A point-of-care non-invasive test for Coronary Artery Disease (CAD) (POC-CAD) has been previously developed and validated. The test requires the simultaneous acquisition of orthogonal voltage gradient (OVG) and photoplethysmogram signals, which is the primary methodology described in this paper. The acquisition of the OVG, a biopotential signal, necessitates the placement of electrodes on the prepared skin of the patient's thorax (arranged similarly to the Frank lead configuration, comprising six bipolar electrodes and a reference electrode) and a hemodynamic sensor on the finger (using a standard transmission modality). The signal is uploaded to a cloud-based system, where engineered features are extracted from the signal and supplied to a machine-learned algorithm to yield the CAD Score. The physician must then interpret the value of the CAD Score in the context of their patient's pre-test probability of CAD, resulting in a post-test probability of CAD. This interpretation can be performed at the level of test positivity and test negativity or at a finer level of granularity; methodologies for each are proposed here based on likelihood ratios. Using the post-test probability, the physician must determine the appropriate next step in the treatment of their patient; several scenarios are used to illustrate this process. Test adoption is only feasible if economically viable; a discussion of the integration of the test into the CAD diagnostic flow and the resultant cost savings to the healthcare system is provided. The economic model demonstrates that cost savings to the healthcare system can be achieved by preventing delayed treatment, which, if left unaddressed, results in disease progression requiring more advanced (and expensive) care.