Lister Hill Center

Bethesda, MD, United States

Lister Hill Center

Bethesda, MD, United States
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Del Fiol G.,University of Utah | Workman T.E.,Lister Hill Center | Gorman P.N.,Oregon Health And Science University
JAMA Internal Medicine | Year: 2014

IMPORTANCE In making decisions about patient care, clinicians raise questions and are unable to pursue or find answers to most of them. Unanswered questions may lead to suboptimal patient care decisions. OBJECTIVE To systematically review studies that examined the questions clinicians raise in the context of patient care decision making. DATA SOURCES MEDLINE (from 1966), CINAHL (from 1982), and Scopus (from 1947), all through May 26, 2011. STUDY SELECTION Studies that examined questions raised and observed by clinicians (physicians, medical residents, physician assistants, nurse practitioners, nurses, dentists, and care managers) in the context of patient care were independently screened and abstracted by 2 investigators. Of 21 710 citations, 72 met the selection criteria. DATA EXTRACTION AND SYNTHESIS Question frequencywas estimated by pooling data from studies with similar methods. MAIN OUTCOMES AND MEASURES Frequency of questions raised, pursued, and answered and questions by type according to a taxonomy of clinical questions. Thematic analysis of barriers to information seeking and the effects of information seeking on decision making. RESULTS In 11 studies, 7012 questions were elicited through short interviews with clinicians after each patient visit. The mean frequency of questions raised was 0.57 (95%CI, 0.38-0.77) per patient seen, and clinicians pursued 51%(36%-66%) of questions and found answers to 78%(67%-88%) of those they pursued. Overall, 34%of questions concerned drug treatment, and 24%concerned potential causes of a symptom, physical finding, or diagnostic test finding. Clinicians' lack of time and doubt that a useful answer exists were the main barriers to information seeking. CONCLUSIONS AND RELEVANCE Clinicians frequently raise questions about patient care in their practice. Although they are effective at finding answers to questions they pursue, roughly half of the questions are never pursued. This picture has been fairly stable over time despite the broad availability of online evidence resources that can answer these questions. Technology-based solutions should enable clinicians to track their questions and provide just-in-time access to high-quality evidence in the context of patient care decision making. Opportunities for improvement include the recent adoption of electronic health record systems and maintenance of certification requirements. © 2014 American Medical Association. All rights reserved.


News Article | October 31, 2016
Site: globenewswire.com

PHILADELPHIA, Oct. 31, 2016 (GLOBE NEWSWIRE) -- Spark Therapeutics (NASDAQ:ONCE), a fully integrated gene therapy company seeking to transform the lives of patients with debilitating genetic diseases by developing investigational, potentially one-time, life-altering treatments, announced today that members of company management or key investigators will present at the following upcoming conferences: Genome Editing for Gene and Cell Therapy, a Herrenhausen Symposium, at the Herrenhausen Palace Conference Center in Hanover, Germany Oxford Global 2nd Annual Cell & Gene Therapy Congress, at the Radisson Blue in London Credit Suisse 25th Annual Healthcare Conference, at The Phoenician in Scottsdale, AZ NEI/FDA Endpoints Workshop, at the Lister Hill Center – NIH Campus, in Bethesda, MD Retina International Interdisciplinary Open Workshop, at the National 4-H Conference Center, in Chevy Chase, MD Stifel 2016 Healthcare Conference, at the Lotte New York Palace Hotel in New York City About Spark Therapeutics Spark Therapeutics, a fully integrated gene therapy company, is seeking to transform the lives of patients with debilitating genetic diseases by developing investigational, potentially one-time, life-altering treatments. Spark Therapeutics’ validated gene therapy platform is being applied to a range of clinical and preclinical programs addressing serious genetic diseases, including inherited retinal diseases, liver-mediated diseases such as hemophilia, and neurodegenerative diseases. Spark Therapeutics’ validated platform successfully has delivered proof-of-concept data with investigational gene therapies in the retina and liver. Spark Therapeutics has reported top-line results from a pivotal Phase 3 clinical trial for its most advanced product candidate, voretigene neparvovec (formerly referred to as SPK-RPE65), a potential treatment of a rare genetic blinding condition. Voretigene neparvovec has received both breakthrough therapy and orphan product designations. Spark Therapeutics’ hemophilia franchise has two lead assets: SPK-9001 in a Phase 1/2 trial for hemophilia B being developed under a collaboration with Pfizer and SPK-8011, a preclinical candidate for hemophilia A to which Spark Therapeutics retains global commercialization rights. To learn more, please visit www.sparktx.com.


Morid M.A.,University of Utah | Fiszman M.,Lister Hill Center | Raja K.,Northwestern University | Jonnalagadda S.R.,Northwestern University | Del Fiol G.,University of Utah
Journal of Biomedical Informatics | Year: 2016

Most patient care questions raised by clinicians can be answered by online clinical knowledge resources. However, important barriers still challenge the use of these resources at the point of care. Objective: To design and assess a method for extracting clinically useful sentences from synthesized online clinical resources that represent the most clinically useful information for directly answering clinicians' information needs. Materials and methods: We developed a Kernel-based Bayesian Network classification model based on different domain-specific feature types extracted from sentences in a gold standard composed of 18 UpToDate documents. These features included UMLS concepts and their semantic groups, semantic predications extracted by SemRep, patient population identified by a pattern-based natural language processing (NLP) algorithm, and cue words extracted by a feature selection technique. Algorithm performance was measured in terms of precision, recall, and F-measure. Results: The feature-rich approach yielded an F-measure of 74% versus 37% for a feature co-occurrence method (p < 0.001). Excluding predication, population, semantic concept or text-based features reduced the F-measure to 62%, 66%, 58% and 69% respectively (p < 0.01). The classifier applied to Medline sentences reached an F-measure of 73%, which is equivalent to the performance of the classifier on UpToDate sentences (p = 0.62). Conclusions: The feature-rich approach significantly outperformed general baseline methods. This approach significantly outperformed classifiers based on a single type of feature. Different types of semantic features provided a unique contribution to overall classification performance. The classifier's model and features used for UpToDate generalized well to Medline abstracts. © 2016 Elsevier Inc.


Mishra R.,University of Utah | Del Fiol G.,University of Utah | Kilicoglu H.,Lister Hill Center | Jonnalagadda S.,Natural Language Processing Group | Fiszman M.,Lister Hill Center
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium | Year: 2013

Clinicians raise several information needs in the course of care. Most of these needs can be met by online health knowledge resources such as UpToDate. However, finding relevant information in these resources often requires significant time and cognitive effort. To design and assess algorithms for extracting from UpToDate the sentences that represent the most clinically useful information for patient care decision making. We developed algorithms based on semantic predications extracted with SemRep, a semantic natural language processing parser. Two algorithms were compared against a gold standard composed of UpToDate sentences rated in terms of clinical usefulness. Clinically useful sentences were strongly correlated with predication frequency (correlation= 0.95). The two algorithms did not differ in terms of top ten precision (53% vs. 49%; p=0.06). Semantic predications may serve as the basis for extracting clinically useful sentences. Future research is needed to improve the algorithms.


Lin M.C.,University of Utah | Vreeman D.J.,Regenstrief Institute Inc. | Vreeman D.J.,Indiana University | McDonald C.J.,Lister Hill Center | And 2 more authors.
Methods of Information in Medicine | Year: 2011

Objectives: We characterized the use of laboratory LOINC® codes in three large in - stitutions, focused on the following questions: 1) How many local codes had been voluntarily mapped to LOINC codes by each institution? 2) Could additional mappings be found by expert manual review for any local codes that were not initially mapped to LOINC codes by the local institution? and 3) Are there any common characteristics of unmapped local codes that might explain why some local codes were not mapped to LOINC codes by the local institution? Methods: With Institutional Review Board (IRB) approval, we obtained deidentified data from three large institutions. We calculated the percentage of local codes that have been mapped to LOINC by personnel at each of the institutions. We also analyzed a sample of unmapped local codes to determine whether any additional LOINC mappings could be made and identify common characteristics that might explain why some local codes did not have mappings. Results: Concept type coverage and concept token coverage (volume of instance data covered) of local codes mapped to LOINC codes were 0.44/0.59, 0.78/0.78 and 0.79/ - 0.88 for ARUP, Intermountain, and Regenstrief, respectively. After additional expert manual mapping, the results showed mapping rates of 0.63/0.72, 0.83/0.80 and 0.88/0.90, respectively. After excluding local codes which were not useful for inter-insti - tutional data exchange, the mapping rates became 0.73/0.79, 0.90/0.99 and 0.93/0.997, respectively. Conclusions: Local codes for two institutions could be mapped to LOINC codes with 99% or better concept token coverage, but mapping for a third institution (a reference laboratory) only achieved 79% concept token coverage. Our research supports the conclusions of others that not all local codes should be assigned LOINC codes. There should also be public discussions to develop more precise rules for when LOINC codes should be assigned. © Schattauer 2011.


Lin M.C.,University of Utah | Vreeman D.J.,Regenstrief Institute Inc. | Vreeman D.J.,Indiana University | McDonald C.J.,Lister Hill Center | And 2 more authors.
Journal of Biomedical Informatics | Year: 2012

Objectives: We wanted to develop a method for evaluating the consistency and usefulness of LOINC code use across different institutions, and to evaluate the degree of interoperability that can be attained when using LOINC codes for laboratory data exchange. Our specific goals were to: (1) Determine if any contradictory knowledge exists in LOINC. (2) Determine how many LOINC codes were used in a truly interoperable fashion between systems. (3) Provide suggestions for improving the semantic interoperability of LOINC. Methods: We collected Extensional Definitions (EDs) of LOINC usage from three institutions. The version space approach was used to divide LOINC codes into small sets, which made auditing of LOINC use across the institutions feasible. We then compared pairings of LOINC codes from the three institutions for consistency and usefulness. Results: The number of LOINC codes evaluated were 1917, 1267 and 1693 as obtained from ARUP, Intermountain and Regenstrief respectively. There were 2022, 2030, and 2301 version spaces among ARUP and Intermountain, Intermountain and Regenstrief and ARUP and Regenstrief respectively. Using the EDs as the gold standard, there were 104, 109 and 112 pairs containing contradictory knowledge and there were 1165, 765 and 1121 semantically interoperable pairs. The interoperable pairs were classified into three levels: (1) Level I - No loss of meaning, complete information was exchanged by identical codes. (2) Level II - No loss of meaning, but processing of data was needed to make the data completely comparable. (3) Level III - Some loss of meaning. For example, tests with a specific 'method' could be rolled-up with tests that were 'methodless'. Conclusions: There are variations in the way LOINC is used for data exchange that result in some data not being truly interoperable across different enterprises. To improve its semantic interoperability, we need to detect and correct any contradictory knowledge within LOINC and add computable relationships that can be used for making reliable inferences about the data. The LOINC committee should also provide detailed guidance on best practices for mapping from local codes to LOINC codes and for using LOINC codes in data exchange. © 2012 Elsevier Inc.


PubMed | University of Utah, Northwestern University and Lister Hill Center
Type: | Journal: Journal of biomedical informatics | Year: 2016

Most patient care questions raised by clinicians can be answered by online clinical knowledge resources. However, important barriers still challenge the use of these resources at the point of care.To design and assess a method for extracting clinically useful sentences from synthesized online clinical resources that represent the most clinically useful information for directly answering clinicians information needs.We developed a Kernel-based Bayesian Network classification model based on different domain-specific feature types extracted from sentences in a gold standard composed of 18 UpToDate documents. These features included UMLS concepts and their semantic groups, semantic predications extracted by SemRep, patient population identified by a pattern-based natural language processing (NLP) algorithm, and cue words extracted by a feature selection technique. Algorithm performance was measured in terms of precision, recall, and F-measure.The feature-rich approach yielded an F-measure of 74% versus 37% for a feature co-occurrence method (p<0.001). Excluding predication, population, semantic concept or text-based features reduced the F-measure to 62%, 66%, 58% and 69% respectively (p<0.01). The classifier applied to Medline sentences reached an F-measure of 73%, which is equivalent to the performance of the classifier on UpToDate sentences (p=0.62).The feature-rich approach significantly outperformed general baseline methods. This approach significantly outperformed classifiers based on a single type of feature. Different types of semantic features provided a unique contribution to overall classification performance. The classifiers model and features used for UpToDate generalized well to Medline abstracts.


Mishra R.,University of Utah | Bian J.,University of Utah | Bian J.,Intermountain Healthcare | Fiszman M.,Lister Hill Center | And 4 more authors.
Journal of Biomedical Informatics | Year: 2014

Objective: The amount of information for clinicians and clinical researchers is growing exponentially. Text summarization reduces information as an attempt to enable users to find and understand relevant source texts more quickly and effortlessly. In recent years, substantial research has been conducted to develop and evaluate various summarization techniques in the biomedical domain. The goal of this study was to systematically review recent published research on summarization of textual documents in the biomedical domain. Materials and methods: MEDLINE (2000 to October 2013), IEEE Digital Library, and the ACM digital library were searched. Investigators independently screened and abstracted studies that examined text summarization techniques in the biomedical domain. Information is derived from selected articles on five dimensions: input, purpose, output, method and evaluation. Results: Of 10,786 studies retrieved, 34 (0.3%) met the inclusion criteria. Natural language processing (17; 50%) and a hybrid technique comprising of statistical, Natural language processing and machine learning (15; 44%) were the most common summarization approaches. Most studies (28; 82%) conducted an intrinsic evaluation. Discussion: This is the first systematic review of text summarization in the biomedical domain. The study identified research gaps and provides recommendations for guiding future research on biomedical text summarization. Conclusion: Recent research has focused on a hybrid technique comprising statistical, language processing and machine learning techniques. Further research is needed on the application and evaluation of text summarization in real research or patient care settings. © 2014 Elsevier Inc.


Jonnalagadda S.R.,Mayo Medical School | Del Fiol G.,University of Utah | Medlin R.,University of North Carolina at Chapel Hill | Weir C.,University of Utah | And 3 more authors.
Journal of the American Medical Informatics Association | Year: 2013

Objective: Online health knowledge resources contain answers to most of the information needs raised by clinicians in the course of care. However, significant barriers limit the use of these resources for decisionmaking, especially clinicians' lack of time. In this study we assessed the feasibility of automatically generating knowledge summaries for a particular clinical topic composed of relevant sentences extracted from Medline citations. Methods: The proposed approach combines information retrieval and semantic information extraction techniques to identify relevant sentences from Medline abstracts. We assessed this approach in two case studies on the treatment alternatives for depression and Alzheimer's disease. Results: A total of 515 of 564 (91.3%) sentences retrieved in the two case studies were relevant to the topic of interest. About one-third of the relevant sentences described factual knowledge or a study conclusion that can be used for supporting information needs at the point of care. Conclusions: The high rate of relevant sentences is desirable, given that clinicians' lack of time is one of the main barriers to using knowledge resources at the point of care. Sentence rank was not significantly associated with relevancy, possibly due to most sentences being highly relevant. Sentences located closer to the end of the abstract and sentences with treatment and comparative predications were likely to be conclusive sentences. Our proposed technical approach to helping clinicians meet their information needs is promising. The approach can be extended for other knowledge resources and information need types.


Hanauer D.A.,University of Michigan | Saeed M.,University of Michigan | Zheng K.,University of Michigan | Mei Q.,University of Michigan | And 3 more authors.
Journal of the American Medical Informatics Association | Year: 2014

Objective: We describe experiments designed to determine the feasibility of distinguishing known from novel associations based on a clinical dataset comprised of International Classification of Disease, V.9 (ICD-9) codes from 1.6 million patients by comparing them to associations of ICD-9 codes derived from 20.5 million Medline citations processed using MetaMap. Associations appearing only in the clinical dataset, but not in Medline citations, are potentially novel. Methods: Pairwise associations of ICD-9 codes were independently identified in both the clinical and Medline datasets, which were then compared to quantify their degree of overlap. We also performed a manual review of a subset of the associations to validate how well MetaMap performed in identifying diagnoses mentioned in Medline citations that formed the basis of the Medline associations. Results: The overlap of associations based on ICD-9 codes in the clinical and Medline datasets was low: only 6.6% of the 3.1 million associations found in the clinical dataset were also present in the Medline dataset. Further, a manual review of a subset of the associations that appeared in both datasets revealed that cooccurring diagnoses from Medline citations do not always represent clinically meaningful associations. Discussion: Identifying novel associations derived from large clinical datasets remains challenging. Medline as a sole data source for existing knowledge may not be adequate to filter out widely known associations. Conclusions: In this study, novel associations were not readily identified. Further improvements in accuracy and relevance for tools such as MetaMap are needed to realize their expected utility.

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