The presence of wild edible plants and determinants influencing their harvest, consumption, and conservation in south eastern Bhutan.

DEFINITION: Wild edible plants (WEPs) grow naturally in self-maintaining ecosystems. WEPs are harvested for consumption, sale, and medicinal uses. We hypothesize that WEPs play a major role in supplying food and generating income for the rural people in a world that is increasingly recognising its emerging conservation issues. We tested this hypothesis by identifying the reasons for harvest, consumption, and conservation of WEPs using focus group discussion, field observations and questionnaire surveys in south eastern Bhutan in late 2019. METHODS: Focused group discussions were held with the local people to identify reasons for harvest and consumption of WEPs. Data on the identified reasons for harvest, consumption, and conserving WEPs were determined using a questionnaire survey with ranking scales for a set of 76 randomly selected households. Representative field-observations and questionnaire surveys were carried out in villages close to forests. Parts of the plant used, how these were consumed, harvest season, and plant (life form) were recorded. The data was subjected to a Kruskal-Wallis rank sum test and weighted averages calculated. RESULT AND CONCLUSION: A total of 120 WEPs belonging to 63 families (including Agaricaceae) were reported. Most of the WEPs recorded were trees (45.0%) then herbs (25.8%), vines (13.3%) and shrubs (10.8%). The commonly consumed plant parts were the fruit (43.3%), shoots (28.3%) and leaves (20.8%). The purposes for harvesting and consumption, conservation of WEPs were significantly (P<0.001) different, while the motivations for collecting WEPs were not. The motivation for collecting WEPs were family consumption > sale > medicinal uses > preservation for future use > insufficient food from cultivated source's. The two most important strategies for conservation were to domesticate the WEPs and cultivate in forests. The findings reveal valuable lessons and insights about the reasons for harvesting, collection, consumption, and conservation of WEPs.

Identifying Patients With Hypoglycemia Using Natural Language Processing: Systematic Literature Review.

BACKGROUND: Accurately identifying patients with hypoglycemia is key to preventing adverse events and mortality. Natural language processing (NLP), a form of artificial intelligence, uses computational algorithms to extract information from text data. NLP is a scalable, efficient, and quick method to extract hypoglycemia-related information when using electronic health record data sources from a large population. OBJECTIVE: The objective of this systematic review was to synthesize the literature on the application of NLP to extract hypoglycemia from electronic health record clinical notes. METHODS: Literature searches were conducted electronically in PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEE Xplore, Google Scholar, and ACL Anthology. Keywords included hypoglycemia, low blood glucose, NLP, and machine learning. Inclusion criteria included studies that applied NLP to identify hypoglycemia, reported the outcomes related to hypoglycemia, and were published in English as full papers. RESULTS: This review (n=8 studies) revealed heterogeneity of the reported results related to hypoglycemia. Of the 8 included studies, 4 (50%) reported that the prevalence rate of any level of hypoglycemia was 3.4% to 46.2%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4% for International Classification of Diseases codes, 25.1% for an NLP algorithm, and 32.2% for combined algorithms. All the reviewed studies applied rule-based NLP algorithms to identify hypoglycemia. CONCLUSIONS: The findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9 or ICD-10 codes and laboratory testing.

Climate change and Australia's primary industries: factors hampering an effective and coordinated response.

Australia's primary production sector operates in one of the world's most variable climates with future climate change posing a challenge to its ongoing sustainability. Recognising this, Australia has invested in understanding climate change risks to primary production with a substantial amount of research produced. Recently, focus on this research space has broadened, with interests from the financial sector and expanded scopes of works from government and industry. These expanded needs require sector- and country-wide assessments to assist with the implementation of climate strategies. We considered the applicability of the current research body for these needs by reviewing 188 peer-reviewed studies that considered the quantitative impacts of climate change on Australia's primary industries. Our broad review includes cropping, livestock, horticulture, forestry and fisheries and biosecurity threats. This is the first such review for Australia, and no other similar country-wide review was found. We reviewed the studies through three lenses, industry diversity, geographic coverage and study comparability. Our results show that all three areas are lacking for sector- and country-wide assessments. Industry diversity was skewed towards cropping and biosecurity threats (64% of all studies) with wheat in particular a major focus (25% of all studies). Geographic coverage at a state level appeared to be evenly distributed across the country; however, when considered in conjunction with industry focus, gaps emerged. Study comparability was found to be very limited due to the use of different historical baseline periods and different impact models. We make several recommendations to assist with future research directions, being (1) co-development of a standard set of method guidelines for impact assessments, (2) filling industry and geographic knowledge gaps, and (3) improving transparency in study method descriptions. Uptake of these recommendations will improve study application and transparency enabling and enhancing responses to climate change in Australia's primary industries.

Taxonomies for synthesizing the evidence on communicating numbers in health: Goals, format, and structure.

Many people, especially those with low numeracy, are known to have difficulty interpreting and applying quantitative information to health decisions. These difficulties have resulted in a rich body of research about better ways to communicate numbers. Synthesizing this body of research into evidence-based guidance, however, is complicated by inconsistencies in research terminology and researcher goals. In this article, we introduce three taxonomies intended to systematize terminology in the literature, derived from an ongoing systematic literature review. The first taxonomy provides a systematic nomenclature for the outcome measures assessed in the studies, including perceptions, decisions, and actions. The second taxonomy is a nomenclature for the data formats assessed, including numbers (and different formats for numbers) and graphics. The third taxonomy describes the quantitative concepts being conveyed, from the simplest (a single value at a single point in time) to more complex ones (including a risk-benefit trade-off and a trend over time). Finally, we demonstrate how these three taxonomies can be used to resolve ambiguities and apparent contradictions in the literature.

An architecture for research computing in health to support clinical and translational investigators with electronic patient data.

OBJECTIVE: Obtaining electronic patient data, especially from electronic health record (EHR) systems, for clinical and translational research is difficult. Multiple research informatics systems exist but navigating the numerous applications can be challenging for scientists. This article describes Architecture for Research Computing in Health (ARCH), our institution's approach for matching investigators with tools and services for obtaining electronic patient data. MATERIALS AND METHODS: Supporting the spectrum of studies from populations to individuals, ARCH delivers a breadth of scientific functions-including but not limited to cohort discovery, electronic data capture, and multi-institutional data sharing-that manifest in specific systems-such as i2b2, REDCap, and PCORnet. Through a consultative process, ARCH staff align investigators with tools with respect to study design, data sources, and cost. Although most ARCH services are available free of charge, advanced engagements require fee for service. RESULTS: Since 2016 at Weill Cornell Medicine, ARCH has supported over 1200 unique investigators through more than 4177 consultations. Notably, ARCH infrastructure enabled critical coronavirus disease 2019 response activities for research and patient care. DISCUSSION: ARCH has provided a technical, regulatory, financial, and educational framework to support the biomedical research enterprise with electronic patient data. Collaboration among informaticians, biostatisticians, and clinicians has been critical to rapid generation and analysis of EHR data. CONCLUSION: A suite of tools and services, ARCH helps match investigators with informatics systems to reduce time to science. ARCH has facilitated research at Weill Cornell Medicine and may provide a model for informatics and research leaders to support scientists elsewhere.

Assessing adverse event reports of hysteroscopic sterilization device removal using natural language processing.

OBJECTIVE: To develop an annotation model to apply natural language processing (NLP) to device adverse event reports and implement the model to evaluate the most frequently experienced events among women reporting a sterilization device removal. METHODS: We included adverse event reports from the Manufacturer and User Facility Device Experience database from January 2005 to June 2018 related to device removal following hysteroscopic sterilization. We used an iterative process to develop an annotation model that extracts six categories of desired information and applied the annotation model to train an NLP algorithm. We assessed the model performance using positive predictive value (PPV, also known as precision), sensitivity (also known as recall), and F1 score (a combined measure of PPV and sensitivity). Using extracted variables, we summarized the reporting source, the presence of prespecified and other patient and device events, additional sterilizations and other procedures performed, and time from implantation to removal. RESULTS: The overall F1 score was 91.5% for labeled items and 93.9% for distinct events after excluding duplicates. A total of 16 535 reports of device removal were analyzed. The most frequently reported patient and device events were abdominal/pelvic/genital pain (N = 13 166, 79.6%) and device dislocation/migration (N = 3180, 19.2%), respectively. Of those reporting an additional sterilization procedure, the majority had a hysterectomy or salpingectomy (N = 7932). One-fifth of the cases that had device removal timing specified reported a removal after 7 years following implantation (N = 2444/11 293). CONCLUSIONS: We present a roadmap to develop an annotation model for NLP to analyze device adverse event reports. The extracted information is informative and complements findings from previous research using administrative data.

ReCiter: An open source, identity-driven, authorship prediction algorithm optimized for academic institutions.

Academic institutions need to maintain publication lists for thousands of faculty and other scholars. Automated tools are essential to minimize the need for direct feedback from the scholars themselves who are practically unable to commit necessary effort to keep the data accurate. In relying exclusively on clustering techniques, author disambiguation applications fail to satisfy key use cases of academic institutions. Algorithms can perfectly group together a set of publications authored by a common individual, but, for them to be useful to an academic institution, they need to programmatically and recurrently map articles to thousands of scholars of interest en masse. Consistent with a savvy librarian's approach for generating a scholar's list of publications, identity-driven authorship prediction is the process of using information about a scholar to quantify the likelihood that person wrote certain articles. ReCiter is an application that attempts to do exactly that. ReCiter uses institutionally-maintained identity data such as name of department and year of terminal degree to predict which articles a given scholar has authored. To compute the overall score for a given candidate article from PubMed (and, optionally, Scopus), ReCiter uses: up to 12 types of commonly available, identity data; whether other members of a cluster have been accepted or rejected by a user; and the average score of a cluster. In addition, ReCiter provides scoring and qualitative evidence supporting why particular articles are suggested. This context and confidence scoring allows curators to more accurately provide feedback on behalf of scholars. To help users to more efficiently curate publication lists, we used a support vector machine analysis to optimize the scoring of the ReCiter algorithm. In our analysis of a diverse test group of 500 scholars at an academic private medical center, ReCiter correctly predicted 98% of their publications in PubMed.

The national landscape of culminating experiences in master's programs in health and biomedical informatics.

Health and biomedical informatics graduate-level degree programs have proliferated across the United States in the last 10 years. To help inform programs on practices in teaching and learning, a survey of master's programs in health and biomedical informatics in the United States was conducted to determine the national landscape of culminating experiences including capstone projects, research theses, internships, and practicums. Almost all respondents reported that their programs required a culminating experience (97%). A paper (not a formal thesis), an oral presentation, a formal course, and an internship were required by ≥50% programs. The most commonly reported purposes for the culminating experience were to help students extend and apply the learning and as a bridge to the workplace. The biggest challenges were students' maturity, difficulty in synthesizing information into a coherent paper, and ability to generate research ideas. The results provide students and program leaders with a summary of pedagogical methods across programs.

Renin-Angiotensin-Aldosterone System Inhibitors and Risk of Covid-19.

BACKGROUND: There is concern about the potential of an increased risk related to medications that act on the renin-angiotensin-aldosterone system in patients exposed to coronavirus disease 2019 (Covid-19), because the viral receptor is angiotensin-converting enzyme 2 (ACE2). METHODS: We assessed the relation between previous treatment with ACE inhibitors, angiotensin-receptor blockers, beta-blockers, calcium-channel blockers, or thiazide diuretics and the likelihood of a positive or negative result on Covid-19 testing as well as the likelihood of severe illness (defined as intensive care, mechanical ventilation, or death) among patients who tested positive. Using Bayesian methods, we compared outcomes in patients who had been treated with these medications and in untreated patients, overall and in those with hypertension, after propensity-score matching for receipt of each medication class. A difference of at least 10 percentage points was prespecified as a substantial difference. RESULTS: Among 12,594 patients who were tested for Covid-19, a total of 5894 (46.8%) were positive; 1002 of these patients (17.0%) had severe illness. A history of hypertension was present in 4357 patients (34.6%), among whom 2573 (59.1%) had a positive test; 634 of these patients (24.6%) had severe illness. There was no association between any single medication class and an increased likelihood of a positive test. None of the medications examined was associated with a substantial increase in the risk of severe illness among patients who tested positive. CONCLUSIONS: We found no substantial increase in the likelihood of a positive test for Covid-19 or in the risk of severe Covid-19 among patients who tested positive in association with five common classes of antihypertensive medications.

Underserved populations with missing race ethnicity data differ significantly from those with structured race/ethnicity documentation.

OBJECTIVE: We aimed to address deficiencies in structured electronic health record (EHR) data for race and ethnicity by identifying black and Hispanic patients from unstructured clinical notes and assessing differences between patients with or without structured race/ethnicity data. MATERIALS AND METHODS: Using EHR notes for 16 665 patients with encounters at a primary care practice, we developed rule-based natural language processing (NLP) algorithms to classify patients as black/Hispanic. We evaluated performance of the method against an annotated gold standard, compared race and ethnicity between NLP-derived and structured EHR data, and compared characteristics of patients identified as black or Hispanic using only NLP vs patients identified as such only in structured EHR data. RESULTS: For the sample of 16 665 patients, NLP identified 948 additional patients as black, a 26%increase, and 665 additional patients as Hispanic, a 20% increase. Compared with the patients identified as black or Hispanic in structured EHR data, patients identified as black or Hispanic via NLP only were older, more likely to be male, less likely to have commercial insurance, and more likely to have higher comorbidity. DISCUSSION: Structured EHR data for race and ethnicity are subject to data quality issues. Supplementing structured EHR race data with NLP-derived race and ethnicity may allow researchers to better assess the demographic makeup of populations and draw more accurate conclusions about intergroup differences in health outcomes. CONCLUSIONS: Black or Hispanic patients who are not documented as such in structured EHR race/ethnicity fields differ significantly from those who are. Relatively simple NLP can help address this limitation.

Alignment of American Association of Colleges of Nursing Graduate-Level Nursing Informatics Competencies With American Medical Informatics Association Health Informatics Core Competencies.

This study yielded a map of the alignment of American Association of Colleges of Nursing Graduate-Level Nursing Informatics Competencies with American Medical Informatics Association Health Informatics Core Competencies in an effort to understand graduate-level accreditation and certification opportunities in nursing informatics. Nursing Informatics Program Directors from the American Medical Informatics Association and a health informatics expert independently mapped the American Association of Colleges of Nursing competencies to the American Medical Informatics Association Health Informatics knowledge, skills, and attitudes. The Nursing Informatics Program Directors' map connected an average of 4.0 American Medical Informatics Association Core Competencies per American Association of Colleges of Nursing competency, whereas the health informatics expert's map connected an average of 5.0 American Medical Informatics Association Core Competencies per American Association of Colleges of Nursing competency. Agreement across the two maps ranged from 14% to 60% per American Association of Colleges of Nursing competency, revealing alignment between the two groups' competencies according to knowledge, skills, and attitudes. These findings suggest that graduates of master's degree programs in nursing, especially those specializing in nursing informatics, will likely be prepared to sit for the proposed Advanced Health Informatics Certification in addition to the American Nurses Credentialing Center bachelor's-level Informatics Nursing Certification. This preliminary map sets the stage for further in-depth mapping of nursing informatics curricula with American Medical Informatics Association Core Competencies and will enable interprofessional conversations around nursing informatics specialty program accreditation, nursing workforce preparation, and nursing informatics advanced certification. Nursing informaticists should examine their need for credentials as key contributors who will address critical health informatics needs.

Automated detection of sudden unexpected death in epilepsy risk factors in electronic medical records using natural language processing.

OBJECTIVE: Sudden unexpected death in epilepsy (SUDEP) is an important cause of mortality in epilepsy. However, there is a gap in how often providers counsel patients about SUDEP. One potential solution is to electronically prompt clinicians to provide counseling via automated detection of risk factors in electronic medical records (EMRs). We evaluated (1) the feasibility and generalizability of using regular expressions to identify risk factors in EMRs and (2) barriers to generalizability. METHODS: Data included physician notes for 3000 patients from one medical center (home) and 1000 from five additional centers (away). Through chart review, we identified three SUDEP risk factors: (1) generalized tonic-clonic seizures, (2) refractory epilepsy, and (3) epilepsy surgery candidacy. Regular expressions of risk factors were manually created with home training data, and performance was evaluated with home test and away test data. Performance was evaluated by sensitivity, positive predictive value, and F-measure. Generalizability was defined as an absolute decrease in performance by <0.10 for away versus home test data. To evaluate underlying barriers to generalizability, we identified causes of errors seen more often in away data than home data. To demonstrate how small revisions can improve generalizability, we removed three "boilerplate" standard text phrases from away notes and repeated performance. RESULTS: We observed high performance in home test data (F-measure range = 0.86-0.90), and low to high performance in away test data (F-measure range = 0.53-0.81). After removing three boilerplate phrases, away performance improved (F-measure range = 0.79-0.89) and generalizability was achieved for nearly all measures. The only significant barrier to generalizability was use of boilerplate phrases, causing 104 of 171 errors (61%) in away data. SIGNIFICANCE: Regular expressions are a feasible and probably a generalizable method to identify variables related to SUDEP risk. Our methods may be implemented to create large patient cohorts for research and to generate electronic prompts for SUDEP counseling.

AMIA Board White Paper: AMIA 2017 core competencies for applied health informatics education at the master's degree level.

This White Paper presents the foundational domains with examples of key aspects of competencies (knowledge, skills, and attitudes) that are intended for curriculum development and accreditation quality assessment for graduate (master's level) education in applied health informatics. Through a deliberative process, the AMIA Accreditation Committee refined the work of a task force of the Health Informatics Accreditation Council, establishing 10 foundational domains with accompanying example statements of knowledge, skills, and attitudes that are components of competencies by which graduates from applied health informatics programs can be assessed for competence at the time of graduation. The AMIA Accreditation Committee developed the domains for application across all the subdisciplines represented by AMIA, ranging from translational bioinformatics to clinical and public health informatics, spanning the spectrum from molecular to population levels of health and biomedicine. This document will be periodically updated, as part of the responsibility of the AMIA Accreditation Committee, through continued study, education, and surveys of market trends.

From Sour Grapes to Low-Hanging Fruit: A Case Study Demonstrating a Practical Strategy for Natural Language Processing Portability.

Natural Language Processing (NLP) holds potential for patient care and clinical research, but a gap exists between promise and reality. While some studies have demonstrated portability of NLP systems across multiple sites, challenges remain. Strategies to mitigate these challenges can strive for complex NLP problems using advanced methods (hard-to-reach fruit), or focus on simple NLP problems using practical methods (low-hanging fruit). This paper investigates a practical strategy for NLP portability using extraction of left ventricular ejection fraction (LVEF) as a use case. We used a tool developed at the Department of Veterans Affair (VA) to extract the LVEF values from free-text echocardiograms in the MIMIC-III database. The approach showed an accuracy of 98.4%, sensitivity of 99.4%, a positive predictive value of 98.7%, and F-score of 99.0%. This experience, in which a simple NLP solution proved highly portable with excellent performance, illustrates the point that simple NLP applications may be easier to disseminate and adapt, and in the short term may prove more useful, than complex applications.

Common terms for rare epilepsies: Synonyms, associated terms, and links to structured vocabularies.

Identifying individuals with rare epilepsy syndromes in electronic data sources is difficult, in part because of missing codes in the International Classification of Diseases (ICD) system. Our objectives were the following: (1) to describe the representation of rare epilepsies in other medical vocabularies, to identify gaps; and (2) to compile synonyms and associated terms for rare epilepsies, to facilitate text and natural language processing tools for cohort identification and population-based surveillance. We describe the representation of 33 epilepsies in 3 vocabularies: Orphanet, SNOMED-CT, and UMLS-Metathesaurus. We compiled terms via 2 surveys, correspondence with parent advocates, and review of web resources and standard vocabularies. UMLS-Metathesaurus had entries for all 33 epilepsies, Orphanet 32, and SNOMED-CT 25. The vocabularies had redundancies and missing phenotypes. Emerging epilepsies (SCN2A-, SCN8A-, KCNQ2-, SLC13A5-, and SYNGAP-related epilepsies) were underrepresented. Survey and correspondence respondents included 160 providers, 375 caregivers, and 11 advocacy group leaders. Each epilepsy syndrome had a median of 15 (range 6-28) synonyms. Nineteen had associated terms, with a median of 4 (range 1-41). We conclude that medical vocabularies should fill gaps in representation of rare epilepsies to improve their value for epilepsy research. We encourage epilepsy researchers to use this resource to develop tools to identify individuals with rare epilepsies in electronic data sources.

Ascertaining Depression Severity by Extracting Patient Health Questionnaire-9 (PHQ-9) Scores from Clinical Notes.

The Patient Health Questionnaire-9 (PHQ-9) is a validated instrument for assessing depression severity. While some electronic health record (EHR) systems capture PHQ-9 scores in a structured format, unstructured clinical notes remain the only source in many settings, which presents data retrieval challenges for research and clinical decision support. To address this gap, we extended the open-source Leo natural language processing (NLP) platform to extract PHQ-9 scores from clinical notes and evaluated performance using EHR data for n=123,703 patients who were prescribed antidepressants. Compared to a reference standard, the NLP method exhibited high accuracy (97%), sensitivity (98%), precision (97%), and F-score (97%). Furthermore, of patients with PHQ-9 scores identified by the NLP method, 31% (n=498) had at least one PHQ-9 score clinically indicative of major depressive disorder (MDD), but lacked a structured ICD-9/10 diagnosis code for MDD. This NLP technique may facilitate accurate identification and stratification of patients with depression.

Navigation in the electronic health record: A review of the safety and usability literature.

OBJECTIVE: Inefficient navigation in electronic health records has been shown to increase users' cognitive load, which may increase potential for errors, reduce efficiency, and increase fatigue. However, navigation has received insufficient recognition and attention in the electronic health record (EHR) literature as an independent construct and contributor to overall usability. Our aims in this literature review were to (1) assess the prevalence of navigation-related topics within the EHR usability and safety research literature, (2) categorize types of navigation actions within the EHR, (3) capture relationships between these navigation actions and usability principles, and (4) collect terms and concepts related to EHR navigation. Our goal was to improve access to navigation-related research in usability. MATERIALS AND METHODS: We applied scoping literature review search methods with the assistance of a reference librarian to identify articles published since 1996 that reported evaluation of the usability or safety of an EHR user interface via user test, analytic methods, or inspection methods. The 4336 references collected from MEDLINE, EMBASE, Engineering Village, and expert referrals were de-duplicated and screened for relevance, and navigation-related concepts were abstracted from the 21 articles eligible for review using a standard abstraction form. RESULTS: Of the 21 eligible articles, 20 (95%) mentioned navigation in results and discussion of usability evaluations. Navigation between pages of the EHR was the more frequently documented type of navigation (86%) compared to navigation within a single page (14%). Navigation actions (e.g., scrolling through a medication list) were frequently linked to specific usability heuristic violations, among which flexibility and efficiency of use, recognition rather than recall, and error prevention were most common. DISCUSSION: Discussion of navigation was prevalent in results across all types of evaluation methods among the articles reviewed. Navigating between multiple screens was frequently identified as a usability barrier. The lack of standard terminology created some challenges to identifying and comparing articles. CONCLUSION: We observed that usability researchers are frequently capturing navigation-related issues even in articles that did not explicitly state navigation as a focus. Capturing and synthesizing the literature on navigation is challenging because of the lack of uniform vocabulary. Navigation is a potential target for normative recommendations for improved interaction design for safer systems. Future research in this domain, including development of normative recommendations for usability design and evaluation, will be facilitated by development of a standard terminology for describing EHR navigation.

Secondary Use of Patients' Electronic Records (SUPER): An Approach for Meeting Specific Data Needs of Clinical and Translational Researchers.

Academic medical centers commonly approach secondary use of electronic health record (EHR) data by implementing centralized clinical data warehouses (CDWs). However, CDWs require extensive resources to model data dimensions and harmonize clinical terminology, which can hinder effective support of the specific and varied data needs of investigators. We hypothesized that an approach that aggregates raw data from source systems, ignores initial modeling typical of CDWs, and transforms raw data for specific research purposes would meet investigator needs. The approach has successfully enabled multiple tools that provide utility to the institutional research enterprise. To our knowledge, this is the first complete description of a methodology for electronic patient data acquisition and provisioning that ignores data harmonization at the time of initial storage in favor of downstream transformation to address specific research questions and applications.

Data management in clinical research: Synthesizing stakeholder perspectives.

OBJECTIVE: This study assesses data management needs in clinical research from the perspectives of researchers, software analysts and developers. MATERIALS AND METHODS: This is a mixed-methods study that employs sublanguage analysis in an innovative manner to link the assessments. We performed content analysis using sublanguage theory on transcribed interviews conducted with researchers at four universities. A business analyst independently extracted potential software features from the transcriptions, which were translated into the sublanguage. This common sublanguage was then used to create survey questions for researchers, analysts and developers about the desirability and difficulty of features. Results were synthesized using the common sublanguage to compare stakeholder perceptions with the original content analysis. RESULTS: Individual researchers exhibited significant diversity of perspectives that did not correlate by role or site. Researchers had mixed feelings about their technologies, and sought improvements in integration, interoperability and interaction as well as engaging with study participants. Researchers and analysts agreed that data integration has higher desirability and mobile technology has lower desirability but disagreed on the desirability of data validation rules. Developers agreed that data integration and validation are the most difficult to implement. DISCUSSION: Researchers perceive tasks related to study execution, analysis and quality control as highly strategic, in contrast with tactical tasks related to data manipulation. Researchers have only partial technologic support for analysis and quality control, and poor support for study execution. CONCLUSION: Software for data integration and validation appears critical to support clinical research, but may be expensive to implement. Features to support study workflow, collaboration and engagement have been underappreciated, but may prove to be easy successes. Software developers should consider the strategic goals of researchers with regard to the overall coordination of research projects and teams, workflow connecting data collection with analysis and processes for improving data quality.

Integrating an Informationist Into Graduate Education: Case Study With Preliminary Results.

An informationist taught, consulted, and mentored graduate students enrolled in a graduate research project course in Health Informatics. An observational cohort study was conducted to determine the effect of an early (first term) and continued (subsequent term) exposure of course-integrated instruction, individual consultations, information resource mentoring, and educational collaboration partnership on the development of information literacy, research skills, and integrative competencies in graduate students. Student progress was assessed by survey, class performance, and faculty feedback. The course-integrated lectures, consultations, mentoring, and educational partnership between the informationist and academic advisors increased the students' course performance, information literacy, and research skills in graduate students.