bluuebunny/crossref_metadata_2025_split huggingface dataset:
An efficient approach for surveillance of childhood diabetes by type derived from electronic health record data: the SEARCH for Diabetes in Youth Study
Authors: Zhong Victor W , Obeid Jihad S , Craig Jean B , Pfaff Emily R , Thomas Joan , Jaacks Lindsay M , Beavers Daniel P , Carey Timothy S , Lawrence Jean M , Dabelea Dana , Hamman Richard F , Bowlby Deborah A , Pihoker Catherine , Saydah Sharon H , Mayer-Davis Elizabeth J 
Published: April 2016

Abstract
Objective To develop an efficient surveillance approach for childhood diabetes by type across 2 large US health care systems, using phenotyping algorithms derived from electronic health record (EHR) data.
Materials and Methods Presumptive diabetes cases &lt;20 years of age from 2 large independent health care systems were identified as those having ≥1 of the 5 indicators in the past 3.5 years, including elevated HbA1c, elevated blood glucose, diabetes-related billing codes, patient problem list, and outpatient anti-diabetic medications. EHRs of all the presumptive cases were manually reviewed, and true diabetes status and diabetes type were determined. Algorithms for identifying diabetes cases overall and classifying diabetes type were either prespecified or derived from classification and regression tree analysis. Surveillance approach was developed based on the best algorithms identified.
Results We developed a stepwise surveillance approach using billing code–based prespecified algorithms and targeted manual EHR review, which efficiently and accurately ascertained and classified diabetes cases by type, in both health care systems. The sensitivity and positive predictive values in both systems were approximately ≥90% for ascertaining diabetes cases overall and classifying cases with type 1 or type 2 diabetes. About 80% of the cases with “other” type were also correctly classified. This stepwise surveillance approach resulted in a &gt;70% reduction in the number of cases requiring manual validation compared to traditional surveillance methods.
Conclusion EHR data may be used to establish an efficient approach for large-scale surveillance for childhood diabetes by type, although some manual effort is still needed.
An informatics research agenda to support precision medicine: seven key areas
Authors: Tenenbaum Jessica D , Avillach Paul , Benham-Hutchins Marge , Breitenstein Matthew K , Crowgey Erin L , Hoffman Mark A , Jiang Xia , Madhavan Subha , Mattison John E , Nagarajan Radhakrishnan , Ray Bisakha , Shin Dmitriy , Visweswaran Shyam , Zhao Zhongming , Freimuth Robert R 
Published: April 2016

AbstractThe recent announcement of the Precision Medicine Initiative by President Obama has brought precision medicine (PM) to the forefront for healthcare providers, researchers, regulators, innovators, and funders alike. As technologies continue to evolve and datasets grow in magnitude, a strong computational infrastructure will be essential to realize PM’s vision of improved healthcare derived from personal data. In addition, informatics research and innovation affords a tremendous opportunity to drive the science underlying PM. The informatics community must lead the development of technologies and methodologies that will increase the discovery and application of biomedical knowledge through close collaboration between researchers, clinicians, and patients. This perspective highlights seven key areas that are in need of further informatics research and innovation to support the realization of PM.
A bioinformatics approach for precision medicine off-label drug drug selection among triple negative breast cancer patients
Authors: Cheng Lijun , Schneider Bryan P , Li Lang 
Published: April 2016

ABSTRACTBackground Cancer has been extensively characterized on the basis of genomics. The integration of genetic information about cancers with data on how the cancers respond to target based therapy to help to optimum cancer treatment.Objective The increasing usage of sequencing technology in cancer research and clinical practice has enormously advanced our understanding of cancer mechanisms. The cancer precision medicine is becoming a reality. Although off-label drug usage is a common practice in treating cancer, it suffers from the lack of knowledge base for proper cancer drug selections. This eminent need has become even more apparent considering the upcoming genomics data.Methods In this paper, a personalized medicine knowledge base is constructed by integrating various cancer drugs, drug-target database, and knowledge sources for the proper cancer drugs and their target selections. Based on the knowledge base, a bioinformatics approach for cancer drugs selection in precision medicine is developed. It integrates personal molecular profile data, including copy number variation, mutation, and gene expression.Results By analyzing the 85 triple negative breast cancer (TNBC) patient data in the Cancer Genome Altar, we have shown that 71.7% of the TNBC patients have FDA approved drug targets, and 51.7% of the patients have more than one drug target. Sixty-five drug targets are identified as TNBC treatment targets and 85 candidate drugs are recommended. Many existing TNBC candidate targets, such as Poly (ADP-Ribose) Polymerase 1 (PARP1), Cell division protein kinase 6 (CDK6), epidermal growth factor receptor, etc., were identified. On the other hand, we found some additional targets that are not yet fully investigated in the TNBC, such as Gamma-Glutamyl Hydrolase (GGH), Thymidylate Synthetase (TYMS), Protein Tyrosine Kinase 6 (PTK6), Topoisomerase (DNA) I, Mitochondrial (TOP1MT), Smoothened, Frizzled Class Receptor (SMO), etc. Our additional analysis of target and drug selection strategy is also fully supported by the drug screening data on TNBC cell lines in the Cancer Cell Line Encyclopedia.Conclusions The proposed bioinformatics approach lays a foundation for cancer precision medicine. It supplies much needed knowledge base for the off-label cancer drug usage in clinics.
Data quality of electronic medical records in Manitoba: do problem lists accurately reflect chronic disease billing diagnoses?
Authors: Singer Alexander , Yakubovich Sari , Kroeker Andrea L , Dufault Brenden , Duarte Roberto , Katz Alan 
Published: April 2016

AbstractObjective To determine problem list completeness related to chronic diseases in electronic medical records (EMRs) and explore clinic and physician factors influencing completeness.Methods A retrospective analysis of primary care EMR data quality related to seven chronic diseases (hypertension, diabetes, asthma, congestive heart failure, coronary artery disease, hypothyroidism, and chronic obstructive pulmonary disorder) in Manitoba, Canada. We included 119 practices in 18 primary care clinics across urban and rural Manitoba. The main outcome measure was EMR problem list completeness. Completeness was measured by comparing the number of EMR-documented diagnoses to the number of billings associated with each disease. We calculated odds ratios for the effect of clinic patient load and salary type on EMR problem list completeness of the 7 chronic diseases.Results Completeness of EMR problem list for each disease varied widely among clinics. Factors that significantly affected EMR problem list completeness included the primary care provider, the patient load, and the clinic’s funding and organization model (ie, salaried, fee-for-service, or residency training clinics). Average rates of completeness were: hypertension, 72%; diabetes, 80%; hypothyroidism, 63%; asthma, 56%; chronic obstructive pulmonary disorder, 43%; congestive heart failure, 54%; and coronary artery disease, 64%.Conclusion This study demonstrates the high variability but generally low quality of problem lists (health condition records) related to 7 common chronic diseases in EMRs. There are systematic physician- and clinic-level factors associated with low data quality completeness. This information may be useful to support improvement in EMR data quality in primary care.
Quality improvement teams, super-users, and nurse champions: a recipe for meaningful use?
Authors: Shea Christopher M , Reiter Kristin L , Weaver Mark A , Albritton Jordan 
Published: April 2016

Abstract
Objective This study assessed whether having an electronic health record (EHR) super-user, nurse champion for meaningful use (MU), and quality improvement (QI) team leading MU implementation is positively associated with MU Stage 1 demonstration.
Methods Data on MU demonstration of 596 providers in 37 ambulatory care clinics came from the clinical data warehouse and administrative systems of UNC Health Care. We surveyed the 37 clinics about champions, super-users, and QI teams. We used generalized estimating equation methods with an independence working correlation matrix to account for clustering within clinics and to weight contributions from each clinic according to clinic size.
Results Having a QI team lead MU implementation was significantly associated with MU demonstration (odds ratio, OR = 3.57, 95% CI, 1.83-6.96, P &lt; .001, Table 2 ). Having neither a nurse champion nor an EHR super-user was significant.
Conclusion Our findings support the alignment of MU with QI efforts by having the QI team lead MU implementation.
Automated identification of molecular effects of drugs (AIMED)
Authors: Fathiamini Safa , Johnson Amber M , Zeng Jia , Araya Alejandro , Holla Vijaykumar , Bailey Ann M , Litzenburger Beate C , Sanchez Nora S , Khotskaya Yekaterina , Xu Hua , Meric-Bernstam Funda , Bernstam Elmer V , Cohen Trevor 
Published: April 2016

AbstractIntroduction Genomic profiling information is frequently available to oncologists, enabling targeted cancer therapy. Because clinically relevant information is rapidly emerging in the literature and elsewhere, there is a need for informatics technologies to support targeted therapies. To this end, we have developed a system for Automated Identification of Molecular Effects of Drugs, to help biomedical scientists curate this literature to facilitate decision support.Objectives To create an automated system to identify assertions in the literature concerning drugs targeting genes with therapeutic implications and characterize the challenges inherent in automating this process in rapidly evolving domains.Methods We used subject-predicate-object triples (semantic predications) and co-occurrence relations generated by applying the SemRep Natural Language Processing system to MEDLINE abstracts and ClinicalTrials.gov descriptions. We applied customized semantic queries to find drugs targeting genes of interest. The results were manually reviewed by a team of experts.Results Compared to a manually curated set of relationships, recall, precision, and F2 were 0.39, 0.21, and 0.33, respectively, which represents a 3- to 4-fold improvement over a publically available set of predications (SemMedDB) alone. Upon review of ostensibly false positive results, 26% were considered relevant additions to the reference set, and an additional 61% were considered to be relevant for review. Adding co-occurrence data improved results for drugs in early development, but not their better-established counterparts.Conclusions Precision medicine poses unique challenges for biomedical informatics systems that help domain experts find answers to their research questions. Further research is required to improve the performance of such systems, particularly for drugs in development.
An in silico framework for integrating epidemiologic and genetic evidence with health care applications: ventilation-related pneumothorax as a case illustration
Authors: Torosyan Yelizaveta , Hu Yuzhi , Hoffman Sarah , Luo Qianlai , Carleton Bruce , Marinac-Dabic Danica 
Published: April 2016

Abstract
Objective To illustrate an in silico integration of epidemiologic and genetic evidence that is being developed at the Center for Devices and Radiological Health/US Food and Drug Administration as part of regulatory research on postmarket device performance. In addition to using conventional epidemiologic evidence from registries, this innovative approach explores the vast potential of open-access omics databases for identifying genetic evidence pertaining to devices.
Material and methods A retrospective analysis of Agency for Healthcare Research and Quality (AHRQ)/Healthcare Cost and Utilization Project (HCUPNet) data (2002–2011) was focused on the ventilation-related iatrogenic pneumothorax (Vent-IP) outcome in discharges with mechanical ventilation (MV) and continuous positive airway pressure (CPAP). The derived epidemiologic evidence was analyzed in conjunction with pre-existing genomic data from Gene Expression Omnibus/National Center for Biotechnology Information and other databases.
Results AHRQ/HCUPNet epidemiologic evidence showed that annual occurrence of Vent-IP did not decrease over a decade. While the Vent-IP risk associated with noninvasive CPAP comprised about 0.5%, the Vent-IP risk due to longer-term MV reached 2%. Along with MV posing an independent risk for Vent-IP, female sex and white race were found to be effect modifiers, resulting in the highest Vent-IP risk among mechanically ventilated white females. The Vent-IP risk was also potentiated by comorbidities associated with spontaneous pneumothorax (SP) and fibrosis. Consistent with the epidemiologic evidence, expression profiling in a number of animal models showed that the expression of several collagens and other SP/fibrosis-related genes was modified by ventilation settings.
Conclusion Integration of complementary genetic evidence into epidemiologic analysis can lead to a cost- and time-efficient discovery of the risk predictors and markers and thus can facilitate more efficient marker-based evaluation of medical product performance.
Optimizing vitamin D naming conventions in computerized order entry to support high-value care
Authors: White Andrew A , McKinney Christy M , Hoffman Noah G , Sutton Paul R 
Published: April 2016

Objective: To reduce wasteful ordering of rare 1,25-OH vitamin D lab tests through use of a noninterruptive decision support tool.
Materials and Methods: We conducted a time series quality improvement study at 2 academic hospitals. The titles of vitamin D tests and the order in which they appeared in search results were changed to reflect the purpose and rarity of the tests. We used interruptive time series analyses to evaluate the changes we made.
Results: The estimated number of monthly tests ordered at the 2 hospitals increased, by 24.8 and 14.2, following the introduction of computerized provider order entry (CPOE) (both P &lt; .001). When we changed the titles of the tests, the estimated number of monthly tests decreased at the 2 hospitals, by 22.1 and 11.3 (both P &lt; .001). The search order did not affect test utilization.
Discussion: Changing catalog names in CPOE systems for infrequently used tests can reduce unintentional overuse. Users may prefer this to interruptive or restrictive interventions.
Conclusion: CPOE vendors and users should refine interfaces by incorporating human factors engineering. Health care institutions should monitor test utilization for unintended changes after CPOE implementation.
An appraisal of published usability evaluations of electronic health records via systematic review
Authors: Ellsworth Marc A , Dziadzko Mikhail , O'Horo John C , Farrell Ann M , Zhang Jiajie , Herasevich Vitaly 
Published: April 2016

Objective: In this systematic review, we aimed to evaluate methodological and reporting trends present in the current literature by investigating published usability studies of electronic health records (EHRs).
Methods: A literature search was conducted for articles published through January 2015 using MEDLINE (Ovid), EMBASE, Scopus, and Web of Science, supplemented by citation and reference list reviews. Studies were included if they tested the usability of hospital and clinic EHR systems in the inpatient, outpatient, emergency department, or operating room setting.
Results: A total of 4848 references were identified for title and abstract screening. Full text screening was performed for 197 articles, with 120 meeting the criteria for study inclusion.
Conclusion: A review of the literature demonstrates a paucity of quality published studies describing scientifically valid and reproducible usability evaluations at various stages of EHR system development. A lack of formal and standardized reporting of EHR usability evaluation results is a major contributor to this knowledge gap, and efforts to improve this deficiency will be one step of moving the field of usability engineering forward.
Lower alert rates by clustering of related drug interaction alerts
Authors: Heringa Mette , Siderius Hidde , Floor-Schreudering Annemieke , de Smet Peter A G M , Bouvy Marcel L 
Published: April 2016

Objective: We aimed to investigate to what extent clustering of related drug interaction alerts (drug-drug and drug-disease interaction alerts) would decrease the alert rate in clinical decision support systems (CDSSs).
Methods: We conducted a retrospective analysis of drug interaction alerts generated by CDSSs in community pharmacies. Frequently generated combinations of alerts were analyzed for associations in a 5% random data sample (dataset 1). Alert combinations with similar management recommendations were defined as clusters. The alert rate was assessed by simulating a CDSS generating 1 alert per cluster per patient instead of separate alerts. The simulation was performed in dataset 1 and replicated in another 5% data sample (dataset 2).
Results: Data were extracted from the CDSSs of 123 community pharmacies. Dataset 1 consisted of 841 572 dispensed prescriptions and 298 261 drug interaction alerts. Dataset 2 was comparable. Twenty-two frequently occurring alert combinations were identified. Analysis of these associated alert combinations for similar management recommendations resulted in 3 clusters (related to renal function, electrolytes, diabetes, and cardiovascular diseases). Using the clusters in alert generation reduced the alert rate within these clusters by 53–70%. The overall number of drug interaction alerts was reduced by 11% in dataset 1 and by 12% in dataset 2. This corresponds to a decrease of 21 alerts per pharmacy per day.
Discussion and conclusion: Using clusters of drug interaction alerts with similar management recommendations in CDSSs can substantially decrease the overall alert rate. Further research is needed to establish the applicability of this concept in daily practice.
Performance test of RM CLEAN and its evaluation with chi-square value
Authors: Miyashita Yoshimitsu , Ideguchi Shinsuke , Takahashi Keitaro 
Published: April 2016

Abstract
RM CLEAN is a standard method to reconstruct the distribution of cosmic magnetic fields and polarized sources along the line of sight (LOS) from the observed polarization spectrum. This method is similar to the CLEAN algorithm for aperture synthesis of radio telescope images but it is rather unclear in what cases RM CLEAN works well. In this paper, we evaluate the performance of RM CLEAN by simulating spectro-polarimetric observations of two compact sources located in the same LOS, varying the relative initial polarization angle and Faraday depth systematically. In particular, we focus on whether the two polarized sources can be resolved in the Faraday depth space and how well the source parameters can be estimated. We confirm the previous studies that two sources cannot be resolved when they are closely located in the Faraday depth space for specific values of the relative initial polarization angle. Further, we calculate the chi-square value for the fit between the mock polarization spectrum data and the one from RM CLEAN. We find that the chi-square value is not always significantly large even when RM CLEAN gives wrong results.
Breast MRI in the Diagnostic and Preoperative Workup Among Medicare Beneficiaries With Breast Cancer
Authors: Onega Tracy , Weiss Julia E. , Buist Diana S.M. , Tosteson Anna N.A. , Henderson Louise M. , Kerlikowske Karla , Goodrich Martha E. , O’Donoghue Cristina , Wernli Karen J. , DeMartini Wendy B. , Virnig Beth A. , Bennette Caroline S. , Hubbard Rebecca A. 
Published: April 2016

Purpose:
We compared the frequency and sequence of breast imaging and biopsy use for the diagnostic and preoperative workup of breast cancer according to breast magnetic resonance imaging (MRI) use among older women.

Materials and Methods:
Using SEER-Medicare data from 2004 to 2010, we identified women with and without breast MRI as part of their diagnostic and preoperative breast cancer workup and measured the number and sequence of breast imaging and biopsy events per woman.

Results:
A total of 10,766 (20%) women had an MRI in the diagnostic/preoperative period, 32,178 (60%) had mammogram and ultrasound, and 10,669 (20%) had mammography alone. MRI use increased across study years, tripling from 2005 to 2009 (9%–29%). Women with MRI had higher rates of breast imaging and biopsy compared with those with mammogram and ultrasound or those with mammography alone (5.8 vs. 4.1 vs. 2.8, respectively). There were 4254 unique sequences of breast events; the dominant patterns for women with MRI were an MRI occurring at the end of the care pathway. Among women receiving an MRI postdiagnosis, 26% had a subsequent biopsy compared with 51% receiving a subsequent biopsy in the subgroup without MRI.

Conclusions:
Older women who receive breast MRI undergo additional breast imaging and biopsy events. There is much variability in the diagnostic/preoperative work-up in older women, demonstrating the opportunity to increase standardization to optimize care for all women.
Mental Health Utilization Patterns During a Stepped, Collaborative Care Effectiveness Trial for PTSD and Depression in the Military Health System
Authors: Belsher Bradley E. , Jaycox Lisa H. , Freed Michael C. , Evatt Daniel P. , Liu Xian , Novak Laura A. , Zatzick Douglas , Bray Robert M. , Engel Charles C. 
Published: April 2016

Background:
Integrated health care models aim to improve access and continuity of mental health services in general medical settings. STEPS-UP is a stepped, centrally assisted collaborative care model designed to improve posttraumatic stress disorder (PTSD) and depression care by providing the appropriate intensity and type of care based on patient characteristics and clinical complexity. STEPS-UP demonstrated improved PTSD and depression outcomes in a large effectiveness trial conducted in the Military Health System. The objective of this study was to examine differences in mental health utilization patterns between patients in the stepped, centrally assisted collaborative care model relative to patients in the collaborative care as usual-treatment arm.

Methods:
Patients with probable PTSD and/or depression were recruited at 6 large military treatment facilities, and 666 patients were enrolled and randomized to STEPS-UP or usual collaborative care. Utilization data acquired from Military Health System administrative datasets were analyzed to determine mental health service use and patterns. Clinical complexity and patient characteristics were based on self-report questionnaires collected at baseline.

Results:
Compared with the treatment as usual arm, STEPS-UP participants received significantly more mental health services and psychiatric medications across primary and specialty care settings during the year of their participation. Patterns of service use indicated that greater clinical complexity was associated with increased service use in the STEPS-UP group, but not in the usual-care group.

Conclusions:
Results suggest that stepped, centrally assisted collaborative care models may increase the quantity of mental health services patients receive, while efficiently matching care on the basis of the clinical complexity of patients.