Impact of Terminology Mapping on Population Health Cohorts IMPaCt

Background and Objectives: The population health care delivery model uses phenotype algorithms in the electronic health record (EHR) system to identify patient cohorts targeted for clinical interventions such as laboratory tests, and procedures. The standard terminology used to identify disease cohorts may contribute to significant variation in error rates for patient inclusion or exclusion. The United States requires EHR systems to support two diagnosis terminologies, the International Classification of Disease (ICD) and the Systematized Nomenclature of Medicine (SNOMED). Terminology mapping enables the retrieval of diagnosis data using either terminology. There are no standards of practice by which to evaluate and report the operational characteristics of ICD and SNOMED value sets used to select patient groups for population health interventions. Establishing a best practice for terminology selection is a step forward in ensuring that the right patients receive the right intervention at the right time. The research question is, “How does the diagnosis retrieval terminology (ICD vs SNOMED) and terminology map maintenance impact population health cohorts?” Aim 1 and 2 explore this question, and Aim 3 informs practice and policy for population health programs. Methods Aim 1: Quantify impact of terminology choice (ICD vs SNOMED) ICD and SNOMED phenotype algorithms for diabetes, chronic kidney disease (CKD), and heart failure were developed using matched sets of codes from the Value Set Authority Center. The performance of the diagnosis-only phenotypes was compared to published reference standard that included diagnosis codes, laboratory results, procedures, and medications. Aim 2: Measure terminology maintenance impact on SNOMED cohorts For each disease state, the performance of a single SNOMED algorithm before and after terminology updates was evaluated in comparison to a reference standard to identify and quantify cohort changes introduced by terminology maintenance. Aim 3: Recommend methods for improving population health interventions The socio-technical model for studying health information technology was used to inform best practice for the use of population health interventions. Results Aim 1: ICD-10 value sets had better sensitivity than SNOMED for diabetes (.829, .662) and CKD (.242, .225) (N=201,713, p Aim 2: Following terminology maintenance the SNOMED algorithm for diabetes increased in sensitivity from (.662 to .683 (p Aim 3: Based on observed social and technical challenges to population health programs, including and in addition to the development and measurement of phenotypes, a practical method was proposed for population health intervention development and reporting

Emotional intelligence training intervention among trainee teachers: a quasi-experimental study

Background: Emotional intelligence (EI) has often been linked to improvements in professional performance. Indeed, generic competencies related to EI have been included in university curricula. However, learning EI involves significant time and effort on the part of students, and this may hinder the acquisition of specific content for each degree. In this study, an intervention to develop EI in higher education students is described and evaluated. Methods: The intervention consisted of eight group sessions performed in a regular course aiming to increase EI. The sessions included strategies and training on perceiving and understanding one’s own emotions and others’ emotions, identifying and understanding the impact one’s own feelings in adopting decisions, expressing one’s own emotions and the stress experienced, and managing both one’s own emotions and emotions of others. Participants were 192 students studying for a Master of Primary Education degree. A quasi-experimental nonequivalent control group pretest-posttest design was adopted. The effectiveness of the intervention was evaluated using multi-level analyses. Results: The results showed a significant improvement in the EI of students in the experimental group compared with the control group. Conclusions: This research demonstrates that it is possible to develop EI in higher education students, without hindering the acquisition of specific content competencies and, therefore, without interfering with their academic performance and without overburdening students with work outside the classroom. Trial registration: The experiment has been registered in the Initial Deposit of the Spanish Center for Sociological Research (CIS). 7/6/2015. http://www.cis.es/cis/opencms/ES/index.html.This research was supported by the Spanish Ministry of Economy and Competitiveness under Grant number EDU2015-64562-R