Public health surveillance systems worldwide depend on a structural bottleneck: physicians must observe disease cases, diagnose them, and manually report them to surveillance authorities. This human-mediated reporting chain introduces delays of days to weeks, suffers from chronic underreporting estimated at 50-90% in developing nations, and creates geographic blind spots in areas with limited healthcare infrastructure. This paper proposes an alternative epidemiological signal: pharmaceutical point-of-sale (POS) data. Every pharmacy transaction-already digitized, geotagged, and timestamped-encodes implicit information about the health state of the purchasing population. We argue that pharmacy sales data, analyzed through AI-driven pattern recognition, can function as a real-time epidemiological surveillance proxy that operates independently of physician reporting compliance. We introduce the Pharmaceutical Signal Framework (PSF), a three-layer architecture connecting pharmacy POS systems to public health response through signal detection and anomaly correlation. We ground this work in the Enterprise Software Perception Dependency Spectrum (EPDS), demonstrating that disease surveillance is a P3 workflow-fully dependent on human observation-that can be partially converted to P1 through already-digital proxy signals. Using India's GST e-invoicing infrastructure as a case, we show that the technical infrastructure for pharmaceutical surveillance already exists; the barrier is architectural and policy-oriented, not technological. We present drug-to-condition signal mappings across three confidence tiers, illustrative detection scenarios for pollutiondriven respiratory disease, waterborne outbreaks, and influenza, and identify validation methodology and ethical constraints.