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Treatment non-adherence is a major problem in the management of chronic pathologies. Research has suggested that education, shared decision making and clear communication were factors that may help mitigate non-adherence. Currently use of many chronic disease therapies is evidence based. Most of the trials carry out complex statistics that are difficult to transmit to the patient. Hence, it is essential to simplify the probability algorithms obtained from regression analyses to aid clarity during the doctor-patient communication. We describe two chronic pathologies (functional hypogonadism and cardiovascular disease prevention) managed in the Metabolic Clinics at the University Hospitals Birmingham NHS Foundation Trust, where graphical illustrations of treatment benefit were created from regression models and used as a communication tool. The BLAST screened cohort audit was coordinated at our secondary care centre and patient level data were available. This allowed us to calculate the probability of mortality for each man with type 2 diabetes and functional hypogonadism using logistic regression analysis. Probability of mortality was plotted against age in men treated/not treated with testosterone replacement and phosphodiesterase 5-inhibitors. Regarding cardiovascular disease prevention, we used the cumulative data from the Cholesterol Treatment Trialist collaboration demonstrating the scale of benefit associated with statins (22% relative risk reduction per 1mmol/L decrease in low density lipoprotein cholesterol). Other low density lipoprotein-cholesterol lowering agents have demonstrated similar benefit. An algorithm calculating the relative risk reduction was derived and this was transferred as a graph, enhancing communication clarity. These two examples we have provided ways in which complex algorithms can be translated into graphs that the lay public can easily comprehend, thus potentially improving healthcare.