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

Future Challenges

An ongoing major challenge is how to present quantitative risk and benefit information to patients in a comprehensible way. Health literacy and numeracy vary widely across patients, suggesting that presentation of information on potential risks and benefits of clinical actions should be customized to specific groups of patients. Development of effective strategies to clearly communicate risk and benefit information to those with low numeracy is very much a work in progress, and there is plenty of room for new ideas on how to advance this agenda.[25]

Another ongoing challenge is to develop prioritization methods that can compare benefits across diverse clinical domains. Will a patient who does not like to take a lot of pills benefit more from starting a statin or treating osteoporosis? Prioritizing across diverse clinical domains is challenging because the benefits of lipid and osteoporosis management are very different (reduced risk of a cardiovascular event or death on the one hand and reduced likelihood of fracture and disability on the other). The traditional resolution of this problem is to quantify all benefits in terms of quality-adjusted life expectancy (QALE). Neither clinicians nor patients, however, are usually fluent in the language of QALE, and benefits of even very effective treatments on QALE are often surprisingly small. For example, among patients with type 2 diabetes in the United Kingdom Prospective Diabetes Study study,[27] intensive lipid control extends QALE 1.42 years,[26] and intensive blood pressure control extends QALE about 1.16 years,[26] but intensive glucose control extends QALE by only 0.27 years, and it did not improve QALE at all in the ACCORD Trial.[8,28]

Recent advances in health care informatics and risk prediction methods enable design of new and more effective types of EHR-linked, Web-based, real-time clinical decision support systems that have high use rates at targeted visits, have high clinician satisfaction rates, and improve patients' clinical outcomes. We anticipate that further progress may occur as risk prediction science improves, better methods of communicating results to patients in customized ways are devised, and ways of prioritizing clinical options across a broader set of clinical domains are developed.[3–5,13,29]