scholarly journals Identifying Patients With High Data Completeness to Improve Validity of Comparative Effectiveness Research in Electronic Health Records Data

2017 ◽  
Vol 103 (5) ◽  
pp. 899-905 ◽  
Author(s):  
Kueiyu Joshua Lin ◽  
Daniel E. Singer ◽  
Robert J. Glynn ◽  
Shawn N. Murphy ◽  
Joyce Lii ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Effectiveness Research ◽  
Health Records ◽  
Data Completeness ◽  
High Data ◽  
Electronic Health

Importance of Health Information Technology, Electronic Health Records, and Continuously Aggregating Data to Comparative Effectiveness Research and Learning Health Care

2012 ◽  
Vol 30 (34) ◽  
pp. 4243-4248 ◽  
Author(s):  
Benjamin J. Miriovsky ◽  
Lawrence N. Shulman ◽  
Amy P. Abernethy
Keyword(s):  
Electronic Health Records ◽  
Real Time ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Clinical Care ◽  
Administrative Databases ◽  
Effectiveness Research ◽  
Health Records ◽  
Patient Level ◽  
Electronic Health

Rapidly accumulating clinical information can support cancer care and discovery. Future success depends on information management, access, use, and reuse. Electronic health records (EHRs) are highlighted as a critical component of evidence development and implementation, but to fully harness the potential of EHRs, they need to be more than electronic renderings of the traditional paper medical chart. Clinical informatics and structured accessible secure data captured through EHR systems provide mechanisms through which EHRs can facilitate comparative effectiveness research (CER). Use of large linked administrative databases to answer comparative questions is an early version of informatics-enabled CER familiar to oncologists. An updated version of informatics-enabled CER relies on EHR-derived structured data linked with supplemental information to provide patient-level information that can be aggregated and analyzed to support hypothesis generation, comparative assessment, and personalized care. As implementation of EHRs continues to expand, electronic databases containing information collected via EHRs will continuously aggregate; aggregating data enhanced with real-time analytics can provide point-of-care evidence to oncologists, tailored to patient-level characteristics. The system learns when clinical care informs research, and insights derived from research are reinvested in care. Challenges must be overcome, including interoperability, standardization, access, and development of real-time analytics.

scholarly journals Caveats for the Use of Operational Electronic Health Record Data in Comparative Effectiveness Research

Medical Care ◽  
2013 ◽  
Vol 51 ◽  
pp. S30-S37 ◽  
Author(s):  
William R. Hersh ◽  
Mark G. Weiner ◽  
Peter J. Embi ◽  
Judith R. Logan ◽  
Philip R.O. Payne ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Health Record ◽  
Effectiveness Research ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Electronic Health

Will Comparative Effectiveness Research Lead to Healthcare Rationing?

2011 ◽  
pp. 1-21
Author(s):  
Mary Brown
Keyword(s):  
Comparative Effectiveness Research ◽  
Comparative Effectiveness ◽  
Data Exchange ◽  
Health Data ◽  
Effectiveness Research ◽  
Advisory Committees ◽  
Healthcare Data ◽  
Practice Approach ◽  
Electronic Health ◽  
Healthcare Rationing

The Affordable Healthcare for America Bill that was signed into law in March 2010 includes support for activities that come under the heading of ‘comparative effectiveness’ research. The bill attempts to accelerate the conversion to electronic health records by all payers and providers who participate in the healthcare payment data stream. Conversion to electronic health data collection and storage solutions will create a large amount of treatment and payment data that is increasingly standardized by health standards organizations which reduces integration issues between technologies. There are federal advisory committees at work on designing the infrastructure needed to support a National Health Information Network (NHIN) that will support the healthcare data exchange required for comparative effectiveness research. The theory behind this work is that the availability of a large portion of existing health data will make it possible for researchers to identify therapies that lead to superior patient outcomes. It is assumed that the superior therapy would become the ‘best practice’ approach to treating a particular ailment. Supporters of comparative effectiveness see this as a strategy for making the system more effective both in terms of good medicine and also in terms of decreased cost. Opponents of comparative effectiveness see it as healthcare rationing and an inappropriate injection of government into the healthcare decision making process. Supporters and opponents have identified both positive and negative consequences to comparative effectiveness and this chapter will analyze the impact and propose some ways to optimize the results of this work.

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