Clinical intervention research with EHR a big data analytics approach

Rupesh Agrawal, Dursun Delen, Bruce Benjamin

Research output: Contribution to conferencePaper

Abstract

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.

Original languageEnglish
StatePublished - 1 Jan 2019
Event25th Americas Conference on Information Systems, AMCIS 2019 - Cancun, Mexico
Duration: 15 Aug 201917 Aug 2019

Conference

Conference25th Americas Conference on Information Systems, AMCIS 2019
CountryMexico
CityCancun
Period15/08/1917/08/19

Fingerprint

Health
Big data
Sleep

Keywords

  • AF
  • Analytics
  • Clinical trial
  • Data quality
  • electronic Health Record
  • OSA

Cite this

Agrawal, R., Delen, D., & Benjamin, B. (2019). Clinical intervention research with EHR a big data analytics approach. Paper presented at 25th Americas Conference on Information Systems, AMCIS 2019, Cancun, Mexico.
Agrawal, Rupesh ; Delen, Dursun ; Benjamin, Bruce. / Clinical intervention research with EHR a big data analytics approach. Paper presented at 25th Americas Conference on Information Systems, AMCIS 2019, Cancun, Mexico.
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Agrawal, R, Delen, D & Benjamin, B 2019, 'Clinical intervention research with EHR a big data analytics approach', Paper presented at 25th Americas Conference on Information Systems, AMCIS 2019, Cancun, Mexico, 15/08/19 - 17/08/19.

Clinical intervention research with EHR a big data analytics approach. / Agrawal, Rupesh; Delen, Dursun; Benjamin, Bruce.

2019. Paper presented at 25th Americas Conference on Information Systems, AMCIS 2019, Cancun, Mexico.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Clinical intervention research with EHR a big data analytics approach

AU - Agrawal, Rupesh

AU - Delen, Dursun

AU - Benjamin, Bruce

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 - electronic Health Record

KW - OSA

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Agrawal R, Delen D, Benjamin B. Clinical intervention research with EHR a big data analytics approach. 2019. Paper presented at 25th Americas Conference on Information Systems, AMCIS 2019, Cancun, Mexico.