The Genomic Prescribing System (GPS) is a novel web-based portal used by physicians which displays interactive, patient-specific, pharmacogenomic results in the form of succinct, electronic clinical consults – all instantaneously available in real time during patient care. Patient-specific results are provided not as raw genetic data, but as a patient-tailored synopsis of the information translated into clinical meaning, and including prescribing recommendations and suggested alternative medications.
The home page for each patient displays current medications, pharmacogenomic results for those medications, levels of evidence, and alternate medications. Information for each medication is displayed as a traffic light signal – a green light means that a medication is likely to work well for that patient, a yellow light indicates that the medication may have undesirable outcomes for the patient, and a red light signals that the patient is likely at a significant risk for toxicity or non-response for that medication. The GPS also displays levels of evidence next to each medication, either a 1, 2, or 3. The strongest evidence is given a Level 1 designation; likewise, evidence that is not as strong is given a Level 3.
To view more information about a medication, providers can click on the medication and view a summary. All summaries are designed to be read in 30 seconds or less, and use clear, concise, and consistent language so that providers can easily absorb the information. From this page, providers are also able to select the primary literature sources to read more about the basis for the summary. In addition, if a healthcare provider is interested in searching for additional information on other medications that the patient is not currently taking, they are able to move to a tab labeled “Search Drug/Diseases” to look up pharmacogenomic information either by the individual drug or by disease.
The GPS has been successfully used in the 1200 Patients Project, the ACCOuNT Project, and the ImPreSS Trial.
Please note the information shown is not real patient data.