Realize it or not, as a nurse practitioner you have most likely used a clinical prediction rule. These algorithms come in quite handy in clinical practice and take much of the guesswork out of making a diagnosis or selecting the appropriate treatment for a patient. As a nurse practitioner in the emergency department for example, I find Wells Criteria for determining both the risk of DVT and pulmonary embolism helpful in determining the course of testing for my patients.
It is important that as nurse practitioners we understand how and why such rules and algorithms are developed if we plan to use them. While the creation and testing of clinical prediction rules could easily take up a semester’s long statistics course, the basics are pretty simple. So, in the spirit of simplification, I’ve outlined ten essentials of clinical prediction rules all confined to the length of a standard Twitter ‘Tweet’.
A clinical predication rule (CPR) is a combination of clinical findings that statistically predicts the probability of a condition or outcome of a treatment.
CPRs use condensed information and the smallest number of indicators possible to help clinicians make informed, efficient decisions.
Commonly used clinical prediction rules include Wells Score, Ranson Criteria, Ottowa Ankle Rules and the Model for End-Stage Liver Disease.
Clinical prediction rules may fall into one of three categories: diagnostic, prognostic or prescriptive.
Diagnostic CPRs are designed to aid in determining the likelihood that a patient has or does not have a specific diagnosis.
Prognostic clinical prediction rules predict the likelihood of a specific outcome, for example mortality.
Clinical prediction rules that identity the best course of treatment or the best intervention for a specific type of patient are termed ‘prescriptive’ CPRs.
The benefit? CPRs have advantages over human clinical decision-making. Rules are statistically tested removing human inconsistency from clinical judgment.
Ultimately, clinical prediction rules can improve patient outcomes, save clinician time, decrease cost of care and increase patient satisfaction.
Taking the guesswork out of clinical decision making and instead relying on tried and true algorithms confers substantial advantages for the nurse practitioner. Relying on statistically based algorithms helps you as an NP make clinically sound decisions ensuring the best course of diagnosis, treatment and ultimately the best possible outcome for your patient. These rules back the decisions you make with science and stats protecting you legally and preventing unnecessary testing, a major pitfall of our medical system. Familiarity with clinical prediction rules pertaining to your specialty area is fundamental to helping guide your practice.
Which clinical prediction rules do you use in your specialty area?