OHDSI MEETINGS THIS WEEK
OHDSI Community Call - Tuesday at 12pm EThttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M59X2V1U61WC9ASID2Z5N3UT95-D1JL&rnd=811649.9868221112
CDM and Vocabulary WG - Tuesday at 1pm EThttps://jjconferencing.webex.com/mw3100/mywebex/default.do?service=1&main_url=%2Fmc3100%2Fe.do%3Fsiteurl%3Djjconferencing%26AT%3DMI%26EventID%3D594628767%26MTID%3Dma32e5ce14c744149f041412fc99040f1%26Host%3DQUhTSwAAAASVgMyDOQd7vb9BN5k-UptJa_Gk1J-zE2cQn8vAktuQMo0Xmp8zuA_NCNpDQIiZrtOZRGYiH4-uB_6yxaZ4Slqe0%26FrameSet%3D2&siteurl=jjconferencing&nomenu=true
Population-Level Estimation (Eastern hemisphere) workgroup meeting - Wednesday at 3pm Hong Kong timehttps://meetings.webex.com/collabs/meetings/join?uuid=M6WE9AOKFETH2VEFPVCZWWBIT0-D1JL
Architecture Working Group - Thursday at 10am EThttps://jjconferencing.webex.com/jjconferencing/j.php?MTID=m3e1ceeca56f1e94c9fcf1ae98c10e02e1
GIS working group meeting - Next Monday (April 23rd) at 10am ET
Simple, modern video meetings for the global workforce. Join from anywhere, including your desktop, browser, mobile device, or video room device.
ANNOUNCEMENTS
2018 OHDSI F2F: Voting for best clinical research question will open this week. Keep an eye out for a forum post inviting you to vote.
OHDSI Europe - Check out photos from the 2018 OHDSI European Symposium here: https://www.ohdsi.org/2018-european-symposium-photos/
2018 OHDSI Symposium - Collaborator showcase
It is often safer to be in chains than to be free.
COMMUNITY PUBLICATIONS
Building PCOR Value and Integrity with Data Quality and Transparency Standards
Comparing a Mobile Decision Support System Versus the Use of Printed Materials for the Implementation of an Evidence-Based Recommendation: Protocol for a Qualitative Evaluation.
J Camacho, AM Medina Ch, Z Landis-Lewis, G Douglas and R Boyce,
JMIR research protocols , Apr 2018 13
The distribution of printed materials is the most frequently used strategy to disseminate and implement clinical practice guidelines, although several studies have shown that the effectiveness of this approach is modest at best. Nevertheless, there is insufficient evidence to support the use of other strategies. Recent research has shown that the use of computerized decision support presents a promising approach to address some aspects of this problem.The aim of this study is to provide qualitative evidence on the potential effect of mobile decision support systems to facilitate the implementation of evidence-based recommendations included in clinical practice guidelines.We will conduct a qualitative study with two arms to compare the experience of primary care physicians while they try to implement an evidence-based recommendation in their clinical practice. In the first arm, we will provide participants with a printout of the guideline article containing the recommendation, while in the second arm, we will provide participants with a mobile app developed after formalizing the recommendation text into a clinical algorithm. Data will be collected using semistructured and open interviews to explore aspects of behavioral change and technology acceptance involved in the implementation process. The analysis will be comprised of two phases. During the first phase, we will conduct a template analysis to identify barriers and facilitators in each scenario. Then, during the second phase, we will contrast the findings from each arm to propose hypotheses about the potential impact of the system.We have formalized the narrative in the recommendation into a clinical algorithm and have developed a mobile app. Data collection is expected to occur during 2018, with the first phase of analysis running in parallel. The second phase is scheduled to conclude in July 2019.Our study will further the understanding of the role of mobile decision support systems in the implementation of clinical practice guidelines. Furthermore, we will provide qualitative evidence to aid decisions made by low- and middle-income countries' ministries of health about investments in these technologies.
Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading.
TC Ong, MG Kahn, BM Kwan, T Yamashita, E Brandt, P Hosokawa, C Uhrich and LM Schilling,
BMC medical informatics and decision making , Sep 2017 13
Electronic health records (EHRs) contain detailed clinical data stored in proprietary formats with non-standard codes and structures. Participating in multi-site clinical research networks requires EHR data to be restructured and transformed into a common format and standard terminologies, and optimally linked to other data sources. The expertise and scalable solutions needed to transform data to conform to network requirements are beyond the scope of many health care organizations and there is a need for practical tools that lower the barriers of data contribution to clinical research networks.We designed and implemented a health data transformation and loading approach, which we refer to as Dynamic ETL (Extraction, Transformation and Loading) (D-ETL), that automates part of the process through use of scalable, reusable and customizable code, while retaining manual aspects of the process that requires knowledge of complex coding syntax. This approach provides the flexibility required for the ETL of heterogeneous data, variations in semantic expertise, and transparency of transformation logic that are essential to implement ETL conventions across clinical research sharing networks. Processing workflows are directed by the ETL specifications guideline, developed by ETL designers with extensive knowledge of the structure and semantics of health data (i.e., "health data domain experts") and target common data model.D-ETL was implemented to perform ETL operations that load data from various sources with different database schema structures into the Observational Medical Outcome Partnership (OMOP) common data model. The results showed that ETL rule composition methods and the D-ETL engine offer a scalable solution for health data transformation via automatic query generation to harmonize source datasets.D-ETL supports a flexible and transparent process to transform and load health data into a target data model. This approach offers a solution that lowers technical barriers that prevent data partners from participating in research data networks, and therefore, promotes the advancement of comparative effectiveness research using secondary electronic health data.
Behavioral Health and the Comprehensive Primary Care (CPC) Initiative: findings from the 2014 CPC behavioral health survey.
K Zivin, BF Miller, B Finke, A Bitton, P Payne, EC Stowe, A Reddy, TJ Day, P Lapin, JL Jin and LL Sessums,
BMC health services research , Aug 29 2017
Incorporating behavioral health care into patient centered medical homes is critical for improving patient health and care quality while reducing costs. Despite documented effectiveness of behavioral health integration (BHI) in primary care settings, implementation is limited outside of large health systems. We conducted a survey of BHI in primary care practices participating in the Comprehensive Primary Care (CPC) initiative, a four-year multi-payer initiative of the Centers for Medicare and Medicaid Services (CMS). We sought to explore associations between practice characteristics and the extent of BHI to illuminate possible factors influencing successful implementation.We fielded a survey that addressed six substantive domains (integrated space, training, access, communication and coordination, treatment planning, and available resources) and five behavioral health conditions (depression, anxiety, pain, alcohol use disorder, and cognitive function). Descriptive statistics compared BHI survey respondents to all CPC practices, documented the availability of behavioral health providers, and primary care and behavioral health provider communication. Bivariate relationships compared provider and practice characteristics and domain scores.One hundred sixty-one of 188 eligible primary care practices completed the survey (86% response rate). Scores indicated basic to good baseline implementation of BHI in all domains, with lowest scores on communication and coordination and highest scores for depression. Higher scores were associated with: having any behavioral health provider, multispecialty practice, patient-centered medical home designation, and having any communication between behavioral health and primary care providers.This study provides useful data on opportunities and challenges of scaling BHI integration linked to primary care transformation. Payment reform models such as CPC can assist in BHI promotion and development.
Improved computational tool for OHDSI: Bayesian penalized regression Separating known risk factors among the large number of potential confoundershttp://www.ohdsi-europe.org/images/symposium-2018/posters/30-Aki-Nishimura.pdf
