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Objective. In this randomized, nonblinded, controlled study, the feasibility and precision of "targeted percutaneous vertebroplasty" ("targeted PVP") for osteoporotic vertebral compression fracture (OVCF) was evaluated. Methods. A total of 42 patients, aged 50 to 87 years, with OVCF were randomly divided into 2 groups: A and B. Group A underwent "targeted PVP," and group B underwent traditional PVP with the guidance of C-arm fluoroscopy. Fluoroscopy times for skin puncture points (FTSPP), total radiation doses (TRD), total fluoroscopy times (TFT), and operation time were set as the main evaluation indicators. Results. FTSPP (1.52 ± 0.51 in group A vs 6.62 ± 2.58 in group B, U < .001), TRD (6.26 ± 1.51 in group A vs 11.32 ± 4.21 in group B, P < .001), TFT (16.57 ± 2.79 in group A vs 26.05 ± 6.18 in group B, P < .001), and operation time (20.05 ± 3.38 in group A vs 25.43 ±5.11 in group B, U < .001) were statistically different in the 2 groups. The incidence of cement leakage that occurred in group A (1/21, 4.76%) was significantly less than that in group B (9/21, 42.9%, P < 0.05). Conclusions. "Targeted PVP" may achieve (1) less skin positioning fluoroscopy times, less total fluoroscopy times and dose, shorter operation time, which is more precise than traditional PVP; (2) less incidence of cement leakage; and (3) visualization of the fractured vertebra, which is probably more valuable for the treatment of complicated OVCF patients. [ABSTRACT FROM AUTHOR]

Percutaneous vertebroplasty (PVP) is considered an effective treatment for the back pain caused by osteoporotic vertebral compression fracture. The accuracy of PVP mainly depends on the surgeons' experience and multiple fluoroscopes during a traditional procedure. Puncture related complications were reported all over the world. To make the surgical procedure more precise and decrease the rate of puncture-related complications, our team applied a three-dimensional printing guide template to PVP to modify the traditional procedure. This protocol introduces how to model target vertebrae DICOM imaging data into three-dimensions in the software, how to simulate operation in this 3-D model, and how to use all of the surgical data to reconstruct a patient specific template for application. Using this template, surgeons can identify suitable puncture points accurately to improve the accuracy of the operation. The whole protocol includes: 1) diagnosis of the osteoporotic vertebral compression fracture; 2) acquisition of CT imaging of the target vertebra; 3) simulation of the operation in the software; 4) design and fabrication of the 3-D printing guide template; and 5) application of the template into an operation procedure.

Problem: Efforts to evaluate and optimize the effectiveness of medical education have been limited by the difficulty of designing medical education research. Longitudinal, epidemiological views of educational outcomes can help overcome limitations, but these approaches require "bigger data"-more learners, sources, and time points. The rich data institutions collect on students and residents can be mined, however, ethical and practical barriers to using these data must first be overcome., Approach: In 2008, the authors established the Research on Medical Education Outcomes (ROMEO) Registry, an educational data registry modeled after patient registries. New York University School of Medicine students, residents, and fellows provide consent for routinely collected educational, performance, quality improvement, and clinical practice data to be compiled into a deidentified, longitudinal database. As of January 2015, this registry included 1,225 residents and fellows across 12 programs (71% consent rate) and 841 medical students (86% consent rate). Procedures ensuring voluntary informed consent are essential to ethical enrollment and data use. Substantial resources are required to provide access to and manage the data., Outcomes: The registry supports educational scholarship. Seventy-two studies using registry data have been presented or published. These focus on evaluating the curriculum, quality of care, and measurement quality and on assessing needs, competencies, skills development, transfer of skills to practice, remediation patterns, and links between education and patient outcomes., Next Steps: The authors are working to integrate assessment of relevant outcomes into the curriculum, maximize both the quantity and quality of the data, and expand the registry across institutions.

A SNOMED CT-encoded problem list will be required to satisfy the Certification Criteria for Stage 2 "Meaningful Use" of the EHR incentive program. ICD-10-CM will be replacing ICD-9-CM as the reimbursement code set in the near future. Having a cross-map from SNOMED CT to ICD-10-CM will promote the use of SNOMED CT as the primary problem list terminology, while easing the transition to ICD-10-CM. This rule-based map will support semi-automatic generation of ICD-10-CM codes from SNOMED CT-encoded data. Among the different types of rules, the age rule is used to handle age-specific code assignment in ICD-10-CM. To supplement the manual process of creation of age rules, a special QA process was implemented to flag maps that were potentially missing age rules. The QA flagged 342 concepts for review (out of 7,277), of which 172 concepts (50.3%) were true positives. Without the special QA, many of the age rules would have been missed.

Two mapping projects are currently underway, creating maps from SNOMED CT to ICD-10 and ICD-10-CM respectively. Even though the two projects belong to different organizations, there has been a lot of synergism between them. The ICD-10-CM map project heavily re-used the mapping methodology, tools and map data developed in the ICD-10 map project. An algorithm was derived to generate candidate ICD-10-CM map records from the ICD-10 map. We evaluated the algorithm in 5,264 SNOMED CT concepts common to both maps. 4,317 ICD-10-CM candidate maps could be generated from the ICD-10 map and 3,341 (77%) of the generated maps agreed with the published map. By priming the mapping process with candidate maps generated from the other map project, significant saving in time and effort in future phases of the two projects can be anticipated. The reasons for the discordance between the generated map and published map were also analyzed.

Medical terminologies are large and complex. Frequently, errors are hidden in this complexity. Our objective is to find such errors, which can be aided by deriving abstraction networks from a large terminology. Abstraction networks preserve important features but eliminate many minor details, which are often not useful for identifying errors. Providing visualizations for such abstraction networks aids auditors by allowing them to quickly focus on elements of interest within a terminology. Previously we introduced area taxonomies and partial area taxonomies for SNOMED CT. In this paper, two advanced, novel kinds of abstraction networks, the relationship-constrained partial area subtaxonomy and the root-constrained partial area subtaxonomy are defined and their benefits are demonstrated. We also describe BLUSNO, an innovative software tool for quickly generating and visualizing these SNOMED CT abstraction networks. BLUSNO is a dynamic, interactive system that provides quick access to well organized information about SNOMED CT.

We analyzed 598 of 63,952 terms employed in problem list entries from seven major healthcare institutions that were not mapped with UMLS to SNOMED CT when preparing the NLM UMLS-CORE problem list subset. We intended to determine whether published or post-coordinated SNOMED concepts could accurately capture the problems as stated by the clinician and to characterize the workload for the local terminology manager. From the terms we analyzed, we estimate that 7.5% of the total terms represent ambiguous statements that require clarification. Of those terms which were unambiguous, we estimate that 38.1% could be encoded using the SNOMED CT January 2011 pre-coordinated (published core) content. 60.4% of unambiguous terms required post-coordination to capture the term meaning within the SNOMED model. Approximately 28.5% of post-coordinated content could not be fully defined and required primitive forms. This left 1.5% of unambiguous terms which were expressed with meaning which could not be represented in SNOMED CT. We estimate from our study that 98.5% of clinical terms unambiguously suggested for the problem list can be equated to published concepts or can be modeled with SNOMED CT but that roughly one in four SNOMED modeled expressions fail to represent the full meaning of the term. Implications for the business model of the local terminology manager and the development of SNOMED CT are discussed.

Three Problem List Terminologies (PLT) were tested using a web-based application simulating a clinical data entry environment to evaluate coverage and coding efficiency. The three PLTs were: the CORE Problem List Subset of SNOMED CT, a clinical subset extracted from the full SNOMED CT and the PLT currently used at the Mayo Clinic. Candidate problem statements were randomly extracted from free text problem list entries contained in two electronic medical record systems. Physician reviewers searched for concepts in one of the three PLTs that most closely matched a problem statement. Altogether 45 reviewers reviewed 15 problems each. The coverage of the much smaller CORE Subset was comparable to Clinical SNOMED for combined exact or partial matches. The CORE Subset required the shortest time to find a concept. This may be related to the smaller size of the pick lists for the CORE Subset.

The goal of this paper is to audit null-annotated parent-child pairs in the UMLS Metathesaurus. We have developed techniques for identifying suspicious pairs with high likelihood of errors by using inconsistencies between the hierarchical relationships of the Metathesaurus and the Semantic Network. Two formal conditions, called semantic inversion and lack of ancestry are investigated. Analyzing two corresponding samples shows that semantic inversion is significantly more likely to indicate an error than lack of ancestry, which in turn is more likely to indicate errors than a consistent configuration. We also discuss cases of parent-child pairs with semantic inversion that may be corrected by disambiguating the child.

Limited resources and the sheer volume of concepts make auditing a large terminology, such as SNOMED CT, a daunting task. It is essential to devise techniques that can aid an auditor by automatically identifying concepts that deserve attention. A methodology for this purpose based on a previously introduced abstraction network (called the p-area taxonomy) for a SNOMED CT hierarchy is presented. The methodology algorithmically gathers concepts appearing in certain overlapping subsets, defined exclusively with respect to the p-area taxonomy, for review. The results of applying the methodology to SNOMED's Specimen hierarchy are presented. These results are compared against a control sample composed of concepts residing in subsets without the overlaps. With the use of the double bootstrap, the concept group produced by our methodology is shown to yield a statistically significant higher proportion of error discoveries.

SNOMED CT is an extensive terminology with an attendant amount of complexity. Two measures are proposed for quantifying that complexity. Both are based on abstraction networks, called the area taxonomy and the partial-area taxonomy, that provide, for example, distributions of the relationships within a SNOMED hierarchy. The complexity measures are employed specifically to track the complexity of versions of the Specimen hierarchy of SNOMED before and after it is put through an auditing process. The pre-audit and post-audit versions are compared. The results show that the auditing process indeed leads to a simplification of the terminology's structure.