the long-term outcome remains poor with progressive neurological deterioration. We report the clinical, biochemical, and molecular features of two Lebanese siblings diagnosed with this disorder and followed for 8 and 15 years, respectively. Variable clinical manifestations and neurological outcome were observed. The patient with earlier onset of symptoms had a severe neurological deterioration while the other developed a milder form of the disease at an older age. Diagnosis was challenging in the absence of the complete biochemical triad and the non-specific clinical presentations. Whole exome sequencing revealed a homozygous variant, p.Phe188del, in the SLC25A15 gene, a French- Canadian founder mutation previously unreported in Arab patients. Hyperammonemia was controlled in both patients but hyperonithinemia persisted. Frequent hyperalaninemia spikes and lactic acidosis occured concomitantly with the onset of seizures in one of the siblings. Variable neurological deterioration and outcome were observed within the same family. This is the first report from the Arab population of the long-term outcome of this devastating neurometabolic disorder.

Unveiling productivity: The interplay of cognitive arousal and expressive typing in remote work.

Cognitive Arousal, frequently elicited by environmental stressors that exceed personal coping resources, manifests in measurable physiological markers, notably in galvanic skin responses. This effect is prominent in cognitive tasks such as composition, where fluctuations in these biomarkers correlate with individual expressiveness. It is crucial to understand the nexus between cognitive arousal and expressiveness. However, there has not been a concrete study that investigates this inter-relation concurrently. Addressing this, we introduce an innovative methodology for simultaneous monitoring of these elements. Our strategy employs Bayesian analysis in a multi-state filtering format to dissect psychomotor performance (captured through typing speed), galvanic skin response or skin conductance (SC), and heart rate variability (HRV). This integrative analysis facilitates the quantification of expressive behavior and arousal states. At the core, we deploy a state-space model connecting one latent psychological arousal condition to neural activities impacting sweating (inferred through SC responses) and another latent state to expressive behavior during typing. These states are concurrently evaluated with model parameters using an expectation-maximization algorithms approach. Assessments using both computer-simulated data and experimental data substantiate the validity of our approach. Outcomes display distinguishable latent state patterns in expressive typing and arousal across different computer software used in office management, offering profound implications for Human-Computer Interaction (HCI) and productivity analysis. This research marks a significant advancement in decoding human productivity dynamics, with extensive repercussions for optimizing performance in telecommuting scenarios.

Characterization of Leptin Secretion in Premenopausal Obese Women Treated with Bromocriptine.

Leptin, a hormone secreted by adipose tissue, is primarily responsible for inhibiting hunger and maintaining energy balance. Improper leptin secretion may result in hyperleptinemia (excess secretion of leptin) or leptin resistance, both of which contribute to obesity. Diagnosing abnormal leptin secretion may help treat this underlying cause of obesity. Therefore, continuous monitoring of the level of leptin may help characterize its secretion dynamics and also help devise an appropriate treatment. In this research, we consider leptin hormone concentration data taken over a 24 hour time period from eighteen healthy premenopausal obese women before and after treatment with a dopamine agonist, bromocriptine, and deconvolve the observed leptin hormone levels to estimate the number, timing, and magnitude of the underlying leptin secretory pulses. We find that there is an overall decrease in leptin secretion, particularly during sleep, but the changes in the secretory and clearance rates, and the number of pulses underlying the secretion process are not statistically significant.Clinical relevance- This work seeks to understand the effect of bromocriptine on leptin secretory dynamics and will help further current understanding of the effect of bromocriptine in relation to obesity.

Characterizing Alterations in Cortisol Secretion During Cardiac Surgery.

Cortisol is a neuroendocrine hormone of the hypothalamus-pituitary-adrenal (HPA) axis secreted from adrenal glands in response to stimulation by adrenocorticotropic hormone (ACTH) from the anterior pituitary and corticotropin releasing hormone (CRH) from the hypothalamus. Cortisol has multiple functionalities in maintaining bodily homeostasis - including anti-inflammatory influences - through its diurnal secretion pattern (which has been studied extensively); its secretion is also increased in response to major traumatic events such as surgery. Due to the adverse health consequences of an abnormal immune response, it is crucial to understand the effect of cortisol in modulating inflammation. To address this physiological issue, we characterize the secretion of cortisol using a high temporal resolution dataset of ten patients undergoing coronary arterial bypass grafting (CABG) surgery, in comparison with a control group not undergoing surgery. We find that cortisol exhibits different pulsatile dynamics in those undergoing cardiac surgery compared to the control subjects. We also summarize the causality of cortisol's relationship with different cytokines (which are one type of inflammatory markers) by performing Granger causality analysis.Clinical relevance- This work documents time-varying patterns of the HPA axis hormone cortisol in the inflammatory response to cardiac surgery and may eventually help improve patients' prognosis post-surgery (or in other conditions) by enabling early detection of an abnormal cortisol or inflammatory response and enabling patient specific remedial interventions.

A Point-Process Approach for Tracking Valence using a Respiration Belt.

Emotional valence is difficult to be inferred since it is related to several psychological factors and is affected by inter- and intra-subject variability. Changes in emotional valence have been found to cause a physiological response in respiration signals. In this study, we propose a state-space model and decode the valence by analyzing a person's respiration pattern. Particularly, we generate a binary point process based on features that are indicative of changes in respiration pattern as a result of an emotional valence response. High valence is typically associated with faster and deeper breathing. As a result, (i)depth of breath, (ii)rate of respiration, and (iii) breathing cycle time are indicators of high valence and used to generate the binary point process representing underlying neural stimuli associated with changes in valence. We utilize an expectation-maximization (EM) framework to decode a hidden valence state and the associated valence index. This predicted valence state is compared to self-reported valence ratings to optimize the parameters and determine the accuracy of the model. The accuracy of the model in predicting high and low valence events is found to be 77% and 73%, respectively. Our study can be applied towards the long term analysis of valence. Additionally, it has applications in a closed-loop system procedures and wearable design paradigm to track and regulate the emotional valence.

Sparse Deconvolution and Causality Analysis of Inflammatory Markers During Cardiac Surgery.

Major bodily trauma such as cardiac surgery elicits (in response to tissue injury and other exogenous surgical factors) a whole-body inflammation response during which specialized signaling proteins called cytokines are synthesized and invoke multiple defense mechanisms. Many proinflammatory and anti-inflammatory cytokines such as interleukins (IL) and tumor necrosis factor (TNF) are produced to initiate bodily repair. Due to the adverse health consequences, including mortality, of a maladaptive cytokine response, understanding their complex dynamics using system-theoretic modeling and analysis may pave the way for controlling the inflammatory response which may eventually improve medical outcomes for patients. To this end, we use clinical data from ten patients undergoing coronary arterial bypass graft surgery to study the response of four cytokines (IL6, IL8, IL10, TNFα) and the neuroendocrine hormone cortisol. We perform deconvolution to obtain the secretory pulses underlying their pulsatile production and analyze causal interactions, mathematically uncovering some interactive relationships found in previous experimental studies.Clinical relevance- This work is a first step towards a mechanistic inference of the inflammatory response to surgery that could eventually help control the inflammatory response and could inform medical interventions to improve patient outcomes.

Stellar scintillation statistics and the impact of aperture averaging on space-to-ground optical communications.

Statistical probability distributions characterizing received optical power fluctuations, or scintillation, enable performance predictions of space-to-ground optical communication systems. In this paper, we present measurements of stellar scintillation over a wide range of elevation angles and turbulence conditions collected simultaneously with a 5 cm and 40 cm telescope aperture, which allows a comparison between minimal and significant aperture averaging conditions. The measured data is compared to a reasonable set of candidate probability distribution functions (PDFs), including lognormal, which is most often cited in the literature for weak to moderate scintillation. For scintillation indices (SIs) less than about 0.2, the Nakagami-m distribution provides the best representation of the collected data for both apertures and imposes a greater lasercom link penalty than a lognormal distribution, which has been inaccurately implemented as the default probability distribution in the literature. For larger values of the SI, the scintillation is best characterized by a Gamma-Gamma distribution. Additionally, the measured temporal covariance for weak to moderate scintillation conditions is found to be in reasonably good agreement with theoretical predictions.

Regulation of brain cognitive states through auditory, gustatory, and olfactory stimulation with wearable monitoring.

Inspired by advances in wearable technologies, we design and perform human-subject experiments. We aim to investigate the effects of applying safe actuation (i.e., auditory, gustatory, and olfactory) for the purpose of regulating cognitive arousal and enhancing the performance states. In two proposed experiments, subjects are asked to perform a working memory experiment called n-back tasks. Next, we incorporate listening to different types of music, drinking coffee, and smelling perfume as safe actuators. We employ signal processing methods to seamlessly infer participants' brain cognitive states. The results demonstrate the effectiveness of the proposed safe actuation in regulating the arousal state and enhancing performance levels. Employing only wearable devices for human monitoring and using safe actuation intervention are the key components of the proposed experiments. Our dataset fills the existing gap of the lack of publicly available datasets for the self-management of internal brain states using wearable devices and safe everyday actuators. This dataset enables further machine learning and system identification investigations to facilitate future smart work environments. This would lead us to the ultimate idea of developing practical automated personalized closed-loop architectures for managing internal brain states and enhancing the quality of life.

Beliefs and Practices on Depression Among Selected Filipino Indigenous Peoples: A Focused Ethnography.

INTRODUCTION: Depression beliefs and practices among indigenous peoples are essential to creating responsive mental health services. The purpose of the study is to explore the cultural beliefs and practices on depression among the Ilocanos, Kankana-eys, and Maranaos indigenous peoples in the Philippines. METHOD: The study employed a focused ethnography research design. The study involved 41 (N = 41) traditional healers and tribal leaders across Ilocano, Kankana-ey, and Maranao ethnic groups in the Philippine Islands. Interviews, reviews of records, and participant observation were used as data gathering tools. RESULTS: Beliefs about depression include magico-spiritual influence, relational problems, economic pressure, and emotional domains. Practices were divided into three domains: preventive, curative, and rehabilitative interventions. DISCUSSION: The depression beliefs and practices of Ilocano, Kankana-ey, and Maranao indigenous peoples are rooted in their tradition, culture, religion, and medical influences largely rooted in magico-spiritual approaches. These findings suggest the inclusion of culturally-based care to address depression.

Isolation, susceptibility profiles and genomic analysis of a colistin-resistant Salmonella enterica serovar Kentucky strain COL-R.

Salmonella enterica serovar Kentucky is a frequent cause for clinical infections in human patients. They are isolated and reported with multidrug resistance from the foods of animal origin from various countries. However, studies inferring the colistin resistance are limited. Hence, the current study reports the genetic factors and genomic analysis of the colistin-resistant Salmonella enterica serovar Kentucky strain COL-R for better understanding of its pathogenic potential and phylogenetic relatedness. The S. Kentucky strain COL-R was successfully isolated from chicken meat during ongoing surveillance of food of animal origin. Antimicrobial susceptibility testing revealed resistance to cefoxitin, erythromycin, gentamicin, tetracycline, and most disturbingly to ciprofloxacin and colistin (broth microdilution method). Whole-genome sequence of the COL-R strain was subjected to various in silico analysis to identify the virulence factors, antimicrobial resistance genes, pathogenicity islands and sequence type. The S. Kentucky COL-R strain belonged to sequence type (ST) 198 with a high probability (0.943) of being a human pathogen. Besides presence of integrated phage in the S. Kentucky COL-R genome, 38 genes conferring resistance to various antimicrobials and disinfectants were also identified. Nucleotide Polymorphism analysis indicated triple mutations in gyrA and parC genes conferring fluoroquinolone resistance. Phylogenomic analysis with 31 other S. Kentucky genomes revealed discernible clusters with S. Kentucky COL-R strain latching onto a cluster of high diversity (geographic location and isolation sources). Taken together, our results document the first occurrence of colistin resistance in a fluoroquinolone resistant S. Kentucky COL-R strain isolated from retail chicken and provide crucial information on the genomic features of the strain. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03559-2.

Deploying Bots in Healthcare to Find Revenue Cycle Improvements.

SUMMARY: Doing more with less has been a business mantra for decades. Healthcare leaders have implemented flex scheduling and job sharing, streamlined workflows, committed to process improvement techniques such as Lean, hired retirees, gained efficiencies from remote work ... and the list goes on. Each tactic has yielded productivity improvements, yet the need to do more with less continues. Postpandemic challenges include staff recruitment and retention, labor inflation, and dwindling margins, all of which must be addressed while maintaining corporate cultures. The journey with bots described here started in this dynamic environment, and the work has not been single-threaded. The organization featured here-an integrated delivery network-has digital front-door and back-end robotic process automation (RPA) projects underway. The digital front-door initiative supports patient self-registration and automates authorizations and insurance verification processes. The back-end patient financial services RPA project replaces and enhances existing technology. Revenue cycle as a multidepartment function is leadership's poster child for RPA, and the revenue cycle team is tasked to demonstrate the value of the technology. This article covers the initial steps and lessons learned in the process.