OHDSI MEETINGS THIS WEEK
Patient Visualization Work Group Meeting - Tuesday at 2pm ET
ANNOUNCEMENTS
OHDSI Symposium 2016 - REGISTER NOW! http://www.ohdsi.org/events/ohdsi-symposium-2016/
OHDSI Symposium 2016 - Tutorial registration now open http://www.ohdsi.org/events/
Morning tutorials (8am-12pm) :Common Data Model & Extract-Transform-Load (ETL) Standardized Vocabularies
Afternoon tutorials (1-5pm) :Cohort Definitions OHDSI Technology Stack
Full-day tutorial (8am-5pm) :Population-level Estimation for Medical Product Safety Surveillance and Comparative Effectiveness Research
Please note that space for each session is very limited. Completion of the registration form does not guarantee you a space in the class. A final attendee list will be determined by tutorial faculty.
The deadline to register is 5pm Friday, July 29th . Selected attendees will be notified in early August.
COMMUNITY PUBLICATIONS
Improving quality of care and patient safety as a priority
U Iqbal, S Syed-Abdul and YC Li,
International journal for quality in health care : journal of the International Society for Quality in Health Care , Oct 2015
Association Between Trauma Center Type and Mortality Among Injured Adolescent Patients.
RB Webman, EA Carter, S Mittal, J Wang, C Sathya, AB Nathens, ML Nance, D Madigan and RS Burd,
JAMA pediatrics , 2016 08 01
Although data obtained from regional trauma systems demonstrate improved outcomes for children treated at pediatric trauma centers (PTCs) compared with those treated at adult trauma centers (ATCs), differences in mortality have not been consistently observed for adolescents. Because trauma is the leading cause of death and acquired disability among adolescents, it is important to better define differences in outcomes among injured adolescents by using national data.To use a national data set to compare mortality of injured adolescents treated at ATCs, PTCs, or mixed trauma centers (MTCs) that treat both pediatric and adult trauma patients and to determine the final discharge disposition of survivors at different center types.Data from level I and II trauma centers participating in the 2010 National Trauma Data Bank (January 1 to December 31, 2010) were used to create multilevel models accounting for center-specific effects to evaluate the association of center characteristics (PTC, ATC, or MTC) on mortality among patients aged 15 to 19 years who were treated for a blunt or penetrating injury. The models controlled for sex; mechanism of injury (blunt vs penetrating); injuries sustained, based on the Abbreviated Injury Scale scores (post-dot values <3 or ≥3 by body region); initial systolic blood pressure; and Glasgow Coma Scale scores. Missing data were managed using multiple imputation, accounting for multilevel data structure. Data analysis was conducted from January 15, 2013, to March 15, 2016.Type of trauma center.Mortality at each center type.Among 29 613 injured adolescents (mean [SD] age, 17.3 [1.4] years; 72.7% male), most were treated at ATCs (20 402 [68.9%]), with the remainder at MTCs (7572 [25.6%]) or PTCs (1639 [5.5%]). Adolescents treated at PTCs were more likely to be injured by a blunt than penetrating injury mechanism (91.4%) compared with those treated at ATCs (80.4%) or MTCs (84.6%). Mortality was higher among adolescents treated at ATCs and MTCs than those treated at PTCs (3.2% and 3.5% vs 0.4%; P < .001). The adjusted odds of mortality were higher at ATCs (odds ratio, 4.19; 95% CI, 1.30-13.51) and MTCs (odds ratio, 6.68; 95% CI, 2.03-21.99) compared with PTCs but was not different between level I and II centers (odds ratio, 0.76; 95% CI, 0.59-0.99).Mortality among injured adolescents was lower among those treated at PTCs, compared with those treated at ATCs and MTCs. Defining resource and patient features that account for these observed differences is needed to optimize adolescent outcomes after injury.
Normalizing acronyms and abbreviations to aid patient understanding of clinical texts: ShARe/CLEF eHealth Challenge 2013, Task 2.
DL Mowery, BR South, L Christensen, J Leng, LM Peltonen, S Salanterä, H Suominen, D Martinez, S Velupillai, N Elhadad, G Savova, S Pradhan and WW Chapman,
Journal of biomedical semantics , Jul 2016 01
The ShARe/CLEF eHealth challenge lab aims to stimulate development of natural language processing and information retrieval technologies to aid patients in understanding their clinical reports. In clinical text, acronyms and abbreviations, also referenced as short forms, can be difficult for patients to understand. For one of three shared tasks in 2013 (Task 2), we generated a reference standard of clinical short forms normalized to the Unified Medical Language System. This reference standard can be used to improve patient understanding by linking to web sources with lay descriptions of annotated short forms or by substituting short forms with a more simplified, lay term.In this study, we evaluate 1) accuracy of participating systems' normalizing short forms compared to a majority sense baseline approach, 2) performance of participants' systems for short forms with variable majority sense distributions, and 3) report the accuracy of participating systems' normalizing shared normalized concepts between the test set and the Consumer Health Vocabulary, a vocabulary of lay medical terms.The best systems submitted by the five participating teams performed with accuracies ranging from 43 to 72 %. A majority sense baseline approach achieved the second best performance. The performance of participating systems for normalizing short forms with two or more senses with low ambiguity (majority sense greater than 80 %) ranged from 52 to 78 % accuracy, with two or more senses with moderate ambiguity (majority sense between 50 and 80 %) ranged from 23 to 57 % accuracy, and with two or more senses with high ambiguity (majority sense less than 50 %) ranged from 2 to 45 % accuracy. With respect to the ShARe test set, 69 % of short form annotations contained common concept unique identifiers with the Consumer Health Vocabulary. For these 2594 possible annotations, the performance of participating systems ranged from 50 to 75 % accuracy.Short form normalization continues to be a challenging problem. Short form normalization systems perform with moderate to reasonable accuracies. The Consumer Health Vocabulary could enrich its knowledge base with missed concept unique identifiers from the ShARe test set to further support patient understanding of unfamiliar medical terms.
