# Predictive analytics > Predictive analytics uses statistical models and machine learning to forecast outcomes such as patient response, trial performance, market uptake and access challenges, helping pharma teams plan under uncertainty. Source: https://www.visfo.health/glossary/predictive-analytics Updated: 2026-08-16T21:55:01.686153+00:00 What is predictive analytics? Predictive analytics uses statistical algorithms, machine learning and modelling techniques to identify patterns in historical or incoming data and forecast future events or outcomes. In pharma, it can estimate patient response, trial performance, market uptake or likely access challenges. A prediction is an informed estimate, not a certainty or proof of causation. It should relate to a defined decision, population and time horizon, with assumptions and limitations made clear. Why does predictive analytics matter in pharmaceutical decision-making? Predictive analytics helps clinical, medical affairs, market access and commercial teams plan under uncertainty. It can support site selection, recruitment forecasting, demand and pricing scenarios, and early access strategy. This may reduce avoidable risk and help teams direct resources towards the most plausible scenarios. Its value depends on whether the output changes a decision. For example, a recruitment forecast may affect site activation, while an access model may identify markets where additional evidence or stakeholder engagement is needed. Models using [real-world data](/glossary/real-world-data-rwd) can also inform decisions across the product lifecycle, provided the data are suitable for the intended use. How is predictive analytics run in practice? A predictive analytics project usually follows a defined sequence: - Set the decision question, target outcome, population and forecast period. - Identify relevant data and assess their completeness, consistency, timeliness and representativeness. - Prepare variables that may explain or predict the outcome, while avoiding information that would not be available when the prediction is made. - Select and train an appropriate statistical or machine-learning model. - Test the model on data not used for training and compare it with a credible baseline. - Review results with clinical, operational, access or commercial experts. - Deploy the output in a workflow, then monitor performance and update the model when conditions or data change. Data may come from trials, operational systems, claims, electronic health records, market research or other sources. If analysis of real-world data produces clinically meaningful findings, those findings may contribute to [real-world evidence](/glossary/real-world-evidence-rwe); the model itself is not automatically evidence. How should a predictive model be evaluated? Evaluation should reflect its intended use. Forecasting models may be assessed by the size and direction of their errors. Classification models may be assessed by how well they distinguish outcomes and whether predicted probabilities match observed outcomes. Teams should also examine performance across relevant patient, site or market groups rather than relying on one overall measure. Good performance during development is not enough. The model should be validated on separate data, compared with current practice and monitored after implementation. Transparent documentation should cover data provenance, assumptions, missing data, uncertainty, potential bias and the circumstances in which the model should not be used. Who owns predictive analytics, and where do teams go wrong? Ownership is usually shared. Data science teams develop and test the model; subject-matter experts define meaningful outcomes and assess plausibility; data owners manage access and quality; and the team making the decision remains accountable for how the prediction is used. Governance, privacy, security and any applicable regulatory requirements should be addressed from the outset. Common errors include starting with available data rather than a decision, treating correlation as causation, using unrepresentative training data, allowing information leakage, selecting a complex model that users cannot interpret, and failing to monitor changing clinical or market conditions. Predictive outputs should inform professional judgement rather than replace it. How does predictive analytics differ from related analytics terms? Descriptive analytics explains what has happened, while diagnostic analytics explores why it happened. Predictive analytics estimates what may happen next. Prescriptive analytics goes further by recommending an action, often using predictions alongside constraints, costs and objectives. Predictive analytics may support an [evidence generation programme](/glossary/evidence-generation-program), but it is not the same as evidence generation or causal analysis. A forecast estimates a future outcome from observed patterns; it does not by itself show that an intervention caused that outcome.