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Clinical prediction models have been widely acknowledged as informative tools providing evidence-based support for clinical decision making. However, prediction models are often underused in clinical practice due to many reasons including missing information upon real-time risk calculation in electronic health records (EHR) system. Existing literature to address this challenge focuses on statistical comparison of various approaches while overlooking the feasibility of their implementation in EHR. In this article, we propose a novel and feasible submodel approach to address this challenge for prediction models developed using the model approximation (also termed "preconditioning") method. The proposed submodel coefficients are equivalent to the corresponding original prediction model coefficients plus a correction factor. Comprehensive simulations were conducted to assess the performance of the proposed method and compared with the existing "one-step-sweep" approach as well as the imputation approach. In general, the simulation results show the preconditioning-based submodel approach is robust to various heterogeneity scenarios and is comparable to the imputation-based approach, while the "one-step-sweep" approach is less robust under certain heterogeneity scenarios. The proposed method was applied to facilitate real-time implementation of a prediction model to identify emergency department patients with acute heart failure who can be safely discharged home., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

The p K a 's of acids are known to depend on the ionic strength of an electrolyte solution, an effect that qualitatively results from electrostatic interactions of the acid and conjugate base with proximal ions. Here, we explore an intramolecular variation on this theme in which a carboxylic acid group is tethered to a pyridinium cationic charge center with increasingly long alkyl linkages in the series Py + -(CH 2 ) n -COOH, with n = 1-7. The effective acidities of the carboxylic acid group in the isolated cations are determined by recording the red-shifts in the frequencies of the acid OH stretches upon attachment to D 2 and N 2 molecules, measured by using cryogenic ion spectroscopy. The short chains indeed lead to substantial increases in acidity, with the effect falling off in the range of n = 4-5. The response of the CO stretch on the acid head group is surprisingly strong and, in contrast to the expected red shift of the OH, displays a blue shift as the chain length is reduced. Electronic structure calculations recover these trends and indicate that the proximal cationic charge center acts to draw electron density away from the acid C═O bond. This acts to blue-shift the CO stretch while weakening the C-C bond that attaches the head group to the chain.

Here we analyze cryogenic ion vibrational spectra of tagged protonated formic acid (PFA) with electronic structure and anharmonic vibrational calculations to establish the isomers generated by electrospray ionization (ESI) followed by buffer gas cooling to ∼25 K. Two isomers are identified (the trans form (E,Z) and the cis form (E,E)) and generated in comparable abundance despite the fact that the calculated E,E structure lies 6.40 kJ mol -1 above the E,Z form. A large (∼60 kJ mol -1 ) barrier separates them such that the E,E form can be kinetically trapped upon cooling in the ion trap. The anticooperativity between the H-bonds of the OH groups is explored by measuring the shift in the D 2 -bound OH fundamental when a second D 2 is attached. Both isomers are observed in the N 2 -tagged counterparts, displaying the expected red-shifted OH bands. These results indicate that ESI generates both isomers and both must be considered when analyzing cluster spectra based on the PFA core ion.

Background: The Vanderbilt Clinical Informatics Center (VCLIC) is based in the Department of Biomedical Informatics (DBMI) and operates across Vanderbilt University Medical Center (VUMC) and Vanderbilt University (VU) with a goal of enabling and supporting clinical informatics research and practice. VCLIC supports several types of applied clinical informatics teaching, including teaching of students in courses, professional education for staff and faculty throughout VUMC, and workshops and conferences that are open to the public., Objectives: In this paper, we provide a detailed accounting of our center and institution's methods of educating and training faculty, staff, students, and trainees from across the academic institution and health system on clinical informatics topics, including formal training programs and informal applied learning sessions., Methods: Through a host of informal learning events, such as workshops, seminars, conference-style events, bite-size instructive videos, and hackathons, as well as several formal education programs, such as the Clinical Informatics Graduate Course, Master's in Applied Clinical Informatics, Medical Student Integrated Science Course, Graduate Medical Education Elective, and Fellowship in Clinical Informatics, VCLIC and VUMC provide opportunities for faculty, students, trainees, and even staff to engage with Clinical Informatics topics and learn related skills., Results: The described programs have trained hundreds of participants from across the academic and clinical enterprises. Of the VCLIC-held events, the majority of attendees indicated through surveys that they were satisfied, with the average satisfaction score being 4.63/5, and all events averaging a satisfaction score of greater than 4. Across the 20 events VCLIC has held, our largest audiences are DBMI, HealthIT operational staff, and students from the medical and nursing schools., Conclusions: VCLIC has created and delivered a successful suite of formal and informal educational events and programs to disseminate clinical informatics knowledge and skills to learners across the academic institution and healthcare system., Competing Interests: The authors declare that they have no conflict of interest., (The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Background:  Over the past 30 years, the American Medical Informatics Association (AMIA) has played a pivotal role in fostering a collaborative community for professionals in biomedical and health informatics. As an interdisciplinary association, AMIA brings together individuals with clinical, research, and computer expertise and emphasizes the use of data to enhance biomedical research and clinical work. The need for a recognition program within AMIA, acknowledging applied informatics skills by members, led to the establishment of the Fellows of AMIA (FAMIA) Recognition Program in 2018., Objectives:  To outline the evolution of the FAMIA program and shed light on its origins, development, and impact. This report explores factors that led to the establishment of FAMIA, considerations affecting its development, and the objectives FAMIA seeks to achieve within the broader context of AMIA., Methods:  The development of FAMIA is examined through a historical lens, encompassing key milestones, discussions, and decisions that shaped the program. Insights into the formation of FAMIA were gathered through discussions within AMIA membership and leadership, including proposals, board-level discussions, and the involvement of key stakeholders. Additionally, the report outlines criteria for FAMIA eligibility and the pathways available for recognition, namely the Certification Pathway and the Long-Term Experience Pathway., Results:  The FAMIA program has inducted five classes, totaling 602 fellows. An overview of disciplines, roles, and application pathways for FAMIA members is provided. A comparative analysis with other fellow recognition programs in related fields showcases the unique features and contributions of FAMIA in acknowledging applied informatics., Conclusion:  Now in its sixth year, FAMIA acknowledges the growing influence of applied informatics within health information professionals, recognizing individuals with experience, training, and a commitment to the highest level of applied informatics and the science associated with it., Competing Interests: The authors have no conflict of interests to disclose regarding this study., (Thieme. All rights reserved.)

Objective: This study aims to investigate the feasibility of using Large Language Models (LLMs) to engage with patients at the time they are drafting a question to their healthcare providers, and generate pertinent follow-up questions that the patient can answer before sending their message, with the goal of ensuring that their healthcare provider receives all the information they need to safely and accurately answer the patient's question, eliminating back-and-forth messaging, and the associated delays and frustrations., Methods: We collected a dataset of patient messages sent between January 1, 2022 to March 7, 2023 at Vanderbilt University Medical Center. Two internal medicine physicians identified 7 common scenarios. We used 3 LLMs to generate follow-up questions: (1) Comprehensive LLM Artificial Intelligence Responder (CLAIR): a locally fine-tuned LLM, (2) GPT4 with a simple prompt, and (3) GPT4 with a complex prompt. Five physicians rated them with the actual follow-ups written by healthcare providers on clarity, completeness, conciseness, and utility., Results: For five scenarios, our CLAIR model had the best performance. The GPT4 model received higher scores for utility and completeness but lower scores for clarity and conciseness. CLAIR generated follow-up questions with similar clarity and conciseness as the actual follow-ups written by healthcare providers, with higher utility than healthcare providers and GPT4, and lower completeness than GPT4, but better than healthcare providers., Conclusion: LLMs can generate follow-up patient messages designed to clarify a medical question that compares favorably to those generated by healthcare providers., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Background: Despite growing interest in patient-reported outcome measures to track the progression of Crohn's disease, frameworks to apply these questionnaires in the preoperative setting are lacking. Using the Short Inflammatory Bowel Disease Questionnaire (sIBDQ), this study aimed to describe the interpretable quality of life thresholds and examine potential associations with future bowel resection in Crohn's disease., Methods: Adult patients with Crohn's disease completing an sIBDQ at a clinic visit between 2020 and 2022 were eligible. A stoplight framework was adopted for sIBDQ scores, including a "Resection Red" zone suggesting poor quality of life that may benefit from discussions about surgery as well as a "Nonoperative Green" zone. Thresholds were identified with both anchor- and distribution-based methods using receiver operating characteristic curve analysis and subgroup percentile scores, respectively. To quantify associations between sIBDQ scores and subsequent bowel resection, multivariable logistic regression models were fit with covariates of age, sex assigned at birth, body mass index, medications, disease pattern and location, resection history, and the Harvey Bradshaw Index. The incremental discriminatory value of the sIBDQ beyond clinical factors was assessed through the area under the receiver operating characteristics curve (AUC) with an internal validation through bootstrap resampling., Results: Of the 2003 included patients, 102 underwent Crohn's-related bowel resection. The sIBDQ Nonoperative Green zone threshold ranged from 61 to 64 and the Resection Red zone from 36 to 38. When adjusting for clinical covariates, a worse sIBDQ score was associated with greater odds of subsequent 90-day bowel resection when considered as a 1-point (odds ratio [OR] [95% CI], 1.05 [1.03-1.07]) or 5-point change (OR [95% CI], 1.27 [1.14-1.41]). Inclusion of the sIBDQ modestly improved discriminative performance (AUC [95% CI], 0.85 [0.85-0.86]) relative to models that included only demographics (0.57 [0.57-0.58]) or demographics with clinical covariates (0.83 [0.83-0.84])., Conclusion: In the decision-making process for bowel resection, disease-specific patient-reported outcome measures may be useful to identify patients with Crohn's disease with poor quality of life and promote a shared understanding of personalized burden., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 Society for Surgery of the Alimentary Tract. Published by Elsevier Inc. All rights reserved.)

Objective: To develop and validate a predictive model for postpartum hemorrhage that can be deployed in clinical care using automated, real-time electronic health record (EHR) data and to compare performance of the model with a nationally published risk prediction tool., Methods: A multivariable logistic regression model was developed from retrospective EHR data from 21,108 patients delivering at a quaternary medical center between January 1, 2018, and April 30, 2022. Deliveries were divided into derivation and validation sets based on an 80/20 split by date of delivery. Postpartum hemorrhage was defined as blood loss of 1,000 mL or more in addition to postpartum transfusion of 1 or more units of packed red blood cells. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC) and was compared with a postpartum hemorrhage risk assessment tool published by the CMQCC (California Maternal Quality Care Collaborative). The model was then programmed into the EHR and again validated with prospectively collected data from 928 patients between November 7, 2023, and January 31, 2024., Results: Postpartum hemorrhage occurred in 235 of 16,862 patients (1.4%) in the derivation cohort. The predictive model included 21 risk factors and demonstrated an AUC of 0.81 (95% CI, 0.79-0.84) and calibration slope of 1.0 (Brier score 0.013). During external temporal validation, the model maintained discrimination (AUC 0.80, 95% CI, 0.72-0.84) and calibration (calibration slope 0.95, Brier score 0.014). This was superior to the CMQCC tool (AUC 0.69 [95% CI, 0.67-0.70], P <.001). The model maintained performance in prospective, automated data collected with the predictive model in real time (AUC 0.82 [95% CI, 0.73-0.91])., Conclusion: We created and temporally validated a postpartum hemorrhage prediction model, demonstrated its superior performance over a commonly used risk prediction tool, successfully coded the model into the EHR, and prospectively validated the model using risk factor data collected in real time. Future work should evaluate external generalizability and effects on patient outcomes; to facilitate this work, we have included the model coefficients and examples of EHR integration in the article., Competing Interests: Financial Disclosure The authors did not report any potential conflicts of interest., (Copyright © 2024 by the American College of Obstetricians and Gynecologists. Published by Wolters Kluwer Health, Inc. All rights reserved.)

In this work, we report the infrared spectrum of the pyrene anion, measured using messenger tagging with up to three Ar atoms. We assign the spectrum using density functional theory and vibrational perturbation theory. We discuss our results in the context of computed and experimental spectra from the literature as well as recent observations from astronomical sources, addressing the question of whether polycyclic aromatic hydrocarbon anions could contribute to the strong infrared emission bands at 3.29 μm from carbon-rich regions of space.

An IR-vacuum ultraviolet (VUV) ion-dip spectroscopy method is utilized to examine the IR spectrum of acetaldehyde oxide (CH3CHOO) in the overtone CH stretch (2νCH) spectral region. IR activation creates a depletion of the ground state population that reduces the VUV photoionization signal on the parent mass channel. IR activation of the more stable and populated syn-CH3CHOO conformer results in rapid unimolecular decay to OH + vinoxy products and makes the most significant contribution to the observed spectrum. The resultant IR-VUV ion-dip spectrum of CH3CHOO is similar to that obtained previously for syn-CH3CHOO using IR action spectroscopy with UV laser-induced fluorescence detection of OH products. The prominent IR features at 5984 and 6081 cm-1 are also observed using UV + VUV photoionization of OH products. Complementary theoretical calculations utilizing a general implementation of second-order vibrational perturbation theory provide new insights on the vibrational transitions that give rise to the experimental spectrum in the overtone CH stretch region. The introduction of physically motivated small shifts of the harmonic frequencies yields remarkably improved agreement between experiment and theory in the overtone CH stretch region. The prominent features are assigned as highly mixed states with contributions from two quanta of CH stretch and/or a combination of CH stretch with an overtone in mode 4. The generality of this approach is demonstrated by applying it to three different levels of electronic structure theory/basis sets, all of which provide spectra that are virtually indistinguishable despite showing large deviations prior to introducing the shifts to the harmonic frequencies., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

Objective: This study aimed to develop and assess the performance of fine-tuned large language models for generating responses to patient messages sent via an electronic health record patient portal., Materials and Methods: Utilizing a dataset of messages and responses extracted from the patient portal at a large academic medical center, we developed a model (CLAIR-Short) based on a pre-trained large language model (LLaMA-65B). In addition, we used the OpenAI API to update physician responses from an open-source dataset into a format with informative paragraphs that offered patient education while emphasizing empathy and professionalism. By combining with this dataset, we further fine-tuned our model (CLAIR-Long). To evaluate fine-tuned models, we used 10 representative patient portal questions in primary care to generate responses. We asked primary care physicians to review generated responses from our models and ChatGPT and rated them for empathy, responsiveness, accuracy, and usefulness., Results: The dataset consisted of 499 794 pairs of patient messages and corresponding responses from the patient portal, with 5000 patient messages and ChatGPT-updated responses from an online platform. Four primary care physicians participated in the survey. CLAIR-Short exhibited the ability to generate concise responses similar to provider's responses. CLAIR-Long responses provided increased patient educational content compared to CLAIR-Short and were rated similarly to ChatGPT's responses, receiving positive evaluations for responsiveness, empathy, and accuracy, while receiving a neutral rating for usefulness., Conclusion: This subjective analysis suggests that leveraging large language models to generate responses to patient messages demonstrates significant potential in facilitating communication between patients and healthcare providers., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objectives: To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts., Materials and Methods: We extracted user comments to alerts generated from September 1, 2022 to September 1, 2023 at Vanderbilt University Medical Center. For a subset of 8 alerts, comment summaries were generated independently by 2 physicians and then separately by GPT-4. We surveyed 5 CDS experts to rate the human-generated and AI-generated summaries on a scale from 1 (strongly disagree) to 5 (strongly agree) for the 4 metrics: clarity, completeness, accuracy, and usefulness., Results: Five CDS experts participated in the survey. A total of 16 human-generated summaries and 8 AI-generated summaries were assessed. Among the top 8 rated summaries, five were generated by GPT-4. AI-generated summaries demonstrated high levels of clarity, accuracy, and usefulness, similar to the human-generated summaries. Moreover, AI-generated summaries exhibited significantly higher completeness and usefulness compared to the human-generated summaries (AI: 3.4 ± 1.2, human: 2.7 ± 1.2, P = .001)., Conclusion: End-user comments provide clinicians' immediate feedback to CDS alerts and can serve as a direct and valuable data resource for improving CDS delivery. Traditionally, these comments may not be considered in the CDS review process due to their unstructured nature, large volume, and the presence of redundant or irrelevant content. Our study demonstrates that GPT-4 is capable of distilling these comments into summaries characterized by high clarity, accuracy, and completeness. AI-generated summaries are equivalent and potentially better than human-generated summaries. These AI-generated summaries could provide CDS experts with a novel means of reviewing user comments to rapidly optimize CDS alerts both online and offline., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Ion receptors are molecular hosts that bind ionic guests, often with great selectivity. The interplay of solvation and ion binding in anion host-guest complexes in solution governs the binding efficiency and selectivity of such ion receptors. To gain molecular-level insight into the intrinsic binding properties of octamethyl calix[4]pyrrole (omC4P) host molecules with halide guest ions, we performed cryogenic ion vibrational spectroscopy (CIVS) of omC4P in complexes with fluoride, chloride, and bromide ions. We interpret the spectra using density functional theory, describing the infrared spectra of these complexes with both harmonic and anharmonic second-order vibrational perturbation theory (VPT2) calculations. The NH stretching modes of the pyrrole moieties serve as sensitive probes of the ion binding properties, as their frequencies encode the ion-receptor interactions. While scaled harmonic spectra reproduce the experimental NH stretching modes of the chloride and bromide complexes in broad strokes, the high proton affinity of fluoride introduces strong anharmonic effects. As a result, the spectrum of F - ·omC4P is not even qualitatively captured by harmonic calculations, but it is recovered very well by VPT2 calculations. In addition, the VPT2 calculations recover the intricate coupling of the NH stretching modes with overtones and combination bands of CH stretching and NH bending modes and with low-frequency vibrations of the omC4P macrocycle, which are apparent for all of the halide ion complexes investigated here. A comparison of the CIVS spectra with infrared spectra of solutions of the same ion-receptor complexes in d 3 -acetonitrile and d 6 -acetone shows how ion solvation changes the ion-receptor interactions for the different halide ions.

Background: Numerous pressure injury prediction models have been developed using electronic health record data, yet hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these models in routine care., Objective: To help bridge the gap between development and implementation, we sought to create a model that was feasible, broadly applicable, dynamic, actionable, and rigorously validated and then compare its performance to usual care (ie, the Braden scale)., Methods: We extracted electronic health record data from 197,991 adult hospital admissions with 51 candidate features. For risk prediction and feature selection, we used logistic regression with a least absolute shrinkage and selection operator (LASSO) approach. To compare the model with usual care, we used the area under the receiver operating curve (AUC), Brier score, slope, intercept, and integrated calibration index. The model was validated using a temporally staggered cohort., Results: A total of 5458 HAPIs were identified between January 2018 and July 2022. We determined 22 features were necessary to achieve a parsimonious and highly accurate model. The top 5 features included tracheostomy, edema, central line, first albumin measure, and age. Our model achieved higher discrimination than the Braden scale (AUC 0.897, 95% CI 0.893-0.901 vs AUC 0.798, 95% CI 0.791-0.803)., Conclusions: We developed and validated an accurate prediction model for HAPIs that surpassed the standard-of-care risk assessment and fulfilled necessary elements for implementation. Future work includes a pragmatic randomized trial to assess whether our model improves patient outcomes., (© Thomas J Reese, Henry J Domenico, Antonio Hernandez, Daniel W Byrne, Ryan P Moore, Jessica B Williams, Brian J Douthit, Elise Russo, Allison B McCoy, Catherine H Ivory, Bryan D Steitz, Adam Wright. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Objective: To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches., Methods: We extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts. We applied XAI techniques to generate global explanations and local explanations. We evaluated the generated suggestions by comparing with alert's historical change logs and stakeholder interviews. Suggestions that either matched (or partially matched) changes already made to the alert or were considered clinically correct were classified as helpful., Results: The final dataset included 2 991 823 firings with 2689 features. Among the 5 machine learning models, the LightGBM model achieved the highest Area under the ROC Curve: 0.919 [0.918, 0.920]. We identified 96 helpful suggestions. A total of 278 807 firings (9.3%) could have been eliminated. Some of the suggestions also revealed workflow and education issues., Conclusion: We developed a data-driven process to generate suggestions for improving alert criteria using XAI techniques. Our approach could identify improvements regarding clinical decision support (CDS) that might be overlooked or delayed in manual reviews. It also unveils a secondary purpose for the XAI: to improve quality by discovering scenarios where CDS alerts are not accepted due to workflow, education, or staffing issues., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: Positive antinuclear antibodies (ANAs) cause diagnostic dilemmas for clinicians. Currently, no tools exist to help clinicians interpret the significance of a positive ANA in individuals without diagnosed autoimmune diseases. We developed and validated a risk model to predict risk of developing autoimmune disease in positive ANA individuals., Methods: Using a de-identified electronic health record (EHR), we randomly chart reviewed 2,000 positive ANA individuals to determine if a systemic autoimmune disease was diagnosed by a rheumatologist. A priori , we considered demographics, billing codes for autoimmune disease-related symptoms, and laboratory values as variables for the risk model. We performed logistic regression and machine learning models using training and validation samples., Results: We assembled training (n = 1030) and validation (n = 449) sets. Positive ANA individuals who were younger, female, had a higher titer ANA, higher platelet count, disease-specific autoantibodies, and more billing codes related to symptoms of autoimmune diseases were all more likely to develop autoimmune diseases. The most important variables included having a disease-specific autoantibody, number of billing codes for autoimmune disease-related symptoms, and platelet count. In the logistic regression model, AUC was 0.83 (95% CI 0.79-0.86) in the training set and 0.75 (95% CI 0.68-0.81) in the validation set., Conclusion: We developed and validated a risk model that predicts risk for developing systemic autoimmune diseases and can be deployed easily within the EHR. The model can risk stratify positive ANA individuals to ensure high-risk individuals receive urgent rheumatology referrals while reassuring low-risk individuals and reducing unnecessary referrals., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Barnado, Moore, Domenico, Green, Camai, Suh, Han, Walker, Anderson, Caruth, Katta, McCoy and Byrne.)

Badger's rule-like correlations between OH stretching frequencies and intensities and the OH bond length are used to develop a spectral mapping procedure for studies of pure and protonated water clusters. This approach utilizes the vibrationally averaged OH bond lengths, which were obtained from diffusion Monte Carlo simulations that were performed using the general potential developed by Yu and Bowman. Good agreement is achieved between the spectra obtained using this approach and previously reported spectra for H + (H 2 O) n clusters, with n = 3, 4, and 5, as well as their perdeuterated analogues. The analysis of the spectra obtained by this spectral mapping approach supports previous work that assigned the spectrum of H + (H 2 O) 6 to a mixture of Eigen and Zundel-like structures. Analysis of the calculated spectra also suggests a reassignment of the frequency of one of the transitions that involves the OH stretching vibration of the OH bonds in the hydronium core in the Eigen-like structure of H + (H 2 O) 6 from 1917 cm -1 to roughly 2100 cm -1 . For D + (D 2 O) 6 , comparison of the measured spectrum to those obtained by using the spectral mapping approach suggests that the carrier of the measured spectrum is one or more of the isomers of D + (D 2 O) 6 that contain a four-membered ring and two flanking water molecules. While there are several candidate structures, the two flanking water molecules most likely form a chain that is bound to the hydronium core.

Collision-induced dissociation (CID) of small, protonated peptides leads to the formation of b-type fragment ions that can occur with several structural motifs driven by different covalent intramolecular bonding arrangements. Here, we characterize the so-called "oxazolone" and "macrocycle" b n ion structures that occur upon CID of oligoglycine peptides (G n ) ions ( n = 2-6). This is determined by acquiring the vibrational band patterns of the cryogenically cooled, D 2 -tagged b n ions obtained using isomer-selective, two-color IR-IR photobleaching and analyzing them with predicted (DFT) harmonic spectra for the candidate structures. Both oxazolone and macrocyclic isomers are formed by b 4 , whereas only oxazolone species are created for b 2 and b 3 and the macrocycle is created for b 5 . As such, n = 4 corresponds to the minimum size where both Oxa and MC forms are present.

Background: Early detection of clinical deterioration among hospitalized patients is a clinical priority for patient safety and quality of care. Current automated approaches for identifying these patients perform poorly at identifying imminent events., Objective: Develop a machine learning algorithm using pager messages sent between clinical team members to predict imminent clinical deterioration., Design: We conducted a large observational study using long short-term memory machine learning models on the content and frequency of clinical pages., Participants: We included all hospitalizations between January 1, 2018 and December 31, 2020 at Vanderbilt University Medical Center that included at least one page message to physicians. Exclusion criteria included patients receiving palliative care, hospitalizations with a planned intensive care stay, and hospitalizations in the top 2% longest length of stay., Main Measures: Model classification performance to identify in-hospital cardiac arrest, transfer to intensive care, or Rapid Response activation in the next 3-, 6-, and 12-hours. We compared model performance against three common early warning scores: Modified Early Warning Score, National Early Warning Score, and the Epic Deterioration Index., Key Results: There were 87,783 patients (mean [SD] age 54.0 [18.8] years; 45,835 [52.2%] women) who experienced 136,778 hospitalizations. 6214 hospitalized patients experienced a deterioration event. The machine learning model accurately identified 62% of deterioration events within 3-hours prior to the event and 47% of events within 12-hours. Across each time horizon, the model surpassed performance of the best early warning score including area under the receiver operating characteristic curve at 6-hours (0.856 vs. 0.781), sensitivity at 6-hours (0.590 vs. 0.505), specificity at 6-hours (0.900 vs. 0.878), and F-score at 6-hours (0.291 vs. 0.220)., Conclusions: Machine learning applied to the content and frequency of clinical pages improves prediction of imminent deterioration. Using clinical pages to monitor patient acuity supports improved detection of imminent deterioration without requiring changes to clinical workflow or nursing documentation., (© 2023. The Author(s).)

Objectives: Geocoding, the process of converting addresses into precise geographic coordinates, allows researchers and health systems to obtain neighborhood-level estimates of social determinants of health. This information supports opportunities to personalize care and interventions for individual patients based on the environments where they live. We developed an integrated offline geocoding pipeline to streamline the process of obtaining address-based variables, which can be integrated into existing data processing pipelines., Methods: POINT is a web-based, containerized, application for geocoding addresses that can be deployed offline and made available to multiple users across an organization. Our application supports use through both a graphical user interface and application programming interface to query geographic variables, by census tract, without exposing sensitive patient data. We evaluated our application's performance using two datasets: one consisting of 1 million nationally representative addresses sampled from Open Addresses, and the other consisting of 3,096 previously geocoded patient addresses., Results: A total of 99.4 and 99.8% of addresses in the Open Addresses and patient addresses datasets, respectively, were geocoded successfully. Census tract assignment was concordant with reference in greater than 90% of addresses for both datasets. Among successful geocodes, median (interquartile range) distances from reference coordinates were 52.5 (26.5-119.4) and 14.5 (10.9-24.6) m for the two datasets., Conclusion: POINT successfully geocodes more addresses and yields similar accuracy to existing solutions, including the U.S. Census Bureau's official geocoder. Addresses are considered protected health information and cannot be shared with common online geocoding services. POINT is an offline solution that enables scalability to multiple users and integrates downstream mapping to neighborhood-level variables with a pipeline that allows users to incorporate additional datasets as they become available. As health systems and researchers continue to explore and improve health equity, it is essential to quickly and accurately obtain neighborhood variables in a Health Insurance Portability and Accountability Act (HIPAA)-compliant way., Competing Interests: None declared., (Thieme. All rights reserved.)

Objective: To develop and validate an approach that identifies patients eligible for lung cancer screening (LCS) by combining structured and unstructured smoking data from the electronic health record (EHR)., Methods: We identified patients aged 50-80 years who had at least one encounter in a primary care clinic at Vanderbilt University Medical Center (VUMC) between 2019 and 2022. We fine-tuned an existing natural language processing (NLP) tool to extract quantitative smoking information using clinical notes collected from VUMC. Then, we developed an approach to identify patients who are eligible for LCS by combining smoking information from structured data and clinical narratives. We compared this method with two approaches to identify LCS eligibility only using smoking information from structured EHR. We used 50 patients with a documented history of tobacco use for comparison and validation., Results: 102,475 patients were included. The NLP-based approach achieved an F1-score of 0.909, and accuracy of 0.96. The baseline approach could identify 5,887 patients. Compared to the baseline approach, the number of identified patients using all structured data and the NLP-based algorithm was 7,194 (22.2 %) and 10,231 (73.8 %), respectively. The NLP-based approach identified 589 Black/African Americans, a significant increase of 119 %., Conclusion: We present a feasible NLP-based approach to identify LCS eligible patients. It provides a technical basis for the development of clinical decision support tools to potentially improve the utilization of LCS and diminish healthcare disparities., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Introduction: The need to rapidly implement telehealth at large scale during the COVID-19 pandemic led to many patients using telehealth for the first time. We assessed the effect of structured pre-visit preparatory telephone calls on success of telehealth visits and examined risk factors for unsuccessful visits., Methods: A retrospective cohort study was carried out of 45,803 adult patients scheduled for a total of 64,447 telehealth appointments between March and July 2020 at an academic medical center. A subset of patients received a structured pre-visit phone call. Demographic factors and inclusion of a pre-visit call were analysed by logistic regression. Primary outcomes were non-completion of any visit and completion of phone-only versus audio-visual telehealth visits., Results: A pre-visit telephone call to a subset of patients significantly increased the likelihood of a successful telehealth visit (OR 0.54; 95% CI: 0.48-0.60). Patients aged 18-30 years, those with non-commercial insurance or those of Black race were more likely to have incomplete visits. Compared to age 18-30, increasing age increased likelihood of a failed video visit: 31-50 years (OR 1.31; 95% CI: 1.13-1.51), 51-70 years (OR 2.98; 2.60-3.42) and >70 years (OR 4.16; 3.58-4.82). Those with non-commercial insurance and those of Black race (OR 1.8; 95% CI 1.67-1.92) were more likely to have a failed video visit., Discussion: A structured pre-call to patients improved the likelihood of a successful video visit during widespread adoption of telehealth. Structured pre-calls to patients may be an important tool to help reduce gaps in utilization among groups.

While the intensity of the OH stretching fundamental transition is strongly correlated to hydrogen-bond strength, the intensity of the corresponding transition to the state with one quantum of excitation in both the OH stretching and HOH bending vibrations in the same water molecule shows a much weaker sensitivity to the hydrogen-bonding environment. The origins of this difference are explored through analyses of the contributions of terms in the expansion of the dipole moment to the calculated intensity. It is found that the leading contribution to the stretch-bend intensity involves the second derivative of the dipole moment with respect to the OH bond length and HOH angle. While this is not surprising, the insensitivity of this derivative to the hydrogen-bonding environment is unexpected. Possible contributions of mode mixing are also explored. While mode mixing leads to splittings of the energies of nearly degenerate excited states, it does not result in significant changes in the sum of the intensities of these transitions. Analysis of changes in the partial charges on the hydrogen atoms upon displacement of the HOH angles shows that these charges generally increase with increasing HOH angle. This effect is partially canceled by a decrease in the charge of the hydrogen atom when a hydrogen bond is broken. The extent of this cancellation increases with the hydrogen bond strength, which is reflected in the observed insensitivity of the intensity of the stretch-bend transition to hydrogen-bond strength.

Background: Clinical trials indicate continuous glucose monitor (CGM) use may benefit adults with type 2 diabetes, but CGM rates and correlates in real-world care settings are unknown., Objective: We sought to ascertain prevalence and correlates of CGM use and to examine rates of new CGM prescriptions across clinic types and medication regimens., Design: Retrospective cohort using electronic health records in a large academic medical center in the Southeastern US., Participants: Adults with type 2 diabetes and a primary care or endocrinology visit during 2021., Main Measures: Age, gender, race, ethnicity, insurance, clinic type, insulin regimen, hemoglobin A1c values, CGM prescriptions, and prescribing clinic type., Key Results: Among 30,585 adults with type 2 diabetes, 13% had used a CGM. CGM users were younger and more had private health insurance (p < .05) as compared to non-users; 72% of CGM users had an intensive insulin regimen, but 12% were not taking insulin. CGM users had higher hemoglobin A1c values (both most recent and most proximal to the first CGM prescription) than non-users. CGM users were more likely to receive endocrinology care than non-users, but 23% had only primary care visits in 2021. For each month in 2021, a mean of 90.5 (SD 12.5) people started using CGM. From 2020 to 2021, monthly rates of CGM prescriptions to new users grew 36% overall, but 125% in primary care. Most starting CGM in endocrinology had an intensive insulin regimen (82% vs. 49% starting in primary care), whereas 28% starting CGM in primary care were not using insulin (vs. 5% in endocrinology)., Conclusion: CGM uptake for type 2 diabetes is increasing rapidly, with most growth in primary care. These trends present opportunities for healthcare system adaptations to support CGM use and related workflows in primary care to support growth in uptake., (© 2023. The Author(s), under exclusive licence to Society of General Internal Medicine.)

Objectives: This literature review summarizes relevant studies from the last three years (2020-2022) related to clinical decision support (CDS) and CDS impact on health disparities and the digital divide. This survey identifies current trends and synthesizes evidence-based recommendations and considerations for future development and implementation of CDS tools., Methods: We conducted a search in PubMed for literature published between 2020 and 2022. Our search strategy was constructed as a combination of the MEDLINE®/PubMed® Health Disparities and Minority Health Search Strategy and relevant CDS MeSH terms and phrases. We then extracted relevant data from the studies, including priority population when applicable, domain of influence on the disparity being addressed, and the type of CDS being used. We also made note of when a study discussed the digital divide in some capacity and organized the comments into general themes through group discussion., Results: Our search yielded 520 studies, with 45 included at the conclusion of screening. The most frequent CDS type in this review was point-of-care alerts/reminders (33.3%). Health Care System was the most frequent domain of influence (71.1%), and Blacks/African Americans were the most frequently included priority population (42.2%). Throughout the literature, we found four general themes related to the technology divide: inaccessibility of technology, access to care, trust of technology, and technology literacy.This survey revealed the diversity of CDS being used to address health disparities and several barriers which may make CDS less effective or potentially harmful to certain populations. Regular examinations of literature that feature CDS and address health disparities can help to reveal new strategies and patterns for improving healthcare., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Objective: To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions., Methods: We supplied summaries of CDS logic to ChatGPT, an artificial intelligence (AI) tool for question answering that uses a large language model, and asked it to generate suggestions. We asked human clinician reviewers to review the AI-generated suggestions as well as human-generated suggestions for improving the same CDS alerts, and rate the suggestions for their usefulness, acceptance, relevance, understanding, workflow, bias, inversion, and redundancy., Results: Five clinicians analyzed 36 AI-generated suggestions and 29 human-generated suggestions for 7 alerts. Of the 20 suggestions that scored highest in the survey, 9 were generated by ChatGPT. The suggestions generated by AI were found to offer unique perspectives and were evaluated as highly understandable and relevant, with moderate usefulness, low acceptance, bias, inversion, redundancy., Conclusion: AI-generated suggestions could be an important complementary part of optimizing CDS alerts, can identify potential improvements to alert logic and support their implementation, and may even be able to assist experts in formulating their own suggestions for CDS improvement. ChatGPT shows great potential for using large language models and reinforcement learning from human feedback to improve CDS alert logic and potentially other medical areas involving complex, clinical logic, a key step in the development of an advanced learning health system., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

The isomerization pathway between the energetically low-lying Zundel and Eigen isomers of the protonated water hexamer was investigated using high-level ab initio calculations including a treatment of zero-point corrections. On the basis of these calculations, the Zundel-Eigen isomerization was found to proceed through a stable intermediate isomer, which consists of a four-membered ring with two single acceptor water molecules. The inclusion of vibrational zero-point energy is shown to be important for accurately establishing the relative energies of the three relevant isomers involved in the Zundel-Eigen isomerization. Diffusion Monte Carlo calculations including anharmonic vibrational effects show that all three isomers of H + (H 2 O) 6 and D + (D 2 O) 6 have well-defined structures. The energetic ordering of the three isomers changes upon deuteration. The implications of these results for the vibrational spectra of H + (H 2 O) 6 and D + (D 2 O) 6 are also discussed.

Background: Chest pain (CP) is the hallmark symptom for acute coronary syndrome (ACS) but is not reported in 20-30% of patients, especially women, elderly, non-white patients, presenting to the emergency department (ED) with an ST-segment elevation myocardial infarction (STEMI)., Methods: We used a retrospective 5-year adult ED sample of 279,132 patients to explore using CP alone to predict ACS, then we incrementally added other ACS chief complaints, age, and sex in a series of multivariable logistic regression models. We evaluated each model's identification of ACS and STEMI., Results: Using CP alone would recommend ECGs for 8% of patients (sensitivity, 61%; specificity, 92%) but missed 28.4% of STEMIs. The model with all variables identified ECGs for 22% of patients (sensitivity, 82%; specificity, 78%) but missed 14.7% of STEMIs. The model with CP and other ACS chief complaints had the highest sensitivity (93%) and specificity (55%), identified 45.1% of patients for ECG, and only missed 4.4% of STEMIs., Conclusion: CP alone had highest specificity but lacked sensitivity. Adding other ACS chief complaints increased sensitivity but identified 2.2-fold more patients for ECGs. Achieving an ECG in 10 min for patients with ACS to identify all STEMIs will be challenging without introducing more complex risk calculation into clinical care., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

High-resolution photoelectron spectra of vibrationally pre-excited vinoxide anions (CH 2 CHO - ) are reported using the recently developed IR-cryo-SEVI technique. This method is combined with a newly developed implementation of vibrational perturbation theory that can readily identify relevant anharmonic couplings among nearly degenerate vibrational states. IR-cryo-SEVI spectra are obtained by resonant infrared excitation of vinoxide anions via the fundamental C-O (ν 4 , 1566 cm -1 ) or isolated C-H (ν 3 , 2540 cm -1 ) stretching vibrations prior to photodetachment. Excitation of the ν 4 mode leads to a well-resolved photoelectron spectrum that is in excellent agreement with a harmonic Franck-Condon simulation. Excitation of the higher-energy ν 3 mode results in a more complicated spectrum that requires consideration of the calculated anharmonic resonances in both the anion and the neutral. From this analysis, information about the zeroth-order states that contribute to the nominal ν 3 wave function in the anion is obtained. In the neutral, we observe anharmonic splitting of the ν 3 fundamental into a polyad feature with peaks at 2737(22), 2 835(18), and 2910(12) cm -1 , for which only the center frequency has been previously reported. Overall, 9 of the 12 fundamental frequencies of the vinoxy radical are extracted from the IR-cryo-SEVI and ground-state cryo-SEVI spectra, most of which are consistent with previous measurements. However, we provide a new estimate of the ν 5 (CH 2 scissoring) fundamental frequency at 1395(11) cm -1 and attribute the discrepancy with previously reported values to a Fermi resonance with the 2ν 11 overtone (CH 2 wagging).

The formation of isomers when trapping floppy cluster ions in a temperature-controlled ion trap is a generally observed phenomenon. This involves collisional quenching of the ions initially formed at high temperature by buffer gas cooling until their internal energies fall below the barriers in the potential energy surface that separate them. Here we explore the kinetics at play in the case of the two isomers adopted by the H + (H 2 O) 6 cluster ion that differ in the proton accommodation motif. One of these is most like the Eigen cation with a tricoordinated hydronium motif (denoted E ), and the other is most like the Zundel ion with the proton equally shared between two water molecules (denoted Z ). After initial cooling to about 20 K in the radiofrequency (Paul) trap, the relative populations of these two spectroscopically distinct isomers are abruptly changed through isomer-selective photoexcitation of bands in the OH stretching region with a pulsed (∼6 ns) infrared laser while the ions are in the trap. We then monitor the relaxation of the vibrationally excited clusters and reformation of the two cold isomers by recording infrared photodissociation spectra with a second IR laser as a function of delay time from the initial excitation. The latter spectra are obtained after ejecting the trapped ions into a time-of-flight photofragmentation mass spectrometer, thus enabling long (∼0.1 s) delay times. Excitation of the Z isomer is observed to display long-lived vibrationally excited states that are collisionally cooled on a ms time scale, some of which quench into the E isomer. These excited E species then display spontaneous interconversion to the Z form on a ∼10 ms time scale. These qualitative observations set the stage for a series of experimental measurements that can provide quantitative benchmarks for theoretical simulations of cluster dynamics and the potential energy surfaces that underlie them.

The structure and spectra of ethyl cation, H + (C 2 H 4 ), and its deuterated analogues are investigated using diffusion Monte Carlo (DMC). These calculations all show that the ground state wave function for H + (C 2 H 4 ) is localized near the minimum energy configuration in which the excess proton is in a bridging configuration, although the amplitude of the vibrational motions of the bridging proton is large. Deuteration of the bridging proton reduces the amplitude of this motion, while deuteration of only the ethylenic hydrogen atoms in H + (C 2 D 4 ) has little effect on the amplitude of the motion of the bridging proton. Excited states that are accessed by spectroscopically observed transitions in H + (C 2 H 4 ) are calculated using fixed-node DMC. The calculated and measured frequencies for the states with one quantum of excitation in the ethylenic CH stretching vibrations show good agreement. We also explore the excited state with one quantum of excitation in the proton transfer vibration of the bridging proton and obtain a frequency of 616 cm -1 for H + (C 2 H 4 ). This frequency increases to 629 cm -1 in H + (C 2 D 4 ). Deuteration decreases this frequency to 491 and 495 cm -1 in D + (C 2 H 4 ) and D + (C 2 D 4 ), respectively. The effects of partial deuteration on the frequencies of the CH stretching vibrations, and the corresponding probability amplitudes are also explored. Finally, we report the vibrationally averaged rotational constants for the four isotopologues of ethyl cation considered in this study.

Objective: To develop and test an accurate deep learning model for predicting new onset delirium in hospitalized adult patients., Methods: Using electronic health record (EHR) data extracted from a large academic medical center, we developed a model combining long short-term memory (LSTM) and machine learning to predict new onset delirium and compared its performance with machine-learning-only models (logistic regression, random forest, support vector machine, neural network, and LightGBM). The labels of models were confusion assessment method (CAM) assessments. We evaluated models on a hold-out dataset. We calculated Shapley additive explanations (SHAP) measures to gauge the feature impact on the model., Results: A total of 331 489 CAM assessments with 896 features from 34 035 patients were included. The LightGBM model achieved the best performance (AUC 0.927 [0.924, 0.929] and F1 0.626 [0.618, 0.634]) among the machine learning models. When combined with the LSTM model, the final model's performance improved significantly (P = .001) with AUC 0.952 [0.950, 0.955] and F1 0.759 [0.755, 0.765]. The precision value of the combined model improved from 0.497 to 0.751 with a fixed recall of 0.8. Using the mean absolute SHAP values, we identified the top 20 features, including age, heart rate, Richmond Agitation-Sedation Scale score, Morse fall risk score, pulse, respiratory rate, and level of care., Conclusion: Leveraging LSTM to capture temporal trends and combining it with the LightGBM model can significantly improve the prediction of new onset delirium, providing an algorithmic basis for the subsequent development of clinical decision support tools for proactive delirium interventions., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

An approach for identifying resonances in vibrational perturbation theory calculations is introduced. This approach makes use of the corrections to the wave functions that are obtained from non-degenerate perturbation theory calculations to identify spaces of states that must be treated with degenerate perturbation theory. Pairs of states are considered to be in resonance if the magnitude of expansion coefficients in the corrections to the wave functions in the non-degenerate perturbation theory calculation is greater than a specified threshold, χ max . This approach is applied to calculations of the vibrational spectra of CH 4 , H 2 CO, HNO 3 , and cc-HOONO. The question of how the identified resonances depend on the value of χ max and how the choice of the resonance spaces affects the calculated vibrational spectrum is further explored for H 2 CO. The approach is also compared to the Martin test [J. M. L. Martin et al., J. Chem. Phys. 103, 2589-2602 (1995)] for calculations of the vibrational spectra of H 2 CO and cc-HOONO.

A scheme for evaluating expansions of the potential and dipole moment surfaces for vibrational perturbation theory is described. The approach is based on numerical differentiation of the Hessian in the coordinates of interest. It is shown that performing these calculations in internal coordinates generates expansions that are transferable among isotopologues of the molecule of interest. Additionally, re-expressing the expansion of the potential in terms of functions of the internal coordinates, for example, cosines of angles or exponential functions of the bond length displacements, provides expansions that can be used for higher-order perturbation theory calculations. The approach is explored and the results are discussed for water, HOD, ammonia, isomers of HNO 3 , and halogenated methane.

Objectives: To improve clinical decision support (CDS) by allowing users to provide real-time feedback when they interact with CDS tools and by creating processes for responding to and acting on this feedback., Methods: Two organizations implemented similar real-time feedback tools and processes in their electronic health record and gathered data over a 30-month period. At both sites, users could provide feedback by using Likert feedback links embedded in all end-user facing alerts, with results stored outside the electronic health record, and provide feedback as a comment when they overrode an alert. Both systems are monitored daily by clinical informatics teams., Results: The two sites received 2,639 Likert feedback comments and 623,270 override comments over a 30-month period. Through four case studies, we describe our use of end-user feedback to rapidly respond to build errors, as well as identifying inaccurate knowledge management, user-interface issues, and unique workflows., Conclusion: Feedback on CDS tools can be solicited in multiple ways, and it contains valuable and actionable suggestions to improve CDS alerts. Additionally, end users appreciate knowing their feedback is being received and may also make other suggestions to improve the electronic health record. Incorporation of end-user feedback into CDS monitoring, evaluation, and remediation is a way to improve CDS., Competing Interests: None declared., (Thieme. All rights reserved.)

The fundamental transitions that contribute to the diffuse OH stretching spectrum of water are known to increase in width and intensity with increasing red shift from the free OH frequency. In contrast, the profile of the higher-energy combination band involving the OH stretching and the intramolecular HOH bending modes displays a qualitatively different spectral shape with a much faster falloff on the lower-energy side. We elucidate the molecular origin of this difference by analyzing the shapes of the combination bands in the IR spectra of cryogenically cooled H 3 O + (H 2 O) 20 and D 3 O + (D 2 O) 20 clusters. The difference in the shapes of the bands is traced to differences in the dependence of their transition dipole matrix elements on the hydrogen-bonding environment. The fact that individual transitions across the combination band envelope have similar intensities makes it a useful way to determine the participation of various sites in extended H-bonding networks.

Background: Patients taking high doses of opioids, or taking opioids in combination with other central nervous system depressants, are at increased risk of opioid overdose. Coprescribing the opioid-reversal agent naloxone is an essential safety measure, recommended by the surgeon general, but the rate of naloxone coprescribing is low. Therefore, we set out to determine whether a targeted clinical decision support alert could increase the rate of naloxone coprescribing., Methods: We conducted a before-after study from January 2019 to April 2021 at a large academic health system in the Southeast. We developed a targeted point of care decision support notification in the electronic health record to suggest ordering naloxone for patients who have a high risk of opioid overdose based on a high morphine equivalent daily dose (MEDD) ≥90 mg, concomitant benzodiazepine prescription, or a history of opioid use disorder or opioid overdose. We measured the rate of outpatient naloxone prescribing as our primary measure. A multivariable logistic regression model with robust variance to adjust for prescriptions within the same prescriber was implemented to estimate the association between alerts and naloxone coprescribing., Results: The baseline naloxone coprescribing rate in 2019 was 0.28 (95% confidence interval [CI], 0.24-0.31) naloxone prescriptions per 100 opioid prescriptions. After alert implementation, the naloxone coprescribing rate increased to 4.51 (95% CI, 4.33-4.68) naloxone prescriptions per 100 opioid prescriptions (P < .001). The adjusted odds of naloxone coprescribing after alert implementation were approximately 28 times those during the baseline period (95% CI, 15-52)., Conclusions: A targeted decision support alert for patients at risk for opioid overdose significantly increased the rate of naloxone coprescribing and was relatively easy to build., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2022 International Anesthesia Research Society.)

A machine-learning based approach for evaluating potential energies for quantum mechanical studies of properties of the ground and excited vibrational states of small molecules is developed. This approach uses the molecular-orbital-based machine learning (MOB-ML) method to generate electronic energies with the accuracy of CCSD(T) calculations at the same cost as a Hartree-Fock calculation. To further reduce the computational cost of the potential energy evaluations without sacrificing the CCSD(T) level accuracy, GPU-accelerated Neural Network Potential Energy Surfaces (NN-PES) are trained to geometries and energies that are collected from small-scale Diffusion Monte Carlo (DMC) simulations, which are run using energies evaluated using the MOB-ML model. The combined NN+(MOB-ML) approach is used in variational calculations of the ground and low-lying vibrational excited states of water and in DMC calculations of the ground states of water, CH 5 + , and its deuterated analogues. For both of these molecules, comparisons are made to the results obtained using potentials that were fit to much larger sets of electronic energies than were required to train the MOB-ML models. The NN+(MOB-ML) approach is also used to obtain a potential surface for C 2 H 5 + , which is a carbocation with a nonclassical equilibrium structure for which there is currently no available potential surface. This potential is used to explore the CH stretching vibrations, focusing on those of the bridging hydrogen atom. For both CH 5 + and C 2 H 5 + the MOB-ML model is trained using geometries that were sampled from an AIMD trajectory, which was run at 350 K. By comparison, the structures sampled in the ground state calculations can have energies that are as much as ten times larger than those used to train the MOB-ML model. For water a higher temperature AIMD trajectory is needed to obtain accurate results due to the smaller thermal energy. A second MOB-ML model for C 2 H 5 + was developed with additional higher energy structures in the training set. The two models are found to provide nearly identical descriptions of the ground state of C 2 H 5 + .

Objective: To quantify initial tidal volume (VT) during neonatal volume-targeted ventilation (VTV) and to characterize the agreement of initial VT with the limited-evidence available., Study Design: We performed a multi-center retrospective observational cohort study in two Neonatal Intensive Care Units evaluating 313 infants who received VTV as the initial ventilation modality prior to postnatal day 14. We generated descriptive statistics and performed multivariable logistic regression analysis to determine factors associated with initial VT use that agreed with available literature., Results: 154 (49%) infants received an initial VT of 5.0 mL/kg (median 5.0 mL/kg, IQR 5.0-5.1). 45 (14%) infants received an initial VT that was congruent with available literature. A birth weight of 700 -<1250 g was significantly associated with an initial VT in agreement with VT literature (aOR 9.4, 95% CI 1.7-50.4)., Conclusions: Most infants receive an initial VT of 5.0 mL/kg., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Objective: We describe the Clickbusters initiative implemented at Vanderbilt University Medical Center (VUMC), which was designed to improve safety and quality and reduce burnout through the optimization of clinical decision support (CDS) alerts., Materials and Methods: We developed a 10-step Clickbusting process and implemented a program that included a curriculum, CDS alert inventory, oversight process, and gamification. We carried out two 3-month rounds of the Clickbusters program at VUMC. We completed descriptive analyses of the changes made to alerts during the process, and of alert firing rates before and after the program., Results: Prior to Clickbusters, VUMC had 419 CDS alerts in production, with 488 425 firings (42 982 interruptive) each week. After 2 rounds, the Clickbusters program resulted in detailed, comprehensive reviews of 84 CDS alerts and reduced the number of weekly alert firings by more than 70 000 (15.43%). In addition to the direct improvements in CDS, the initiative also increased user engagement and involvement in CDS., Conclusions: At VUMC, the Clickbusters program was successful in optimizing CDS alerts by reducing alert firings and resulting clicks. The program also involved more users in the process of evaluating and improving CDS and helped build a culture of continuous evaluation and improvement of clinical content in the electronic health record., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

We report vibrational spectra of the H 2 -tagged, cryogenically cooled X -  · HOCl (X = Cl, Br, and I) ion-molecule complexes and analyze the resulting band patterns with electronic structure calculations and an anharmonic theoretical treatment of nuclear motions on extended potential energy surfaces. The complexes are formed by "ligand exchange" reactions of X -  · (H 2 O) n clusters with HOCl molecules at low pressure (∼10 -2  mbar) in a radio frequency ion guide. The spectra generally feature many bands in addition to the fundamentals expected at the double harmonic level. These "extra bands" appear in patterns that are similar to those displayed by the X -  · HOD analogs, where they are assigned to excitations of nominally IR forbidden overtones and combination bands. The interactions driving these features include mechanical and electronic anharmonicities. Particularly intense bands are observed for the v = 0 → 2 transitions of the out-of-plane bending soft modes of the HOCl molecule relative to the ions. These involve displacements that act to break the strong H-bond to the ion, which give rise to large quadratic dependences of the electric dipoles (electronic anharmonicities) that drive the transition moments for the overtone bands. On the other hand, overtone bands arising from the intramolecular OH bending modes of HOCl are traced to mechanical anharmonic coupling with the v = 1 level of the OH stretch (Fermi resonances). These interactions are similar in strength to those reported earlier for the X -  · HOD complexes.

Importance: Understanding the differences and potential synergies between traditional clinician assessment and automated machine learning might enable more accurate and useful suicide risk detection., Objective: To evaluate the respective and combined abilities of a real-time machine learning model and the Columbia Suicide Severity Rating Scale (C-SSRS) to predict suicide attempt (SA) and suicidal ideation (SI)., Design, Setting, and Participants: This cohort study included encounters with adult patients (aged ≥18 years) at a major academic medical center. The C-SSRS was administered during routine care, and a Vanderbilt Suicide Attempt and Ideation Likelihood (VSAIL) prediction was generated in the electronic health record. Encounters took place in the inpatient, ambulatory surgical, and emergency department settings. Data were collected from June 2019 to September 2020., Main Outcomes and Measures: Primary outcomes were the incidence of SA and SI, encoded as International Classification of Diseases codes, occurring within various time periods after an index visit. We evaluated the retrospective validity of the C-SSRS, VSAIL, and ensemble models combining both. Discrimination metrics included area under the receiver operating curve (AUROC), area under the precision-recall curve (AUPR), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV)., Results: The cohort included 120 398 unique index visits for 83 394 patients (mean [SD] age, 51.2 [20.6] years; 38 107 [46%] men; 45 273 [54%] women; 13 644 [16%] Black; 63 869 [77%] White). Within 30 days of an index visit, the combined models had higher AUROC (SA: 0.874-0.887; SI: 0.869-0.879) than both the VSAIL (SA: 0.729; SI: 0.773) and C-SSRS (SA: 0.823; SI: 0.777) models. In the highest risk-decile, ensemble methods had PPV of 1.3% to 1.4% for SA and 8.3% to 8.7% for SI and sensitivity of 77.6% to 79.5% for SA and 67.4% to 70.1% for SI, outperforming VSAIL (PPV for SA: 0.4%; PPV for SI: 3.9%; sensitivity for SA: 28.8%; sensitivity for SI: 35.1%) and C-SSRS (PPV for SA: 0.5%; PPV for SI: 3.5%; sensitivity for SA: 76.6%; sensitivity for SI: 68.8%)., Conclusions and Relevance: In this study, suicide risk prediction was optimal when leveraging both in-person screening (for acute measures of risk in patient-reported suicidality) and historical EHR data (for underlying clinical factors that can quantify a patient's passive risk level). To improve suicide risk classification, prediction systems could combine pretrained machine learning with structured clinician assessment without needing to retrain the original model.