scholarly journals Electronic health records contain dispersed risk factor information that could be used to prevent breast and ovarian cancer

2020 ◽  
Vol 27 (9) ◽  
pp. 1443-1449
Author(s):  
Thomas H Payne ◽  
Lue Ping Zhao ◽  
Calvin Le ◽  
Peter Wilcox ◽  
Troy Yi ◽  
...  

Abstract Objective The genetic testing for hereditary breast cancer that is most helpful in high-risk women is underused. Our objective was to quantify the risk factors for heritable breast and ovarian cancer contained in the electronic health record (EHR), to determine how many women meet national guidelines for referral to a cancer genetics professional but have no record of a referral. Methods and Materials We reviewed EHR records of a random sample of women to determine the presence and location of risk-factor information meeting National Comprehensive Cancer Network (NCCN) guidelines for a further genetic risk evaluation for breast and/or ovarian cancer, and determine whether the women were referred for such an evaluation. Results A thorough review of the EHR records of 299 women revealed that 24 (8%) met the NCCN criteria for referral for a further genetic risk evaluation; of these, 12 (50%) had no referral to a medical genetics clinic. Conclusions Half of the women whose EHR records contain risk-factor information meeting the criteria for further genetic risk evaluation for heritable forms of breast and ovarian cancer were not referred.

2021 ◽  
Vol 12 (02) ◽  
pp. 245-250
Author(s):  
Alexander L. Kostrinsky-Thomas ◽  
Fuki M. Hisama ◽  
Thomas H. Payne

Abstract Background Clinicians express concern that they may be unaware of important information contained in voluminous scanned and other outside documents contained in electronic health records (EHRs). An example is “unrecognized EHR risk factor information,” defined as risk factors for heritable cancer that exist within a patient's EHR but are not known by current treating providers. In a related study using manual EHR chart review, we found that half of the women whose EHR contained risk factor information meet criteria for further genetic risk evaluation for heritable forms of breast and ovarian cancer. They were not referred for genetic counseling. Objectives The purpose of this study was to compare the use of automated methods (optical character recognition with natural language processing) versus human review in their ability to identify risk factors for heritable breast and ovarian cancer within EHR scanned documents. Methods We evaluated the accuracy of the chart review by comparing our criterion standard (physician chart review) versus an automated method involving Amazon's Textract service (Amazon.com, Seattle, Washington, United States), a clinical language annotation modeling and processing toolkit (CLAMP) (Center for Computational Biomedicine at The University of Texas Health Science, Houston, Texas, United States), and a custom-written Java application. Results We found that automated methods identified most cancer risk factor information that would otherwise require clinician manual review and therefore is at risk of being missed. Conclusion The use of automated methods for identification of heritable risk factors within EHRs may provide an accurate yet rapid review of patients' past medical histories. These methods could be further strengthened via improved analysis of handwritten notes, tables, and colloquial phrases.


2009 ◽  
Vol 13 (3) ◽  
pp. 307-317 ◽  
Author(s):  
Malinee Pongsavee ◽  
Vichanan Yamkamon ◽  
Sumana Dakeng ◽  
Pornchai O-Charoenrat ◽  
Duncan R. Smith ◽  
...  

2016 ◽  
pp. 70-72
Author(s):  
D. Z. Mamarasulova ◽  
Y. S. Mamadalieva ◽  
Z. A. Ergasheva ◽  
U. D. Azizov

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