Association Between Procalcitonin and Antibiotics in Children With Community-Acquired Pneumonia.

OBJECTIVE: To determine whether empirical antibiotic initiation and selection for children with pneumonia was associated with procalcitonin (PCT) levels when results were blinded to clinicians. METHODS: We enrolled children <18 years with radiographically confirmed pneumonia at 2 children's hospitals from 2014 to 2019. Blood for PCT was collected at enrollment (blinded to clinicians). We modeled associations between PCT and (1) antibiotic initiation and (2) antibiotic selection (narrow versus broad-spectrum) using multivariable logistic regression models. To quantify potential stewardship opportunities, we calculated proportions of noncritically ill children receiving antibiotics who also had a low likelihood of bacterial etiology (PCT <0.25 ng/mL) and those receiving broad-spectrum therapy, regardless of PCT level. RESULTS: We enrolled 488 children (median PCT, 0.37 ng/mL; interquartile range [IQR], 0.11-2.38); 85 (17%) received no antibiotics (median PCT, 0.32; IQR, 0.09-1.33). Among the 403 children receiving antibiotics, 95 (24%) received narrow-spectrum therapy (median PCT, 0.24; IQR, 0.08-2.52) and 308 (76%) received broad-spectrum (median PCT, 0.46; IQR, 0.12-2.83). In adjusted analyses, PCT values were not associated with antibiotic initiation (odds ratio [OR], 1.02, 95% confidence interval [CI], 0.97%-1.06%) or empirical antibiotic selection (OR 1.07; 95% CI, 0.97%-1.17%). Of those with noncritical illness, 246 (69%) were identified as potential targets for antibiotic stewardship interventions. CONCLUSION: Neither antibiotic initiation nor empirical antibiotic selection were associated with PCT values. Whereas other factors may inform antibiotic treatment decisions, the observed discordance between objective likelihood of bacterial etiology and antibiotic use suggests important opportunities for stewardship.

A Bayesian approach for detecting a disease that is not being modeled.

Over the past decade, outbreaks of new or reemergent viruses such as severe acute respiratory syndrome (SARS) virus, Middle East respiratory syndrome (MERS) virus, and Zika have claimed thousands of lives and cost governments and healthcare systems billions of dollars. Because the appearance of new or transformed diseases is likely to continue, the detection and characterization of emergent diseases is an important problem. We describe a Bayesian statistical model that can detect and characterize previously unknown and unmodeled diseases from patient-care reports and evaluate its performance on historical data.

The design and evaluation of a Bayesian system for detecting and characterizing outbreaks of influenza.

The prediction and characterization of outbreaks of infectious diseases such as influenza remains an open and important problem. This paper describes a framework for detecting and characterizing outbreaks of influenza and the results of testing it on data from ten outbreaks collected from two locations over five years. We model outbreaks with compartment models and explicitly model non-influenza influenza-like illnesses.

Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology.

BACKGROUND: Health care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy. OBJECTIVE: The aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems. METHODS: We describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States. RESULTS: The majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present. CONCLUSIONS: Syndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks.

Short-Term Elevation of Fine Particulate Matter Air Pollution and Acute Lower Respiratory Infection.

RATIONALE: Nearly 60% of U.S. children live in counties with particulate matter less than or equal to 2.5 µm in aerodynamic diameter (PM2.5) concentrations above air quality standards. Understanding the relationship between ambient air pollution exposure and health outcomes informs actions to reduce exposure and disease risk. OBJECTIVES: To evaluate the association between ambient PM2.5 levels and healthcare encounters for acute lower respiratory infection (ALRI). METHODS: Using an observational case-crossover design, subjects (n = 146,397) were studied if they had an ALRI diagnosis and resided on Utah's Wasatch Front. PM2.5 air pollution concentrations were measured using community-based air quality monitors between 1999 and 2016. Odds ratios for ALRI healthcare encounters were calculated after stratification by ages 0-2, 3-17, and 18 or more years. MEASUREMENTS AND MAIN RESULTS: Approximately 77% (n = 112,467) of subjects were 0-2 years of age. The odds of ALRI encounter for these young children increased within 1 week of elevated PM2.5 and peaked after 3 weeks with a cumulative 28-day odds ratio of 1.15 per +10 µg/m3 (95% confidence interval, 1.12-1.19). ALRI encounters with diagnosed and laboratory-confirmed respiratory syncytial virus and influenza increased following elevated ambient PM2.5 levels. Similar elevated odds for ALRI were also observed for older children, although the number of events and precision of estimates were much lower. CONCLUSIONS: In this large sample of urban/suburban patients, short-term exposure to elevated PM2.5 air pollution was associated with greater healthcare use for ALRI in young children, older children, and adults. Further exploration is needed of causal interactions between PM2.5 and ALRI.

A Bayesian system to detect and characterize overlapping outbreaks.

Outbreaks of infectious diseases such as influenza are a significant threat to human health. Because there are different strains of influenza which can cause independent outbreaks, and influenza can affect demographic groups at different rates and times, there is a need to recognize and characterize multiple outbreaks of influenza. This paper describes a Bayesian system that uses data from emergency department patient care reports to create epidemiological models of overlapping outbreaks of influenza. Clinical findings are extracted from patient care reports using natural language processing. These findings are analyzed by a case detection system to create disease likelihoods that are passed to a multiple outbreak detection system. We evaluated the system using real and simulated outbreaks. The results show that this approach can recognize and characterize overlapping outbreaks of influenza. We describe several extensions that appear promising.

The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance.

OBJECTIVES: This study evaluates the accuracy and portability of a natural language processing (NLP) tool for extracting clinical findings of influenza from clinical notes across two large healthcare systems. Effectiveness is evaluated on how well NLP supports downstream influenza case-detection for disease surveillance. METHODS: We independently developed two NLP parsers, one at Intermountain Healthcare (IH) in Utah and the other at University of Pittsburgh Medical Center (UPMC) using local clinical notes from emergency department (ED) encounters of influenza. We measured NLP parser performance for the presence and absence of 70 clinical findings indicative of influenza. We then developed Bayesian network models from NLP processed reports and tested their ability to discriminate among cases of (1) influenza, (2) non-influenza influenza-like illness (NI-ILI), and (3) 'other' diagnosis. RESULTS: On Intermountain Healthcare reports, recall and precision of the IH NLP parser were 0.71 and 0.75, respectively, and UPMC NLP parser, 0.67 and 0.79. On University of Pittsburgh Medical Center reports, recall and precision of the UPMC NLP parser were 0.73 and 0.80, respectively, and IH NLP parser, 0.53 and 0.80. Bayesian case-detection performance measured by AUROC for influenza versus non-influenza on Intermountain Healthcare cases was 0.93 (using IH NLP parser) and 0.93 (using UPMC NLP parser). Case-detection on University of Pittsburgh Medical Center cases was 0.95 (using UPMC NLP parser) and 0.83 (using IH NLP parser). For influenza versus NI-ILI on Intermountain Healthcare cases performance was 0.70 (using IH NLP parser) and 0.76 (using UPMC NLP parser). On University of Pisstburgh Medical Center cases, 0.76 (using UPMC NLP parser) and 0.65 (using IH NLP parser). CONCLUSION: In all but one instance (influenza versus NI-ILI using IH cases), local parsers were more effective at supporting case-detection although performances of non-local parsers were reasonable.

A study of the transferability of influenza case detection systems between two large healthcare systems.

OBJECTIVES: This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases. METHODS: A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients' diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH). At each site, we manually built a rule-based NLP and trained a Bayesain network classifier from over 40,000 ED encounters between Jan. 2008 and May. 2010 using feature selection, machine learning, and expert debiasing approach. Transferability of a BCD in this study may be impacted by seven factors: development (source) institution, development parser, application (target) institution, application parser, NLP transfer, BN transfer, and classification task. We employed an ANOVA analysis to study their impacts on BCD performance. RESULTS: Both BCDs discriminated well between influenza and non-influenza on local test cases (AUCs > 0.92). When tested for transferability using the other institution's cases, BCDUPMC discriminations declined minimally (AUC decreased from 0.95 to 0.94, p<0.01), and BCDIH discriminations declined more (from 0.93 to 0.87, p<0.0001). We attributed the BCDIH decline to the lower recall of the IH parser on UPMC notes. The ANOVA analysis showed five significant factors: development parser, application institution, application parser, BN transfer, and classification task. CONCLUSION: We demonstrated high influenza case detection performance in two large healthcare systems in two geographically separated regions, providing evidentiary support for the use of automated case detection from routinely collected electronic clinical notes in national influenza surveillance. The transferability could be improved by training Bayesian network classifier locally and increasing the accuracy of the NLP parser.

Cross-immunity between strains explains the dynamical pattern of paramyxoviruses.

Viral respiratory tract diseases pose serious public health problems. Our ability to predict and thus, be able to prepare for outbreaks is strained by the complex factors driving the prevalence and severity of these diseases. The abundance of diseases and transmission dynamics of strains are not only affected by external factors, such as weather, but also driven by interactions among viruses mediated by human behavior and immunity. To untangle the complex out-of-phase annual and biennial pattern of three common paramyxoviruses, Respiratory Syncytial Virus (RSV), Human Parainfluenza Virus (HPIV), and Human Metapneumovirus (hMPV), we adopt a theoretical approach that integrates ecological and immunological mechanisms of disease interactions. By estimating parameters from multiyear time series of laboratory-confirmed cases from the intermountain west region of the United States and using statistical inference, we show that models of immune-mediated interactions better explain the data than those based on ecological competition by convalescence. The strength of cross-protective immunity among viruses is correlated with their genetic distance in the phylogenetic tree of the paramyxovirus family.

Predicting asthma control deterioration in children.

BACKGROUND: Pediatric asthma affects 7.1 million American children incurring an annual total direct healthcare cost around 9.3 billion dollars. Asthma control in children is suboptimal, leading to frequent asthma exacerbations, excess costs, and decreased quality of life. Successful prediction of risk for asthma control deterioration at the individual patient level would enhance self-management and enable early interventions to reduce asthma exacerbations. We developed and tested the first set of models for predicting a child's asthma control deterioration one week prior to occurrence. METHODS: We previously reported validation of the Asthma Symptom Tracker, a weekly asthma self-monitoring tool. Over a period of two years, we used this tool to collect a total of 2912 weekly assessments of asthma control on 210 children. We combined the asthma control data set with patient attributes and environmental variables to develop machine learning models to predict a child's asthma control deterioration one week ahead. RESULTS: Our best model achieved an accuracy of 71.8 %, a sensitivity of 73.8 %, a specificity of 71.4 %, and an area under the receiver operating characteristic curve of 0.757. We also identified potential improvements to our models to stimulate future research on this topic. CONCLUSIONS: Our best model successfully predicted a child's asthma control level one week ahead. With adequate accuracy, the model could be integrated into electronic asthma self-monitoring systems to provide real-time decision support and personalized early warnings of potential asthma control deteriorations.

A systematic review of predictive modeling for bronchiolitis.

PURPOSE: Bronchiolitis is the most common cause of illness leading to hospitalization in young children. At present, many bronchiolitis management decisions are made subjectively, leading to significant practice variation among hospitals and physicians caring for children with bronchiolitis. To standardize care for bronchiolitis, researchers have proposed various models to predict the disease course to help determine a proper management plan. This paper reviews the existing state of the art of predictive modeling for bronchiolitis. Predictive modeling for respiratory syncytial virus (RSV) infection is covered whenever appropriate, as RSV accounts for about 70% of bronchiolitis cases. METHODS: A systematic review was conducted through a PubMed search up to April 25, 2014. The literature on predictive modeling for bronchiolitis was retrieved using a comprehensive search query, which was developed through an iterative process. Search results were limited to human subjects, the English language, and children (birth to 18 years). RESULTS: The literature search returned 2312 references in total. After manual review, 168 of these references were determined to be relevant and are discussed in this paper. We identify several limitations and open problems in predictive modeling for bronchiolitis, and provide some preliminary thoughts on how to address them, with the hope to stimulate future research in this domain. CONCLUSIONS: Many problems remain open in predictive modeling for bronchiolitis. Future studies will need to address them to achieve optimal predictive models.

Predicting the start week of respiratory syncytial virus outbreaks using real time weather variables.

BACKGROUND: Respiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons. Reliable prediction of the week these outbreaks will start, based on readily available data, could help pediatric hospitals better prepare for large outbreaks. METHODS: Naïve Bayes (NB) classifier models were constructed using weather data from 1985-2008 considering only variables that are available in real time and that could be used to forecast the week in which an RSV outbreak will occur in Salt Lake County, Utah. Outbreak start dates were determined by a panel of experts using 32,509 records with ICD-9 coded RSV and bronchiolitis diagnoses from Intermountain Healthcare hospitals and clinics for the RSV seasons from 1985 to 2008. RESULTS: NB models predicted RSV outbreaks up to 3 weeks in advance with an estimated sensitivity of up to 67% and estimated specificities as high as 94% to 100%. Temperature and wind speed were the best overall predictors, but other weather variables also showed relevance depending on how far in advance the predictions were made. The weather conditions predictive of an RSV outbreak in our study were similar to those that lead to temperature inversions in the Salt Lake Valley. CONCLUSIONS: We demonstrate that Naïve Bayes (NB) classifier models based on weather data available in real time have the potential to be used as effective predictive models. These models may be able to predict the week that an RSV outbreak will occur with clinical relevance. Their clinical usefulness will be field tested during the next five years.

Feasibility of elementary school children's use of hand gel and facemasks during influenza season.

BACKGROUND: The feasibility of non-pharmacologic interventions to prevent influenza's spread in schools is not well known. OBJECTIVES: To determine the acceptability of, adherence with, and barriers to the use of hand gel and facemasks in elementary schools. INTERVENTION: We provided hand gel and facemasks to 20 teachers and their students over 4 weeks. Gel use was promoted for the first 2 weeks; mask use was promoted for the second 2 weeks. OUTCOMES: Acceptability, adherence, and barriers were measured by teachers' responses on weekly surveys. Mask use was also measured by observation. RESULTS: The weekly survey response rate ranged from 70% to 100%. Averaged over 2 weeks, 89% of teachers thought gel use was not disruptive (week 1--17/20, week 2--16/17), 95% would use gel next winter (week 1--19/20, week 2--16/17), and 97% would use gel in a pandemic (week 1--20/20, week 2--16/17). Averaged over 2 weeks, 39% thought mask use was not disruptive (week 1--6/17, week 2--6/14), 35% would use masks next winter (week 1--5/17, week 2--6/14), and 97% would use masks in a pandemic (week 1--16/17, week 2--14/14). About 70% estimated that their students used hand gel ≥ 4 x/day for both weeks (week 1--14/20, week 2--13/17). Students' mask use declined over time with 59% of teachers (10/17) estimating regular mask use during week 1 and 29% (4/14) during week 2. By observation, 30% of students wore masks in week 1, while 15% wore masks in week 2. Few barriers to gel use were identified; barriers to mask use were difficulty reading facial expressions and physical discomfort. CONCLUSIONS: Hand gel use is a feasible strategy in elementary schools. Acceptability and adherence with facemasks was low, but some students and teachers did use facemasks for 2 weeks, and most teachers would use masks in their classroom in a pandemic.

Rotavirus gastroenteritis and seizures in young children.

In this retrospective cohort study, a clinical and administrative database of children hospitalized at Primary Children's Medical Center, Salt Lake City, Utah, between January 1, 2002, and December 31, 2006, was used to identify those with laboratory-confirmed rotavirus infections and at least one seizure. In all, 59 children were identified, 34 of whom (58%) had no other potential medical explanation for their seizures. Of these 34 children, 23 (68%) were afebrile at seizure onset and 11 were febrile. Electroencephalography was performed for 21 of the 34 children (62%); all findings were normal, except for a child with slowing related to cerebral edema. Twenty-six of the 34 children (76%) had neuroimaging studies; all findings were normal, except for the child with cerebral edema and a child with an incidental arachnoid cyst. Twenty of the 34 children (59%) had a lumbar puncture; again, all findings were normal. All 34 children recovered uneventfully, including the 6 children who spent at least 1 day in an intensive care unit. Follow-up data on 27 of these children identified 2 children (7%) who required chronic anticonvulsant therapy. The results indicate that seizures associated with rotavirus infection are a relatively benign neurologic condition in young children. With few exceptions, neurodiagnostic studies do not influence management or outcome.

Urgent care providers' knowledge and attitude about public health reporting and pertussis control measures: implications for informatics.

OBJECTIVES: We assessed urgent care providers' knowledge about public health reporting, guidelines, and actions for the prevention and control of pertussis; attitudes about public health reporting and population-based data; and perception of reporting practices in their clinic. METHODS: We identified the 106 providers (95% are physicians) employed in 28 urgent care clinics owned by Intermountain Healthcare located throughout Utah and Southern Idaho. We performed a descriptive, cross-sectional survey and assessed providers' knowledge, attitudes, beliefs, and behaviors associated with population-based data and public health mandates and recommendations. The online survey was completed between November 1, 2007, and February 29, 2008. RESULTS: Among 63 practicing urgent care providers (60% response rate), 19 percent knew that clinically diagnosed pertussis was reportable, and only half (52%) the providers correctly responded about current pertussis vaccination recommendations. Most (35%-78%) providers did not know the prevention and control measures performed by public health practitioners after reporting occurs, including contact tracing, testing, treatment, and prophylaxis. Half (48%) the providers did not know that health department personnel can prescribe antibiotics for contacts of a reported case, and only 22 percent knew that health department personnel may perform diagnostic testing on contacts. Attitudes about reporting are variable, and reporting responsibility is diffused. CONCLUSION: To improve our ability to meet public health goals, systems need to be designed that engage urgent care providers in the public health process, improve their knowledge and attitude about reporting, and facilitate the flow of information between urgent care and public health settings.

Should the pertussis case definition for public health reporting be refined?

BACKGROUND: The surveillance case definition for confirmed pertussis requires that an individual with a positive polymerase chain reaction (PCR) result for Bordetella pertussis have 2 weeks or more of cough and at least one of the following: paroxysmal coughing, inspiratory "whoop," or posttussive vomiting. OBJECTIVES: Determine (1) proportion of individuals with a positive PCR result who met additional criteria for surveillance confirmed pertussis, (2) whether the likelihood of PCR-positive individuals meeting additional elements of surveillance case definition varied by age or vaccination status, and (3) whether elements of the current case definition influence the likelihood of pertussis confirmation in PCR-positive individuals. METHODS: Pertussis PCR results were compared with case investigation data. RESULTS: Eighty-eight percent (165/188) of PCR-positive individuals met requirements for confirmed pertussis. Sixty-one percent (14/23) of PCR-positive individuals who had less than 2 weeks but more than 1 week of cough had at least one other reported sign or symptom. Fourteen (100%) reported paroxysmal coughing, 7 (50%) "whoop," and 7 (50%) posttussive vomiting. Infants who met case definition were more likely to have reported apnea than were older individuals (15/17 vs 45/86, OR = 6.8, 95% CI = 1.4-64.2). CONCLUSIONS: Decreasing cough duration from 2 weeks or more to more than 1 week would result in 95 percent of those with positive PCR results meeting confirmation criteria for pertussis. Apnea should be considered an additional sign for pertussis confirmation in infants.

Quality of care for children hospitalized with asthma.

OBJECTIVES: The goals were (1) to identify evidence-based clinical process measures that are appropriate, feasible, and reliable for assessing the quality of inpatient asthma care for children and (2) to evaluate provider compliance with these measures. METHODS: Key asthma quality measures were identified by using a modified Rand appropriateness method, combining a literature review of asthma care evidence with a consensus panel. The feasibility and reliability of obtaining these measures were determined through manual chart review. Provider compliance with these measures was evaluated through retrospective manual chart review of data for 252 children between 2 and 17 years of age who were admitted to a tertiary care children's hospital in 2005 because of asthma exacerbations. RESULTS: Nine appropriate, feasible, reliable, clinical process measures of inpatient asthma care were identified. Provider compliance with these measures was as follows: acute asthma severity assessment at admission, 39%; use of systemic corticosteroid therapy, 98%; use of oral (not intravenous) systemic corticosteroid therapy, 87%; use of ipratropium bromide restricted to <24 hours after admission, 71%; use of albuterol delivered with a metered-dose inhaler (not nebulizer) for children >5 years of age, 20%; documented chronic asthma severity assessment, 22%; parental participation in an asthma education class, 33%; written asthma action plan, 5%; scheduled follow-up appointment with the primary care provider at discharge, 22%. CONCLUSIONS: Nine appropriate, feasible, reliable, clinical process measures of inpatient asthma care were identified. Provider compliance across these measures was highly variable but generally low. Our study highlights opportunities for improvement in the provision of asthma care for hospitalized children. Future studies are needed to confirm these findings in other inpatient settings.

Clinician use and acceptance of population-based data about respiratory pathogens: implications for enhancing population-based clinical practice.

Front line health care providers (HCPs) play a central role in endemic (pertussis), epidemic (influenza) and pandemic (avian influenza) infectious disease outbreaks. Effective preparedness for this role requires access to and awareness of population-based data (PBD). We investigated the degree to which this is currently achieved among HCPs in Utah by surveying a sample about access, awareness and attitudes concerning PBD in clinical practice. We found variability in the number and nature (national vs. local, pushed vs. pulled) of PBD sources accessed by HCPs, with a subset using multiple sources and using them frequently. We found that HCPs believe PBD improves their clinical performance and that they cannot rely on their own practice to remain informed. These findings suggest that an integrated system, which interprets PBD from multiple sources and optimizes the delivery of PBD may facilitate preparedness of HCPs through the application of PBD in routine clinical practice.

Forecasting hospital census at a tertiary care children's hospital.

Developing a forecasting tool for patient census allows for improved staffing, better resource utilization and mobilization, and improved timing of educational campaigns around the disease control process. Using a neural network approach we evaluated several different models and variables for predicting patient census prospectively. These initial studies enabled selection of a subset of predictor variables and show that different network models, and variables must be used based on the season.

Informing the front line about common respiratory viral epidemics.

The nature of clinical medicine is to focus on individuals rather than the populations from which they originate. This orientation can be problematic in the context of acute healthcare delivery during routine winter outbreaks of viral respiratory disease where an individuals likelihood of viral infection depends on knowledge of local disease incidence. The level of interest in and perceived utility of community and regional infection data for front line clinicians providing acute care is unclear. Based on input from clinicians, we developed an automated analysis and reporting system that delivers pathogen-specific epidemic curves derived from a viral panel that tests for influenza, RSV, adenovirus, parainfluenza and human metapneumovirus. Surveillance summaries were actively e-mailed to clinicians practicing in emergency, urgent and primary care settings and posted on a web site for passive consumption. We demonstrated the feasibility and sustainability of a system that provides both timely and clinically useful surveillance information.