Marilyn M. Hallock
Rush University Medical Center
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Featured researches published by Marilyn M. Hallock.
Artificial Intelligence in Medicine | 2013
Julio C. Silva; Shital Shah; Dino P. Rumoro; Jamil D. Bayram; Marilyn M. Hallock; Gillian S. Gibbs; Michael J. Waddell
BACKGROUND A highly sensitive real-time syndrome surveillance system is critical to detect, monitor, and control infectious disease outbreaks, such as influenza. Direct comparisons of diagnostic accuracy of various surveillance systems are scarce. OBJECTIVE To statistically compare sensitivity and specificity of multiple proprietary and open source syndrome surveillance systems to detect influenza-like illness (ILI). METHODS A retrospective, cross-sectional study was conducted utilizing data from 1122 patients seen during November 1–7, 2009 in the emergency department of a single urban academic medical center. The study compared the Geographic Utilization of Artificial Intelligence in Real-time for Disease Identification and Alert Notification (GUARDIAN) system to the Complaint Coder (CoCo) of the Real-time Outbreak Detection System (RODS), the Symptom Coder (SyCo) of RODS, and to a standardized report generated via a proprietary electronic medical record (EMR) system. Sensitivity, specificity, and accuracy of each classifiers ability to identify ILI cases were calculated and compared to a manual review by a board-certified emergency physician. Chi-square and McNemars tests were used to evaluate the statistical difference between the various surveillance systems.ResultsThe performance of GUARDIAN in detecting ILI in terms of sensitivity, specificity, and accuracy, as compared to a physician chart review, was 95.5%, 97.6%, and 97.1%, respectively. The EMR-generated reports were the next best system at identifying disease activity with a sensitivity, specificity, and accuracy of 36.7%, 99.3%, and 83.2%, respectively. RODS (CoCo and SyCo) had similar sensitivity (35.3%) but slightly different specificity (CoCo = 98.9%; SyCo = 99.3%). The GUARDIAN surveillance system with its multiple data sources performed significantly better compared to CoCo (χ2 = 130.6, p < 0.05), SyCo (χ2 = 125.2, p < 0.05), and EMR-based reports (χ2 = 121.3, p < 0.05). In addition, similar significant improvements in the accuracy (>12%) and sensitivity (>47%) were observed for GUARDIAN with only chief complaint data as compared to RODS (CoCo and SyCo) and EMR-based reports. CONCLUSION In our study population, the GUARDIAN surveillance system, with its ability to utilize multiple data sources from patient encounters and real-time automaticity, demonstrated a more robust performance when compared to standard EMR-based reports and the RODS systems in detecting ILI. More large-scale studies are needed to validate the study findings, and to compare the performance of GUARDIAN in detecting other infectious diseases.
Annals of Emergency Medicine | 2018
Michael Gottlieb; Joshua DeMott; Marilyn M. Hallock; Gary D. Peksa
Study objective The addition of antibiotics to standard incision and drainage is controversial, with earlier studies demonstrating no significant benefit. However, 2 large, multicenter trials have recently been published that have challenged the previous literature. The goal of this review was to determine whether systemic antibiotics for abscesses after incision and drainage improve cure rates. Methods PubMed, the Cumulative Index of Nursing and Allied Health Literature, Scopus, the Cochrane Database of Systematic Reviews, the Cochrane Central Register of Controlled Trials, and bibliographies of selected articles were assessed for all randomized controlled trials comparing adjuvant antibiotics with placebo in the treatment of drained abscesses, with an outcome of treatment failure assessed within 21 days. Data were dual extracted into a predefined worksheet and quality analysis was performed with the Cochrane Risk of Bias tool. Results Four studies (n=2,406 participants) were identified. There were 89 treatment failures (7.7%) in the antibiotic group and 150 (16.1%) in the placebo group. The calculated risk difference was 7.4% (95% confidence interval [CI] 2.8% to 12.1%), with an odds ratio for clinical cure of 2.32 (95% CI 1.75 to 3.08) in favor of the antibiotic group. There was also a decreased incidence of new lesions in the antibiotic group (risk difference –10.0%, 95% CI –12.8% to –7.2%; odds ratio 0.32, 95% CI 0.23 to 0.44), with a minimally increased risk of minor adverse events (risk difference 4.4%, 95% CI 1.0% to 7.8%; odds ratio 1.29, 95% CI 1.06 to 1.58). Conclusion The use of systemic antibiotics for skin and soft tissue abscesses after incision and drainage resulted in an increased rate of clinical cure. Providers should consider the use of antibiotics while balancing the risk of adverse events.
Online Journal of Public Health Informatics | 2015
Shital Shah; Dino P. Rumoro; Gordon M. Trenholme; Gillian S. Gibbs; Marilyn M. Hallock; Michael J. Waddell
Description of a statistical model to account for weather variation in influenza-like illness surveillance.
Infection Control and Hospital Epidemiology | 2015
Shital Shah; Dino P. Rumoro; Marilyn M. Hallock; Gordon M. Trenholme; Gillian S. Gibbs; Julio C. Silva; Michael J. Waddell
Emerging Health Threats Journal | 2011
Julio C. Silva; Dino P. Rumoro; Marilyn M. Hallock; Shital Shah; Gillian S. Gibbs; Michael J. Waddell
Online Journal of Public Health Informatics | 2013
Julio C. Silva; Shital Shah; Dino P. Rumoro; Marilyn M. Hallock; Gillian S. Gibbs; Michael J. Waddell
American journal of disaster medicine | 2012
Dino P. Rumoro; Jamil D. Bayram; Julio C. Silva; Shital Shah; Marilyn M. Hallock; Gillian S. Gibbs; Michael J. Waddell
Emerging Health Threats Journal | 2011
Dino P. Rumoro; Shital Shah; Julio C. Silva; Marilyn M. Hallock; Gillian S. Gibbs; Michael J. Waddell
Emerging Health Threats Journal | 2011
Dino P. Rumoro; Julio C. Silva; Marilyn M. Hallock; Shital Shah; Gillian S. Gibbs; Michael J. Waddell
Online Journal of Public Health Informatics | 2017
Dino P. Rumoro; Shital Shah; Gillian S. Gibbs; Marilyn M. Hallock; Gordon M. Trenholme; Michael J. Waddell