Reporting molecular diagnostic results in an EHR is typically unstructured and unwieldy she stated. Text is entered into a reporting template, and that form is then scanned and uploaded into the EHR as an image file, rendering it noncomputable. Another challenge associated with this type of reporting is the sheer amount of data to be reported. In her example, Levy highlighted that variance on 40 different mutations had to be reported at the same time. Not only does that require an increase in data points, but the complexity associated with this variance information must also be reflected in the system. Information must be reported in a way that is clinically useful for physicians, in order to help inform them of the findings’ clinical significance. Levy emphasized that much of this information is actionable only through its ability to link a patient’s results to clinical trial eligibility, and traditional reporting mechanisms do not possess this ability.
Levy noted that approaches to addressing these challenges are varied. Visualization of test results, complete with color coordination and coding, has proved very helpful at Vanderbilt, allowing researchers and clinicians to quickly scan information and identify positive findings. Findings are reported in a structured way, so that there is an entity, an attribute, and a value behind each piece of data. Moreover, information and results recorded in the EHR are linked directly to a database that provides information on the clinical significance of a patient’s particular mutation variant, thereby identifying potential targeted therapies. Further guidance is provided through inclusion of relevant, summarized clinical trial literature, which links clinicians to full, PubMed sources should they need to see additional information on the significance of the trial to their patient’s care. The data management system also links the EHR to a clinical trials database, providing clinicians with the means to identify relevant trial eligibility criteria. All of these strategies, Levy emphasized, offer promise for the effective and efficient incorporation of complex and varied digital data into the process of cancer care.
Levy finished her discussion by looking to the future, contemplating how to make systems like Vanderbilt’s sustainable and scalable with respect to content generation as well as content dissemination. Aggregation of institutional data, she suggested, is critical for rendering the data clinically useful. Information from patient diagnosis, treatment, and treatment response should be aggregated and transformed to computable, standardized data for improved and more effective clinical decision support. Moreover, the records incorporated into this type of database should be combined with other data, including patient-reported outcomes as well as cost information, both of which would be beneficial to understanding treatment comparative effectiveness. Given the complexity of genome-directed cancer treatment and translational informatics on the whole, Levy underscored her experiences with the importance of triangulation of data from multiple sources