Data scientists often deliver polished predictive models only to hear radio silence from the requestors. The article recounts a typical cycle: a stakeholder asks for a model, the team builds a proof‑of‑concept, obtains approval, and spends weeks fine‑tuning hundreds of features and hyperparameters. Despite high accuracy, the solution frequently remains unused. Without stakeholder buy‑in, the effort yields no impact on patient outcomes or business decisions.

In healthcare, clinicians favor trusted workflows over opaque algorithms. A model that cannot be explained invites doubt, prompting doctors to stick with familiar procedures. The author advises delivering a concise model brief that outlines target population, key features, and business‑aligned metrics in plain language. Linking outputs to real actions encourages clinicians to trust the predictions. This reduces the explainability‑accuracy gap and helps stakeholders see practical value.

Even a technically superior model fails if it disrupts operations. Embedding predictions into existing systems—such as integrating with Epic electronic health records—lets clinicians access insights without extra steps. Teams that ship a minimum viable version quickly, leverage their extensive feature library, and iterate based on feedback increase adoption rates. Check‑ins keep the project aligned with clinical priorities and prevent abandonment. Successful deployment hinges on usability.