Strategies to Prioritize Clinical Options in Primary Care

Patrick J. O'Connor, MD, MA, MPH; JoAnn M. Sperl-Hillen, MD; Karen L. Margolis, MD, MPH; Thomas E. Kottke, MD, MSPH


Ann Fam Med. 2017;15(1):10-13. 

In This Article

How to Prioritize Evidence-based Clinical Options

Primary care clinicians have always intuitively prioritized treatment options, but both benefits and risks of treatment are often not estimated accurately. For some clinical services, such as smoking cessation and certain immunizations, intuition is adequate. But beyond smoking and immunizations, intuitive estimation of potential benefit of multiple clinical options is very challenging.

Several alternative methods are available to identify and prioritize evidence-based clinical options with the most potential benefit to a given patient at a given point in time.[12] With respect to cardiovascular risk factor management, risk prediction equations, such as the American College of Cardiology/American Heart Association (ACC/AHA) Cardiovascular Disease risk equations,[13,14] can be used to estimate the benefit of various clinical actions using the following 3-step approach: (1) use the risk equation to estimate a person's cardiovascular risk using current clinical data, (2) run the risk equation again, replacing 1 sub-optimal clinical value (eg, an elevated blood pressure) with a potentially improved clinical value (anticipated improved blood pressure after treatment), and (3) subtract the results to estimate the potential reduction in cardiovascular risk that may be achieved by better blood pressure control. The potential benefits associated with better blood pressure management, cholesterol management, smoking cessation, or other clinical options can then be similarly estimated and then prioritized based on potential clinical benefit.[15,16]

This approach to prioritization has a number of important limitations. Risk estimates are necessarily derived from groups of people and thus cannot precisely predict future risk for one person. The benefits of stopping smoking are not the same as the benefits of never having smoked. The full benefits of improved cardiovascular risk factor control do not kick in immediately, and benefit estimates assume that the improved risk factor control will be sustained.

Current cardiovascular risk prediction tools are based on relatively small cohort studies that began in the 1950s, when many current drug classes were not available, aspirin use was low, smoking rates were high, and cardiac care was primitive when judged by today's standards.[17] For these and other reasons, the ACC/AHA and most other cardiovascular risk equations are somewhat obsolete and tend to overestimate event and death rates.[18] Despite such limitations, explicit estimation of benefits and risks of treatment options is usually far more accurate than intuitive risk estimation by either clinician or patient.[19] The availability of large databases that include detailed clinical data on millions of patients and novel analytic approaches, such as marginal structural models and machine learning, will likely lead to improved risk prediction and prioritization methods in the near future.[20–22] It may be difficult, however, to explain these complex statistical approaches to clinicians and patients, who thus may be skeptical of their results.