A prognosis is an educated prediction about the likely outcome of a disease or condition. It is based on your history, diagnosis, treatment response, and other factors.
It can be a useful tool for healthcare providers to help patients make decisions about their care. However, prognosis information is often kept confidential.
Diagnosis comes from Greek meaning “to distinguish or discern.” It refers to a medical professional’s determination of what type of disease, disorder, or condition is affecting you. This can include physical examinations, diagnostic testing, and research done on your situation (such as a family history).
Prognosis, on the other hand, is a prediction of how something is likely to turn out. It’s based on what’s known about the disease, your specific circumstances, and what’s available to help you.
Case 1: A doctor gives you a diagnosis of appendicitis, after you’ve had an exam and X-rays. Then they tell you that your condition is likely to require an appendectomy, and that if you have the surgery, you’ll probably recover fully.
The word prognosis is often used interchangeably with diagnosis, but the distinction is important in medicine. A good prognosis should tell you the likely outcome of a treatment or procedure, and it may even predict how long you’ll be sick.
Prognosis is the prediction of the expected outcomes of treatment. It is not a fixed thing and can change over time, depending on factors like the stage of the disease when you have it or if your cancer responds well to therapy.
Prognoses are often based on statistics from studies looking at people who have the same disease as you. These statistics are often dated and don’t take into account individual differences.
Fortunately, there are many tools that help us interpret and use the prognostic information we get from these studies. These include life-table methods, prognostic indices and models.
A team of researchers at UCSF has created an online tool called ePrognosis that provides easy access to 16 community based mortality indices. These indices are rated based on their ability to predict mortality risk.
Prognosis is the art of predicting the outcome of a medical procedure or event. The science is a complex endeavor involving many facets including data collection, statistical analysis, and decision-making. A good prognostic model must account for a variety of factors, such as patient age, clinical stage, and risk factors, in order to achieve the desired level of accuracy.
Identifying the best prognostic model for your specific case requires the study of the literature, as well as in-depth conversations with your peers. Once a prognostic model has been selected, it’s time to put the model to the test. There are many tests and experiments to consider, but the most effective tests involve multiple measures such as a comparison with an alternative model, a randomized control trial (RCT), and a pilot trial to test the performance of each model over time. The results are then compared to determine which model is most suitable for your patient. Eventually, you will be left with an unbiased and objective evaluation of the best model for your patient.
Eprognosis is a crucial part of cancer care. It helps clinicians understand how much time patients have left before death and enables them to make treatment decisions. However, prognostic error is widespread and only about 20% of estimates are accurate [1,2].
The accuracy of clinical survival predictions can be influenced by the type of disease, the method used to make the prediction, the experience of the professional making it, and the length of time that has passed since the patient was diagnosed. For example, prognosis of 3 days is usually judged to be inaccurate if the patient dies within 48 hours of being admitted to hospital.
Accuracy of survival estimates was related in univariate analysis to diagnosis (chi-squared for trend, 2 = 23.3, p 0.0001) and palliative care team (chi-squared for trend, 5.0, df 4, p 0.001). In multivariate analysis accuracy was also significantly associated with the minimum estimate and maximum estimate.