Using Multiple Array Sensing and Non-Invasive Data Capture As a Model For Polypharmacy Error Detection

Abstract

Developing standards and technology models that will facilitate e-prescribing is one of the key action items in the federal government's plan to build a nationwide electronic health information infrastructure in the United States. E-prescribing has the potential to drive change in the healthcare industry, but the unavailability of diagnostic testing and detection equipment outside of clinical settings makes expanded collection and use of information problematic. Most solutions are provider-based, and limited by organization-wide startup & maintenance costs, and risk-averse data distribution policies. Objective, consumer-provided standardized data can facilitate the use of distributed information networks in polypharmacy detection and avoidance. In this technology review we propose here one promising model for polypharmacy management and integrated diagnostics through the use of breath-based, multiple array sensing and data capture.