Faculty Bibliography

The landscape of drug abuse is shifting. Traditional means of characterizing these changes, such as national surveys or voluntary reporting by frontline clinicians, can miss changes in usage the emergence of novel drugs. Delays in detecting novel drug usage patterns make it difficult to evaluate public policy aimed at altering drug abuse. Increasingly, newer methods to inform frontline providers to recognize symptoms associated with novel drugs or methods of administration are needed. The growth of social networks may address this need. The objective of this manuscript is to introduce tools for using data from social networks to characterize drug abuse. We outline a structured approach to analyze social media in order to capture emerging trends in drug abuse by applying powerful methods from artificial intelligence, computational linguistics, graph theory, and agent-based modeling. First, we describe how to obtain data from social networks such as Twitter using publicly available automated programmatic interfaces. Then, we discuss how to use artificial intelligence techniques to extract content useful for purposes of toxicovigilance. This filtered content can be employed to generate real-time maps of drug usage across geographical regions. Beyond describing the real-time epidemiology of drug abuse, techniques from computational linguistics can uncover ways that drug discussions differ from other online conversations. Next, graph theory can elucidate the structure of networks discussing drug abuse, helping us learn what online interactions promote drug abuse and whether these interactions differ among drugs. Finally, agent-based modeling relates online interactions to psychological archetypes, providing a link between epidemiology and behavior. An analysis of social media discussions about drug abuse patterns with computational linguistics, graph theory, and agent-based modeling permits the real-time monitoring and characterization of trends of drugs of abuse. These tools provide a powerful complement to existing methods of toxicovigilance.

As hospitals incorporate information technology (IT), their operations become increasingly vulnerable to technological breakdowns and attacks. Proper emergency management and business continuity planning require an approach to identify, mitigate, and work through IT downtime. Hospitals can prepare for these disasters by reviewing case studies. This case study details the disruption of computer operations at Mount Sinai Medical Center (MSMC), an urban academic teaching hospital. The events, and MSMC's response, are narrated and the impact on hospital operations is analyzed. MSMC's disaster management strategy prevented computer failure from compromising patient care, although walkouts and time-to-disposition in the emergency department (ED) notably increased. This incident highlights the importance of disaster preparedness and mitigation. It also demonstrates the value of using operational data to evaluate hospital responses to disasters. Quantifying normal hospital functions, just as with a patient's vital signs, may help quantitatively evaluate and improve disaster management and business continuity planning.

Monoarticular arthritis presentations in the emergency department are increasing as the population ages and gets heavier. Many etiologies--from trauma to infection to autoimmune-mediated inflammation--are associated with significant disability or early mortality, and their treatments are associated with adverse effects. A systematic approach to evaluating patients with monoarticular arthritic complaints is important for relieving pain, diagnosing systemic illness, and unmasking true arthritis emergencies. Septic arthritis is a rapidly destructive process that can cause significant disability in a matter of hours or days, with relatively high mortality. Other causes of monoarticular arthritis may cause disability in the long term. In all cases, accurate diagnosis and appropriate therapies are crucial for resuming activities and preventing long-term deficits. This review examines the diagnosis and treatment of monoarticular arthritis, with a focus on recent evidence in the diagnosis of septic arthritis and new research on gout therapies. Modalities for pain control and new techniques for imaging are discussed.

OBJECTIVES/OBJECTIVE:The primary study aim was to examine the variations in crowding when an emergency department (ED) initiates ambulance diversion. METHODS:This retrospective, multicenter study included nine geographically disparate EDs. Daily ED operational variables were collected during a 12-month period (January 2009 to December 2009), including total number of ED visits, mean overall length of stay (LOS), number of ED beds, and hours on ambulance diversion. The primary outcome variable was the "ED workload rate," a surrogate marker for daily ED crowding. It was calculated as the total number of daily ED visits multiplied by the overall mean LOS (in hours) and divided by the number of ED beds available for acute treatment in a given day. The primary predictor variables were ambulance diversion, as a dichotomous variable of whether or not an ED went on diversion at least once during a 24-hour period, diversion hour quintiles, and sites. RESULTS:The annual ED census ranged from 43,000 to 101,000 patients. The percentage of days that an ED went on diversion at least once varied from 4.9% to 86.6%. On days with ambulance diversion, the mean ED workload rate varied from 17.1 to 62.1 patient LOS hours per ED bed among sites. The magnitude of variation in ED workload rate was similar on days without ambulance diversion. Differences in ED workload rate varied among sites, ranging from 1.0 to 6.0 patient LOS hours per ED bed. ED workload rate was higher on average on diversion days compared to nondiversion days. The mean difference between diversion and nondiversion was statistically significant for the majority of sites. CONCLUSIONS:There was marked variation in ED workload rates and whether or not ambulance diversion occurred during a 24-hour period. This variability in initiating ambulance diversion suggests different or inconsistently applied decision-making criteria for initiating diversion.

The participants of the Electronic Collaboration working group of the 2010 Academic Emergency Medicine consensus conference developed recommendations and research questions for improving regional quality of care through the use of electronic collaboration. A writing group devised a working draft prior to the meeting and presented this to the breakout session at the consensus conference for input and approval. The recommendations include: 1) patient health information should be available electronically across the entire health care delivery system from the 9-1-1 call to the emergency department (ED) visit through hospitalization and outpatient care, 2) relevant patient health information should be shared electronically across the entire health care delivery system, 3) Web-based collaborative technologies should be employed to facilitate patient transfer and timely access to specialists, 4) personal health record adoption should be considered as a way to improve patient health, and 5) any comprehensive reform of regionalization in emergency care must include telemedicine. The workgroup emphasized the need for funding increases so that research in this new and exciting area can expand.

OBJECTIVES/OBJECTIVE:The objectives were to measure the financial impact of implementing a fully integrated emergency department information system (EDIS) and determine the length of time to "break even" on the initial investment. METHODS:A before-and-after study design was performed using a framework of analysis consisting of four 15-month phases: 1) preimplementation, 2) peri-implementation, 3) postimplementation, and 4) sustained effects. Registration and financial data were reviewed. Costs and rates of professional and facility charges and receipts were calculated for the phases in question and compared against monthly averages for covariates such as volume, collections rates, acuity, age, admission rate, and insurance status with an autoregressive time series analysis using a segmented model. The break-even point was calculated by measuring cumulative monthly receipts for the last three study phases in excess of the average monthly receipts from the preimplementation phase, corrected for change in volume, and then plotting this against cumulative overall cost. RESULTS:Time to break even on the initial EDIS investment was less than 8 months. Total revenue enhancement at the end of the 5-year study period was $16,138,953 with an increase of 69.40% in charges and 70.06% in receipts. This corresponds to an increase in receipts per patient from $50 to $90 for professional services and $131 to $183 for facilities charges. Other than volume, there were no significant changes in trends for covariates between the preimplementation and sustained-effects periods. CONCLUSIONS:A comprehensive EDIS implementation with process redesign resulted in sustained increases in professional and facility revenues and a rapid initial break-even point. .