Most illnesses today are measured in terms of their effects on daily activities, but who do not always consider the outcomes based on the patient’s perspectives. Many clinical studies instead apply standardized measures that identify quality of life as an important outcome. Advancing research methodologies, including new approaches to clinical research, should inform this discussion by centering medical decision making on the preferences of
the most important stakeholder—the patient.
Published Online: June 20, 2014
Robert M. Kaplan, PhD, chief science officer, Agency for Healthcare Research and Quality
Discussions of how to measure health are often characterized by 2 themes: first, illness and premature death are undesirable, so at least 1 component of health is avoidance of serious illness and mortality; and second, the effects of illness and disability on everyday functioning and quality of life are important considerations as well.1 Disease and disability would typically disrupt the usual activities of daily living. For example, cancer or heart disease may shorten life expectancy and can reduce a person’s capacity to engage in meaningful life activities in the years prior to death. Even relatively minor illnesses can have disruptive effects. A common cold, for example, can interfere with social and work activities, but symptoms usually improve in a relatively short time. However, chronic illnesses, such as recurrent low back pain, can result in permanent disruption of enjoyable life activities.2 A comprehensive conceptualization of wellness, therefore, must consider risk of death, reduced quality of life, and duration of health states.1,3,4
Over the last few decades, the Agency for Healthcare Research and Quality (AHRQ), clinicians, policy makers, and patient advocates have demonstrated a growing interest in measuring patient-reported outcomes. Most illnesses are now evaluated in terms of their effects on usual life activities. For example, modern medicine applies common laboratory tests, such as the blood chemistry panel, to assess wellness. Although these tests are clinically instructive, they are not always correlated with outcomes from the perspective of the patient, nor are they direct measures of the disease process. We often refer to the tests as “surrogate” markers because they serve as proxy measures of the disease process. Sometimes the surrogate markers do not correlate with either life expectancy or outcomes when viewed from the patient’s perspective.5
Many clinical studies now apply standardized measures that address a patient’s quality of life. Figure 1 summarizes the number of scientific papers identified in PubMed between 1972 and 2012 that included the “quality of life” key-word phrase. In 1972, PubMed did not identify any papers with this phrase; but by 2012, more than 11,000 articles containing the phrase were identified. This trend has continued in recent years, with a 78% increase in the 5 years between 2007 and 2012. During the last 4 decades, many new quality-of-life tools have become available. These tools allow for a more sophisticated analysis of patient-reported outcomes specific to a variety of illnesses such as cancer,6 diabetes,7 and heart disease.8
Identifying quality of life as an important outcome was perhaps first highlighted in the 1988 Shattuck Lecture by Paul M. Ellwood Jr, MD.9 Dr Ellwood advocated for what he referred to as “a technology of patient experience.” In contrast to managing symptoms, Dr Ellwood emphasized the importance of managing patient outcomes. He saw medical care that factored in quality of life as relying on 4 techniques:
1) Developing standards and guidelines that match treatments with patient desires
2) Measuring patient well-being and functioning
3) Using normative data to interpret patient outcomes within the context of other people
4) Disseminating information in ways that could affect decision makers
This approach puts the patient at the center of healthcare and uses patient-centered reports to offer guidance and perspective for clinical care. More recently, use of these methods has come to be known as patient-centered outcomes research (PCOR). The use of PCOR is well represented in the Affordable Care Act and in the Patient-Centered Outcomes Research Institute (PCORI). At the center of PCOR is the measurement of outcomes from the patient perspective.
Although PCOR may seem uncontroversial, the PCOR perspective often leads to different conclusions than more traditional biomedical research. PCOR gives preference to outcomes with reference to only 2 central measures: length of life and quality of life.10,11 The central premise of PCOR is that the goal of medicine and public health is to lengthen human life and/or improve its quality during the years that people survive. This perspective argues that physiological measures are important only if they relate to life duration or life quality. Blood pressure, for example, is a meaningful biological measure because it is highly predictive of early death or disability associated with myocardial infarction (MI) or stroke. Other measures less clearly relate to the twin objectives of improved life quality or lengthened life expectancy. Catecholamine variations in response to acute stress, for example, are less clearly related to the objectives and outcomes researchers focus on.
Another perspective arising from PCOR is the focus on all-cause mortality as opposed to disease-specific mortality.12 A variety of large clinical trials have demonstrated reductions in 1 cause of death but have shown compensatory increases in other causes of death.13 Trials on screening mammography, for example, frequently show that breast cancer screening leads to a reduction in breast cancer mortality. Yet the same trials often fail to show that breast cancer screening increases overall life expectancy.14 Although breast cancer deaths might be reduced, other causes of death are increased.15
Another example of this conundrum is illustrated by the Physicians’ Health Study. In this landmark study, approximately 22,000 physicians were randomly assigned to take either 325 mg of aspirin every other day or placebo. When the data were first analyzed, significantly fewer physicians in the low-dose aspirin group had died of MI than those in the placebo group. However, considering all causes of cardiovascular death, the number of physicians who had died was exactly the same in the aspirin and placebo groups (Figure 2). All of the deaths occurred during the study period and all were considered premature deaths. Aspirin may have changed what was recorded on the death certificate, but it did not extend participants’ life expectancy.16 Considering the specific cause of death (MI) would lead to the conclusion that using aspirin as a primary preventive was highly effective. From the outcomes perspective, though, aspirin had no effect. From the perspective of the patient, we would argue that people and families are more concerned about the person’s vital status and less concerned about a specific cause of death.
Many of the controversies in contemporary medicine are related to the differences between the PCOR perspective and the emphasis on more traditional surrogate markers. For example, a current debate centers on the aggressive management of high blood pressure in the elderly. Even though aggressive management of blood pressure in older adults may reduce the chances of stroke, there is some concern that it will result in an increase in heart attacks, diminished cognitive functioning, and an increase in falls.17 Laser focus on measures of blood pressure may miss the bigger, and perhaps more important, quality-of-life picture that is most important to patients. There are also examples of studies that fail to achieve changes in biological surrogate markers, but do find changes in important patient-reported outcomes. For example, rehabilitation for patients with chronic obstructive pulmonary disease rarely results in changes in measures of lung function. Yet, these studies consistently observe improvements in functional status and quality of life.18
Differences in conclusions between the PCOR perspective and traditional investigation are not rare. In large clinical trials, for example, it is common to observe changes in biological process variables without finding differences in life expectancy or health-related quality of life.19 We expect continuing methodological discussions about the value of patient-reported experience. PCOR is a relatively new area of research investigation, and we need to learn more about how to reliably assess patient experience and how to integrate the patient perspective into the clinical decision- making process. To date, the best evidence from PCOR is rarely absorbed into clinical care.20 But AHRQ and PCORI are committed to a rigorous research agenda on these topics. Advancing research methodologies, including new approaches to clinical research, should inform this discussion by centering medical decision- making on the preferences of the most important stakeholder— the patient.
Author Affiliation: Robert M. Kaplan, PhD, is chief science officer at the Agency for Healthcare Research and Quality, Rockville, MD.
Address Correspondence to: Associate editorial director Nicole Beagin: firstname.lastname@example.org; 609-716-7777 ext. 131.
1. Kaplan RM, Ries AL. Quality of life: concept and definition. COPD. 2007;4(3):263- 271.
2. Smith WB, Safer MA. Effects of present pain level on recall of chronic pain and medication use. Pain. 1993;55(3):355-361.
3. Brown DS, Jia H, Zack MM, Thompson WW, Haddix AC, Kaplan RM. Using health-related quality of life and quality-adjusted life expectancy for effective public health surveillance and prevention. Expert Rev Pharmacoecon Outcomes Res. 2013;13(4):425-427.
4. Cherepanov D, Palta M, Fryback DG, Robert SA, Hays RD, Kaplan RM. Gender differences in multiple underlying dimensions of health-related quality of life are associated with sociodemographic and socioeconomic status. Med Care. 2011;49(11):1021-1030.
5. Kaplan RM. Diseases, Diagnoses, and Dollars. New York, NY: Springer; 2009.
6. Yost KJ, Thompson CA, Eton DT, et al. The Functional Assessment of Cancer Therapy - General (FACT-G) is valid for monitoring quality of life in patients with non-Hodgkin lymphoma. Leuk Lymphoma. 2013;54(2):290-297.
7. Speight J, Sinclair AJ, Browne JL, Woodcock A, Bradley C. Assessing the impact of diabetes on the quality of life of older adults living in a care home: validation of the ADDQoL Senior. Diabet Med. 2013;30(1):74-80.
8. Fteropoulli T, Stygall J, Cullen S, Deanfield J, Newman SP. Quality of life of adult congenital heart disease patients: a systematic review of the literature. Cardiol Young. 2013;23(4):473-485.
9. Ellwood PM. Shattuck lecture--outcomes management: a technology of patient experience. New Engl J Med. 1988;318(23):1549-1556.
10. Kaplan RM. Two pathways to prevention. Am Psychol. 2000;55(4):382-396.
11. Kaplan RM. Quality of life: an outcomes perspective. Arch Phys Med Rehabil. 2002;83(12, suppl 2):S44-S50.
12. Kaplan RM. Behavior as the central outcome in health care. Am Psychol. 1990; 45(11):1211-1220.
13. Kaplan RM. The Ziggy theorem: toward an outcomes-focused health psychology. Health Psychol. 1994;13(6):451-460.
14. Gøtzsche PC, Jorgensen KJ. Screening for breast cancer with mammography. Cochrane Database Syst Rev. 2013;6:CD001877.
15. Miller AB, Wall C, Baines CJ, Sun P, To T, Narod SA. Twenty-five-year follow-up for breast cancer incidence and mortality of the Canadian National Breast Screening Study: randomised screening trial. BMJ. 2014;348:g366.
16. Kaplan RM. Health outcome models for policy analysis. Health Psychol. 1989;8(6): 723-735.
17. Mancia G, Grassi G. Aggressive blood pressure lowering is dangerous: the J-curve: pro side of the arguement. Hypertension. 2014;63(1):29-36.
18. Ries AL, Kaplan RM, Limberg TM, Prewitt LM. Effects of pulmonary rehabilitation on physiologic and psychosocial outcomes in patients with chronic obstructive pulmonary disease. Ann Intern Med. 1995;122(11):823-832.
19. Gerstein HC, Miller ME, et al.; Action to Control Cardiovascular Risk in Diabetes Study G. Effects of intensive glucose lowering in type 2 diabetes. New Engl J Med. 2008;358(24):2545-2559.
20. Han PK, Kobrin S, Breen N, et al. National evidence on the use of shared decision making in prostate-specific antigen screening. Ann Fam Med. 2013;11(4):306-314.