A clinical prediction rule is a guideline based on medical studies that provides information on the likelihood of an outcome or probability of disease. These rules can assist with the decision making process for ill patients who need medical interventions. They can also play a role in policies developed by hospitals and similar facilities to standardize care, reduce the risk of infection, and protect patients. Clinical prediction rules are designed to work in concert with other medical information, rather than being applied on their own.
For example, researchers might conduct a study on patients with a particular type of fracture. They would look at different kinds of treatment to determine the probability of various outcomes in relation to those treatments. This could establish a clinical prediction rule that might inform guidance on best practice for patients with that kind of fracture. The study might show that surgery to pin and stabilize the fracture with a specific device tends to result in lower complications and faster healing time, for instance, or that people who receive physical therapy do better.
Studies may be able to illustrate a patient’s risk level, on the basis of evaluations of similar patients. Hospitals can use a clinical prediction rule to classify people as low, medium, and high risk, and this may inform treatment. Patients at high risk of immune compromise, for instance, could be isolated to limit the chance of contracting infections. Members of a particular population who tend to have a higher risk of disease could be subject to interventions to reduce transmission or increase vaccination with the goal of preventing it.
Development of a clinical prediction rule may involve a number of studies to explore and corroborate evidence. The goal is to design repeatable studies that be verified through testing to confirm their results. If groups of researchers get radically different results, they can analyze the data to explore why. It might be the result of errors like using different classifications or having a wider range of patients. Other problems might involve bias or failure to select a representative sample.
Clinicians in specific areas of medical practice may read trade journals and attend conferences to keep up with the latest clinical prediction rules. Presentations at these events can provide information about peer-reviewed studies on this subject. A clinical prediction rule may be used by medical personnel for activities like developing a clinical pathway, a decision tree that standardizes medical treatment to ensure that no steps in diagnosis or treatment are missed.