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BACKGROUND Tibial shaft fractures are the most common injuries preceding acute compartment syndrome (ACS), so it is important to understand the incidence of and risk factors for ACS after pediatric tibial shaft fractures. The purposes of this study were to determine the rate at which ACS occurs and if any patient or fracture characteristics are significantly associated with developing ACS. METHODS All patients aged 5 to 17 years treated for a tibial shaft fracture at a level 1 pediatric trauma center, a level 1 adult trauma center, and an outpatient orthopaedic practice between 2008 and 2016 were retrospectively identified. Demographics, mechanisms of injury, and fracture characteristics were collected from the medical records. Radiographs were reviewed by study authors. ACS was diagnosed clinically or by intracompartmental pressure measurement. Univariable analysis was performed using the Fisher exact test for nominal variables and simple logistic regression for continuous variables. Multivariable analysis was performed using stepwise logistic regression. RESULTS Among 515 patients with 517 tibial shaft fractures, 9 patients (1.7%) with 10 (1.9%) fractures developed ACS at a mean age of 15.2 years compared with a mean age of 11 years in patients without ACS (P=0.001). One patient with bilateral tibial fractures developed ACS bilaterally. Age greater than 14 years (P=0.006), higher body mass index (P

Educators need efficient and effective means to track students' clinical experiences to monitor their progress toward competency goals.To validate an electronic scoring system that rates medical students' clinical notes for relevance to priority topics of the medical school curriculum.The Vanderbilt School of Medicine Core Clinical Curriculum enumerates 25 core clinical problems (CCP) that graduating medical students must understand. Medical students upload clinical notes pertinent to each CCP to a web-based dashboard, but criteria for determining relevance of a note and consistent uploading practices by students are lacking. The Vanderbilt Learning Portfolio (VLP) system automates both tasks by rating relevance for each CCP and uploading the note to the student's electronic dashboard. We validated this electronic scoring system by comparing the relevance of 265 clinical notes written by third year medical students to each of the 25 core patient problems as scored by VLP verses an expert panel of raters.We established the threshold score which yielded 75% positive prediction of relevance for 16 of the 25 clinical problems to expert opinion.Automated scoring of student's clinical notes provides a novel, efficient and standardized means of tracking student's progress toward institutional competency goals.

Clinical task, or “to-do” lists are a common element in the physician document known as signout. Such lists are used to capture and track patient care plan items, supporting daily workflow and collaborative patient management continuity across care transitions. While physician task lists have been shown to be important to patient safety, the tasks themselves have not been systematically examined for their subject matter, structure, or components. A manual sublanguage analysis of 500 signout tasks was conducted, and a hierarchical conceptual model for clinical tasks was inductively constructed. Tasks were classified by action type (Assess, Order, Communicate, Perform) and corresponding components. The most common task action types were Assess and Order. The most common task components were “What” type components such as Tests, including subtypes Laboratory and Imaging. This study yielded several important design considerations for future electronic health record systems that support collaborative clinical task management.

Objective Although electronic notes have advantages compared to handwritten notes, they take longer to write and promote information redundancy in electronic health records (EHRs). We sought to quantify redundancy in clinical documentation by studying collections of physician notes in an EHR. Design and methods We implemented a retrospective design to gather all electronic admission, progress, resident signout and discharge summary notes written during 100 randomly selected patient admissions within a 6 month period. We modified and applied a Levenshtein edit-distance algorithm to align and compare the documents written for each of the 100 admissions. We then identified and measured the amount of text duplicated from previous notes. Finally, we manually reviewed the content that was conserved between note types in a subsample of notes. Measurements We measured the amount of new information in a document, which was calculated as the number of words that did not match with previous documents divided by the length, in words, of the document. Results are reported as the percentage of information in a document that had been duplicated from previously written documents. Results Signout and progress notes proved to be particularly redundant, with an average of 78% and 54% information duplicated from previous documents respectively. There was also significant information duplication between document types (eg, from an admission note to a progress note). Conclusion The study established the feasibility of exploring redundancy in the narrative record with a known sequence alignment algorithm used frequently in the field of bioinformatics. The findings provide a foundation for studying the usefulness and risks of redundancy in the EHR.

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.