• Ashley Kieran Clift.
• Department of Surgery & Cancer, Imperial College London, London, UK.
• Brit J Hosp Med. 2025 Jan 24; 86 (1): 161-6.

AbstractPredictive algorithms have myriad potential clinical decision-making implications from prognostic counselling to improving clinical trial efficiency. Large observational (or "real world") cohorts are a common data source for the development and evaluation of such tools. There is significant optimism regarding the benefits and use cases for risk-based care, but there is a notable disparity between the volume of clinical prediction models published and implementation into healthcare systems that drive and realise patient benefit. Considering the perspective of a clinician or clinical researcher that may encounter clinical predictive algorithms in the near future as a user or developer, this editorial: (1) discusses the ways in which prediction models built using observational data could inform better clinical decisions; (2) summarises the main steps in producing a model with special focus on key appraisal factors; and (3) highlights recent work driving evolution in the ways that we should conceptualise, build and evaluate these tools.

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