Operational outcomes of community-to-academic emergency department patient transfers.

Background: Many patients require inter-hospital transfer (IHT) to tertiary Emergency Departments (EDs) to access specialty services. The purpose of this study is to determine operational outcomes for patients undergoing IHT to a tertiary academic ED, with an emphasis on timing and specialty consult utilization.

Methods: This study was a retrospective observational cohort study at a tertiary academic hospital from 10/1/21-9/30/22.

Does initiating care in alternate care sites decrease time to disposition in the emergency department?

Objectives: During the coronavirus disease 2019 (COVID-19) pandemic surge, alternate care sites (ACS) such as the waiting room or hospital lobby were created amongst hospitals nationwide to help alleviate emergency department (ED) overflow. Despite the end of the pandemic surge, many of these ACS remain functional given the burden of prolonged ED wait times, with providers now utilizing the waiting room or ACS to initiate care. Therefore, the objective of this study is to evaluate if initiating patient care in ACS helps to decrease time to disposition.

Geriatric End-of-Life Screening Tool Prediction of 6-Month Mortality in Older Patients.

Importance: Emergency department (ED) visits by older adults with life-limiting illnesses are a critical opportunity to establish patient care end-of-life preferences, but little is known about the optimal screening criteria for resource-constrained EDs.

Objectives: To externally validate the Geriatric End-of-Life Screening Tool (GEST) in an independent population and compare it with commonly used serious illness diagnostic criteria.

Design, Setting, And Participants: This prognostic study assessed a cohort of patients aged 65 years and older who were treated in a tertiary care ED in Boston, Massachusetts, from 2017 to 2021.

Assessment of respiratory rate monitoring in the emergency department.

Objectives: This study aimed to compare the different respiratory rate (RR) monitoring methods used in the emergency department (ED): manual documentation, telemetry, and capnography.

Methods: This is a retrospective study using recorded patient monitoring data. The study population includes patients who presented to a tertiary care ED between January 2020 and December 2022.

Postoperative Opioid Prescribing via Rule-Based Guidelines Derived from In-Hospital Consumption: An Assessment of Efficacy Based on Postdischarge Opioid Use.

Background: Many institutions have developed operation-specific guidelines for opioid prescribing. These guidelines rarely incorporate in-hospital opioid consumption, which is highly correlated with consumption. We compare outcomes of several patient-centered approaches to prescribing that are derived from in-hospital consumption, including several experimental, rule-based prescribing guidelines and our current institutional guideline.

Comparing Rationale for Opioid Prescribing Decisions after Surgery with Subsequent Patient Consumption: A Survey of the Highest Quartile of Prescribers.

Background: Opioid prescribing patterns, including those after surgery, have been implicated as a significant contributor to the US opioid crisis. A plethora of interventions-from nudges to reminders-have been deployed to improve prescribing behavior, but reasons for persistent outlier behavior are often unknown.

Study Design: Our institution employs multiple prescribing resources and a near real-time, feedback-based intervention to promote appropriate opioid prescribing.

Machine Learning Methods for Predicting Patient-Level Emergency Department Workload.

Background: Work Relative Value Units (wRVUs) are a component of many compensation models, and a proxy for the effort required to care for a patient. Accurate prediction of wRVUs generated per patient at triage could facilitate real-time load balancing between physicians and provide many practical operational and clinical benefits.

Objective: We examined whether deep-learning approaches could predict the wRVUs generated by a patient's visit using data commonly available at triage.

Machine learning nonresponse adjustment of patient-reported opioid consumption data to enable consumption-informed postoperative opioid prescribing guidelines.

Background: Post-discharge opioid consumption is a crucial patient-reported outcome informing opioid prescribing guidelines, but its collection is resource-intensive and vulnerable to inaccuracy due to nonresponse bias.

Methods: We developed a post-discharge text message-to-web survey system for efficient collection of patient-reported pain outcomes. We prospectively recruited surgical patients at Beth Israel Deaconess Medical Center in Boston, Massachusetts from March 2019 through October 2020, sending an SMS link to a secure web survey to quantify opioids consumed after discharge from hospitalization.

Use of an Automated Discharge Instruction Module to Improve Outpatient Follow-Up for Emergency Department Patients with Elevated Blood Pressure.

Introduction: Hypertension is often incidentally discovered in the emergency department (ED); these patients may benefit from close follow-up. We developed a module to automatically include discharge instructions for patients with elevated blood pressure (BP) in the ED, aiming to improve 30-day follow-up.

Aim: This study sought to determine if automated discharge instructions for patients with elevated blood pressure in the ED improved 30-day follow-up with a patient's primary care physician (PCP).

Evaluation of U.S. state opioid prescribing restrictions using patient opioid consumption patterns from a single, urban, academic institution.

Since 2017, states, insurers, and pharmacies have placed blanket limits on the duration and quantity of opioid prescriptions. In many states, overlapping duration and daily dose limits yield maximum prescription limits of 150-350 morphine milligram equivalents (MMEs). There is limited knowledge of how these restrictions compare with actual patient opioid consumption; while changes in prescription patterns and opioid misuse rates have been studied, these are, at best, weak proxies for actual pain control consumption.

An Automated Tobacco Cessation Intervention for Emergency Department Discharged Patients.

Introduction: Nearly 14% of US adults currently smoke cigarettes. Cigarette smoking causes more than 480,000 deaths each year in the United States. Emergency department (ED) patients are frequently asked for their use of tobacco.

Who's at Risk in a Changing Climate? Mapping Electricity-Dependent Patient Populations in a Coastal City.

Climate change is causing increasingly frequent extreme weather events. This pilot study demonstrates a GIS-based approach for assessing risk to electricity-dependent patients of a coastal academic medical center during future hurricanes.  Methods: A single-center retrospective chart review was conducted and the spatial distribution of patients with prescriptions for nebulized medications was mapped.

A visual representation of microbiological culture data improves comprehension: a randomized controlled trial.

Objective: While the judicious use of antibiotics takes past microbiological culture results into consideration, this data's typical format in the electronic health record (EHR) may be unwieldy when incorporated into clinical decision-making. We hypothesize that a visual representation of sensitivities may aid in their comprehension.

Materials And Methods: A prospective parallel unblinded randomized controlled trial was undertaken at an academic urban tertiary care center.

Reducing Emergency Department Transfers from Skilled Nursing Facilities Through an Emergency Physician Telemedicine Service.

Introduction: Transfers of skilled nursing facility (SNF) residents to emergency departments (ED) are linked to morbidity, mortality and significant cost, especially when transfers result in hospital admissions. This study investigated an alternative approach for emergency care delivery comprised of SNF-based telemedicine services provided by emergency physicians (EP). We compared this on-site emergency care option to traditional ED-based care, evaluating hospital admission rates following care by an EP.

Deep-learning approaches to identify critically Ill patients at emergency department triage using limited information.

Study Objective: Triage quickly identifies critically ill patients, facilitating timely interventions. Many emergency departments (EDs) use emergency severity index (ESI) or abnormal vital sign triggers to guide triage. However, both use fixed thresholds, and false activations are costly.

Emergency physicians' active patient queues over the course of a shift.

Objectives: When emergency physicians see new patients in an ad libitum system, they see fewer patients as the shift progresses. However, it is unclear if this reflects a decreasing workload, as patient assessments often span many hours. We sought to investigate whether the size of a physician's queue of active patients similarly declines over a shift.

Comparing Clinician Consensus Recommendations to Patient-reported Opioid Use Across Multiple Hospital Systems.

Objective: We compare consensus recommendations for 5 surgical procedures to prospectively collected patient consumption data. To address local variation, we combined data from multiple hospitals across the country.

Summary Of Background Data: One approach to address the opioid epidemic has been to create prescribing consensus reports for common surgical procedures.

Assessment of Unintentional Duplicate Orders by Emergency Department Clinicians Before and After Implementation of a Visual Aid in the Electronic Health Record Ordering System.

Importance: Electronic health records allow teams of clinicians to simultaneously care for patients, but an unintended consequence is the potential for duplicate orders of tests and medications.

Objective: To determine whether a simple visual aid is associated with a reduction in duplicate ordering of tests and medications.

Design, Setting, And Participants: This cohort study used an interrupted time series model to analyze 184 694 consecutive patients who visited the emergency department (ED) of an academic hospital with 55 000 ED visits annually.

Improving documentation of presenting problems in the emergency department using a domain-specific ontology and machine learning-driven user interfaces.

Objectives: To determine the effect of a domain-specific ontology and machine learning-driven user interfaces on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED).

Methods: As part of a quality improvement project, we simultaneously implemented three interventions: a domain-specific ontology, contextual autocomplete, and top five suggestions. Contextual autocomplete is a user interface that ranks concepts by their predicted probability which helps nurses enter data about a patient's presenting problems.

Derivation and validation of a machine learning record linkage algorithm between emergency medical services and the emergency department.

Objective: Linking emergency medical services (EMS) electronic patient care reports (ePCRs) to emergency department (ED) records can provide clinicians access to vital information that can alter management. It can also create rich databases for research and quality improvement. Unfortunately, previous attempts at ePCR and ED record linkage have had limited success.

Consensus Development of a Modern Ontology of Emergency Department Presenting Problems-The Hierarchical Presenting Problem Ontology (HaPPy).

Objective: Numerous attempts have been made to create a standardized "presenting problem" or "chief complaint" list to characterize the nature of an emergency department visit. Previous attempts have failed to gain widespread adoption as they were not freely shareable or did not contain the right level of specificity, structure, and clinical relevance to gain acceptance by the larger emergency medicine community. Using real-world data, we constructed a presenting problem list that addresses these challenges.

Point of Care Image Capture with a Custom Smartphone Application: Experience with an Encounter-Based Workflow.

Medical documentation is one of the primary methods by which physicians share clinical information and impressions over time with one another. As the adage says, "a picture is worth a thousand words," and physicians have started leveraging consumer mobile technology to share images with one another. However, image sharing often uses short message service texting and similar methods, which can be noncompliant with privacy regulations and can also limit the ability to communicate information longitudinally and across specialties.

Variations in Opioid Prescribing Behavior by Physician Training.

Introduction: Opioid abuse has reached epidemic proportions in the United States. Patients often present to the emergency department (ED) with painful conditions seeking analgesic relief. While there is known variability in the prescribing behaviors of emergency physicians, it is unknown if there are differences in these behaviors based on training level or by resident specialty.

Mobile device ownership among emergency department patients.

Background: The landscape of mobile devices is changing and their present use by patients for healthcare purposes is unknown. An understanding of current attitudes and usage may help increase patient engagement through mobile applications. This study sought to determine characteristics of mobile device ownership among Emergency Department patients, patients' feelings regarding their use in healthcare, and desired functionality in mobile applications.

The Effect of Signed-Out Emergency Department Patients on Resident Productivity.

Background: Transitions of care and patient hand-offs between physicians have important implications for patient care. However, what effect caring for signed-out patients has on providing care to new patients and education is unclear.

Objective: We sought to determine whether the number of patients a physician receives in sign-out affects productivity.