Associating co-authorship patterns with publications in high-impact journals.

OBJECTIVES: To develop a method for investigating co-authorship patterns and author team characteristics associated with the publications in high-impact journals through the integration of public MEDLINE data and institutional scientific profile data. METHODS: For all current researchers at Columbia University Medical Center, we extracted their publications from MEDLINE authored between years 2007 and 2011 and associated journal impact factors, along with author academic ranks and departmental affiliations obtained from Columbia University Scientific Profiles (CUSP). Chi-square tests were performed on co-authorship patterns, with Bonferroni correction for multiple comparisons, to identify team composition characteristics associated with publication impact factors. We also developed co-authorship networks for the 25 most prolific departments between years 2002 and 2011 and counted the internal and external authors, inter-connectivity, and centrality of each department. RESULTS: Papers with at least one author from a basic science department are significantly more likely to appear in high-impact journals than papers authored by those from clinical departments alone. Inclusion of at least one professor on the author list is strongly associated with publication in high-impact journals, as is inclusion of at least one research scientist. Departmental and disciplinary differences in the ratios of within- to outside-department collaboration and overall network cohesion are also observed. CONCLUSIONS: Enrichment of co-authorship patterns with author scientific profiles helps uncover associations between author team characteristics and appearance in high-impact journals. These results may offer implications for mentoring junior biomedical researchers to publish on high-impact journals, as well as for evaluating academic progress across disciplines in modern academic medical centers.

Automatic generation of investigator bibliographies for institutional research networking systems.

OBJECTIVE: Publications are a key data source for investigator profiles and research networking systems. We developed ReCiter, an algorithm that automatically extracts bibliographies from PubMed using institutional information about the target investigators. METHODS: ReCiter executes a broad query against PubMed, groups the results into clusters that appear to constitute distinct author identities and selects the cluster that best matches the target investigator. Using information about investigators from one of our institutions, we compared ReCiter results to queries based on author name and institution and to citations extracted manually from the Scopus database. Five judges created a gold standard using citations of a random sample of 200 investigators. RESULTS: About half of the 10,471 potential investigators had no matching citations in PubMed, and about 45% had fewer than 70 citations. Interrater agreement (Fleiss' kappa) for the gold standard was 0.81. Scopus achieved the best recall (sensitivity) of 0.81, while name-based queries had 0.78 and ReCiter had 0.69. ReCiter attained the best precision (positive predictive value) of 0.93 while Scopus had 0.85 and name-based queries had 0.31. DISCUSSION: ReCiter accesses the most current citation data, uses limited computational resources and minimizes manual entry by investigators. Generation of bibliographies using named-based queries will not yield high accuracy. Proprietary databases can perform well but requite manual effort. Automated generation with higher recall is possible but requires additional knowledge about investigators.

An electronic health record based on structured narrative.

OBJECTIVE: To develop an electronic health record that facilitates rapid capture of detailed narrative observations from clinicians, with partial structuring of narrative information for integration and reuse. DESIGN: We propose a design in which unstructured text and coded data are fused into a single model called structured narrative. Each major clinical event (e.g., encounter or procedure) is represented as a document that is marked up to identify gross structure (sections, fields, paragraphs, lists) as well as fine structure within sentences (concepts, modifiers, relationships). Marked up items are associated with standardized codes that enable linkage to other events, as well as efficient reuse of information, which can speed up data entry by clinicians. Natural language processing is used to identify fine structure, which can reduce the need for form-based entry. VALIDATION: The model is validated through an example of use by a clinician, with discussion of relevant aspects of the user interface, data structures and processing rules. DISCUSSION: The proposed model represents all patient information as documents with standardized gross structure (templates). Clinicians enter their data as free text, which is coded by natural language processing in real time making it immediately usable for other computation, such as alerts or critiques. In addition, the narrative data annotates and augments structured data with temporal relations, severity and degree modifiers, causal connections, clinical explanations and rationale. CONCLUSION: Structured narrative has potential to facilitate capture of data directly from clinicians by allowing freedom of expression, giving immediate feedback, supporting reuse of clinical information and structuring data for subsequent processing, such as quality assurance and clinical research.