Leveraging EHR Data for Outcomes and Comparative Effectiveness Research in Oncology

Along with the increasing adoption of electronic health records (EHRs) are expectations that data collected within EHRs will be readily available for outcomes and comparative effectiveness research. Yet the ability to effectively share and reuse data depends on implementing and configuring EHRs with these goals in mind from the beginning. Data sharing and integration must be planned both locally as well as nationally. The rich data transmission and semantic infrastructure developed by the National Cancer Institute (NCI) for research provides an excellent example of moving beyond paper-based paradigms and exploiting the power of semantically robust, network-based systems, and engaging both domain and informatics expertise. Similar efforts are required to address current challenges in sharing EHR data.

[1]  J Morrissey HIMSS (Healthcare Information and Management Systems Society) meeting sees explosive growth. , 1995, Modern healthcare.

[2]  Kashner Tm Agreement between administrative files and written medical records: a case of the Department of Veterans Affairs. , 1998 .

[3]  T. M. Kashner,et al.  Agreement between administrative files and written medical records: a case of the Department of Veterans Affairs. , 1998, Medical care.

[4]  Ashokkumar A. Patel,et al.  The development of common data elements for a multi-institute prostate cancer tissue bank: The Cooperative Prostate Cancer Tissue Resource (CPCTR) experience , 2005, BMC Cancer.

[5]  JoAnn E Manson,et al.  Accuracy of Administrative Coding for Type 2 Diabetes in Children, Adolescents, and Young Adults , 2007, Diabetes Care.

[6]  JRobert Beck,et al.  The Cancer Biomedical Informatics Grid (caBIG): infrastructure and applications for a worldwide research community. , 2007, Studies in health technology and informatics.

[7]  Sherri de Coronado,et al.  NCI Thesaurus: A semantic model integrating cancer-related clinical and molecular information , 2007, J. Biomed. Informatics.

[8]  D. Ludwig,et al.  Accuracy of Administrative Coding for Type 2 Diabetes in Children, Adolescents, and Young Adults , 2007, Diabetes Care.

[9]  Anders Grimsmo,et al.  Instant availability of patient records, but diminished availability of patient information: A multi-method study of GP's use of electronic patient records , 2008, BMC Medical Informatics Decis. Mak..

[10]  Guy Doumeingts,et al.  Architectures for enterprise integration and interoperability: Past, present and future , 2008, Comput. Ind..

[11]  K. Bailey,et al.  Identifying In-Hospital Venous Thromboembolism (VTE): A Comparison of Claims-Based Approaches With the Rochester Epidemiology Project VTE Cohort , 2008, Medical care.

[12]  S. Greenfield,et al.  Comparative Effectiveness Research: A Report From the Institute of Medicine , 2009, Annals of Internal Medicine.

[13]  C Ohmann,et al.  Future Developments of Medical Informatics from the Viewpoint of Networked Clinical Research , 2009, Methods of Information in Medicine.

[14]  B. Dean,et al.  Review: Use of Electronic Medical Records for Health Outcomes Research , 2009, Medical care research and review : MCRR.

[15]  Chalapathy Neti,et al.  Rapid-learning system for cancer care. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[16]  F. Mowat,et al.  Use of electronic medical records in oncology outcomes research , 2010, ClinicoEconomics and outcomes research : CEOR.

[17]  D. Blumenthal,et al.  The "meaningful use" regulation for electronic health records. , 2010, The New England journal of medicine.

[18]  Kai Zheng,et al.  Handling anticipated exceptions in clinical care: investigating clinician use of 'exit strategies' in an electronic health records system , 2011, J. Am. Medical Informatics Assoc..

[19]  T. Hoff Deskilling and adaptation among primary care physicians using two work innovations , 2011, Health care management review.

[20]  Hua Xu,et al.  Data from clinical notes: a perspective on the tension between structure and flexible documentation , 2011, J. Am. Medical Informatics Assoc..

[21]  Kai Zheng,et al.  Collaborative search in electronic health records , 2011, J. Am. Medical Informatics Assoc..

[22]  Christopher G. Chute,et al.  Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network experience , 2011, J. Am. Medical Informatics Assoc..

[23]  Barry Smith,et al.  The HL7 Approach to Semantic Interoperability , 2011, ICBO.

[24]  C. Chute,et al.  Electronic Medical Records for Genetic Research: Results of the eMERGE Consortium , 2011, Science Translational Medicine.

[25]  Lucila Ohno-Machado,et al.  Natural language processing: an introduction , 2011, J. Am. Medical Informatics Assoc..

[26]  McGinnis Jm,et al.  Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care: Workshop Series Summary , 2011 .

[27]  Nianwen Xue,et al.  Natural language processing and the oncologic history: is there a match? , 2011, Journal of oncology practice.

[28]  Jason Wang,et al.  Validity of electronic health record-derived quality measurement for performance monitoring , 2012, J. Am. Medical Informatics Assoc..

[29]  Susan Tiefenbrun BUFFALO (New York) , 2012 .

[30]  Reuben R. McDaniel,et al.  Same organization, same electronic health records (EHRs) system, different use: exploring the linkage between practice member communication patterns and EHR use patterns in an ambulatory care setting , 2011, J. Am. Medical Informatics Assoc..

[31]  Erin Holve,et al.  The Electronic Data Methods (EDM) Forum for Comparative Effectiveness Research (CER) , 2012, Medical care.

[32]  Blackford Middleton,et al.  Method of electronic health record documentation and quality of primary care , 2012, J. Am. Medical Informatics Assoc..

[33]  Nedjeljko Frančula The National Academies Press , 2013 .

[34]  Ramana V. Davuluri,et al.  Biomedical Informatics for Cancer Research , 2014 .