Biomedical Resources Research Articles

Unanswered clinical questions: a survey of specialists and primary care providers.

ObjectiveWith the myriad of cases presented to clinicians every day at our integrated academic health system, clinical questions are bound to arise. Clinicians need to recognize these knowledge gaps and act on them. However, for many reasons, clinicians might not seek answers to these questions. Our goal was to investigate the rationale and process behind these unanswered clinical questions. Subsequently, we explored the use of biomedical information resources among specialists and primary care providers and identified ways to promote more informed clinical decision making.MethodsWe conducted a survey to assess how practitioners identify and respond to information gaps, their background knowledge of search tools and strategies, and their usage of and comfort level with technology.ResultsMost of the 292 respondents encountered clinical questions at least a few times per week. While the vast majority often or always pursued answers, time was the biggest barrier for not following through on questions. Most respondents did not have any formal training in searching databases, were unaware of many digital resources, and indicated a need for resources and services that could be provided at the point of care.ConclusionsWhile the reasons for unanswered clinical questions varied, thoughtful review of the responses suggested that a combination of educational strategies, embedded librarian services, and technology applications could help providers pursue answers to their clinical questions, enhance patient safety, and contribute to patient-based, self-directed learning.

Biomedical Resource Oncology and Data Mining to Enable Resource Discovery in Medical, Medicinal, Clinical, Pharmaceutical, Chemical and Translational Research and Their Applications in Cancer Research

Biomedical Resource Oncology and Data Mining to Enable Resource Discovery in Medical, Medicinal, Clinical, Pharmaceutical, Chemical and Translational Research and Their Applications in Cancer Research

“Life Given Straight from the Heart”: Settler Militarism, Biopolitics, and Public Health in Hawai‘i during World War II

This essay analyzes the interconnected regimes of US settler colonialism and militarization in Hawai‘i by examining the expansion of public health programs under martial law during World War II. I explore the biopolitics of wartime public health programs that targeted health and hygiene practices as a means of military surveillance, territorial organization, and population control. The mandatory immunization program vaccinated individuals in order to maintain an uncontaminated military base in Hawai‘i and produce able “Americanized” bodies that could contribute to the US military project. The Honolulu Blood Bank collected donations from these bodies as supplements to military strength and promoted blood donation through racialized tropes of patriotism and loyalty. I explore how the US military government employed a provisional wartime “racial liberalism” in Hawai‘i in order to incorporate all peoples in these programs regardless of race, yet these projects simultaneously intensified the classification and regulation of racially differentiated groups and erased Hawaiian indigeneity. As Hawai‘i became a biopolitical center of the Pacific War, the coercive conditions of martial law and wartime racial discourse compelled all peoples in Hawai‘i to cultivate biological health, only to then surrender it for the extraction of biomedical resources—all in the name of military security.

Editor’s Note

Editor’s Note Mari Yoshihara This issue of American Quarterly goes into production in the wake of the 2016 election, and by the time this issue goes to print, Donald Trump will be serving as president of the United States. Even as American studies scholars have long dedicated ourselves to understanding white supremacy, patriarchy, racial capitalism, settler colonialism, and neoliberalism, the election results have been a tremendous shock. The values, policies, and behavior represented by Trump, his supporters, and those he is choosing as his staff are deeply in opposition to the fundamental principles of the ASA community. The election results challenge us to seriously reflect on what it means to do American studies and renew our commitments as scholars, teachers, and activists in this world. What the United States and the world are about to face under the Trump administration—many aspects of which will try to undermine the gains of decades of social activism, others of which will reinforce the injustices and inequalities structured into American society long before this election—all the more underscores the critical importance of rigorous intersectional analysis of American history and society and engaged pedagogy and activism. It is, then, perhaps fitting that we open this issue with an essay on the question of entrance to the White House. Kate Masur examines African Americans’ demands for recognition at White House social functions and whites’ varied reactions during the Lincoln administration. Other essays in this issue also address race, biopower, and regulation of body and space in different political contexts. Juliet Nebolon focuses on public health programs in World War II Hawai‘i to analyze the interconnected regimes of US settler colonialism and militarization, illustrating how wartime racial liberalism compelled all people in Hawai‘i to cultivate health, which then led to the extraction of biomedical resources in the name of military security. John Bloom looks at mandatory bicycle regulation and bicycle stakeouts in Washington, DC, in 1963–2009 as a mechanism for policing of racial boundaries in public spaces, which fore-shadowed the racialized law and order policies practiced across the nation. Joseph Darda and Theresa Geller both analyze the cultural narratives about the state and war in film and television. Darda chronicles how the George H. W. Bush administration revised the cultural narrative of the Vietnam War by militarizing the humanitarian ethic of the antiwar movement in the Persian Gulf and how the militarization of humanitarian affect contained liberal anti-war [End Page v] discourse. Geller’s study of The X-Files uses Fredric Jameson’s methods to illustrate the show’s allegorization of the dialectical relationship between the governmental conspiracy and the effects of its unchecked power on marginalized communities. This issue carries four book reviews on new scholarship in diverse topics. In her review of three books that reassess the analytic of intersectionality, Jennifer C. Nash problematizes the impulse toward a “correct” reading and historicization of intersectionality and calls for a sustained interrogation of the ways in which “black woman” haunts the unconscious of the fields. This review provides a useful context for Mecca Jamilah Sullivan’s discussion of three works on black sexuality, erotic labor, and pornography. Patrick Wilz reviews four books on discourses of opposition in the twentieth century. John Worsencroft’s review examines four works on militarized state, community, and citizenship. In an event review, Kristin Moriah discusses the paradox of the seeming ascendance of ethnic diversity on the popular stage in her review of the 2016 Broadway musical Shuffle Along, or The Making of the Musical Sensation of 1921 and All That Followed. [End Page vi] Copyright © 2017 The American Studies Association

The Human Phenotype Ontology in 2017.

Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

ExplorinG frailty and mild cognitive impairmEnt in kidney tRansplantation to predict biomedicAl, psychosocial and health cost outcomeS (GERAS): protocol of a nationwide prospective cohort study.

To present the rationale, design and methodology of the GERAS project, which examines whether assessment of frailty and mild cognitive impairment could enhance risk prediction for biomedical, psychosocial outcomes and foster efficient resource allocation in kidney transplantation. For the burgeoning cohort of older patients considered for kidney transplantation, evidence gaps regarding frailty and mild cognitive impairment limit clinical decision-making and medical management. As known risk factors for 'hard' clinical outcomes in chronic illness, both require further study in transplantation. Integrating these and other bio-psychosocial factors into a comprehensive pre-transplant patient assessment will provide insights regarding economic implications and may improve risk prediction. A nation-wide multi-centre prospective cohort study nested in the Swiss Transplant Cohort Study. Our nationally representative convenience sample includes 250 adult kidney transplant recipients. Data sources include the Swiss Transplant Cohort Study and primary data collected at time of transplantation, 6months, 1 and 2years post-transplant via established measures (the Montreal Cognitive Assessment, Psychosocial Questionnaire, Fried Frailty Instrument and a blood analysis), investigator-developed instruments and datasets compiled by hospitals' management control units, sickness funds, the Swiss Federal Statistical Office and the European Renal Association. Descriptive, competing risk survival and mixed effects analyses will be performed. Research Ethics Committee approval was obtained in January 2016. This pioneering project jointly examines frailty and mild cognitive impairment from bio-psychosocial and health economic perspectives. Results may significantly inform risk prediction, care tailoring and resource optimization to improve health outcomes in the ageing kidney transplant cohort.

Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource.

Biomedical literature articles and narrative content from Electronic Health Records (EHRs) both constitute rich sources of disease-phenotype information. Phenotype concepts may be mentioned in text in multiple ways, using phrases with a variety of structures. This variability stems partly from the different backgrounds of the authors, but also from the different writing styles typically used in each text type. Since EHR narrative reports and literature articles contain different but complementary types of valuable information, combining details from each text type can help to uncover new disease-phenotype associations. However, the alternative ways in which the same concept may be mentioned in each source constitutes a barrier to the automatic integration of information. Accordingly, identification of the unique concepts represented by phrases in text can help to bridge the gap between text types. We describe our development of a novel method, PhenoNorm, which integrates a number of different similarity measures to allow automatic linking of phenotype concept mentions to known concepts in the UMLS Metathesaurus, a biomedical terminological resource. PhenoNorm was developed using the PhenoCHF corpus—a collection of literature articles and narratives in EHRs, annotated for phenotypic information relating to congestive heart failure (CHF). We evaluate the performance of PhenoNorm in linking CHF-related phenotype mentions to Metathesaurus concepts, using a newly enriched version of PhenoCHF, in which each phenotype mention has an expert-verified link to a concept in the UMLS Metathesaurus. We show that PhenoNorm outperforms a number of alternative methods applied to the same task. Furthermore, we demonstrate PhenoNorm’s wider utility, by evaluating its ability to link mentions of various other types of medically-related information, occurring in texts covering wider subject areas, to concepts in different terminological resources. We show that PhenoNorm can maintain performance levels, and that its accuracy compares favourably to other methods applied to these tasks.

Smart Spaces for Adaptive Information Integration in Bioinformatics

Bioinformatics is reaping the benefits of advances in Semantic Web technology thanks to the growing number of available biomedical web resources and portals. Although positive in general, this abundance poses practical challenges to researchers who must be skilled in techniques for retrieving and integrating sparse and complex contents, and thereof calls for more “intelligent” and user friendly ways of interaction to easily get information. With the aim of making available the intelligent functionality of smart systems, this paper presents SSAIIB (Smart Spaces for Adaptive Information Integration in Bioinformatics), a reference framework for designing bioinformatics smart applications that support discovering, aggregating and delivering contents from web resources according to user’s goals, tasks and concerns. The “Smart Spaces” are software environments whose smartness lies in their ability to adaptively accomplish specific user’s activities such as the exploiting content from biomedical resources, integrating data captured from different sources, supporting data analytics, etc. SSAIIB is structured around two main technologies: service oriented architectures and software agents. In particular, it relies on mechanisms for dynamically assembling suitable services and the use of agents as a natural metaphor for both modelling user’s activities and accessing web resources. A case study is presented that shows the application of SSAIIB to the design and the implementation of a smart space for annotating biomedical texts.

BIOMedical Search Engine Framework: Lightweight and customized implementation of domain-specific biomedical search engines

Background and objectivesText mining and semantic analysis approaches can be applied to the construction of biomedical domain-specific search engines and provide an attractive alternative to create personalized and enhanced search experiences. Therefore, this work introduces the new open-source BIOMedical Search Engine Framework for the fast and lightweight development of domain-specific search engines. The rationale behind this framework is to incorporate core features typically available in search engine frameworks with flexible and extensible technologies to retrieve biomedical documents, annotate meaningful domain concepts, and develop highly customized Web search interfaces. MethodsThe BIOMedical Search Engine Framework integrates taggers for major biomedical concepts, such as diseases, drugs, genes, proteins, compounds and organisms, and enables the use of domain-specific controlled vocabulary. Technologies from the Typesafe Reactive Platform, the AngularJS JavaScript framework and the Bootstrap HTML/CSS framework support the customization of the domain-oriented search application. Moreover, the RESTful API of the BIOMedical Search Engine Framework allows the integration of the search engine into existing systems or a complete web interface personalization. ResultsThe construction of the Smart Drug Search is described as proof-of-concept of the BIOMedical Search Engine Framework. This public search engine catalogs scientific literature about antimicrobial resistance, microbial virulence and topics alike. The keyword-based queries of the users are transformed into concepts and search results are presented and ranked accordingly. The semantic graph view portraits all the concepts found in the results, and the researcher may look into the relevance of different concepts, the strength of direct relations, and non-trivial, indirect relations. The number of occurrences of the concept shows its importance to the query, and the frequency of concept co-occurrence is indicative of biological relations meaningful to that particular scope of research. Conversely, indirect concept associations, i.e. concepts related by other intermediary concepts, can be useful to integrate information from different studies and look into non-trivial relations. ConclusionsThe BIOMedical Search Engine Framework supports the development of domain-specific search engines. The key strengths of the framework are modularity and extensibilityin terms of software design, the use of open-source consolidated Web technologies, and the ability to integrate any number of biomedical text mining tools and information resources. Currently, the Smart Drug Search keeps over 1,186,000 documents, containing more than 11,854,000 annotations for 77,200 different concepts. The Smart Drug Search is publicly accessible at http://sing.ei.uvigo.es/sds/. The BIOMedical Search Engine Framework is freely available for non-commercial use at https://github.com/agjacome/biomsef.

Cohort profile of the UK Biobank: diagnosis and characteristics of cerebrovascular disease

PurposeThe UK Biobank is a large-scale biomedical resource, containing sociodemographic and medical information, including data on a previous diagnosis of stroke or transient ischaemic attack (TIA). We described these participants...

A method for the development of disease-specific reference standards vocabularies from textual biomedical literature resources.

Disease-specific vocabularies are fundamental to many knowledge-based intelligent systems and applications like text annotation, cohort selection, disease diagnostic modeling, and therapy recommendation. Reference standards are critical in the development and validation of automated methods for disease-specific vocabularies. The goal of the present study is to design and test a generalizable method for the development of vocabulary reference standards from expert-curated, disease-specific biomedical literature resources. We formed disease-specific corpora from literature resources like textbooks, evidence-based synthesized online sources, clinical practice guidelines, and journal articles. Medical experts annotated and adjudicated disease-specific terms in four classes (i.e., causes or risk factors, signs or symptoms, diagnostic tests or results, and treatment). Annotations were mapped to UMLS concepts. We assessed source variation, the contribution of each source to build disease-specific vocabularies, the saturation of the vocabularies with respect to the number of used sources, and the generalizability of the method with different diseases. The study resulted in 2588 string-unique annotations for heart failure in four classes, and 193 and 425 respectively for pulmonary embolism and rheumatoid arthritis in treatment class. Approximately 80% of the annotations were mapped to UMLS concepts. The agreement among heart failure sources ranged between 0.28 and 0.46. The contribution of these sources to the final vocabulary ranged between 18% and 49%. With the sources explored, the heart failure vocabulary reached near saturation in all four classes with the inclusion of minimal six sources (or between four to seven sources if only counting terms occurred in two or more sources). It took fewer sources to reach near saturation for the other two diseases in terms of the treatment class. We developed a method for the development of disease-specific reference vocabularies. Expert-curated biomedical literature resources are substantial for acquiring disease-specific medical knowledge. It is feasible to reach near saturation in a disease-specific vocabulary using a relatively small number of literature sources.

Preventive and Therapeutic Effects of Chinese Herbal Compounds against Hepatocellular Carcinoma.

Traditional Chinese Medicines, unique biomedical and pharmaceutical resources, have been widely used for hepatocellular carcinoma (HCC) prevention and treatment. Accumulated Chinese herb-derived compounds with significant anti-cancer effects against HCC have been identified. Chinese herbal compounds are effective in preventing carcinogenesis, inhibiting cell proliferation, arresting cell cycle, inducing apoptosis, autophagy, cell senescence and anoikis, inhibiting epithelial-mesenchymal transition, metastasis and angiogenesis, regulating immune function, reversing drug resistance and enhancing the effects of chemotherapy in HCC. This paper comprehensively reviews these compounds and their effects on HCC. Finally, the perspectives and rational application of herbal compounds for HCC management are discussed.

Resource Disambiguator for the Web: Extracting Biomedical Resources and Their Citations from the Scientific Literature.

The NIF Registry developed and maintained by the Neuroscience Information Framework is a cooperative project aimed at cataloging research resources, e.g., software tools, databases and tissue banks, funded largely by governments and available as tools to research scientists. Although originally conceived for neuroscience, the NIF Registry has over the years broadened in the scope to include research resources of general relevance to biomedical research. The current number of research resources listed by the Registry numbers over 13K. The broadening in scope to biomedical science led us to re-christen the NIF Registry platform as SciCrunch. The NIF/SciCrunch Registry has been cataloging the resource landscape since 2006; as such, it serves as a valuable dataset for tracking the breadth, fate and utilization of these resources. Our experience shows research resources like databases are dynamic objects, that can change location and scope over time. Although each record is entered manually and human-curated, the current size of the registry requires tools that can aid in curation efforts to keep content up to date, including when and where such resources are used. To address this challenge, we have developed an open source tool suite, collectively termed RDW: Resource Disambiguator for the (Web). RDW is designed to help in the upkeep and curation of the registry as well as in enhancing the content of the registry by automated extraction of resource candidates from the literature. The RDW toolkit includes a URL extractor from papers, resource candidate screen, resource URL change tracker, resource content change tracker. Curators access these tools via a web based user interface. Several strategies are used to optimize these tools, including supervised and unsupervised learning algorithms as well as statistical text analysis. The complete tool suite is used to enhance and maintain the resource registry as well as track the usage of individual resources through an innovative literature citation index honed for research resources. Here we present an overview of the Registry and show how the RDW tools are used in curation and usage tracking.

Biobanks and Their Clinical Application and Informatics Challenges.

Biobanks are one of the most important biomedical research resources and contribute to the development of biomarker detection, molecular diagnosis, translational medicine, and multidisciplinary disease research, as well as studies of interactions between genetic and environmental or lifestyle factors. Aiming for the wide clinical application of biobanks, biobanking efforts have recently switched from a focus on accumulating samples to both formalizing and sustaining collections in light of the rapid progress in the fields of personalized medicine and bioinformatics analysis. With the emergence of novel molecular diagnostic technologies, although the bioinformatics platform of biobanks ensures reliable bioinformatics analysis of patient samples, there are a series of challenges facing biobanks in terms of the overall harmonization of policies, integrated processes, and local informatics solutions across the network. Further, there is a controversy regarding the increased role of ethical boards, governance, and accreditation bodies in ensuring that collected samples have sufficient informatics capabilities to be used in biobanks. In this volume, we present a selection of current issues on the inevitable challenges of the clinical application of biobanks in informatics.

Exploiting Biomedical Web Resources: A Case Study

An increasing number of web resources continue to be extensively used by healthcare operators to obtain more accurate diagnostic results. In particular, health care is reaping the benefits of technological advances in genomic for facing the demand of genetic tests that allow a better comprehension of diagnostic results. Within this context, Gene Ontology (GO) is a popular and effective mean for extracting knowledge from a list of genes and evaluating their semantic similarity. This paper investigates about the potential and any limits of GO ontology as support for capturing information about a set of genes which are supposed to play a significant role in a pathological condition. In particular, we present a case study that exploits some biomedical web resources for devising several groups of functionally coherent genes and experiments about the evaluation of their semantic similarity over GO. Due to the GO structure and content, results reveal limitations that not affect the evaluation of the semantic similarity when genes exhibit simple correlations but influence the estimation of the relatedness of genes belonging to complex organizations.

Cell toxicity of cocoon extracts for biomedical resources

Cell toxicity of cocoon extracts for biomedical resources

Semantic biomedical resource discovery: a Natural Language Processing framework.

BackgroundA plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate. In parallel, specialized repositories are been developed, indexing numerous clinical and biomedical tools. The main drawback of such repositories is the difficulty in locating appropriate resources for a clinical or biomedical decision task, especially for non-Information Technology expert users. In parallel, although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications has been slow and lags behind progress in the general NLP domain.The aim of the present study is to investigate the use of semantics for biomedical resources annotation with domain specific ontologies and exploit Natural Language Processing methods in empowering the non-Information Technology expert users to efficiently search for biomedical resources using natural language.MethodsA Natural Language Processing engine which can “translate” free text into targeted queries, automatically transforming a clinical research question into a request description that contains only terms of ontologies, has been implemented. The implementation is based on information extraction techniques for text in natural language, guided by integrated ontologies. Furthermore, knowledge from robust text mining methods has been incorporated to map descriptions into suitable domain ontologies in order to ensure that the biomedical resources descriptions are domain oriented and enhance the accuracy of services discovery. The framework is freely available as a web application at (http://calchas.ics.forth.gr/).ResultsFor our experiments, a range of clinical questions were established based on descriptions of clinical trials from the ClinicalTrials.gov registry as well as recommendations from clinicians. Domain experts manually identified the available tools in a tools repository which are suitable for addressing the clinical questions at hand, either individually or as a set of tools forming a computational pipeline. The results were compared with those obtained from an automated discovery of candidate biomedical tools. For the evaluation of the results, precision and recall measurements were used. Our results indicate that the proposed framework has a high precision and low recall, implying that the system returns essentially more relevant results than irrelevant.ConclusionsThere are adequate biomedical ontologies already available, sufficiency of existing NLP tools and quality of biomedical annotation systems for the implementation of a biomedical resources discovery framework, based on the semantic annotation of resources and the use on NLP techniques. The results of the present study demonstrate the clinical utility of the application of the proposed framework which aims to bridge the gap between clinical question in natural language and efficient dynamic biomedical resources discovery.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0200-4) contains supplementary material, which is available to authorized users.

Is it Time for Cognitive Bioinformatics?

The concept of cognitive bioinformatics has been proposed for structuring of knowledge in the field of molecular biology. While cognitive science is considered as “thinking about the process of thinking”, cognitive bioinformatics strives to capture the process of thought and analysis as applied to the challenging intersection of diverse fields such as biology, informatics, and computer science collectively known as bioinformatics. Ten years ago cognitive bioinformatics was introduced as a model of the analysis performed by scientists working with molecular biology and biomedical web resources. At present, the concept of cognitive bioinformatics can be examined in the context of the opportunities represented by the information “data deluge” of life sciences technologies. The unbalanced nature of accumulating information along with some challenges poses currently intractable problems for researchers. The solutions to these problems at the micro-and macro-levels are considered with regards to the role of cognitive approaches in the field of bioinformatics.

How to avoid haste and waste in occupational, environmental and public health research.

The public requires of the medical research community success. The public ultimately provides money for medical research for one purpose only—to generate improvements in patient care. Medical researchers, editors, and peer reviewers should be under no illusions: the public does not support research for the pleasure of watching a cultural event. If improved medical care is not delivered, support for medical research—and hence for medical journals—will dwindle and atrophy.—Horrobin1 The 2014 Lancet series about waste in research posed the question “Why is research that might transform healthcare and reduce health problems not being successfully produced?”2 Indeed, widely disseminated,3 ,4 albeit challenged5 ,6 estimates suggest that most published research findings are false or exaggerated and that 85% of biomedical research resources are wasted.7 Waste is conceptualised in this context as inefficiencies in all aspects of research, including setting priorities, design and conduct of research, regulation and management of research, and failure to publish results.2 We know our successes in identifying strong causes,8 that is, risk factors which causally contribute to disease in a large proportion of cases: the examples of asbestos-related, smoking-related and drinking water-related diseases illustrate the enormous rewards of successful occupational, environmental and public health research. However, in the recent past, potential risk factors of relatively small effects which may have major impacts on public health proved much more difficult to study. One current challenge in this regard is ‘The Plastics Puzzle’9: what research do we need to falsify the hypothesis that ubiquitous synthetic monomers, such as bisphenol A (BPA), and additives to plastics with expected half-lives of centuries, accumulate in wildlife and humans to possibly become a critical burden?10–14 This journal has continuously addressed facets of how we learn from,15 conduct16 and make …

What Factors Influence the Production of Orthopaedic Research in East Africa? A Qualitative Analysis of Interviews

Research addressing the burden of musculoskeletal disease in low- and middle-income countries does not reflect the magnitude of the epidemic in these countries as only 9% of the world's biomedical resources are devoted to addressing problems that affect the health of 90% of the world's population. Little is known regarding the barriers to and drivers of orthopaedic surgery research in such resource-poor settings, the knowledge of which would help direct specific interventions for increasing research capacity and help surgeons from high-income countries support the efforts of our colleagues in low- and middle-income countries. We sought to identify through surveying academic orthopaedic surgeons in East Africa: (1) barriers impeding research, (2) factors that support or drive research, and (3) factors that were identified by some surgeons as barriers and others as drivers (what we term barrier-driver overlap) as they considered the production of clinical research in resource-poor environments. Semistructured interviews were conducted with 21 orthopaedic surgeon faculty members at four academic medical centers in Ethiopia, Kenya, Tanzania, and Uganda. Qualitative content analysis of the interviews was conducted using methods based in grounded theory. Grounded theory begins with qualitative data, such as interview transcripts, and analyzes the data for repeated ideas or concepts which then are coded and grouped into categories which allow for identification of subjects or problems that may not have been apparent previously to the interviewer. We identified and quantified 19 barriers to and 21 drivers of orthopaedic surgery research (mentioned n=1688 and n=1729, respectively). Resource, research process, and institutional domains were identified to categorize the barriers (n=7, n=5, n=7, respectively) and drivers (n=7, n=8, n=6, respectively). Resource barriers (46%) were discussed more often by interview subjects compared with the research process (26%) and institutional barriers (28%). Drivers of research discussed at least once were proportionally similar across the three domains. Some themes such as research ethics boards, technology, and literature access occurred with similar frequency as barriers to and drivers of orthopaedic surgery research. The barriers we identified most often among East African academic orthopaedic faculty members focused on resources to accomplish research, followed by institutional barriers, and method or process barriers. Drivers to be fostered included a desire to effect change, collaboration with colleagues, and mentorship opportunities. The identified barriers and drivers of research in East Africa provide a targeted framework for interventions and collaborations with surgeons and organizations from high-resource settings looking to be involved in global health.