The Task Force finding is based on evidence from a systematic review of 11 studies (search period 1966 - 2002). The review was conducted on behalf of the Task Force by a team of specialists in systematic review methods, and in research, practice, and policy related to cancer prevention and control.
There is no information for this section.
Eleven studies qualified for the systematic review.
There was generally consistent evidence that IDM interventions improve:
- Accuracy of beliefs
- Risk perceptions
- A combination of these
However, there was little or no evidence about whether these interventions:
- Result in individuals participating in decision making at a level they desire
- Result in decisions that are consistent with individual values and preferences
- Affect screening rates, especially among high-risk populations (e.g., older, non-white, low-income)
An economic review of this intervention was not conducted because the Task Force did not have enough information to determine if the intervention works.
Applicability of this intervention across different settings and populations was not assessed because the Task Force did not have enough information to determine if the intervention works.
Each Community Preventive Services Task Force (Task Force) review identifies critical evidence gaps—areas where information is lacking. Evidence gaps can exist whether or not a recommendation is made. In cases when the Task Force finds insufficient evidence to determine whether an intervention strategy works, evidence gaps encourage researchers and program evaluators to conduct more effectiveness studies. When the Task Force recommends an intervention, evidence gaps highlight missing information that would help users determine if the intervention could meet their particular needs. For example, evidence may be needed to determine where the intervention will work, with which populations, how much it will cost to implement, whether it will provide adequate return on investment, or how users should structure or deliver the intervention to ensure effectiveness. Finally, evidence may be missing for outcomes different from those on which the Task Force recommendation is based.
Identified Evidence Gaps
Results from the Community Guide review indicate that there were not enough studies to determine the effectiveness of informed decision making (IDM). Thus, numerous research issues remain.
More work is needed on the effect of these interventions on all of the outcomes in the conceptual framework, especially on recommendation outcomes other than knowledge, beliefs, and perceptions of risk. Few studies reported individuals' participation in decision making, and only one of those reported whether participation was at a desired level. It is not possible to know from the published reports whether questions about this issue were not asked or whether current instruments are not sufficiently sensitive to discriminate different levels of patient interest in participation, causing investigators not to report the data. If the problem is the latter, more sensitive measures of patient desire for participation should be developed.
The medical decision-making field has given considerable attention to assessing patient preferences for health states—that is, the quality of life in a particular health situation. Health economists call these preferences “utilities” and use them, among other purposes, to inform cost-utility analyses. This research needs, however, to be extended to accurate and feasible ways to assess preferences in clinical encounters and to ensure that patient decisions are congruent with individual preferences and values.
Because most of the included studies in this review addressed prostate cancer, additional work on other cancer screenings would be welcome. Additional studies are needed in community contexts outside of clinical settings. Similarly, studies are needed that focus on providers and healthcare systems to promote shared decision making (SDM) instead of, or in addition to, directly targeting individuals. Studies with providers and in healthcare systems should measure provider and system outcomes, but should also measure the client outcomes that are the ultimate goal of these programs and policies.
Social and demographic variables have been shown to affect individuals' desire for involvement in healthcare decisions and may also affect the effectiveness of IDM interventions. To date, IDM seems to be more acceptable to younger and more educated patients. However, this may be a consequence both of how questions are asked and of patients' confidence. More empirical work is needed in diverse populations, such as nonwhite, older, and medically underserved populations. Achieving IDM in such populations is a challenging but desirable goal.
Although the study designs and executions of available studies in this review were generally strong, some measurement issues need additional attention. Sensitive, appropriate measures are still needed of individual involvement in decision making and the match between decisions and preferences or values. In addition, work is needed on how best to elicit patient preferences and respond to them in nonthreatening, time-sensitive, and culturally appropriate ways.
Although much work has already been published in the risk communication literature about how to communicate complex information involving probabilities to individuals, additional work is still needed on appropriate and feasible ways of communicating technical information so that it is helpful and not overwhelming. Additional empirical work on people's information needs and preferences for level of involvement in decision making, how those needs and preferences might evolve over time, and how best to meet those needs and preferences would also be useful. Finally, more work is needed on whether IDM or SDM increases or decreases the use of effective services.
It is known that, at least for some diseases (e.g., breast cancer), individuals overestimate both the disease risks and the benefits of screening. IDM could help patients achieve a more realistic perspective on risks and benefits. In particular, quantitative risk models, which clearly show patients the risks and benefits of screening in terms of their personal characteristics, would allow patients to take personal risk factors into account when making healthcare decisions. Such techniques, which permit individualization of the risks and benefits, might help people to make better-informed decisions.