Skip to main content
Premium Trial:

Request an Annual Quote

Mount Sinai's CLIPMERGE Supports the Use of Genomic Data, EMRs for Personalized Medicine

Premium

The Mount Sinai Medical Center has launched a new research program called the Clinical Implementation of Personalized Medicine through Electronic Health Records and Genomics, or CLIPMERGE, which aims to implement a genomics-enabled clinical decision-support engine for physicians.

Underlying the program is the CLIPMERGE data management and analysis platform, which stores variant information collected from genotyped patients and links this data to clinical information in patients’ electronic medical records in order to give doctors real-time therapeutic and diagnostic guidance based on their patient’s genetic profile.

Mount Sinai is preparing to launch a pilot program in October called CLIPMERGE PGx that will focus on informing physicians about gene-drug interactions at the point of care in order to improve treatment decisions. To do that, Mount Sinai is recruiting 1,500 patients from a pool of more than 25,000 candidates that are enrolled in its BioMe Biobank. Once a patient consents to participate, the hospital collects a saliva sample, extracts DNA, and analyzes it for genetic variants that may affect the patient’s response to treatments.

These variants are then housed in the CLIPMERGE platform, where they remain until a patient in the study is prescribed a medication that the system deems unlikely to be effective or to have a high chance of side effects. These decisions are made based on a set of rules derived from drug-gene interactions and their impact on patient response from the Clinical Pharmacogenetics Implementation Consortium. When this happens, CLIPMERGE sends an alert to the clinician through the patient’s EMR.

The alert provides reasons to back the suggested change in the prescription, including supporting references from the literature as well as alternative medications or doses that the physician could offer to the patient that might be more effective, Omri Gottesman, a Mount Sinai physician-scientist and principal investigator of the program, told BioInform.

Gottesman is also the lead author of a recent Clinical Pharmacology and Therapeutics paper that describes the CLIPMERGE PGx program.

The pilot doesn’t have an end date. The researchers plan to keep updating the system as new information becomes available and also to extend it to other clinical sites outside Mount Sinai.

Gottesman said that one goal of the PGx pilot is to figure out the best ways of interacting with clinicians without adding to their workflow, as well as gauge whether the information they receive is helpful and understandable.

The Mount Sinai investigators will also evaluate the logistics of implementing the program at multiple institutions while catering to different clinicians and working with different EMR systems and workflows, he said. That’s one of the reasons why the team decided to develop CLIPMERGE as a standalone system rather than embed the platform in its internal EMR system. “It makes more sense to be able to write things once and run them anywhere than to have to do it at each instance,” he explained.

Also, it will be relatively easy to update the system with new information about variants as it becomes available in the literature, Gottesman said. If the data were locked in a commercial platform, updating it would likely be less efficient, he said.

The Mount Sinai team hopes to eventually scale up the program to include support for other kinds of genetic applications beyond pharmacogenomics. For example, it could be used to provide clinicians with information about disease-gene interactions for diagnostics and treatment purposes.

“We think there is a lot of potential utility here,” Gottesman said. “We are using the CLIPMEREGE PGx program to learn about these operational and process issues associated with implementing genomic medicine,” but “really the goal of the program [and] the platform is to have a [general] mechanism to deal with known associations between genotype and phenotype — wherever that information comes from,” he said.