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<p>Pharmaceutical marketing analytics effectiveness has traditionally been evaluated using financial return on investment (ROI), with limited integration of patient-centered health outcomes into optimization objectives. This study introduces the CARE framework—causal intelligence, accountability analytics, responsible engagement, and equity outcomes—as a structured analytical architecture for reframing marketing evaluation around return on health (ROH). Rather than considering health impact as a secondary consequence of revenue optimization, CARE integrates causal inference, transparency mechanisms, and fairness constraints directly within the optimization objective. The study employs a simulation-based validation design using synthetic datasets parameterized with publicly reported healthcare adherence, access, and affordability distributions. The analytical architecture integrates marketing mix modeling (MMM), multi-touch attribution (MTA), and incrementality testing within a governance-constrained causal estimation structure. In the simulated environment, patients were assigned to treatment and control groups to enable causal effect estimation. Double machine learning techniques were used to isolate the independent effect of marketing exposure while controlling demographic, socioeconomic, and clinical confounding factors. The optimization procedure also incorporated fairness constraints to prevent disproportionate allocation of resources across population subgroups. Simulation analyses examine how redefining the optimization objective from ROI to ROH changes resource allocation and targeting strategies. Within the modeled environment, health-oriented objective functions shift investment across patient segments and therapeutic areas relative to revenue-maximizing benchmarks, generating comparatively higher projected adherence, therapy initiation, and equity-adjusted outcome indicators. These results reflect model-based analytical demonstrations rather than findings from real-world deployment. The contribution of this study is conceptual and methodological. CARE formalizes a reproducible framework for integrating causal measurement, algorithmic accountability, and equity constraints into healthcare marketing analytics, providing a structured foundation for future for more accountable and health-focused decision making in data-driven marketing systems.</p>

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From return on investment to return on health: the CARE framework for AI governance, accountability, and equitable transformation in healthcare systems

  • 期刊论文

  • 发表日期：

    2026年5月31日

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  • Thi Phuong Lan Nguyen

摘要

Pharmaceutical marketing analytics effectiveness has traditionally been evaluated using financial return on investment (ROI), with limited integration of patient-centered health outcomes into optimization objectives. This study introduces the CARE framework—causal intelligence, accountability analytics, responsible engagement, and equity outcomes—as a structured analytical architecture for reframing marketing evaluation around return on health (ROH). Rather than considering health impact as a secondary consequence of revenue optimization, CARE integrates causal inference, transparency mechanisms, and fairness constraints directly within the optimization objective. The study employs a simulation-based validation design using synthetic datasets parameterized with publicly reported healthcare adherence, access, and affordability distributions. The analytical architecture integrates marketing mix modeling (MMM), multi-touch attribution (MTA), and incrementality testing within a governance-constrained causal estimation structure. In the simulated environment, patients were assigned to treatment and control groups to enable causal effect estimation. Double machine learning techniques were used to isolate the independent effect of marketing exposure while controlling demographic, socioeconomic, and clinical confounding factors. The optimization procedure also incorporated fairness constraints to prevent disproportionate allocation of resources across population subgroups. Simulation analyses examine how redefining the optimization objective from ROI to ROH changes resource allocation and targeting strategies. Within the modeled environment, health-oriented objective functions shift investment across patient segments and therapeutic areas relative to revenue-maximizing benchmarks, generating comparatively higher projected adherence, therapy initiation, and equity-adjusted outcome indicators. These results reflect model-based analytical demonstrations rather than findings from real-world deployment. The contribution of this study is conceptual and methodological. CARE formalizes a reproducible framework for integrating causal measurement, algorithmic accountability, and equity constraints into healthcare marketing analytics, providing a structured foundation for future for more accountable and health-focused decision making in data-driven marketing systems.