• Peter Croft, Douglas G Altman, Jonathan J Deeks, Kate M Dunn, Alastair D Hay, Harry Hemingway, Linda LeResche, George Peat, Pablo Perel, Steffen E Petersen, Richard D Riley, Ian Roberts, Michael Sharpe, Richard J Stevens, Danielle A Van Der Windt, Von KorffMichaelM, and Adam Timmis.
• Arthritis Research UK Primary Care Centre, Research Institute for Primary Care and Health Sciences, Keele University, Keele ST5 5BG, Staffordshire, UK. p.r.croft@keele.ac.uk.
• Bmc Med. 2015 Jan 30; 13: 2020.

BackgroundDiagnosis is the traditional basis for decision-making in clinical practice. Evidence is often lacking about future benefits and harms of these decisions for patients diagnosed with and without disease. We propose that a model of clinical practice focused on patient prognosis and predicting the likelihood of future outcomes may be more useful.DiscussionDisease diagnosis can provide crucial information for clinical decisions that influence outcome in serious acute illness. However, the central role of diagnosis in clinical practice is challenged by evidence that it does not always benefit patients and that factors other than disease are important in determining patient outcome. The concept of disease as a dichotomous 'yes' or 'no' is challenged by the frequent use of diagnostic indicators with continuous distributions, such as blood sugar, which are better understood as contributing information about the probability of a patient's future outcome. Moreover, many illnesses, such as chronic fatigue, cannot usefully be labelled from a disease-diagnosis perspective. In such cases, a prognostic model provides an alternative framework for clinical practice that extends beyond disease and diagnosis and incorporates a wide range of information to predict future patient outcomes and to guide decisions to improve them. Such information embraces non-disease factors and genetic and other biomarkers which influence outcome.SummaryPatient prognosis can provide the framework for modern clinical practice to integrate information from the expanding biological, social, and clinical database for more effective and efficient care.

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