Deep Business Analytics and Artificial Complementary Intelligence: A Pre-Emptive Control Framework for Human–AI Integration in Digital Health Commodity Networks

Vollständiger Abstract

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Digital health commodity networks process billions of transactions annually, yet European health systems exhibit diagnostic lags of 30–180 days between anomalous events and institutional detection. Existing governance architectures are retrospective by design, and no existing framework combines pre-emptive predictive control with a principled human–AI cognitive-boundary taxonomy for this setting. This study introduces Deep Business Analytics (DBA), a pre-emptive management control system extending Simons’ four levers of control with a fifth—Predictive Feedforward Control—implemented via Long Short-Term Memory (LSTM) neural networks integrated with a Balanced Scorecard KPI layer. DBA is governed by Artificial Complementary Intelligence (ACI), a four-domain cognitive boundary taxonomy specifying where algorithmic governance is appropriate and where human clinical judgment must retain sovereignty, operationalised within a four-layer Predictive Governance Architecture (PGA). Empirical grounding draws on two deployments: a longitudinal case study at Royal Liverpool Hospital NHS Trust conducted over 28 consecutive days in 2023 (>10 million timestep records; RMSE = 0.00436) and a practitioner case study of 15 million GKV prescriptions processed in 2023 (VisionXY7; approximately 95% anomaly detection accuracy; Governance Velocity Improvement Ratio≈180:1). The ACI boundary taxonomy identifies two governance domains structurally unsuitable for autonomous AI decision-making. DBA and ACI together constitute an integrated, EU AI Act Annex III-compliant architecture that transforms health network AI governance from retrospective detection to pre-emptive control with principled human–AI boundaries.