We demonstrate how the payoff time framework can be adapted to inform real world decisions at the point of care and has the potential to reduce unnecessary health care utilization. While this framework is still maturing, the current report shows that it can be modified to use generic prognostic models, include frailty, and to consider interactions between comorbidities or frailty and diseases targeted by practice guidelines.
Pilot-testing the payoff time framework is particularly important because preventive guidelines may not be implemented in care for reasons beyond a provider’s control (eg, lack of health insurance, poor adherence), or may get lost in the vast spectrum of competing demands. For example, if providers aimed to apply every preventive guideline to a typical patient panel, they would need to devote 7 additional hours each day beyond time already spent managing chronic disease and addressing acute complaints.29
Clinicians do not have any systematic method to prioritize among preventive guidelines and do not have time to implement them all.30
Therefore, linking the payoff time framework to a clinical reminder alone, without also incorporating a decision support system that facilitates information gathering, prioritization, and implementation, is likely to be unsuccessful. Accordingly, when we pilot-test this framework, we plan to integrate it into the electronic medical record, and to use nonphysician staff to collect information. Other important questions that need to be addressed by pilot testing include how well the payoff time performs in actual clinical practice (eg, what is the utility of this framework over usual care?), whether it slows down clinical workflow or poses an unacceptable time burden, and whether it represents good value. Piloting testing may also inform future efforts to decrease the number of data elements required.
Perhaps the most important question addressed by pilot testing may involve its acceptability for patients (eg, how to “frame” the shared decision making triggered by payoff time information31
), particularly for those who prefer to continue screening even in the face of high risk or low reward. Ideally, clinical decisions should reflect a shared decision-making process between patient and physician that incorporates the individual values and priorities of a particular patient. Even if a patient’s life expectancy is longer than the payoff time for a guideline, that guideline may not be advised because it does not reflect that patient’s preferences. Conversely, even if a patient’s life expectancy is shorter than the payoff time for a guideline, that guideline may be an appropriate topic for discussion. This discussion should not take the form of discouraging pronouncements such as “You’re not going to live long enough to need this test,” but rather could be introduced by statements such as “Every patient is different, and what is best for 1 person may not be best for another. Chances are that you are more likely to be harmed, or will not benefit, from this test.”
It is important to note that the payoff time framework is flexible, and is not limited to colorectal cancer screening, or for that matter, preventive care practice guidelines. It is applicable to any guideline likely to result in short-term harms but longer-term benefits (eg, repair of abdominal aortic aneurysms), and therefore may eventually be applied to other practice guidelines. The payoff time framework is substantially different than other published approaches for individualizing practice guidelines because they require qualitative valuations that are often complex and are difficult to perform and apply at the point of care.1–3,32,33
In contrast, this framework was designed “from the ground up” to be applicable at the point of care and to inform clinical decision support systems, because of its systematic and quantitative approach.
Our modified framework still has important limitations. Although we have enhanced its generalizability and feasibility by linking it to a prognostic model not developed for any particular disease, disease-specific prognostic models may be more accurate when they are available. For example, if a patient has severe congestive heart failure, the Seattle Heart Failure model34
may yield a more accurate estimate of life expectancy than the model of Lee et al. There will be many situations in which it is not possible to apply this framework, because it may either underestimate benefits or overestimate harms (eg,, case 3, in which that patient’s particularly high risk of colorectal cancer may lead to underestimation of benefits from screening), or because factors external to the provider may limit guideline implementation (eg, lack of health insurance, lack of geographic proximity to providers, or patient-specific adherence barriers such as alcohol abuse, drug abuse, or mental health disorders). Our approach uses population-based data to inform decisions about individual patients and inferences may not always be appropriate, a caveat common to all applications of evidence to clinical decisions.
While the main limitation of this framework is its need to be validated in a clinical care setting, it has important strengths that make further study worthwhile. The framework may be a counterbalance to the blind application of guidelines that are inappropriate for individual patients, and may counter the unintended consequences of pay-for-performance and quality benchmarks. It could perhaps stimulate thoughtful physician-patient discussion regarding preferences for screening. If it makes valid predictions, it can be applied in a format that minimizes or eliminates the physician role in collecting information, and therefore may be compatible with emerging models for primary care such as the patient-centered medical home.35
Most importantly, application of the payoff time framework has the potential to limit inappropriate care, thereby increasing quality of care while reducing costs.