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1.  Association Between Pediatric Clinical Trials and Global Burden of Disease 
Pediatrics  2014;133(1):78-87.
The allocation of research resources should favor conditions responsible for the greatest disease burden. This is particularly important in pediatric populations, which have been underrepresented in clinical research. Our aim was to measure the association between the focus of pediatric clinical trials and burden of disease and to identify neglected clinical domains.
We performed a cross-sectional study of clinical trials by using trial records in All trials started in 2006 or after and studying patient-level interventions in pediatric populations were included. Age-specific measures of disease burden were obtained for 21 separate conditions for high-, middle-, and low-income countries. We measured the correlation between number of pediatric clinical trials and disease burden for each condition.
Neuropsychiatric conditions and infectious diseases were the most studied conditions globally in terms of number of trials (874 and 847 trials, respectively), while intentional injuries (5 trials) and maternal conditions (4 trials) were the least studied. Clinical trials were only moderately correlated with global disease burden (r = 0.58, P = .006). Correlations were also moderate within each of the country income levels, but lowest in low-income countries (r = .47, P = .03). Globally, the conditions most understudied relative to disease burden were injuries (–260 trials for unintentional injuries and –160 trials for intentional injuries), nutritional deficiencies (–175 trials), and respiratory infections (–171 trials).
Pediatric clinical trial activity is only moderately associated with pediatric burden of disease, and least associated in low-income countries. The mismatch between clinical trials and disease burden identifies key clinical areas for focus and investment.
PMCID: PMC3876184  PMID: 24344112
clinical trials; burden of disease; pediatric research
2. as a Data Source for Semi-Automated Point-Of-Care Trial Eligibility Screening 
PLoS ONE  2014;9(10):e111055.
Implementing semi-automated processes to efficiently match patients to clinical trials at the point of care requires both detailed patient data and authoritative information about open studies.
To evaluate the utility of the registry as a data source for semi-automated trial eligibility screening.
Eligibility criteria and metadata for 437 trials open for recruitment in four different clinical domains were identified in Trials were evaluated for up to date recruitment status and eligibility criteria were evaluated for obstacles to automated interpretation. Finally, phone or email outreach to coordinators at a subset of the trials was made to assess the accuracy of contact details and recruitment status.
24% (104 of 437) of trials declaring on open recruitment status list a study completion date in the past, indicating out of date records. Substantial barriers to automated eligibility interpretation in free form text are present in 81% to up to 94% of all trials. We were unable to contact coordinators at 31% (45 of 146) of the trials in the subset, either by phone or by email. Only 53% (74 of 146) would confirm that they were still recruiting patients.
Because has entries on most US and many international trials, the registry could be repurposed as a comprehensive trial matching data source. Semi-automated point of care recruitment would be facilitated by matching the registry's eligibility criteria against clinical data from electronic health records. But the current entries fall short. Ultimately, improved techniques in natural language processing will facilitate semi-automated complex matching. As immediate next steps, we recommend augmenting data entry forms to capture key eligibility criteria in a simple, structured format.
PMCID: PMC4205089  PMID: 25334031
3.  A Bayesian dynamic model for influenza surveillance 
Statistics in medicine  2006;25(11):1803-1825.
The severe acute respiratory syndrome (SARS) epidemic, the growing fear of an influenza pandemic and the recent shortage of flu vaccine highlight the need for surveillance systems able to provide early, quantitative predictions of epidemic events. We use dynamic Bayesian networks to discover the interplay among four data sources that are monitored for influenza surveillance. By integrating these different data sources into a dynamic model, we identify in children and infants presenting to the pediatric emergency department with respiratory syndromes an early indicator of impending influenza morbidity and mortality. Our findings show the importance of modelling the complex dynamics of data collected for influenza surveillance, and suggest that dynamic Bayesian networks could be suitable modelling tools for developing epidemic surveillance systems.
PMCID: PMC4128871  PMID: 16645996
dynamic Bayesian networks; influenza surveillance; syndromic data
4.  Premarket Safety and Efficacy Studies for ADHD Medications in Children 
PLoS ONE  2014;9(7):e102249.
Attention-deficit hyperactivity disorder (ADHD) is a chronic condition and pharmacotherapy is the mainstay of treatment, with a variety of ADHD medications available to patients. However, it is unclear to what extent the long-term safety and efficacy of ADHD drugs have been evaluated prior to their market authorization. We aimed to quantify the number of participants studied and their length of exposure in ADHD drug trials prior to marketing.
We identified all ADHD medications approved by the Food and Drug Administration (FDA) and extracted data on clinical trials performed by the sponsor and used by the FDA to evaluate the drug’s clinical efficacy and safety. For each ADHD medication, we measured the total number of participants studied and the length of participant exposure and identified any FDA requests for post-marketing trials.
A total of 32 clinical trials were conducted for the approval of 20 ADHD drugs. The median number of participants studied per drug was 75 (IQR 0, 419). Eleven drugs (55%) were approved after <100 participants were studied and 14 (70%) after <300 participants. The median trial length prior to approval was 4 weeks (IQR 2, 9), with 5 (38%) drugs approved after participants were studied <4 weeks and 10 (77%) after <6 months. Six drugs were approved with requests for specific additional post-marketing trials, of which 2 were performed.
Clinical trials conducted for the approval of many ADHD drugs have not been designed to assess rare adverse events or long-term safety and efficacy. While post-marketing studies can fill in some of the gaps, better assurance is needed that the proper trials are conducted either before or after a new medication is approved.
PMCID: PMC4090185  PMID: 25007171
5.  Are Meaningful Use Stage 2 certified EHRs ready for interoperability? Findings from the SMART C-CDA Collaborative 
Background and objective
Upgrades to electronic health record (EHR) systems scheduled to be introduced in the USA in 2014 will advance document interoperability between care providers. Specifically, the second stage of the federal incentive program for EHR adoption, known as Meaningful Use, requires use of the Consolidated Clinical Document Architecture (C-CDA) for document exchange. In an effort to examine and improve C-CDA based exchange, the SMART (Substitutable Medical Applications and Reusable Technology) C-CDA Collaborative brought together a group of certified EHR and other health information technology vendors.
Materials and methods
We examined the machine-readable content of collected samples for semantic correctness and consistency. This included parsing with the open-source BlueButton.js tool, testing with a validator used in EHR certification, scoring with an automated open-source tool, and manual inspection. We also conducted group and individual review sessions with participating vendors to understand their interpretation of C-CDA specifications and requirements.
We contacted 107 health information technology organizations and collected 91 C-CDA sample documents from 21 distinct technologies. Manual and automated document inspection led to 615 observations of errors and data expression variation across represented technologies. Based upon our analysis and vendor discussions, we identified 11 specific areas that represent relevant barriers to the interoperability of C-CDA documents.
We identified errors and permissible heterogeneity in C-CDA documents that will limit semantic interoperability. Our findings also point to several practical opportunities to improve C-CDA document quality and exchange in the coming years.
PMCID: PMC4215060  PMID: 24970839
C-CDA; Meaningful Use; Interoperability; Data Exchange; EHR
6.  What an open source clinical trial community can learn from hackers 
Science translational medicine  2012;4(132):132cm5.
Open sharing of clinical trial data has been proposed as a way to address the gap between the production of clinical evidence and the decision-making of physicians. Since a similar gap has already been addressed in the software industry by the open source software movement, we examine how the social and technical principles of the movement can be used to guide the growth of an open source clinical trial community.
PMCID: PMC4059195  PMID: 22553248
7.  Analysis of Pediatric Clinical Drug Trials for Neuropsychiatric Conditions 
Pediatrics  2013;131(6):1125-1131.
Neuropsychiatric conditions represent a large and increasing disease burden in children. A number of drugs are available for the treatment of these conditions, but most drugs have not been adequately tested in children, and off-label drug use remains widespread. We sought to define and quantify recent and ongoing clinical research on the use of neuropsychiatric drugs in children.
Drug trials registered in between 2006 and 2011 and studying neuropsychiatric conditions were selected and classified based on the drug’s Food and Drug Administration (FDA) approval status in children. We measured the proportion of trials seeking to expand the use of a drug to pediatric patients and the proportion of available drugs studied in children.
Only 10% of neuropsychiatric trials focused on children. Of 303 drugs studied in both pediatric and adult populations, 90% lacked FDA approval in children and 97% were not approved in children for the indication studied. However, only 19% of all neuropsychiatric drugs were under study in pediatric populations, with as few as 8% of either antidepressant or antipsychotic drugs. Overall, 76% of pediatric drug trials examined a drug previously unapproved in children and 26% explored the use of a drug for a new indication.
Despite the rising prevalence of neuropsychiatric disease and the paucity of FDA-approved pediatric drugs, only a small proportion of trials focus on pediatric populations and these trials cover only a fraction of available drugs. This deficiency is most pronounced for depression and schizophrenia.
PMCID: PMC4074660  PMID: 23650305
pediatric research; off-label drug use; pediatric drug approval; neuropsychiatric conditions
8.  Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): Architecture 
We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, clinical data research network, which leverages the $48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative ‘apps’ to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components.
PMCID: PMC4078286  PMID: 24821734
Electronic Health Record; Learning Health System; Clinical Trials; Patient Engagement; Distributed Computing
9.  Participatory Medicine: A Home Score for Streptococcal Pharyngitis Enabled by Real-Time Biosurveillance 
Annals of internal medicine  2013;159(9):577-583.
Consensus guidelines recommend that adults at low risk for group A streptococcal (GAS) pharyngitis be neither tested nor treated
To help patients decide when to visit a clinician for the evaluation of sore throat.
Retrospective cohort study.
A national chain of retail health clinics.
71 776 patients aged 15 years or older with pharyngitis who visited a clinic from September 2006 to December 2008.
The authors created a score using information from patient-reported clinical variables plus the incidence of local disease and compared it with the Centor score and other traditional scores that require clinician-elicited signs.
If patients aged 15 years or older with sore throat did not visit a clinician when the new score estimated the likelihood of GAS pharyngitis to be less than 10% instead of having clinicians manage their symptoms following guidelines that use the Centor score, 230 000 visits would be avoided in the United States each year and 8500 patients with GAS pharyngitis who would have received antibiotics would not be treated with them.
Real-time information about the local incidence of GAS pharyngitis, which is necessary to calculate the new score, is not currently available.
A patient-driven approach to pharyngitis diagnosis that uses this new score could save hundreds of thousands of visits annually by identifying patients at home who are unlikely to require testing or treatment.
Primary Funding Source
Centers for Disease Control and Prevention and the National Library of Medicine, National Institutes of Health.
PMCID: PMC3953456  PMID: 24189592
10.  Is Biblioleaks Inevitable? 
In 2014, the vast majority of published biomedical research is still hidden behind paywalls rather than open access. For more than a decade, similar restrictions over other digitally available content have engendered illegal activity. Music file sharing became rampant in the late 1990s as communities formed around new ways to share. The frequency and scale of cyber-attacks against commercial and government interests has increased dramatically. Massive troves of classified government documents have become public through the actions of a few. Yet we have not seen significant growth in the illegal sharing of peer-reviewed academic articles. Should we truly expect that biomedical publishing is somehow at less risk than other content-generating industries? What of the larger threat—a “Biblioleaks” event—a database breach and public leak of the substantial archives of biomedical literature? As the expectation that all research should be available to everyone becomes the norm for a younger generation of researchers and the broader community, the motivations for such a leak are likely to grow. We explore the feasibility and consequences of a Biblioleaks event for researchers, journals, publishers, and the broader communities of doctors and the patients they serve.
PMCID: PMC4019771  PMID: 24755534
bibliographic databases; compromising of data; open access; public access to information; peer-to-peer architectures
11.  A novel, privacy-preserving cryptographic approach for sharing sequencing data 
DNA samples are often processed and sequenced in facilities external to the point of collection. These samples are routinely labeled with patient identifiers or pseudonyms, allowing for potential linkage to identity and private clinical information if intercepted during transmission. We present a cryptographic scheme to securely transmit externally generated sequence data which does not require any patient identifiers, public key infrastructure, or the transmission of passwords.
Materials and methods
This novel encryption scheme cryptographically protects participant sequence data using a shared secret key that is derived from a unique subset of an individual’s genetic sequence. This scheme requires access to a subset of an individual’s genetic sequence to acquire full access to the transmitted sequence data, which helps to prevent sample mismatch.
We validate that the proposed encryption scheme is robust to sequencing errors, population uniqueness, and sibling disambiguation, and provides sufficient cryptographic key space.
Access to a set of an individual’s genotypes and a mutually agreed cryptographic seed is needed to unlock the full sequence, which provides additional sample authentication and authorization security. We present modest fixed and marginal costs to implement this transmission architecture.
It is possible for genomics researchers who sequence participant samples externally to protect the transmission of sequence data using unique features of an individual’s genetic sequence.
PMCID: PMC3555340  PMID: 23125421
genomic privacy; genomic encryption; genomic sequencing; transmission of genomic data; cryptography; biorepository research
12.  The Effects of Industry Sponsorship on Comparator Selection in Trial Registrations for Neuropsychiatric Conditions in Children 
PLoS ONE  2013;8(12):e84951.
Pediatric populations continue to be understudied in clinical drug trials despite the increasing use of pharmacotherapy in children, particularly with psychotropic drugs. Most pertinent to the clinical selection of drug interventions are trials directly comparing drugs against other drugs. The aim was to measure the prevalence of active drug comparators in neuropsychiatric drug trials in children and identify the effects of funding source on comparator selection. We analyzed the selection of drugs and drug comparisons in clinical trials registered between January 2006 and May 2012. Completed and ongoing interventional trials examining treatments for six neuropsychiatric conditions in children were included. Networks of drug comparisons for each condition were constructed using information about the trial study arms. Of 421 eligible trial registrations, 228 (63,699 participants) were drug trials addressing ADHD (106 trials), autism spectrum disorders (47), unipolar depression (16), seizure disorders (38), migraines and other headaches (15), or schizophrenia (11). Active drug comparators were used in only 11.0% of drug trials while 44.7% used a placebo control and 44.3% no drug or placebo comparator. Even among conditions with well-established pharmacotherapeutic options, almost all drug interventions were compared to a placebo. Active comparisons were more common among trials without industry funding (17% vs. 8%, p=0.04). Trials with industry funding differed from non-industry trials in terms of the drugs studied and the comparators selected. For 73% (61/84) of drugs and 90% (19/21) of unique comparisons, trials were funded exclusively by either industry or non-industry. We found that industry and non-industry differed when choosing comparators and active drug comparators were rare for both groups. This gap in pediatric research activity limits the evidence available to clinicians treating children and suggests a need to reassess the design and funding of pediatric trials in order to optimize the information derived from pediatric participation in clinical trials.
PMCID: PMC3871546  PMID: 24376857
13.  Early Detection of Poor Adherers to Statins: Applying Individualized Surveillance to Pay for Performance 
PLoS ONE  2013;8(11):e79611.
Medication nonadherence costs $300 billion annually in the US. Medicare Advantage plans have a financial incentive to increase medication adherence among members because the Centers for Medicare and Medicaid Services (CMS) now awards substantive bonus payments to such plans, based in part on population adherence to chronic medications. We sought to build an individualized surveillance model that detects early which beneficiaries will fall below the CMS adherence threshold.
This was a retrospective study of over 210,000 beneficiaries initiating statins, in a database of private insurance claims, from 2008-2011. A logistic regression model was constructed to use statin adherence from initiation to day 90 to predict beneficiaries who would not meet the CMS measure of proportion of days covered 0.8 or above, from day 91 to 365. The model controlled for 15 additional characteristics. In a sensitivity analysis, we varied the number of days of adherence data used for prediction.
Lower adherence in the first 90 days was the strongest predictor of one-year nonadherence, with an odds ratio of 25.0 (95% confidence interval 23.7-26.5) for poor adherence at one year. The model had an area under the receiver operating characteristic curve of 0.80. Sensitivity analysis revealed that predictions of comparable accuracy could be made only 40 days after statin initiation. When members with 30-day supplies for their first statin fill had predictions made at 40 days, and members with 90-day supplies for their first fill had predictions made at 100 days, poor adherence could be predicted with 86% positive predictive value.
To preserve their Medicare Star ratings, plan managers should identify or develop effective programs to improve adherence. An individualized surveillance approach can be used to target members who would most benefit, recognizing the tradeoff between improved model performance over time and the advantage of earlier detection.
PMCID: PMC3817130  PMID: 24223977
14.  A susceptible-infected model of early detection of respiratory infection outbreaks on a background of influenza 
Journal of theoretical biology  2006;241(4):954-963.
The threat of biological warfare and the emergence of new infectious agents spreading at a global scale have highlighted the need for major enhancements to the public health infrastructure. Early detection of epidemics of infectious diseases requires both real-time data and real-time interpretation of data. Despite moderate advancements in data acquisition, the state of the practice for real-time analysis of data remains inadequate. We present a nonlinear mathematical framework for modeling the transient dynamics of influenza, applied to historical data sets of patients with influenza-like illness. We estimate the vital time-varying epidemiological parameters of infections from historical data, representing normal epidemiological trends. We then introduce simulated outbreaks of different shapes and magnitudes into the historical data, and estimate the parameters representing the infection rates of anomalous deviations from normal trends. Finally, a dynamic threshold-based detection algorithm is devised to assess the timeliness and sensitivity of detecting the irregularities in the data, under a fixed low false-positive rate. We find that the detection algorithm can identify such designated abnormalities in the data with high sensitivity with specificity held at 97%, but more importantly, early during an outbreak. The proposed methodology can be applied to a broad range of influenza-like infectious diseases, whether naturally occurring or a result of bioterrorism, and thus can be an integral component of a real-time surveillance system.
PMCID: PMC3754793  PMID: 16556450
SI model; Transients; Early detection; Infectious disease; Outbreaks; Biosurveillance
15.  Pediatric Versus Adult Drug Trials for Conditions With High Pediatric Disease Burden 
Pediatrics  2012;130(2):285-292.
Optimal treatment decisions in children require sufficient evidence on the safety and efficacy of pharmaceuticals in pediatric patients. However, there is concern that not enough trials are conducted in children and that pediatric trials differ from those performed in adults. Our objective was to measure the prevalence of pediatric studies among clinical drug trials and compare trial characteristics and quality indicators between pediatric and adult drug trials.
For conditions representing a high burden of pediatric disease, we identified all drug trials registered in with start dates between 2006 and 2011 and tracked the resulting publications. We measured the proportion of pediatric trials and subjects for each condition and compared pediatric and adult trial characteristics and quality indicators.
For the conditions selected, 59.9% of the disease burden was attributable to children, but only 12.0% (292/2440) of trials were pediatric (P < .001). Among pediatric trials, 58.6% were conducted without industry funding compared with 35.0% of adult trials (P < .001).
Fewer pediatric compared with adult randomized trials examined safety outcomes (10.1% vs 16.9%, P = .008). Pediatric randomized trials were slightly more likely to be appropriately registered before study start (46.9% vs 39.3%, P = .04) and had a modestly higher probability of publication in the examined time frame (32.8% vs 23.2%, P = .04).
There is substantial discrepancy between pediatric burden of disease and the amount of clinical trial research devoted to pediatric populations. This may be related in part to trial funding, with pediatric trials relying primarily on government and nonprofit organizations.
PMCID: PMC3408692  PMID: 22826574
clinical trials; evidence-based medicine; pediatrics; medication use; research subjects
16.  Inpatient Growth and Resource Use in 28 Children’s Hospitals 
JAMA pediatrics  2013;167(2):170-177.
To compare inpatient resource use trends for healthy children and children with chronic health conditions of varying degrees of medical complexity.
Retrospective cohort analysis.
Twenty-eight US children’s hospitals.
A total of 1 526 051 unique patients hospitalized from January 1, 2004, through December 31, 2009, who were assigned to 1 of 5 chronic condition groups using 3M’s Clinical Risk Group software.
Main Outcome Measures
Trends in the number of patients, hospitalizations, hospital days, and charges analyzed with linear regression.
Between 2004 and 2009, hospitals experienced a greater increase in the number of children hospitalized with vs without a chronic condition (19.2% vs 13.7% cumulative increase, P < .001). The greatest cumulative increase (32.5%) was attributable to children with a significant chronic condition affecting 2 or more body systems, who accounted for 19.2% (n=63 203) of patients, 27.2% (n=111 685) of hospital discharges, 48.9% (n=1.1 million) of hospital days, and 53.2% ($9.2 billion) of hospital charges in 2009. These children had a higher percentage of Medicaid use (56.5% vs 49.7%; P<.001) compared with children without a chronic condition. Cerebral palsy (9179 [14.6%]) and asthma (13 708 [21.8%]) were the most common primary diagnosis and comorbidity, respectively, observed among these patients.
Patients with a chronic condition increasingly used more resources in a group of children’s hospitals than patients without a chronic condition. The greatest growth was observed in hospitalized children with chronic conditions affecting 2 or more body systems. Children’s hospitals must ensure that their inpatient care systems and payment structures are equipped to meet the protean needs of this important population of children.
PMCID: PMC3663043  PMID: 23266509
17.  Scalable Decision Support at the Point of Care: A Substitutable Electronic Health Record App for Monitoring Medication Adherence 
Non-adherence to prescribed medications is a serious health problem in the United States, costing an estimated $100 billion per year. While poor adherence should be addressable with point of care health information technology, integrating new solutions with existing electronic health records (EHR) systems require customization within each organization, which is difficult because of the monolithic software design of most EHR products.
The objective of this study was to create a published algorithm for predicting medication adherence problems easily accessible at the point of care through a Web application that runs on the Substitutable Medical Apps, Reusuable Technologies (SMART) platform. The SMART platform is an emerging framework that enables EHR systems to behave as “iPhone like platforms” by exhibiting an application programming interface for easy addition and deletion of third party apps. The app is presented as a point of care solution to monitoring medication adherence as well as a sufficiently general, modular application that may serve as an example and template for other SMART apps.
The widely used, open source Django framework was used together with the SMART platform to create the interoperable components of this app. Django uses Python as its core programming language. This allows statistical and mathematical modules to be created from a large array of Python numerical libraries and assembled together with the core app to create flexible and sophisticated EHR functionality. Algorithms that predict individual adherence are derived from a retrospective study of dispensed medication claims from a large private insurance plan. Patients’ prescription fill information is accessed through the SMART framework and the embedded algorithms compute adherence information, including predicted adherence one year after the first prescription fill. Open source graphing software is used to display patient medication information and the results of statistical prediction of future adherence on a clinician-facing Web interface.
The user interface allows the physician to quickly review all medications in a patient record for potential non-adherence problems. A gap-check and current medication possession ratio (MPR) threshold test are applied to all medications in the record to test for current non-adherence. Predictions of 1-year non-adherence are made for certain drug classes for which external data was available. Information is presented graphically to indicate present non-adherence, or predicted non-adherence at one year, based on early prescription fulfillment patterns. The MPR Monitor app is installed in the SMART reference container as the “MPR Monitor”, where it is publically available for use and testing. MPR is an acronym for Medication Possession Ratio, a commonly used measure of adherence to a prescribed medication regime. This app may be used as an example for creating additional functionality by replacing statistical and display algorithms with new code in a cycle of rapid prototyping and implementation or as a framework for a new SMART app.
The MPR Monitor app is a useful pilot project for monitoring medication adherence. It also provides an example that integrates several open source software components, including the Python-based Django Web framework and python-based graphics, to build a SMART app that allows complex decision support methods to be encapsulated to enhance EHR functionality.
PMCID: PMC3815431  PMID: 23876796
electronic health record; personal electronic health record; hospital information systems; medical informatics applications; accountable care organizations; medication adherence
18.  Twitter as a Sentinel in Emergency Situations: Lessons from the Boston Marathon Explosions 
PLoS Currents  2013;5:ecurrents.dis.ad70cd1c8bc585e9470046cde334ee4b.
Immediately following the Boston Marathon attacks, individuals near the scene posted a deluge of data to social media sites. Previous work has shown that these data can be leveraged to provide rapid insight during natural disasters, disease outbreaks and ongoing conflicts that can assist in the public health and medical response. Here, we examine and discuss the social media messages posted immediately after and around the Boston Marathon bombings, and find that specific keywords appear frequently prior to official public safety and news media reports. Individuals immediately adjacent to the explosions posted messages within minutes via Twitter which identify the location and specifics of events, demonstrating a role for social media in the early recognition and characterization of emergency events. *Christopher Cassa and Rumi Chunara contributed equally to this work.
PMCID: PMC3706072  PMID: 23852273
19.  Twitter as a Sentinel in Emergency Situations: Lessons from the Boston Marathon Explosions 
PLoS Currents  2013;5:ecurrents.dis.ad70cd1c8bc585e9470046cde334ee4b.
Immediately following the Boston Marathon attacks, individuals near the scene posted a deluge of data to social media sites. Previous work has shown that these data can be leveraged to provide rapid insight during natural disasters, disease outbreaks and ongoing conflicts that can assist in the public health and medical response. Here, we examine and discuss the social media messages posted immediately after and around the Boston Marathon bombings, and find that specific keywords appear frequently prior to official public safety and news media reports. Individuals immediately adjacent to the explosions posted messages within minutes via Twitter which identify the location and specifics of events, demonstrating a role for social media in the early recognition and characterization of emergency events. *Christopher Cassa and Rumi Chunara contributed equally to this work.
PMCID: PMC3706072  PMID: 23852273
20.  Large-Scale Validation of the Centor and McIsaac Scores to Predict Group A Streptococcal Pharyngitis 
Archives of internal medicine  2012;172(11):847-852.
The Centor and McIsaac scores guide testing and treatment for group A streptococcal (GAS) pharyngitis in patients presenting with a sore throat, but were derived on relatively small samples. We perform a national-scale validation of the prediction models on a large, geographically diverse population.
Analysis of data collected from 206,870 patients 3 years and above who presented with a painful throat to a United States national retail health chain, from September 2006-December 2008. Main outcome meaures were the proportions of patients testing positive for GAS pharyngitis according to Centor and McIsaac scores (both scales 0-4).
For patients 15 years and older, 23% (95% confidence interval (CI) 22%-23%) tested GAS positive including 7% (7-8%) of those with a Centor score of 0, 12% (11-12%) with 1, 21% (21-22%) with 2, 38% (38-39%) with 3, and 57% (56-58%) with 4. For patients 3 years and older, 27% (95% CI 27-27%) tested GAS positive with 8% (8-9%) of those testing positive with McIsaac score 0, 14% (13-14%) with 1, 23% (23-23%) with 2, 37% (37-37%) with 3, and 55% (55-56%) with 4. 95% CI’s overlapped between the MinuteClinic derived probabilities and the prior reports.
Our study validates the Centor and McIsaac scores and more precisely classifies risk of GAS infection among patients presenting with a painful throat to a retail health chain.
PMCID: PMC3627733  PMID: 22566485
21.  Improved Diagnostic Accuracy of Group A Streptococcal Pharyngitis Using Real-Time Biosurveillance 
Annals of internal medicine  2011;155(6):10.7326/0003-4819-155-6-201109200-00002.
Clinical prediction rules do not incorporate real time incidence data to adjust estimates of disease risk in symptomatic patients.
To measure the value of integrating local incidence data into a clinical decision rule for diagnosing group A streptococcal (GAS) pharyngitis in patients age 15 years and older.
Retrospective analysis of clinical and biosurveillance predictors of GAS pharyngitis.
Large U.S.-based retail-health chain.
82,062 patient visits for pharyngitis.
Accuracy of the Centor score, was compared with that of a biosurveillance-responsive score, essentially an adjusted Centor score based on real-time GAS pharyngitis information from the 14 days prior to a patient’s visit – the recent local proportion positive (RLPP).
Increased RLPP correlated with likelihood of GAS pharyngitis (r2 =0.79, p<0.001). Local incidence data enhanced diagnostic models. For example, when RLPP >0.30, managing patients with Centor scores of 1 as if scores were 2 would identify 62,537 previously missed patients annually while misclassifying 18,446 patients without GAS pharyngitis. Decreasing the score of patients with Centor values of 3 by one point for RLPP <0.20, would spare unnecessary antibiotics for 166,616 patients while missing 18,812 true positives.
Analyses were conducted retrospectively. Real time regional GAS pharyngitis data are generally not yet available to clinicians.
Incorporating live biosurveillance data into clinical guidelines for GAS pharyngitis and other communicable diseases should be considered to reduce missed cases when the contemporaneous incidence is elevated and spare unnecessary antibiotics when the contemporaneous incidence is low. Delivering epidemiologic data to the point of care will enable the use of real-time pre-test probabilities in medical decision-making.
Primary Funding Source
The Mentored Public Health Research Scientist Development Award K01 HK000055 from the Centers for Disease Control and Prevention and R01 LM007677 from the National Library of Medicine, National Institutes of Health.
PMCID: PMC3651845  PMID: 21930851
22.  A Biosurveillance-driven Home Score to Guide Strep Pharyngitis Treatment 
1. To derive and validate an accurate clinical prediction model (“home score”) to estimate a patient’s risk of group A streptococcal (GAS) pharyngitis before a health care visit based only on history and real-time local biosurveillance, and to compare its accuracy to traditional clinical prediction models composed of history and physical exam features. 2. To examine the impact of a home score on patient and public health outcomes.
GAS pharyngitis affects hundreds of millions of individuals globally each year, and over 12 million seek care in the United States annually for sore throat. Clinicians cannot differentiate GAS from other causes of acute pharyngitis based on the oropharynx exam, so consensus guidelines recommend use of clinical scores to classify GAS risk and guide management of adults with acute pharyngitis. When the clinical score is low, consensus guidelines agree patients should neither be tested nor treated for GAS. A prediction model that could identify very-low risk patients prior to an ambulatory visit could reduce low-yield, unnecessary visits for a most common outpatient condition. We recently showed that real-time biosurveillance can further identify patients at low-risk of GAS. With increasing emphasis on patient-centric health care and the well-documented barriers impeding clinicians’ incorporation of prediction models into medical practice, this presents an opportunity to create a patient-centric model for GAS pharyngitis based on history and recent local epidemiology. We refer to this model as the “home score,” because it is designed for use prior to a physical exam.
Analysis of data collected from 110,208 patients 3 years and older who presented with pharyngitis to a national retail health chain, from 2006–08. Practitioners collected standardized historical and physical exam information based on algorithm-driven care, and all patients with pharyngitis were tested for GAS. We used a previously validated biosurveillance variable reflecting disease incidence called recent local proportion positive (RLPP), which represents the proportion of patients who tested GAS positive in a local market in the previous 14 days. To derive the “home score,” candidate variables were restricted to demographic factors, historical items and the RLPP, while physical exam variables (such as exudate), were excluded. Multivariate analytic techniques were used to identify predictors of GAS. For each home score (0–100), we calculated the percent of patients who tested positive, and we examined the relationship between the home score and GAS positivity. Standard metrics (sensitivity, specificity, positive and negative predictive value, and AUC) were used to compare the performance of the home score to standard scores. We computed the number of patients aged >= 15 years who, according to the home score, were at low risk for GAS, and therefore might avoid or delay a trip to a medical provider. Outcomes included the numbers of reduced visits and the number of additional missed GAS cases compared to the standard Centor score approach (Do not test/Do not treat if Centor score is 0–1). To facilitate comparison across different risk thresholds, we calculated outcomes for hypothetical cohorts of 1000 patients, and extrapolated these findings to provide the impact on 12 million annual national pharyngitis visits.
The 3 best predictors were fever (OR 2.43, 95%CI 2.33–2.54), absence of cough (1.71,1.63–1.80) and RLPP (1.04,1.04–1.04 per unit change in RLPP). Using a home score cutoff of 0.10 to identify adults at low risk would save 230,000 ambulatory visits annually while missing only 8500 additional GAS cases. At a 0.20 cutoff, 2.9 million visits would be saved, and 320,000 more cases missed each year. There was a strong correlation between the percent testing positive and the home score (r-square=0.98). As the home score increases, there is a linear increase in the risk of GAS (slope=1.02). The home score AUC was 0.66, approaching the Centor score (0.69) even without any physical exam information.
A biosurveillance-driven home score to guide treatment of strep pharyngitis could save millions of visits annually by identifying patients in the pre-visit setting who would be unlikely to be tested or treated.
PMCID: PMC3692866
biosurveillance; pharyngitis; retail health
23.  SMART Platforms: Building the App Store for Biosurveillance 
To enable public health departments to develop “apps” to run on electronic health records (EHRs) for (1) biosurveillance and case reporting and (2) delivering alerts to the point of care. We describe a novel health information technology platform with substitutable apps constructed around core services enabling EHRs to function as iPhone-like platforms.
Health care information is a fundamental source of data for biosurveillance, yet configuring EHRs to report relevant data to health departments is technically challenging, labor intensive, and often requires custom solutions for each installation. Public health agencies wishing to deliver alerts to clinicians also must engage in an endless array of one-off systems integrations.
Despite a $48B investment in HIT, and meaningful use criteria requiring reporting to biosurveillance systems, most vendor electronic health records are architected monolithically, making modification difficult for hospitals and physician practices. An alternative approach is to reimagine EHRs as iPhone-like platforms supporting substitutable apps-based functionality. Substitutability is the capability inherent in a system of replacing one application with another of similar functionality.
Substitutability requires that the purchaser of an app can replace one application with another without being technically expert, without requiring re-engineering other applications that they are using, and without having to consult or require assistance of any of the vendors of previously installed or currently installed applications. Apps necessarily compete with each other promoting progress and adaptability.
The Substitutable Medical Applications, Reusable Technologies (SMART) Platforms project is funded by a $15M grant from Office of the National Coordinator of Health Information Technology’s Strategic Health IT Advanced Research Projects (SHARP) Program. All SMART standards are open and the core software is open source.
The SMART project promotes substitutability through an application programming interface (API) that can be adopted as part of a “container” built around by a wide variety of HIT, providing readonly access to the underlying data model and a software development toolkit to readily create apps. SMART containers are HIT systems, that have implemented the SMART API or a portion of it. Containers marshal data sources and present them consistently across the SMART API. SMART applications consume the API and are substitutable.
SMART provides a common platform supporting an “app store for biosurveillance” as an approach to enabling one stop shopping for public health departments—to create an app once, and distribute it everywhere.
Further, such apps can be readily updated or created—for example, in the case of an emerging infection, an app may be designed to collect additional data at emergency department triage. Or a public health department may widely distribute an app, interoperable with any SMART-enabled EMR, that delivers contextualized alerts when patient electronic records are opened, or through background processes.
SMART has sparked an ecosystem of apps developers and attracted existing health information technology platforms to adopt the SMART API—including, traditional, open source, and next generation EHRs, patient-facing platforms and health information exchanges. SMART-enabled platforms to date include the Cerner EMR, the WorldVista EHR, the OpenMRS EHR, the i2b2 analytic platform, and the Indivo X personal health record. The SMART team is working with the Mirth Corporation, to SMART-enable the HealthBridge and Redwood MedNet Health Information Exchanges. We have demonstrated that a single SMART app can run, unmodified, in all of these environments, as long as the underlying platform collects the required data types. Major EHR vendors are currently adapting the SMART API for their products.
The SMART system enables nimble customization of any electronic health record system to create either a reporting function (outgoing communication) or an alerting function (incoming communication) establishing a technology for a robust linkage between public health and clinical environments.
PMCID: PMC3692876
Electronic health records; Biosurveillance; Informatics; Application Programming Interfaces
24.  Development of a Scalable Pharmacogenomic Clinical Decision Support Service 
Advances in sequencing technology are making genomic data more accessible within the healthcare environment. Published pharmacogenetic guidelines attempt to provide a clinical context for specific genomic variants; however, the actual implementation to convert genomic data into a clinical report integrated within an electronic medical record system is a major challenge for any hospital. We created a two-part solution that integrates with the medical record system and converts genetic variant results into an interpreted clinical report based on published guidelines. We successfully developed a scalable infrastructure to support TPMT genetic testing and are currently testing approximately two individuals per week in our production version. We plan to release an online variant to clinical interpretation reporting system in order to facilitate translation of pharmacogenetic information into clinical practice.
PMCID: PMC3814487  PMID: 24303299
25.  App Store for EHRs and Patients Both  
The Substitutable Medical Applications, Reusable Technologies (SMART) Platforms project ( ) seeks to develop an iPhone-like health information technology platform with substitutable apps constructed around core services. It is funded by a grant from the Office of the National Coordinator of Health Information Technology’s Strategic Health IT Advanced Research Projects (SHARP) Program. SMART technologies enable existing electronic health records and HIT platforms to run substitutable apps. Substitutability is the capability inherent in a system of replacing one application with another of similar functionality. We created a patient-facing SMART instance using the open source Indivo personally controlled health record (PCHR).
The SMART “read-only” API has been deployed on multiple systems, including the Cerner installation at Boston Children’s Hospital and the World Vista EHR. We sought to SMART-enable Indivo, the open source reference PCHR upon which HealthVault and other PCHRs were modeled. PCHRs provide patients with a secure repository of their health information that can be exposed to apps across a programming interface. We updated the open source Indivo PCHR to support the SMART API, enabling Indivo to act as a patient-facing apps platform, running the same or similar versions of apps that face clinicians.
PMCID: PMC3845767  PMID: 24303239

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