Name
Title | ||
|---|---|---|
CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record. |
Abstract | ||
|---|---|---|
Objective Clinicians' ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS). Materials and Methods The National Institutes of Health (NIH)-sponsored Clinical Sequencing Exploratory Research and Electronic Medical Records & Genomics EHR Working Groups conducted a multiphase, iterative process involving working group discussions and 2 surveys in order to determine how genetic and genomic information are currently displayed in EHRs, envision optimal uses for different types of genetic or genomic information, and prioritize areas for EHR improvement. Results There is substantial heterogeneity in how genetic information enters and is documented in EHR systems. Most institutions indicated that genetic information was displayed in multiple locations in their EHRs. Among surveyed institutions, genetic information enters the EHR through multiple laboratory sources and through clinician notes. For laboratory-based data, the source laboratory was the main determinant of the location of genetic information in the EHR. The highest priority recommendation was to address the need to implement CDS mechanisms and content for decision support for medically actionable genetic information. Conclusion Heterogeneity of genetic information flow and importance of source laboratory, rather than clinical content, as a determinant of information representation are major barriers to using genetic information optimally in patient care. Greater effort to develop interoperable systems to receive and consistently display genetic and/or genomic information and alert clinicians to genomic-dependent improvements to clinical care is recommended. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1093/jamia/ocv065 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Keywords | Field | DocType |
genetics,electronic health records,translational research,clinical decision support,survey | Working group,Data mining,Information flow (information theory),Interoperability,Decision support system,Knowledge management,Medical record,Translational research,Clinical decision support system,Exploratory research,Medicine | Journal |
Volume | Issue | ISSN |
22 | 6 | 1067-5027 |
Citations | PageRank | References |
9 | 0.87 | 5 |
Authors | ||
23 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Brian H Shirts | 1 | 13 | 2.72 |
| joseph salama | 2 | 9 | 0.87 |
| Samuel J. Aronson | 3 | 19 | 5.10 |
| wendy k chung | 4 | 9 | 2.22 |
| s w gray | 5 | 9 | 0.87 |
| Lucia Hindorff | 6 | 222 | 16.27 |
| Gail P Jarvik | 7 | 115 | 11.15 |
| sharon e plon | 8 | 9 | 1.55 |
| elena m stoffel | 9 | 9 | 1.21 |
| P Tarczy-Hornoch | 10 | 553 | 53.80 |
| eliezer m van allen | 11 | 9 | 2.22 |
| karen e weck | 12 | 9 | 0.87 |
| Christopher G Chute | 13 | 2349 | 282.57 |
| Robert R Freimuth | 14 | 93 | 13.73 |
| robert w grundmeier | 15 | 10 | 3.67 |
| Andrea Hartzler | 16 | 164 | 25.55 |
| Rongling Li | 17 | 44 | 4.22 |
| Peggy Peissig | 18 | 189 | 23.83 |
| Josh F. Peterson | 19 | 82 | 15.68 |
| Luke V. Rasmussen | 20 | 105 | 23.74 |
| Justin Starren | 21 | 375 | 56.02 |
| marc s williams | 22 | 26 | 1.80 |
| Casey Lynnette Overby | 23 | 58 | 7.82 |