TY - CONF
T1 - Clinical intervention research with EHR a big data analytics approach
AU - Agrawal, Rupesh
AU - Delen, Dursun
AU - Benjamin, Bruce
N1 - Funding Information:
The data quality, integrity of the EHR data are current challenges to EHR-based interventional studies, along with concerns of various bias and data privacy challenges (Bowman, 2013; Hong et al., 2015; Weng, 2017; Raman et al., 2018). We argue that current level of quality and accuracy of EHR data is not at par, to support the full potential of the EHR system; however, there is constant awareness and general agreement to improve the current data quality expectation (Payne et al., 2015; Raman et al., 2018). For example, the data should be correct, and current (Zozus et al. 2014; Scholte et al., 2016; Weiskopf et al., 2017). Consequently, research-work, and industry initiatives have led the effort for improving data quality (Kahn et al., 2012; Dziadkowiec et al. 2014; Kahn et al., 2016; Qualls et al., 2018). Targeted initiatives a launched to tackle the data quality challenges, e.g., Observational Health Data Science and Initiative (OHDSI), funded by the National Institute of Health. OHDSI’s focuses on data standardization, data characterization, quality improvement, and PCORI, where the latter has developed standards for data integrity and rigorous analyses.
Funding Information:
This work was conducted with the data from the Cerner Corporation?s HealthFacts data warehouse of electronic medical records, provided by Oklahoma State University (OSU), Center for Health Systems Innovation (CHSI). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the Cerner Corporation, OSU or CHSI.
Publisher Copyright:
© 2019 Association for Information Systems. All rights reserved.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Electronic Health Record adoption has reached its saturation point, near 100%; however, it has not been fully utilized for clinical intervention research outcomes. Traditional clinical trial studies for medical intervention guidelines have proven to be time-demanding, expensive, with limited coverage and effectiveness. This study aims to discover conclusive evidence with high significance to the established clinical correlation between obstructive sleep apnea and related comorbidities, along with an application of analytics to establish the impact of obstructive sleep apnea on stroke risk rate using a large, feature-rich electronic health record database. Long term goal of this research is to use a variety of data sources, including electronic health record data, along with big data analytics tools to develop a new stroke-risk stratification score. The scoring system is expected to be able to generate the granular measures for a patient-centric dose-drug combination, consequently reducing the side effects while improving the treatment plan.
AB - Electronic Health Record adoption has reached its saturation point, near 100%; however, it has not been fully utilized for clinical intervention research outcomes. Traditional clinical trial studies for medical intervention guidelines have proven to be time-demanding, expensive, with limited coverage and effectiveness. This study aims to discover conclusive evidence with high significance to the established clinical correlation between obstructive sleep apnea and related comorbidities, along with an application of analytics to establish the impact of obstructive sleep apnea on stroke risk rate using a large, feature-rich electronic health record database. Long term goal of this research is to use a variety of data sources, including electronic health record data, along with big data analytics tools to develop a new stroke-risk stratification score. The scoring system is expected to be able to generate the granular measures for a patient-centric dose-drug combination, consequently reducing the side effects while improving the treatment plan.
KW - AF
KW - Analytics
KW - Clinical trial
KW - Data quality
KW - OSA
KW - electronic Health Record
UR - http://www.scopus.com/inward/record.url?scp=85084019170&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:85084019170
T2 - 25th Americas Conference on Information Systems, AMCIS 2019
Y2 - 15 August 2019 through 17 August 2019
ER -