Publications

2014

A, Johnson, Bickel J, and Lebel A. (2014) 2014. “Pediatric Migraine Prescription Patterns at a Large Academic Hospital”. Pediatric Neurology 51 (5): 706-12.

BACKGROUND: Here we report the prescription patterns by drug type, age, and sex of patients at a large academic pediatric hospital. Because there are few guidelines based on outcome studies in pediatric migraine, physician treatment approaches in children vary.

METHODS:Using the i2b2 query tool, we determined that over an approximately 4 year period, 4839 patients between the ages of 2 and 17 years were observed at Boston Children's Hospital for migraine with or without aura, 59% women and 41% men.

RESULTS:The most common medications prescribed to this population were sumatriptan, amitriptyline, topiramate, ondansetron, and cyproheptadine.

CONCLUSIONS:Our findings support recent data regarding choices of medication in the pediatric population and additionally support current studies and future investigation into controlled trials in the pediatric population.

KEYWORDS:amitriptyline; migraine; pediatric; pharmacotherapy; prescription; sumatriptan

Mandl KD, Kohane IS, McFadden D, Weber GM, Natter M, Mandel J, Schneeweiss S, et al. 2014. “Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): Architecture”. J Am Med Inform Assoc. 21 (4): 615-20.

We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) 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.

EK, Johnson, Broder-Fingert S, Tanpowpong P, Bickel J, Lightdale JR, and Nelson CP. (2014) 2014. “Use of the I2b2 Research Query Tool to Conduct a Matched Case-Control Clinical Research Study: Advantages, Disadvantages and Methodological Considerations”. BMC Med Res Methodol. 14 (16).

BACKGROUND: A major aim of the i2b2 (informatics for integrating biology and the bedside) clinical data informatics framework aims to create an efficient structure within which patients can be identified for clinical and translational research projects.Our objective was to describe the respective roles of the i2b2 research query tool and the electronic medical record (EMR) in conducting a case-controlled clinical study at our institution.

METHODS: We analyzed the process of using i2b2 and the EMR together to generate a complete research database for a case-control study that sought to examine risk factors for kidney stones among gastrostomy tube (G-tube) fed children.

RESULTS: Our final case cohort consisted of 41/177 (23%) of potential cases initially identified by i2b2, who were matched with 80/486 (17%) of potential controls. Cases were 10 times more likely to be excluded for inaccurate coding regarding stones vs. inaccurate coding regarding G-tubes. A majority (67%) of cases were excluded due to not meeting clinical inclusion criteria, whereas a majority of control exclusions (72%) occurred due to inadequate clinical data necessary for study completion. Full dataset assembly required complementary information from i2b2 and the EMR.

CONCLUSIONS: i2b2 was critical as a query analysis tool for patient identification in our case-control study. Patient identification via procedural coding appeared more accurate compared with diagnosis coding. Completion of our investigation required iterative interplay of i2b2 and the EMR to assemble the study cohort.

PMID:24479726  
PMCID: PMC3909388

2013

AJ, McMurry, Murphy SN, MacFadden D, Weber G, Simons WW, Orechia J, Bickel J, et al. (2013) 2013. “SHRINE: Enabling Nationally Scalable Multi-Site Disease Studies.”. PLoS One. 8 (3): e55811.

Abstract- Results of medical research studies are often contradictory or cannot be reproduced. One reason is that there may not be enough patient subjects available for observation for a long enough time period. Another reason is that patient populations may vary considerably with respect to geographic and demographic boundaries thus limiting how broadly the results apply. Even when similar patient populations are pooled together from multiple locations, differences in medical treatment and record systems can limit which outcome measures can be commonly analyzed. In total, these differences in medical research settings can lead to differing conclusions or can even prevent some studies from starting. We thus sought to create a patient research system that could aggregate as many patient observations as possible from a large number of hospitals in a uniform way. We call this system the 'Shared Health Research Information Network', with the following properties: (1) reuse electronic health data from everyday clinical care for research purposes, (2) respect patient privacy and hospital autonomy, (3) aggregate patient populations across many hospitals to achieve statistically significant sample sizes that can be validated independently of a single research setting, (4) harmonize the observation facts recorded at each institution such that queries can be made across many hospitals in parallel, (5) scale to regional and national collaborations. The purpose of this report is to provide open source software for multi-site clinical studies and to report on early uses of this application. At this time SHRINE implementations have been used for multi-site studies of autism co-morbidity, juvenile idiopathic arthritis, peripartum cardiomyopathy, colorectal cancer, diabetes, and others. The wide range of study objectives and growing adoption suggest that SHRINE may be applicable beyond the research uses and participating hospitals named in this report.

PMID: 23533569; PMCID: PMC3591385.

2012

IS, Kohane, McMurry A, Weber G, MacFadden D, Rappaport L, Kunkel L, Bickel J, et al. (2012) 2012. “The Co-Morbidity Burden of Children and Young Adults With Autism Spectrum Disorders”. PLoS One 7 (4): e33224.

OBJECTIVES:Use electronic health records Autism Spectrum Disorder (ASD) to assess the comorbidity burden of ASD in children and young adults.

STUDY DESIGN:A retrospective prevalence study was performed using a distributed query system across three general hospitals and one pediatric hospital. Over 14,000 individuals under age 35 with ASD were characterized by their co-morbidities and conversely, the prevalence of ASD within these comorbidities was measured. The comorbidity prevalence of the younger (Age<18 years) and older (Age 18-34 years) individuals with ASD was compared.

RESULTS:19.44% of ASD patients had epilepsy as compared to 2.19% in the overall hospital population (95% confidence interval for difference in percentages 13.58-14.69%), 2.43% of ASD with schizophrenia vs. 0.24% in the hospital population (95% CI 1.89-2.39%), inflammatory bowel disease (IBD) 0.83% vs. 0.54% (95% CI 0.13-0.43%), bowel disorders (without IBD) 11.74% vs. 4.5% (95% CI 5.72-6.68%), CNS/cranial anomalies 12.45% vs. 1.19% (95% CI 9.41-10.38%), diabetes mellitus type I (DM1) 0.79% vs. 0.34% (95% CI 0.3-0.6%), muscular dystrophy 0.47% vs 0.05% (95% CI 0.26-0.49%), sleep disorders 1.12% vs. 0.14% (95% CI 0.79-1.14%). Autoimmune disorders (excluding DM1 and IBD) were not significantly different at 0.67% vs. 0.68% (95% CI -0.14-0.13%). Three of the studied comorbidities increased significantly when comparing ages 0-17 vs 18-34 with p<0.001: Schizophrenia (1.43% vs. 8.76%), diabetes mellitus type I (0.67% vs. 2.08%), IBD (0.68% vs. 1.99%) whereas sleeping disorders, bowel disorders (without IBD) and epilepsy did not change significantly.

CONCLUSIONS:The comorbidities of ASD encompass disease states that are significantly overrepresented in ASD with respect to even the patient populations of tertiary health centers. This burden of comorbidities goes well beyond those routinely managed in developmental medicine centers and requires broad multidisciplinary management that payors and providers will have to plan for.

PMID: 22511918; PMCID: PMC3325235.

2011

SP, Narus, Srivastava R, Gouripeddi R, Livne OE, Mo P, Bickel JP, Regt D, et al. (2011) 2011. “Federating Clinical Data from Six Pediatric Hospitals: Process and Initial Results from the PHIS+ Consortium.”. AMIA Annu Symp Proc., 994-1003.

Integrating clinical data with administrative data across disparate electronic medical record systems will help improve the internal and external validity of comparative effectiveness research. The Pediatric Health Information System (PHIS) currently collects administrative information from 43 pediatric hospital members of the Child Health Corporation of America (CHCA). Members of the Pediatric Research in Inpatient Settings (PRIS) network have partnered with CHCA and the University of Utah Biomedical Informatics Core to create an enhanced version of PHIS that includes clinical data. A specialized version of a data federation architecture from the University of Utah ("FURTHeR") is being developed to integrate the clinical data from the member hospitals into a common repository ("PHIS+") that is joined with the existing administrative data. We report here on our process for the first phase of federating lab data, and present initial results.

PMID: 22195159; PMCID: PMC3243196.

2010

SL, Pham, Bickel JP, Moritz ML, and Levin JE. (2010) 2010. “Discovering Knowledge on Pediatric Fluid Therapy and Dysnatremias from Quantitative Data Found in Electronic Medical Records”. AMIA Annu Symp Proc., no. 2010: 652-6.

It is accepted that intravenous fluid (IVF) therapy can result in hospital-acquired dysnatremias in pediatric patients, with associated morbidity and mortality. There is interest in improving IVF therapy to prevent dysnatremias, but the optimal approach is controversial. In this study, we develop Natremia Deviation and Intravenous Renderer (NaDIR), a tool that preprocesses large volumes of electronic medical record data obtained from an academic pediatric hospital in order to analyze (1) IVF therapy, (2) the epidemiology of dysnatremias, and (3) the impact of IVFs on changes in serum sodium (ΔS(Na)). We then applied NaDIR to 3,256 inpatient records over a 3 month period, which revealed (1) a 19.9% incidence of dysnatremias, (2) a significant increase in lengths of stay associated with dysnatremias, and (3) a novel linear relationship between ΔS(Na) and IVF tonicity. This demonstrates that EMR data that can be readily analyzed to discover epidemiologic and predictive knowledge.

PMID: 21347059, PMCID: PMC3041455

2003

AR, Doben, Bickel JP, and McGee JB. (2003) 2003. “A Web and Handheld Based Diagnosis & Procedure Tracking System.”. AMIA Annu Symp Proc., no. 2003: 831.

Personal computing devices such as personal organizers, handheld PC's, and tablet PC's are becoming common tools in clinical care and medical education. There is an increasing need for these devices to track various tasks students and medical trainees perform. In particular, in undergraduate medical education, there is a need for tracking the depth and breadth of each student's clinical encounters over the course of his or her education. The authors have developed an application which allows for easy and rapid deployment of a tracking system for medical students' experiences during their clinical training years.

PMID: 14728336, PMCID: PMC1479897