OBJECTIVE: This report presents statistics on ambulatory care visits to physician offices, hospital outpatient departments, and hospital emergency departments. Ambulatory medical care utilization is described in terms of patient, practice, facility, and visit characteristics. Office-based care is further subdivided into the categories of primary care, surgical specialties, and medical specialties. METHODS: Data from the 2001 and 2002 National Ambulatory Medical Care Surveys (NAMCS) and National Hospital Ambulatory Medical Care Surveys (NHAMCS) were combined to produce averaged annual estimates of ambulatory medical care utilization. RESULTS: Patients in the United States made an estimated 1.1 billion visits per year in 2001 and 2002 (annual average) to physician offices, hospital outpatient departments, and emergency departments, a rate of 3.8 visits per person annually. This marks the first time that the annual estimate of visits has surpassed the billion mark and is also a significant increase from the 1999-2000 estimate. The change was primarily driven by a jump in the number of visits to primary care physicians. The distribution of visits by patient age, sex, race, expected source of payment, geographic region, and whether the visit occurred in a metropolitan statistical area (MSA) varied across ambulatory care settings. Females had higher visit rates than males to all settings except office-based surgical specialists and emergency departments (ED). Black persons had higher visit rates than white persons to hospital outpatient and emergency departments, but lower visit rates to office-based surgical and medical specialists. Visits to emergency departments were more likely to be patient-paid or no charge, possibly reflecting a lack of private health insurance, than were visits to physician offices. Visit rates to office-based medical specialists were more than double in MSAs compared with non-MSAs.
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In this paper, we consider the academic department ranking system of Italy, which is based on a performance index named Indice Standardizzato di Performance Dipartimentale (ISPD). While critiques to the ISPD have been moved for its marked tendency to polarization, we here formalize a yet unexplored determinant of this phenomenon, that is, the presence of within-department homogeneity among the standardized scores used to build the index. We account for this intra-departmental correlation by modeling it as a function of departments' size. The proposed model, estimated via Maximum Likelihood, allows to build a fairer ranking procedure via the definition of a properly adjusted version of the ISPD. The estimation framework is also adapted to fit publicly available data, which are coarsened by rounding and/or left-truncated. To this end, a novel probability distribution termed Betoidal is introduced. Empirical evidence in favor of the proposed model is found in the 2017 and 2022 data. Moreover, a simulation study shows that the adjusted index significantly overcomes not only the original ISPD, but also other more data-demanding competing proposals.
Many cyberattacks succeed because they exploit flaws at the human level. To address this problem, organizations rely on security awareness programs, which aim to make employees more resilient against social engineering. While some works have suggested that such programs should account for contextual relevance, the common praxis in research is to adopt a "general" viewpoint. For instance, instead of focusing on department-specific issues, prior user studies sought to provide organization-wide conclusions. Such a protocol may lead to overlooking vulnerabilities that affect only specific subsets of an organization. In this paper, we tackle such an oversight. First, through a systematic literature review, we provide evidence that prior literature poorly accounted for department-specific needs. Then, we carry out a multi-company and mixed-methods study focusing on two pivotal departments: human resources (HR) and accounting. We explore three dimensions: threats faced by these departments; topics covered in the security-awareness campaigns delivered to these departments; and delivery methods that maximize the effectiveness of such campaigns. We begin by interviewing 16 employees of a mul
Electronic health records (EHRs) are central to clinical prediction, but existing methods either rely on correlation-driven deep models or use single large language models (LLMs), making it difficult to support multidisciplinary clinical reasoning. Recent multi-agent systems (MAS) provide a promising alternative, yet current EHR-grounded MAS methods still suffer from weak evidence differentiation across agents and redundant multi-round interaction. We propose D2MDT, a Department-aware MultiDisciplinary Team Consultation with Deliberation for Efficient clinical prediction. D2MDT first constructs structured EHR evidence and consultation-ready semantic evidence for multi-agent consultation. It then assigns patient-specific department perspectives to doctor agents and retrieves complementary evidence for collaborative consultation. To improve efficiency, D2MDT further introduces residual deliberation, which updates only unresolved consensus rather than replaying the full discussion history. Finally, D2MDT fuses the refined consensus report with structured EHR representations for prediction. Experiments on mortality prediction show that D2MDT improves both predictive performance and con
Medical emergency departments are complex systems in which patients must be treated according to priority rules based on the severity of their condition. We develop a model of emergency departments using Petri nets with priorities, described by nonmonotone piecewise linear dynamical systems. The collection of stationary solutions of such systems forms a "phase diagram", in which each phase corresponds to a subset of bottleneck resources (like senior doctors, interns, nurses, consultation rooms, etc.). Since the number of phases is generally exponential in the number of resources, developing automated methods is essential to tackle realistic models. We develop a general method to compute congestion diagrams. A key ingredient is a polynomial time algorithm to test whether a given "policy" (configuration of bottleneck tasks) is achievable by a choice of resources. This is done by reduction to a feasibility problem for an unusual class of lexicographic polyhedra. Furthermore, we show that each policy uniquely determines the system's throughput. We apply our approach to a case study, analyzing a simplified model of an emergency department from Assistance Publique - Hôpitaux de Paris.
Hospitalizations that follow closely on the heels of one or more emergency department visits are often symptoms of missed opportunities to form a proper diagnosis. These diagnostic errors imply a failure to recognize the need for hospitalization and deliver appropriate care, and thus also bear important connotations for patient safety. In this paper, we show how data mining techniques can be applied to a large existing hospitalization data set to learn useful models that predict these upcoming hospitalizations with high accuracy. Specifically, we use an ensemble of logistics regression, naïve Bayes and association rule classifiers to successfully predict hospitalization within 3, 7 and 14 days of an emergency department discharge. Aside from high accuracy, one of the advantages of the techniques proposed here is that the resulting classifier is easily inspected and interpreted by humans so that the learned rules can be readily operationalized. These rules can then be easily distributed and applied directly by physicians in emergency department settings to predict the risk of early admission prior to discharging their emergency department patients.
Objectives: Prior event rate ratio (PERR) is a promising approach to control confounding in observational and real-world evidence research. One of its assumptions is that occurrence of outcome events does not influence later event rate, or in other words, absence of 'event dependence'. This study proposes, evaluates and illustrates a bias reduction method when this assumption is violated. Study Design and Setting: We propose the conditional frailty method for implementation of PERR in the presence of event dependence and evaluate its performance by simulation. We demonstrate the use of the method with a study of emergency department visit rate and palliative care in patients with advanced cancer in Singapore. Results: Simulations showed that, in the presence of negative (positive) event dependence, the crude PERR estimate of treatment effect was biased towards (away from) the null value. The proposed method successfully reduced the bias, with median of absolute level of relative bias at about 5%. Dynamic random-intercept modelling revealed positive event dependence in emergency department visits among patients with advanced cancer. While conventional time-to-event regression analys
This study evaluates fine-tuned Llama 3.2 models for extracting vaccine-related information from emergency department triage notes to support near real-time vaccine safety surveillance. Prompt engineering was used to initially create a labeled dataset, which was then confirmed by human annotators. The performance of prompt-engineered models, fine-tuned models, and a rule-based approach was compared. The fine-tuned Llama 3 billion parameter model outperformed other models in its accuracy of extracting vaccine names. Model quantization enabled efficient deployment in resource-constrained environments. Findings demonstrate the potential of large language models in automating data extraction from emergency department notes, supporting efficient vaccine safety surveillance and early detection of emerging adverse events following immunization issues.
We present comparative case study of three physics department culture from different institutions using the experiences undergraduate women. The three studies conducted in the United States include Johnson's 2020 study in a small physics department at a small predominantly White liberal arts college, Santana and Singh's 2023 study at a large predominantly White research institution, and Santana and Singh's 2024 study in a medium-sized physics department at a small predominantly White private liberal arts college. Using synergistic frameworks such as Standpoint Theory, Domains of Power, and the Holistic Ecosystem for Learning Physics in an Inclusive and Equitable Environment and reflections from undergraduate women, we aim to understand how those in the position of power, e.g., instructors, have important roles in establishing and maintaining safe, equitable, and inclusive environments for undergraduate students. Their accounts help us contrast the experiences of undergraduate women in physics departments with very different cultures. This comparative analysis is especially important for reflecting upon what can be done to improve the physics culture so that historically marginalize
Digital twins (DTs) help improve real-time monitoring and decision-making in water distribution systems. However, their connectivity makes them easy targets for cyberattacks such as scanning, denial-of-service (DoS), and unauthorized access. Small and medium-sized enterprises (SMEs) that manage these systems often do not have enough budget or staff to build strong cybersecurity teams. To solve this problem, we present a Virtual Cybersecurity Department (VCD), an affordable and automated framework designed for SMEs. The VCD uses open-source tools like Zabbix for real-time monitoring, Suricata for network intrusion detection, Fail2Ban to block repeated login attempts, and simple firewall settings. To improve threat detection, we also add a machine-learning-based IDS trained on the OD-IDS2022 dataset using an improved ensemble model. This model detects cyber threats such as brute-force attacks, remote code execution (RCE), and network flooding, with 92\% accuracy and fewer false alarms. Our solution gives SMEs a practical and efficient way to secure water systems using low-cost and easy-to-manage tools.
Approximately one-third of adults search the internet for health information before visiting an emergency department (ED), with 75% encountering inaccurate content. This study examines how such searches influence patient care. We conducted an observational study of ED visits over a 12-month period, surveying 214 of 576 patients about pre-ED internet use. Data on demographics, comorbidities, acuity, orders, prescriptions, and dispositions were extracted. Patients who searched were typically younger, healthier, and more educated. Most used a general search engine to ask symptom-related questions. Compared to non-searchers, they were less likely to receive lab tests (RR 0.78, p=0.053), imaging (RR 0.75, p=0.094), medications (RR 0.67, p=0.038), or admission (RR 0.68, p=0.175). They were more likely to leave against medical advice (RR 1.67, p=0.067) and receive opioids (RR 1.56, p=0.151). Findings suggest inaccurate health information may contribute to mismatched expectations and altered care delivery.
The DAF-MIT AI Accelerator is a collaboration between the United States Department of the Air Force (DAF) and the Massachusetts Institute of Technology (MIT). This program pioneers fundamental advances in artificial intelligence (AI) to expand the competitive advantage of the United States in the defense and civilian sectors. In recent years, AI Accelerator projects have developed and launched public challenge problems aimed at advancing AI research in priority areas. Hallmarks of AI Accelerator challenges include large, publicly available, and AI-ready datasets to stimulate open-source solutions and engage the wider academic and private sector AI ecosystem. This article supplements our previous publication, which introduced AI Accelerator challenges. We provide an update on how ongoing and new challenges have successfully contributed to AI research and applications of AI technologies.
Although bibliometric studies for the assessment of scientific output at university-level are relatively common, data for performance at department-level rarely exist. In this paper we develop a methodology and tools for conducting department-level assessments, and conduct for the first time a complete bibliometric study of scientific output of all university departments in Greece. The study is based on data from Scopus about the number of scientific publications and respective citations of all faculty members in each department for the period 2017-2021. Code scripts were developed using R to query the Scopus database and automatically generate statistics, as well as an online application to view the results. The results reveal interesting facts about the scientific impact of greek university departments, which vary between universities and thematic areas. The developed tools can be used for continuous monitoring and evaluation of university departments worldwide.
Adaptive remote instruction has led to important lessons for the future, including rediscovery of known pedagogical principles in new contexts and new insights for supporting remote learning. Studying one computer science department that serves residential and remote undergraduate and graduate students, we conducted interviews with stakeholders in the department (n=26) and ran a department-wide student survey (n=102) during the four academic quarters from spring 2020 to spring 2021. Our case study outlines what the instructors did, summarizes what instructors and students say about courses during this period, and provides recommendations for CS departments with similar scope going forward. Specific insights address: (1) how instructional components are best structured for students; (2) how students are assessed for their learning; and (3) how students are supported in student-initiated components of learning. The institution is a large U.S. research university that has a history of online programs including online enrollment in regular on-campus courses and large-scale open enrollment courses. Our recommendations to instructors across the scope of this department may also be applic
Nowadays, the increase in patient demand and the decline in resources are lengthening patient waiting times in many chemotherapy oncology departments. Therefore, enhancing healthcare services is necessary to reduce patient complaints. Reducing the patient waiting times in the oncology departments represents one of the main goals of healthcare manager. Simulation models are considered an effective tool for identifying potential ways to improve patient flow in oncology departments. This paper presents a new agent-based simulation model designed to be configurable and adaptable to the needs of oncology departments which have to interact with an external pharmacy. When external pharmacies are utilized, a courier service is needed to deliver the individual therapies from the pharmacy to the oncology department. An oncology department located in southern Italy was studied through the simulation model and different scenarios were compared with the aim of selecting the department configuration capable of reducing the patient waiting times.
Emergency department (ED) crowding is a global public health issue that has been repeatedly associated with increased mortality. Predicting future service demand would enable preventative measures aiming to eliminate crowding along with it's detrimental effects. Recent findings in our ED indicate that occupancy ratios exceeding 90% are associated with increased 10-day mortality. In this paper, we aim to predict these crisis periods using retrospective data from a large Nordic ED with a LightGBM model. We provide predictions for the whole ED and individually for it's different operational sections. We demonstrate that afternoon crowding can be predicted at 11 a.m. with an AUC of 0.82 (95% CI 0.78-0.86) and at 8 a.m. with an AUC up to 0.79 (95% CI 0.75-0.83). Consequently we show that forecasting mortality-associated crowding using anonymous administrative data is feasible.
In this work, we introduce the Multiple Embedding Model for EHR (MEME), an approach that serializes multimodal EHR tabular data into text using pseudo-notes, mimicking clinical text generation. This conversion not only preserves better representations of categorical data and learns contexts but also enables the effective employment of pretrained foundation models for rich feature representation. To address potential issues with context length, our framework encodes embeddings for each EHR modality separately. We demonstrate the effectiveness of MEME by applying it to several decision support tasks within the Emergency Department across multiple hospital systems. Our findings indicate that MEME outperforms traditional machine learning, EHR-specific foundation models, and general LLMs, highlighting its potential as a general and extendible EHR representation strategy.
Environments such as shopping malls, airports, or hospital emergency departments often experience crowding, with many people simultaneously requesting service. Crowding is highly noisy, with sudden overcrowding "spikes". Past research has either focused on average behavior or used context-specific non-generalizable models. Here we show that a stochastic population model, previously applied to a broad range of natural phenomena, can aptly describe hospital emergency-department crowding, using data from five-year minute-by-minute emergency-department records. The model provides reliable forecasting of the crowding distribution. Overcrowding is highly sensitive to the patient arrival-flux and length-of-stay: a 10% increase in arrivals triples the probability of overcrowding events. Expediting patient exit-rate to shorten the typical length-of-stay by just 20 minutes (8.5%) reduces severe overcrowding events by 50%. Such forecasting is crucial in prevention and mitigation of breakdown events. Our results demonstrate that despite its high volatility, crowding follows a dynamic behavior common to many natural systems.
Healthcare systems are challenged to deliver high-quality and efficient care. Studying patient flow in a hospital is particularly fundamental as it demonstrates effectiveness and efficiency of a hospital. Since hospital is a collection of physically nearby services under one administration, its performance and outcome are shaped by the interaction of its discrete components. Coordination of processes at different levels of organizational structure of a hospital can be studied using network analysis. Hence, this article presents a data-driven static and temporal network of departments. Both networks are directed and weighted and constructed using seven years' (2010-2016) empirical data of 24902 Acute Coronary Syndrome (ACS) patients. The ties reflect an episode-based transfer of ACS patients from department to department in a hospital. The weight represents the number of patients transferred among departments. As a result, the underlying structure of a network of departments that deliver healthcare for ACS patients is described, the main departments and their role in the diagnosis and treatment process of ACS patients are identified, the role of departments over seven years is analy