Study: EMRs can yield data for genetic research
Extracting patient data that already exists in EMRs could expedite the process of collecting data for genetic studies, according to “Electronic Medical Records for Genetic Research: Results of the eMERGE Consortium,” a study published in Science Translational Medicine.
In the study, Abel Kho, MD, an assistant professor of medicine at Chicago’s Northwestern University Feinberg School of Medicine and a physician at Northwestern Memorial Hospital, and colleagues investigated whether patients' EMR data captured through routine care could identify disease phenotypes with sufficient positive and negative predictive values for use in genome-wide association studies.
The researchers extracted patient information in EMRs from routine doctors’ visits at five institutions that belong to the Electronic Medical Records and Genomics Network (eMERGE) Consortium. Each organization used different EMR software, according to the authors, yet all EMRs provided information that allowed researchers to accurately identify patients with five kinds of diseases or health conditions: cardiac conduction, cataracts, dementia, peripheral arterial disease and type 2 diabetes.
The five institutions that participated in the study collected genetic samples for research. Patients agreed to the use of their records for studies, according to Northwestern.
“Using data from five different sets of EMRs, we have identified five disease phenotypes with positive predictive values of 73 to 98 percent and negative predictive values of 98 to 100 percent,” wrote Kho and colleagues.
“Most EMRs captured key information (diagnoses, medications, laboratory tests) used to define phenotypes in a structured format. We identified natural language processing as an important tool to improve case identification rates," stated the authors. "Efforts and incentives to increase the implementation of interoperable EMRs will markedly improve the availability of clinical data for genomics research.”
The research was supported by the National Human Genome Research Institute with additional funding from the National Institute of General Medical Sciences, Northwestern stated.
In the study, Abel Kho, MD, an assistant professor of medicine at Chicago’s Northwestern University Feinberg School of Medicine and a physician at Northwestern Memorial Hospital, and colleagues investigated whether patients' EMR data captured through routine care could identify disease phenotypes with sufficient positive and negative predictive values for use in genome-wide association studies.
The researchers extracted patient information in EMRs from routine doctors’ visits at five institutions that belong to the Electronic Medical Records and Genomics Network (eMERGE) Consortium. Each organization used different EMR software, according to the authors, yet all EMRs provided information that allowed researchers to accurately identify patients with five kinds of diseases or health conditions: cardiac conduction, cataracts, dementia, peripheral arterial disease and type 2 diabetes.
The five institutions that participated in the study collected genetic samples for research. Patients agreed to the use of their records for studies, according to Northwestern.
“Using data from five different sets of EMRs, we have identified five disease phenotypes with positive predictive values of 73 to 98 percent and negative predictive values of 98 to 100 percent,” wrote Kho and colleagues.
“Most EMRs captured key information (diagnoses, medications, laboratory tests) used to define phenotypes in a structured format. We identified natural language processing as an important tool to improve case identification rates," stated the authors. "Efforts and incentives to increase the implementation of interoperable EMRs will markedly improve the availability of clinical data for genomics research.”
The research was supported by the National Human Genome Research Institute with additional funding from the National Institute of General Medical Sciences, Northwestern stated.