Effects of type and level of training on variation in physician knowledge in the use and acquisition of blood cultures: a cross sectional survey

BACKGROUND: Blood culture (BCX) use is often sub-optimal, and is a user-dependent diagnostic test. Little is known about physician training and BCX-related knowledge. We sought to assess variations in caregiver BCX-related knowledge, and their relation to medical training. METHODS: We developed and piloted a self-administered BCX-related knowledge survey instrument. Expert opinion, literature review, focus groups, and mini-pilots reduced > 100 questions in multiple formats to a final questionnaire with 15 scored content items and 4 covariate identifiers. This questionnaire was used in a cross-sectional survey of physicians, fellows, residents and medical students at a large urban public teaching hospital. The responses were stratified by years/level of training, type of specialty training, self-reported practical and theoretical BCX-related instruction. Summary scores were derived from participant responses compared to a 95% consensus opinion of infectious diseases specialists that matched an evidence based reference standard. RESULTS: There were 291 respondents (Attendings = 72, Post-Graduate Year (PGY) = 3 = 84, PGY2 = 42, PGY1 = 41, medical students = 52). Mean scores differed by training level (Attending = 85.0, PGY3 = 81.1, PGY2 = 78.4, PGY1 = 75.4, students = 67.7) [p ≤ 0.001], and training type (Infectious Diseases = 96.1, Medicine = 81.7, Emergency Medicine = 79.6, Surgery = 78.5, Family Practice = 76.5, Obstetrics-Gynecology = 74.4, Pediatrics = 74.0) [p ≤ 0.001]. Higher summary scores were associated with self-reported theoretical [p ≤ 0.001] and practical [p = 0.001] BCX-related training. Linear regression showed level and type of training accounted for most of the score variation. CONCLUSION: Higher mean scores were associated with advancing level of training and greater subject-related training. Notably, house staff and medical students, who are most likely to order and/or obtain BCXs, lack key BCX-related knowledge. Targeted education may improve utilization of this important diagnostic tool

Predicting Need for Hospitalization of Patients with Pandemic (H1N1) 2009, Chicago, Illinois, USA

In the absence of established guidelines for hospitalization of patients with pandemic (H1N1) 2009, we studied emergency department patients to identify clinical parameters that predict need for hospitalization. Independent predictors of hospitalization include multiple high-risk medical conditions, dyspnea, and hypoxia. These findings are easily applicable, with a 79% positive predictive value for hospitalization

Evolution of Vaccinia Virus-Specific CD8(+) Cytotoxic T-Lymphocyte Responses in Primary Vaccinees and Revaccinees

Determination of successful vaccination with vaccinia virus is based on visual confirmation of a dermal response (take). Some revaccinees do not manifest a take, which may be due to a preexisting immunity rather than to poor technique or inadequate virus. Cytotoxic T-lymphocyte (CTL) response appears to be the most important immune defense in limiting response to vaccination. We evaluated vaccinia virus-specific CTL responses in revaccinees. Subjects with and without takes displayed comparable CTL responses. Vaccinia virus-specific CD8(+) CTL responses may be useful in interpreting the response to vaccination, particularly in individuals who are revaccinated and have difficult-to-interpret visual takes

A Syndrome Definition Validation Approach for Zika Virus

ObjectiveTo develop and validate a Zika virus disease syndrome definitionwithin the GUARDIAN (Geographic Utilization of ArtificialIntelligence in Real-Time for Disease Identification and AlertNotification) surveillance system.IntroductionIn 2016, the World Health Organization declared Zika virus aglobal public health emergency. Zika infection during pregnancycan cause microcephaly and other fetal brain defects. To facilitateclinicians’ ability to detect Zika, various syndrome definitions havebeen developed.MethodsTo create and validate a detailed syndrome definition for Zika,we utilized the literature based methodology developed anddocumented by GUARDIAN researchers.1,2The syndrome definitionutilized clinical signs and symptoms that were documented inhistorical Zika cases.A testing sample of 1000 randomly selected emergency departmentcases (i.e., true negative cases) and 200 synthetically generated cases(i.e., true positive cases) was created. These 1,200 sample cases wereevaluated by the GUARDIAN surveillance system to determine theprobability of matching the Zika syndrome definition. A probabilityof≥90% was utilized to designate positive Zika cases.We identified the main signs and symptoms contributing to theidentification of Zika cases and conducted statistical performancemetrics. Clinical review of the false positive and false negative casesalong with a sample of true positive and true negative cases wasconducted by a board certified emergency physician.ResultsThe Zika syndrome definition was developed with eleven articles(six used for developing the syndrome definition, and five used fortesting the definition). The sample size for these articles was between1 and 72 positive Zika cases, with a total of 139 cases across the11 articles. The article with the most number of Zika cases wasbased on pregnant women with rash. The publication timeframefor the articles was from 1962 to 2016. Some of the main signsand symptoms from the historical cases that contribute to the Zikasyndrome definition are presented in Table 1. The initial results forthe sample testing data showed accuracy, sensitivity, and specificitywere 94.7%, 93%, and 95% respectively. There were a total of14 false negative and 50 false positive cases.ConclusionsThe initial Zika syndrome definition utilized by the GUARDIANsurveillance system contains similar signs and symptoms to thecurrent CDC case definition, but also includes additional signs andsymptoms such as pruritus/itching, malaise/fatigue/generalizedweakness, headache, retro-orbital pain, myalgia/muscle pain, andlymphadenopathy In addition, the GUARDIAN system provides therelative importance of identified signs and symptoms and allows forproactive surveillance of emergency department patients in real-time.Though we did not include epidemiologic risk factors, such as travel toan infected region or contact with an infected person in the syndromedefinition, GUARDIAN has above 90% sensitivity and specificity.Thus, inclusion of epidemiologic risk factors would further enhancethe early detection of Zika, when used with the appropriate high riskpopulation.Table 1. Main signs and symptoms of Zika syndrome definition*Signs and symptoms included in the Centers for Disease Control andPrevention (CDC)’s Zika clinical case definitio

Utility of Natural Language Processing for Clinical Quality Measures Reporting

ObjectiveTo explain the utility of using an automated syndromic surveillanceprogram with advanced natural language processing (NLP) to improveclinical quality measures reporting for influenza immunization.IntroductionClinical quality measures (CQMs) are tools that help measure andtrack the quality of health care services. Measuring and reportingCQMs helps to ensure that our health care system is deliveringeffective, safe, efficient, patient-centered, equitable, and timely care.The CQM for influenza immunization measures the percentage ofpatients aged 6 months and older seen for a visit between October1 and March 31 who received (or reports previous receipt of) aninfluenza immunization. Centers for Disease Control and Preventionrecommends that everyone 6 months of age and older receive aninfluenza immunization every season, which can reduce influenza-related morbidity and mortality and hospitalizations.MethodsPatients at a large academic medical center who had a visit toan affiliated outpatient clinic during June 1 - 8, 2016 were initiallyidentified using their electronic medical record (EMR). The 2,543patients who were selected did not have documentation of influenzaimmunization in a discrete field of the EMR. All free text notes forthese patients between August 1, 2015 and March 31, 2016 wereretrieved and analyzed using the sophisticated NLP built withinGeographic Utilization of Artificial Intelligence in Real-Timefor Disease Identification and Alert Notification (GUARDIAN)– a syndromic surveillance program – to identify any mention ofinfluenza immunization. The goal was to identify additional cases thatmet the CQM measure for influenza immunization and to distinguishdocumented exceptions. The patients with influenza immunizationmentioned were further categorized by GUARDIAN NLP intoReceived, Recommended, Refused, Allergic, and Unavailable.If more than one category was applicable for a patient, they wereindependently counted in their respective categories. A descriptiveanalysis was conducted, along with manual review of a sample ofcases per each category.ResultsFor the 2,543 patients who did not have influenza immunizationdocumentation in a discrete field of the EMR, a total of 78,642 freetext notes were processed using GUARDIAN. Four hundred fiftythree (17.8%) patients had some mention of influenza immunizationwithin the notes, which could potentially be utilized to meet the CQMinfluenza immunization requirement. Twenty two percent (n=101)of patients mentioned already having received the immunizationwhile 34.7% (n=157) patients refused it during the study time frame.There were 27 patients with the mention of influenza immunization,who could not be differentiated into a specific category. The numberof patients placed into a single category of influenza immunizationwas 351 (77.5%), while 75 (16.6%) were classified into more thanone category. See Table 1.ConclusionsUsing GUARDIAN’s NLP can identify additional patients whomay meet the CQM measure for influenza immunization or whomay be exempt. This tool can be used to improve CQM reportingand improve overall influenza immunization coverage by using it toalert providers. Next steps involve further refinement of influenzaimmunization categories, automating the process of using the NLPto identify and report additional cases, as well as using the NLP forother CQMs.Table 1. Categorization of influenza immunization documentation within freetext notes of 453 patients using NL