Differential Effects of an Emergency Department-to-Home Care Transitions Intervention in an Older Adult Population: A Latent Class Analysis.

BACKGROUND: Older adults frequently return to the emergency department (ED) within 30 days of a visit. High-risk patients can differentially benefit from transitional care interventions. Latent class analysis (LCA) is a model-based method used to segment the population and test intervention effects by subgroup. OBJECTIVES: We aimed to identify latent classes within an older adult population from a randomized controlled trial evaluating the effectiveness of an ED-to-home transitional care program and test whether class membership modified the intervention effect. RESEARCH DESIGN: Participants were randomized to receive the Care Transitions Intervention or usual care. Study staff collected outcomes data through medical record reviews and surveys. We performed LCA and logistic regression to evaluate the differential effects of the intervention by class membership. SUBJECTS: Participants were ED patients (age 60 y and above) discharged to a community residence. MEASURES: Indicator variables for the LCA included clinically available and patient-reported data from the initial ED visit. Our primary outcome was ED revisits within 30 days. Secondary outcomes included ED revisits within 14 days, outpatient follow-up within 7 and 30 days, and self-management behaviors. RESULTS: We interpreted 6 latent classes in this study population. Classes 1, 4, 5, and 6 showed a reduction in ED revisit rates with the intervention; classes 2 and 3 showed an increase in ED revisit rates. In class 5, we found evidence that the intervention increased outpatient follow-up within 7 and 30 days (odds ratio: 1.81, 95% CI: 1.13-2.91; odds ratio: 2.24, 95% CI: 1.25-4.03). CONCLUSIONS: Class membership modified the intervention effect. Population segmentation is an important step in evaluating a transitional care intervention.

Quantifying and reducing inequity in average treatment effect estimation.

BACKGROUND: Across studies of average treatment effects, some population subgroups consistently have lower representation than others which can lead to discrepancies in how well results generalize. METHODS: We develop a framework for quantifying inequity due to systemic disparities in sample representation and a method for mitigation during data analysis. Assuming subgroup treatment effects are exchangeable, an unbiased sample average treatment effect estimator will have lower mean-squared error, on average across studies, for subgroups with less representation when treatment effects vary. We present a method for estimating average treatment effects in representation-adjusted samples which enables subgroups to optimally leverage information from the full sample rather than only their own subgroup's data. Two approaches for specifying representation adjustment are offered-one minimizes average mean-squared error for each subgroup separately and the other balances minimization of mean-squared error and equal representation. We conduct simulation studies to compare the performance of the proposed estimators to several subgroup-specific estimators. RESULTS: We find that the proposed estimators generally provide lower mean squared error, particularly for smaller subgroups, relative to the other estimators. As a case study, we apply this method to a subgroup analysis from a published study. CONCLUSIONS: We recommend the use of the proposed estimators to mitigate the impact of disparities in representation, though structural change is ultimately needed.

Multimethod Process Evaluation of a Community Paramedic Delivered Care Transitions Intervention for Older Emergency Department Patients.

OBJECTIVE: We assessed fidelity of delivery and participant engagement in the implementation of a community paramedic coach-led Care Transitions Intervention (CTI) program adapted for use following emergency department (ED) visits. METHODS: The adapted CTI for ED-to-home transitions was implemented at three university-affiliated hospitals in two cities from 2016 to 2019. Participants were aged ≥60 years old and discharged from the ED within 24 hours of arrival. In the current analysis, participants had to have received the CTI. Community paramedic coaches collected data on program delivery and participant characteristics at each transition contact via inventories and assessments. Participants provided commentary on the acceptability of the adapted CTI. Using a multimethod approach, the CTI implementation was assessed quantitatively for site- and coach-level differences. Qualitatively, barriers to implementation and participant satisfaction with the CTI were thematically analyzed. RESULTS: Of the 863 patient participants, 726 (84.1%) completed their home visits. Cancellations were usually patient-generated (94.9%). Most planned follow-up visits were successfully completed (94.6%). Content on the planning for red flags and post-discharge goal setting was discussed with high rates of fidelity overall (95% and greater), while content on outpatient follow-up was lower overall (75%). Differences in service delivery between the two sites existed for the in-person visit and the first phone follow-up, but the differences narrowed as the study progressed. Participants showed a 24.6% increase in patient activation (i.e., behavioral adoption) over the 30-day study period (p < 0.001).Overall, participants reported that the program was beneficial for managing their health, the quality of coaching was high, and that the program should continue. Not all participants felt that they needed the program. Community paramedic coaches reported barriers to CTI delivery due to patient medical problems and difficulties with phone visit coordination. Coaches also noted refusal to communicate or engage with the intervention as an implementation barrier. CONCLUSIONS: Community paramedic coaches delivered the adapted CTI with high fidelity across geographically distant sites and successfully facilitated participant engagement, highlighting community paramedics as an effective resource for implementing such patient-centered interventions.

The electrophysiological masking level difference: effects of age and mediation of hearing and cognition.

OBJECTIVE: Evaluate the conceptual framework that age effects on the electrophysiological binaural masking level difference (MLD) are partially mediated by age-related hearing loss and/or global cognitive function via mediation analysis. DESIGN: Participants underwent a series of audiometric tests. The MLD was measured via cortical auditory evoked potentials using a speech stimulus (/ɑ/) in speech-weighted background noise. We used mediation analyses to determine the total effect, natural direct effects, and natural indirect effects, which are displayed as regression coefficients ([95% CI]; p value). STUDY SAMPLE: Twenty-eight individuals aged 19-87 years (mean [SD]: 53.3 [25.2]), recruited from the community. RESULTS: Older age had a significant total effect on the MLD (-0.69 [95% CI: -0.96, -0.45]; p < 0.01). Neither pure tone average (-0.11 [95% CI: -0.43, 0.24; p = 0.54] nor global cognitive function (-0.02 [95% CI: -0.13, 0.02]; p = 0.55) mediated the relationship of age and the MLD and effect sizes were small. Results were insensitive to use of alternative hearing measures or inclusion of interaction terms. CONCLUSIONS: The electrophysiological MLD may be an age-sensitive measure of binaural temporal processing that is minimally affected by age-related hearing loss and global cognitive function.

Value estimation and latent-state update-related neural activity during fear conditioning predict posttraumatic stress disorder symptom severity.

Learning theories of posttraumatic stress disorder (PTSD) purport that fear-learning processes, such as those that support fear acquisition and extinction, are impaired. Computational models designed to capture specific processes involved in fear learning have primarily assessed model-free, or trial-and-error, reinforcement learning (RL). Although previous studies indicated that aspects of model-free RL are disrupted among individuals with PTSD, research has yet to identify whether model-based RL, which is inferential and contextually driven, is impaired. Given empirical evidence of aberrant contextual modulation of fear in PTSD, the present study sought to identify whether model-based RL processes are altered during fear conditioning among women with interpersonal violence (IPV)-related PTSD (n = 85) using computational modeling. Model-free, hybrid, and model-based RL models were applied to skin conductance responses (SCR) collected during fear acquisition and extinction, and the model-based RL model was found to provide the best fit to the SCR data. Parameters from the model-based RL model were carried forward to neuroimaging analyses (voxel-wise and independent component analysis). Results revealed that reduced activity within visual processing regions during model-based updating uniquely predicted higher PTSD symptoms. Additionally, after controlling for model-based updating, greater value estimation encoding within the left frontoparietal network during fear acquisition and reduced value estimation encoding within the dorsomedial prefrontal cortex during fear extinction predicted greater PTSD symptoms. Results provide evidence of disrupted RL processes in women with assault-related PTSD, which may contribute to impaired fear and safety learning, and, furthermore, may relate to treatment response (e.g., poorer response to exposure therapy).

Scalar Assessment of the Family Caregiver Activation in Transitions Tool: An Exploratory Factor Analysis.

The Family Caregiver Activation in Transitions (FCAT) tool in its current, non-scalar form is not pragmatic for clinical use as each item is scored and intended to be interpreted individually. The purpose of the current study was to create a scalar version of the FCAT to facilitate better care communications between hospital staff and family caregivers. We also assessed the scale's validity by comparing the scalar version of the measure against patient health measures. Data were collected from 463 family caregiver-patient dyads from January 2016 to July 2019. An exploratory factor analysis was performed on the 10-item FCAT, resulting in a statistically homogeneous six-item scale focused on current caregiving activation factors. The measure was then compared against patient health measures, with no significant biases found. The six-item scalar FCAT can provide hospital staff insight into the level of caregiver activation occurring in the patient's health care and help tailor care transition needs for family caregiver-patient dyads. [Journal of Gerontological Nursing, 48(12), 35-42.].

A digital self-report survey of mood for bipolar disorder.

OBJECTIVES: Bipolar disorder (BP) is commonly researched in digital settings. As a result, standardized digital tools are needed to measure mood. We sought to validate a new survey that is brief, validated in digital form, and able to separately measure manic and depressive severity. METHODS: We introduce a 6-item digital survey, called digiBP, for measuring mood in BP. It has three depressive items (depressed mood, fidgeting, fatigue), two manic items (increased energy, rapid speech), and one mixed item (irritability); and recovers two scores (m and d) to measure manic and depressive severity. In a secondary analysis of individuals with BP who monitored their symptoms over 6 weeks (n = 43), we perform a series of analyses to validate the digiBP survey internally, externally, and as a longitudinal measure. RESULTS: We first verify a conceptual model for the survey in which items load onto two factors ("manic" and "depressive"). We then show weekly averages of m and d scores from digiBP can explain significant variation in weekly scores from the Young Mania Rating Scale (R2  = 0.47) and SIGH-D (R2  = 0.58). Lastly, we examine the utility of the survey as a longitudinal measure by predicting an individual's future m and d scores from their past m and d scores. CONCLUSIONS: While further validation is warranted in larger, diverse populations, these validation analyses should encourage researchers to consider digiBP for their next digital study of BP.

Predicting hearing aid use in adults: the Beaver Dam Offspring Study.

OBJECTIVE: The purpose of this study was to (i) develop a model that predicts hearing aid (HA) use and (ii) determine if model fit is improved by adding factors not typically collected in audiological evaluations. DESIGN: Two models were created and evaluated. The "clinical" model used factors typically collected during audiologic clinical evaluations. The "expanded" model considered additional clinical, health and lifestyle factors to determine if the model fit could be improved (compared to clinical model). Models were created with least absolute shrinkage and selection operator (LASSO) logistic regression with 10-fold cross validation. Predictive ability was evaluated via receiver operating characteristic curves and concordance statistics (c-statistics). STUDY SAMPLE: This study included 275 participants from the Beaver Dam Offspring Study, a prospective longitudinal cohort study of aging, with a treatable level of hearing loss and no HA use at baseline. RESULTS: The clinical and expanded models report predictors important for HA use. The c-statistics of the clinical (0.80) and expanded (0.79) models were not significantly different (p = 0.41). CONCLUSIONS: Similar predictive abilities of models suggest audiological evaluations perform well in predicting HA use.

The Average Effect of Emergency Department Admission on Readmission and Mortality for Older Adults With Chest Pain.

BACKGROUND: Many older adults (65+) present to the Emergency Department (ED) with chest pain, but do not have otherwise clear clinical indication of whether they should be admitted or discharged. This uncertainty leads to decisions that are highly variable-in addition to already being costly-which could have adverse consequences, since older adults are particularly vulnerable from hospitalization. OBJECTIVE: The objective of this study was to determine whether admitting versus discharging an older adult presenting to the ED with chest pain reduces risk of mortality and readmission. STUDY DESIGN: Electronic health records were curated from an academic hospital system between January 1, 2014, and September 27, 2018. Average effects of admission on 30-day readmission and mortality were estimated using a new causal inference approach based on a latent-variable model of the admission process. Additional analyses assessed moderators and robustness of estimates. SUBJECTS: Older patients (n=3090) presenting to University of Wisconsin Hospital ED. MEASURES: Readmission and mortality within 25, 30, and 35 days of discharge from the ED for discharged patients or the hospital for admitted patients RESULTS:: For older chest pain patients, admission is estimated to lower the 30-day risk of readmission by 42.8% (95% confidence interval: 41.0%-44.6%) but increase the 30-day risk of mortality by 0.8% (95% confidence interval: 0.4%-1.2%). Individuals with higher hierarchical conditional category scores or diabetes with complications have both lower 30-day risk of readmission and higher 30-day risk of mortality compared with their counterparts (P≤0.02). CONCLUSIONS: Our findings suggest ED admission may prevent readmission at the cost of increasing mortality risk for older chest pain patients, especially those with comorbidity. Additional studies are needed to validate these findings.

A flexible and generalizable model of online latent-state learning.

Many models of classical conditioning fail to describe important phenomena, notably the rapid return of fear after extinction. To address this shortfall, evidence converged on the idea that learning agents rely on latent-state inferences, i.e. an ability to index disparate associations from cues to rewards (or penalties) and infer which index (i.e. latent state) is presently active. Our goal was to develop a model of latent-state inferences that uses latent states to predict rewards from cues efficiently and that can describe behavior in a diverse set of experiments. The resulting model combines a Rescorla-Wagner rule, for which updates to associations are proportional to prediction error, with an approximate Bayesian rule, for which beliefs in latent states are proportional to prior beliefs and an approximate likelihood based on current associations. In simulation, we demonstrate the model's ability to reproduce learning effects both famously explained and not explained by the Rescorla-Wagner model, including rapid return of fear after extinction, the Hall-Pearce effect, partial reinforcement extinction effect, backwards blocking, and memory modification. Lastly, we derive our model as an online algorithm to maximum likelihood estimation, demonstrating it is an efficient approach to outcome prediction. Establishing such a framework is a key step towards quantifying normative and pathological ranges of latent-state inferences in various contexts.

Rescorla-Wagner Models with Sparse Dynamic Attention.

The Rescorla-Wagner (R-W) model describes human associative learning by proposing that an agent updates associations between stimuli, such as events in their environment or predictive cues, proportionally to a prediction error. While this model has proven informative in experiments, it has been posited that humans selectively attend to certain cues to overcome a problem with the R-W model scaling to large cue dimensions. We formally characterize this scaling problem and provide a solution that involves limiting attention in a R-W model to a sparse set of cues. Given the universal difficulty in selecting features for prediction, sparse attention faces challenges beyond those faced by the R-W model. We demonstrate several ways in which a naive attention model can fail explain those failures and leverage that understanding to produce a Sparse Attention R-W with Inference framework (SAR-WI). The SAR-WI framework not only satisfies a constraint on the number of attended cues, it also performs as well as the R-W model on a number of natural learning tasks, can correctly infer associative strengths, and focuses attention on predictive cues while ignoring uninformative cues. Given the simplicity of proposed alterations, we hope this work informs future development and empirical validation of associative learning models that seek to incorporate sparse attention.

A latent variable approach to potential outcomes for emergency department admission decisions.

In emergency departments (EDs), care providers continuously weigh admissions against continued monitoring and treatment often without knowing their condition and health needs. To understand the decision process and its causal effect on outcomes, an observational study must contend with unobserved/missing information and a lack of exchangeability between admitted and discharged patients. Our goal was to provide a general framework to evaluate admission decisions from electronic healthcare records (EHRs). We describe admission decisions as a decision-making process in which the patient's health needs is a binary latent variable. We estimate latent health needs from EHR with only partial knowledge of the decision process (ie, initial evaluation, admission decision, length of stay). Estimated latent health needs are then used to understand the admission decision and the decision's causal impact on outcomes. For the latter, we assume potential outcomes are stochastically independent from the admission decision conditional on latent health needs. As a case study, we apply our approach to over 150 000 patient encounters with the ED from the University of Michigan Health System collected from August 2012 through July 2015. We estimate that while admitting a patient with higher latent needs reduces the 30-day risk of revisiting the ED or later being admitted through the ED by over 79%, admitting a patient with lower latent needs actually increases these 30-day risks by 3.0% and 7.6%, respectively.

Effects of Sleep, Physical Activity, and Shift Work on Daily Mood: a Prospective Mobile Monitoring Study of Medical Interns.

BACKGROUND: Although short sleep, shift work, and physical inactivity are endemic to residency, a lack of objective, real-time information has limited our understanding of how these problems impact physician mental health. OBJECTIVE: To understand how the residency experience affects sleep, physical activity, and mood, and to understand the directional relationships among these variables. DESIGN: A prospective longitudinal study. SUBJECTS: Thirty-three first-year residents (interns) provided data from 2 months pre-internship through the first 6 months of internship. MAIN MEASURES: Objective real-time assessment of daily sleep and physical activity was assessed through accelerometry-based wearable devices. Mood scaled from 1 to 10 was recorded daily using SMS technology. Average compliance rates prior to internship for mood, sleep, and physical activity were 77.4, 80.2, and 93.7%, and were 78.8, 53.0, and 79.9% during internship. KEY RESULTS: After beginning residency, interns lost an average of 2 h and 48 min of sleep per week (t = - 3.04, p < .01). Mood and physical activity decreased by 7.5% (t = - 3.67, p < .01) and 11.5% (t = - 3.15, p < .01), respectively. A bidirectional relationship emerged between sleep and mood during internship wherein short sleep augured worse mood the next day (b = .12, p < .001), which, in turn, presaged shorter sleep the next night (b = .06, p = .03). Importantly, the effect of short sleep on mood was twice as large as mood's effect on sleep. Lastly, substantial shifts in sleep timing during internship (sleeping ≥ 3 h earlier or later than pre-internship patterns) led to shorter sleep (earlier: b = - .36, p < .01; later: b = - 1.75, p < .001) and poorer mood (earlier: b = - .41, p < .001; later: b = - .41, p < .001). CONCLUSIONS: Shift work, short sleep, and physical inactivity confer a challenging environment for physician mental health. Efforts to increase sleep opportunity through designing shift schedules to allow for adequate opportunity to resynchronize the circadian system and improving exercise compatibility of the work environment may improve mood in this depression-vulnerable population.

Newborn Screening Collection and Delivery Processes in Michigan Birthing Hospitals: Strategies to Improve Timeliness.

Objectives This study aimed to determine which steps in the newborn screening collection and delivery processes contribute to delays and identify strategies to improve timeliness. Methods Data was analyzed from infants (N = 94,770) who underwent newborn screening at 83 hospitals in Michigan between April 2014 and March 2015. Linear mixed effects models estimated effects of hospital and newborn characteristics on times between steps in the process, whereas simulation explored how to improve timeliness through adjustments to schedules for the state laboratory and for specimen pickup from hospitals. Results Time from collection to receipt of arrival to the state laboratory varied greatly with collection timing (P < 0.001), with specimens collected on Friday or Saturday delayed an average of 9-12 h compared to other specimens. Simulation estimates shifting specimen pickup from 6 p.m. Sunday-Friday to 9 p.m. Sunday-Friday could lead to an additional 12.6% of specimens received by the Michigan laboratory within 60 h of birth. Conclusions for Practice The time between when a specimen is collected and received by the laboratory can be a significant bottleneck in the newborn screening process. Modifying hospital pickup schedules appears to be a simple way to improve timeliness.

Dashboarding to Monitor Machine-Learning-Based Clinical Decision Support Interventions.

BACKGROUND: Existing monitoring of machine-learning-based clinical decision support (ML-CDS) is focused predominantly on the ML outputs and accuracy thereof. Improving patient care requires not only accurate algorithms but also systems of care that enable the output of these algorithms to drive specific actions by care teams, necessitating expanding their monitoring. OBJECTIVES: In this case report, we describe the creation of a dashboard that allows the intervention development team and operational stakeholders to govern and identify potential issues that may require corrective action by bridging the monitoring gap between model outputs and patient outcomes. METHODS: We used an iterative development process to build a dashboard to monitor the performance of our intervention in the broader context of the care system. RESULTS: Our investigation of best practices elsewhere, iterative design, and expert consultation led us to anchor our dashboard on alluvial charts and control charts. Both the development process and the dashboard itself illuminated areas to improve the broader intervention. CONCLUSION: We propose that monitoring ML-CDS algorithms with regular dashboards that allow both a context-level view of the system and a drilled down view of specific components is a critical part of implementing these algorithms to ensure that these tools function appropriately within the broader care system.

Addressing heterogeneous populations in latent variable settings through robust estimation.

Individuals routinely differ in how they present with psychiatric illnesses and in how they respond to treatment. This heterogeneity, when overlooked in data analysis, can lead to misspecified models and distorted inferences. While several methods exist to handle various forms of heterogeneity in latent variable models, their implementation in applied research requires additional layers of model crafting, which might be a reason for their underutilization. In response, we present a robust estimation approach based on the expectation-maximization (EM) algorithm. Our method makes minor adjustments to EM to enable automatic detection of population heterogeneity and to recognize individuals who are inadequately explained by the assumed model. Each individual is associated with a probability that reflects how likely their data were to have been generated from the assumed model. The individual-level probabilities are simultaneously estimated and used to weight each individual's contribution in parameter estimation. We examine the utility of our approach for Gaussian mixture models and linear factor models through several simulation studies, drawing contrasts with the EM algorithm. We demonstrate that our method yields inferences more robust to population heterogeneity or other model misspecifications than EM does. We hope that the proposed approach can be incorporated into the model-building process to improve population-level estimates and to shed light on subsets of the population that demand further attention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Quantifying aberrant approach-avoidance conflict in psychopathology: A review of computational approaches.

Making effective decisions during approach-avoidance conflict is critical in daily life. Aberrant decision-making during approach-avoidance conflict is evident in a range of psychological disorders, including anxiety, depression, trauma-related disorders, substance use disorders, and alcohol use disorders. To help clarify etiological pathways and reveal novel intervention targets, clinical research into decision-making is increasingly adopting a computational psychopathology approach. This approach uses mathematical models that can identify specific decision-making related processes that are altered in mental health disorders. In our review, we highlight foundational approach-avoidance conflict research, followed by more in-depth discussion of computational approaches that have been used to model behavior in these tasks. Specifically, we describe the computational models that have been applied to approach-avoidance conflict (e.g., drift-diffusion, active inference, and reinforcement learning models), and provide resources to guide clinical researchers who may be interested in applying computational modeling. Finally, we identify notable gaps in the current literature and potential future directions for computational approaches aimed at identifying mechanisms of approach-avoidance conflict in psychopathology.

Detection of differential depressive symptom patterns in a cohort of perinatal women: an exploratory factor analysis using a robust statistics approach.

Background: Postpartum depression can take many forms. Different symptom patterns could have divergent implications for how we screen, diagnose, and treat postpartum depression. We sought to utilise a recently developed robust estimation algorithm to automatically identify differential patterns in depressive symptoms and subsequently characterise the individuals who exhibit different patterns. Methods: Depressive symptom data (N = 548) were collected from women with neuropsychiatric illnesses at two U.S. urban sites participating in a longitudinal observational study of stress across the perinatal period. Data were collected from Emory University between 1994 and 2012 and from the University of Arkansas for Medical Sciences between 2012 and 2017. We conducted an exploratory factor analysis of Beck Depression Inventory (BDI) items using a robust expectation-maximization algorithm, rather than a conventional expectation-maximization algorithm. This recently developed method enabled automatic detection of differential symptom patterns. We described differences in symptom patterns and conducted unadjusted and adjusted analyses of associations of symptom patterns with demographics and psychiatric histories. Findings: 53 (9.7%) participants were identified by the algorithm as having a different pattern of reported symptoms compared to other participants. This group had more severe symptoms across all items-especially items related to thoughts of self-harm and self-judgement-and differed in how their symptoms related to underlying psychological constructs. History of social anxiety disorder (OR: 4.0; 95% CI [1.9, 8.1]) and history of childhood trauma (for each 5-point increase, OR: 1.2; 95% CI [1.1, 1.3]) were significantly associated with this differential pattern after adjustment for other covariates. Interpretation: Social anxiety disorder and childhood trauma are associated with differential patterns of severe postpartum depressive symptoms, which might warrant tailored strategies for screening, diagnosis, and treatment to address these comorbid conditions. Funding: There are no funding sources to declare.

Effectiveness of an Emergency Department-Based Machine Learning Clinical Decision Support Tool to Prevent Outpatient Falls Among Older Adults: Protocol for a Quasi-Experimental Study.

BACKGROUND: Emergency department (ED) providers are important collaborators in preventing falls for older adults because they are often the first health care providers to see a patient after a fall and because at-home falls are often preceded by previous ED visits. Previous work has shown that ED referrals to falls interventions can reduce the risk of an at-home fall by 38%. Screening patients at risk for a fall can be time-consuming and difficult to implement in the ED setting. Machine learning (ML) and clinical decision support (CDS) offer the potential of automating the screening process. However, it remains unclear whether automation of screening and referrals can reduce the risk of future falls among older patients. OBJECTIVE: The goal of this paper is to describe a research protocol for evaluating the effectiveness of an automated screening and referral intervention. These findings will inform ongoing discussions about the use of ML and artificial intelligence to augment medical decision-making. METHODS: To assess the effectiveness of our program for patients receiving the falls risk intervention, our primary analysis will be to obtain referral completion rates at 3 different EDs. We will use a quasi-experimental design known as a sharp regression discontinuity with regard to intent-to-treat, since the intervention is administered to patients whose risk score falls above a threshold. A conditional logistic regression model will be built to describe 6-month fall risk at each site as a function of the intervention, patient demographics, and risk score. The odds ratio of a return visit for a fall and the 95% CI will be estimated by comparing those identified as high risk by the ML-based CDS (ML-CDS) and those who were not but had a similar risk profile. RESULTS: The ML-CDS tool under study has been implemented at 2 of the 3 EDs in our study. As of April 2023, a total of 1326 patient encounters have been flagged for providers, and 339 unique patients have been referred to the mobility and falls clinic. To date, 15% (45/339) of patients have scheduled an appointment with the clinic. CONCLUSIONS: This study seeks to quantify the impact of an ML-CDS intervention on patient behavior and outcomes. Our end-to-end data set allows for a more meaningful analysis of patient outcomes than other studies focused on interim outcomes, and our multisite implementation plan will demonstrate applicability to a broad population and the possibility to adapt the intervention to other EDs and achieve similar results. Our statistical methodology, regression discontinuity design, allows for causal inference from observational data and a staggered implementation strategy allows for the identification of secular trends that could affect causal associations and allow mitigation as necessary. TRIAL REGISTRATION: ClinicalTrials.gov NCT05810064; https://www.clinicaltrials.gov/study/NCT05810064. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48128.

Frontoparietal network activity during model-based reinforcement learning updates is reduced among adolescents with severe sexual abuse.

Trauma and trauma-related disorders are characterized by impaired learning processes, including reinforcement learning (RL). Identifying which aspects of learning are altered by trauma is critical endeavor, as this may reveal key mechanisms of impairment and potential intervention targets. There are at least two types of RL that have been delineated using computational modeling: model-free and model-based RL. Although these RL processes differentially predict decision-making behavior, most research has examined the impact of trauma on model-free RL. Currently unclear whether model-based RL, which involves building abstract and nuanced representations of stimulus-outcome relationships, is impaired among individuals with a history of trauma. The present study sought to test the hypothesis of impaired model-based RL among adolescent females exposed to assaultive trauma. Participants (n = 60; 29 without a history of assault and 31 with a history of assault with and without PTSD) completed a three-arm bandit task during fMRI acquisition. Two computational models compared the degree to which participants' task behavior fit the use of a model-free versus model-based RL strategy. Although a history of assaultive trauma did not predict poorer model-based RL, greater sexual abuse severity predicted less use of model-based compared to model-free RL. Additionally, severe sexual abuse predicted less left frontoparietal network encoding of model-based RL updates. Altered model-based RL, which supports goal-directed behavior, may be an important route through which clinical impairment emerges among individuals with a history of severe sexual abuse and should be examined further in future studies.