Modeling variation of clinical team processes with multiple sequence alignment.

Our objective was to model process variation of Emergency Medical Service teams responding to simulated pediatric emergencies and determine if sequence alignment distinguishes performance quality. We performed a retrospective process analysis by watching and coding activities in videos from standardized simulations of 42 Emergency Medical Service teams. Teams were classified into high- or low-performing groups based on the Clinical Teamwork Scale™. Activities were coded according to resuscitation tasks, performer, and times. We used ClustalG to align task sequences within and between groups, and measured similarity. Teams within and between performance levels had an average sequence similarity of 52 ± 7% and 50 ± 7%. Teams performed clinically appropriate tasks that varied in prioritization, for example, performing compressions or connecting the EKG monitor early. There was no statistical difference in gross similarity between groups but specific differences in prioritization may have had clinically meaningful implications. Alignment could improve by accounting for task duration and concurrency.

Eliciting the Intension of Drug Value Sets - Principles and Quality Assurance Applications.

Value sets (VSs) used in electronic clinical quality measures are lists of codes from standard terminologies ("extensional" VSs), whose purpose ("intension") is not always explicitly stated. We elicited the intension for the 09/01/2014 release of extensional medication value sets by comparison to drug classes from the October 2014 release of RxClass. Value sets matched drug classes if they shared common ingredients, as evidenced by Jaccard similarity score. We elicited the intension of 80 extensional value sets. The average Jaccard similarity was 0.65 for single classes and 0.80 for combination classes, with 34% (27/80) of the value sets having high similarity scores. Manual review by a pharmacist indicated 51% (41/80) of the drug classes selected as the best mapping for a value set matched the intension reflected in that value set name. This approach has the potential for facilitating the development and maintenance of medication value sets.

Discovering synergistic qualities of published authors to enhance translational research.

Translational research is the process of bringing together basic scientific research and improvements in patient care. This process, by its very nature, requires a wide range of skills and resources, typically not found within any single individual. This project investigates the synergistic features of published researchers at the Oregon Clinical and Translational Research Institute(OCTRI) to see how scientists with different specializations can be brought together to improve translational research.The investigated features were author connectivity and complementarity of research subjects. Author connectivity was measured by taking the Average Path Length (APL) and cluster coefficients [1] over an OCTRIcoauthor network. A high degree of connectivity, or low APL value, would indicate that a researcher has participated in many collaborations and published many papers with other OCTRI researchers. This would imply that they have some experience we could leverage to build teams for translational research. Subject complementarity was established by using pairs of frequently co-occurring MeSH terms. Those terms were then used to bridge researchers together through indirect Swanson matching [2] and present evidence of topic synergy, or potential collaborative synergy.Our initial investigation supports the development of a collaborative browsing tool to assist the creation of new translational research teams. Such a tool is being developed at the OCTRI and will include a user-centric evaluation in the near future.