Electronic Health Record-Based Surveillance for Community Transmitted COVID-19 in the Emergency Department

Introduction: SARS-CoV-2, a novel coronavirus, manifests as a respiratory syndrome (COVID-19) and is the cause of an ongoing pandemic. The response to COVID-19 in the United States has been hampered by an overall lack of diagnostic testing capacity. To address uncertainty about ongoing levels of SARS-CoV-2 community transmission early in the pandemic, we aimed to develop a surveillance tool using readily available emergency department (ED) operations data extracted from the electronic health record (EHR). This involved optimizing the identification of acute respiratory infection (ARI)-related encounters and then comparing metrics for these encounters before and after the confirmation of SARS-CoV-2 community transmission.Methods: We performed an observational study using operational EHR data from two Midwest EDs with a combined annual census of over 80,000. Data were collected three weeks before and after the first confirmed case of local SARS-CoV-2 community transmission. To optimize capture of ARI cases, we compared various metrics including chief complaint, discharge diagnoses, and ARI-related orders. Operational metrics for ARI cases, including volume, pathogen identification, and illness severity, were compared between the pre- and post-community transmission timeframes using chi-square tests of independence.Results: Compared to our combined definition of ARI, chief complaint, discharge diagnoses, and isolation orders individually identified less than half of the cases. Respiratory pathogen testing was the top performing individual ARI definition but still only identified 72.2% of cases. From the pre to post periods, we observed significant increases in ED volumes due to ARI and ARI cases without identified pathogen.Conclusion: Certain methods for identifying ARI cases in the ED may be inadequate and multiple criteria should be used to optimize capture. In the absence of widely available SARS-CoV-2 testing, operational metrics for ARI-related encounters, especially the proportion of cases involving negative pathogen testing, are useful indicators for active surveillance of potential COVID-19 related ED visits

Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department

BACKGROUND: Falls among older adults are both a common reason for presentation to the emergency department, and a major source of morbidity and mortality. It is critical to identify fall patients quickly and reliably during, and immediately after, emergency department encounters in order to deliver appropriate care and referrals. Unfortunately, falls are difficult to identify without manual chart review, a time intensive process infeasible for many applications including surveillance and quality reporting. Here we describe a pragmatic NLP approach to automating fall identification. METHODS: In this single center retrospective review, 500 emergency department provider notes from older adult patients (age 65 and older) were randomly selected for analysis. A simple, rules-based NLP algorithm for fall identification was developed and evaluated on a development set of 1084 notes, then compared with identification by consensus of trained abstractors blinded to NLP results. RESULTS: The NLP pipeline demonstrated a recall (sensitivity) of 95.8%, specificity of 97.4%, precision of 92.0%, and F1 score of 0.939 for identifying fall events within emergency physician visit notes, as compared to gold standard manual abstraction by human coders. CONCLUSIONS: Our pragmatic NLP algorithm was able to identify falls in ED notes with excellent precision and recall, comparable to that of more labor-intensive manual abstraction. This finding offers promise not just for improving research methods, but as a potential for identifying patients for targeted interventions, quality measure development and epidemiologic surveillance

Dietary fat and carbohydrates differentially alter insulin sensitivity during caloric restriction

BACKGROUND AND AIMS: We determined the effects of acute and chronic calorie restriction with either a low-fat, high-carbohydrate diet or a low-carbohydrate diet on hepatic and skeletal muscle insulin sensitivity. METHODS: Twenty-two obese subjects (body-mass index, 36.5±0.8kg/m(2)) were randomized to a high-carbohydrate (>180g/d) or low-carbohydrate (<60g/d) energy-deficit diet. A euglycemic–hyperinsulinemic clamp, muscle biopsies, and magnetic resonance spectroscopy were used to determine insulin action, cellular insulin signaling and intrahepatic triglyceride content before, after 48 h, and after ~11 wks (7% weight loss) of diet therapy. RESULTS: At 48 h, intrahepatic triglyceride content decreased more in the low-carbohydrate than the high-carbohydrate diet group (29.6±4.8% vs. 8.9±1.4%; P<0.05), but was similar in both groups after 7% weight loss (low-carbohydrate diet, 38.0±4.5% vs. high-carbohydrate diet, 44.5±13.5%). Basal glucose production rate decreased more in the low-carbohydrate than the high-carbohydrate diet group at 48 h (23.4±2.2% vs. 7.2±1.4%, P<0.05) and after 7% weight loss (20.0±2.4% vs. 7.9±1.2%, P<0.05). Insulin-mediated glucose uptake did not change at 48 h, but increased similarly in both groups after 7% weight loss (48.4±14.3%, P<0.05). In both groups, insulin-stimulated phosphorylation of Jun N-terminal kinase decreased by 29±13% and phosphorylation of Akt and insulin receptor substrate -1 increased by 35±9% and 36±9%, respectively, after 7% weight loss (all p<0.05). CONCLUSION: Moderate calorie restriction causes temporal changes in liver and skeletal muscle metabolism; 48 h of calorie restriction affects the liver (intrahepatic triglyceride content, hepatic insulin sensitivity, and glucose production), whereas moderate weight loss affects muscle (insulin-mediated glucose uptake and insulin signaling)

Call Me Dr Ishmael: Trends in Electronic Health Record Notes Available at Emergency Department Visits and Admissions

OBJECTIVES: Numerous studies have identified information overload as a key issue for electronic health records (EHRs). This study describes the amount of text data across all notes available to emergency physicians in the EHR, trended over the time since EHR establishment. MATERIALS AND METHODS: We conducted a retrospective analysis of EHR data from a large healthcare system, examining the number of notes and a corresponding number of total words and total tokens across all notes available to physicians during patient encounters in the emergency department (ED). We assessed the change in these metrics over a 17-year period between 2006 and 2023. RESULTS: The study cohort included 730 968 ED visits made by 293 559 unique patients and a total note count of 132 574 964. The median note count for all encounters in 2006 was 5 (IQR 1-16), accounting for 1735 (IQR 447-5521) words. By the last full year of the study period, 2022, the median number of notes had grown to 359 (IQR 84-943), representing 359 (IQR 84-943) words. Note and word counts were higher for admitted patients. DISCUSSION: The volume of notes available for review by providers has increased by over 30-fold in the 17 years since the implementation of the EHR at a large health system. The task of reviewing these notes has become commensurately more difficult. These data point to the critical need for new strategies and tools for filtering, synthesizing, and summarizing information to achieve the promise of the medical record

Academic Detailing as a Health Information Technology Implementation Method: Supporting the Design and Implementation of an Emergency Department-Based Clinical Decision Support Tool to Prevent Future Falls

BACKGROUND: Clinical decision support (CDS) tools that incorporate machine learning-derived content have the potential to transform clinical care by augmenting clinicians\u27 expertise. To realize this potential, such tools must be designed to fit the dynamic work systems of the clinicians who use them. We propose the use of academic detailing-personal visits to clinicians by an expert in a specific health IT tool-as a method for both ensuring the correct understanding of that tool and its evidence base and identifying factors influencing the tool\u27s implementation. OBJECTIVE: This study aimed to assess academic detailing as a method for simultaneously ensuring the correct understanding of an emergency department-based CDS tool to prevent future falls and identifying factors impacting clinicians\u27 use of the tool through an analysis of the resultant qualitative data. METHODS: Previously, our team designed a CDS tool to identify patients aged 65 years and older who are at the highest risk of future falls and prompt an interruptive alert to clinicians, suggesting the patient be referred to a mobility and falls clinic for an evidence-based preventative intervention. We conducted 10-minute academic detailing interviews (n=16) with resident emergency medicine physicians and advanced practice providers who had encountered our CDS tool in practice. We conducted an inductive, team-based content analysis to identify factors that influenced clinicians\u27 use of the CDS tool. RESULTS: The following categories of factors that impacted clinicians\u27 use of the CDS were identified: (1) aspects of the CDS tool\u27s design (2) clinicians\u27 understanding (or misunderstanding) of the CDS or referral process, (3) the busy nature of the emergency department environment, (4) clinicians\u27 perceptions of the patient and their associated fall risk, and (5) the opacity of the referral process. Additionally, clinician education was done to address any misconceptions about the CDS tool or referral process, for example, demonstrating how simple it is to place a referral via the CDS and clarifying which clinic the referral goes to. CONCLUSIONS: Our study demonstrates the use of academic detailing for supporting the implementation of health information technologies, allowing us to identify factors that impacted clinicians\u27 use of the CDS while concurrently educating clinicians to ensure the correct understanding of the CDS tool and intervention. Thus, academic detailing can inform both real-time adjustments of a tool\u27s implementation, for example, refinement of the language used to introduce the tool, and larger scale redesign of the CDS tool to better fit the dynamic work environment of clinicians

Simultaneous Determination of 3-mercaptopyruvate and Cobinamide in Plasma by Liquid Chromatography–tandem Mass Spectrometry

The current suite of Food and Drug Administration (FDA) approved antidotes (i.e., sodium nitrite, sodium thiosulfate, and hydroxocobalamin) are effective for treating cyanide poisoning, but individually, each antidote has major limitations (e.g., large effective dosage or delayed onset of action). To mitigate these limitations, next-generation cyanide antidotes are being investigated, including 3-mercaptopyruvate (3-MP) and cobinamide (Cbi). Analytical methods capable of detecting these therapeutics individually and simultaneously (for combination therapy) are essential for the development of 3-MP and Cbi as potential cyanide antidotes. Therefore, a liquid chromatography–tandem mass-spectrometry method for the simultaneous analysis of 3-MP and Cbi was developed. Sample preparation of 3-MP consisted of spiking plasma with an internal standard (13C3-3-MP), precipitation of plasma proteins, and derivatizing 3-MP with monobromobimane to produce 3-mercaptopyruvate-bimane. Preparation of Cbi involved denaturing plasma proteins with simultaneous addition of excess cyanide to convert each Cbi species to dicyanocobinamide (Cbi(CN)2). The limits of detection for 3-MP and Cbi were 0.5 μM and 0.2 μM, respectively. The linear ranges were 2–500 μM for 3-MP and 0.5–50 μM for Cbi. The accuracy and precision for 3-MP were 100 ± 9% and \u3c8.3% relative standard deviation (RSD), respectively. For Cbi(CN)2, the accuracy was 100 ± 13% and the precision was \u3c9.5% RSD. The method presented here was used to determine 3-MP and Cbi from treated animals and may ultimately facilitate FDA approval of these antidotes for treatment of cyanide poisoning

Multisite Evaluation of Prediction Models for Emergency Department Crowding Before and During the COVID-19 Pandemic

OBJECTIVE: To develop a machine learning framework to forecast emergency department (ED) crowding and to evaluate model performance under spatial and temporal data drift. MATERIALS AND METHODS: We obtained 4 datasets, identified by the location: 1-large academic hospital and 2-rural hospital, and time period: pre-coronavirus disease (COVID) (January 1, 2019-February 1, 2020) and COVID-era (May 15, 2020-February 1, 2021). Our primary target was a binary outcome that is equal to 1 if the number of patients with acute respiratory illness that were ED boarding for more than 4 h was above a prescribed historical percentile. We trained a random forest and used the area under the curve (AUC) to evaluate out-of-sample performance for 2 experiments: (1) we evaluated the impact of sudden temporal drift by training models using pre-COVID data and testing them during the COVID-era, (2) we evaluated the impact of spatial drift by testing models trained at location 1 on data from location 2, and vice versa. RESULTS: The baseline AUC values for ED boarding ranged from 0.54 (pre-COVID at location 2) to 0.81 (COVID-era at location 1). Models trained with pre-COVID data performed similarly to COVID-era models (0.82 vs 0.78 at location 1). Models that were transferred from location 2 to location 1 performed worse than models trained at location 1 (0.51 vs 0.78). DISCUSSION AND CONCLUSION: Our results demonstrate that ED boarding is a predictable metric for ED crowding, models were not significantly impacted by temporal data drift, and any attempts at implementation must consider spatial data drift

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) DERR1-10.2196/4812

The Catalytic Mechanism of Electron-Bifurcating Electron Transfer Flavoproteins (ETFs) Involves an Intermediary Complex with NAD\u3csup\u3e+\u3c/sup\u3e

Electron bifurcation plays a key role in anaerobic energy metabolism, but it is a relatively new discovery, and only limited mechanistic information is available on the diverse enzymes that employ it. Herein, we focused on the bifurcating electron transfer flavoprotein (ETF) from the hyperthermophilic archaeon Pyrobaculum aerophilum. The EtfABCX enzyme complex couples NADH oxidation to the endergonic reduction of ferredoxin and exergonic reduction of menaquinone. We developed a model for the enzyme structure by using nondenaturing MS, cross-linking, and homology modeling in which EtfA, -B, and -C each contained FAD, whereas EtfX contained two [4Fe-4S] clusters. On the basis of analyses using transient absorption, EPR, and optical titrations with NADH or inorganic reductants with and without NAD+, we propose a catalytic cycle involving formation of an intermediary NAD+-bound complex. A charge transfer signal revealed an intriguing interplay of flavin semiquinones and a protein conformational change that gated electron transfer between the low- and high-potential pathways. We found that despite a common bifurcating flavin site, the proposed EtfABCX catalytic cycle is distinct from that of the genetically unrelated bifurcating NADH-dependent ferredoxin NADP+ oxidoreductase (NfnI). The two enzymes particularly differed in the role of NAD+, the resting and bifurcating-ready states of the enzymes, how electron flow is gated, and the two two-electron cycles constituting the overall four-electron reaction. We conclude that P. aerophilum EtfABCX provides a model catalytic mechanism that builds on and extends previous studies of related bifurcating ETFs and can be applied to the large bifurcating ETF family