VALUE JUDGMENTS AND TRADEOFFS IN CHOOSING QUANTITATIVE INFORMATION AND FRAMING FOR A DECISION AID

Purpose:  Experts recommend that decision aids (DAs) for screening should present quantitative information about baseline risk, risk reduction, and probability of positive and negative outcomes.  But there is uncertainty about the impact of such information, especially for patients with low numeracy.  We are conducting a trial where patients eligible for colorectal cancer (CRC) screening are randomized to view a DA that includes quantitative information vs. one that does not. Both DAs describe two available and approved CRC screening tests that differ in invasiveness, sensitivity, and frequency:  colonoscopy vs. stool blood testing with the fecal immunochemical test (FIT).  This paper reports key considerations and decisions made by our team about what quantitative information to include in the DA and how to frame that information.

 

Method: Experts in health communication, epidemiology and screening, risk communication, biostatistics, and bioethics created narrated messages and visuals for the DA in biweekly meetings and via email.  When differences in expert opinions were encountered, they were discussed until consensus was reached.  Draft versions of the DA were presented to patients and community members for feedback regarding acceptability, understandability, and satisfaction.

Result: The Table lists five areas where we faced challenging decisions about whether to include specific types of quantitative information and how to frame them.  For each decision, previous research and feedback from patients and community members did not provide definitive guidance. 

Six goals influenced our decisions. Two goals that supported including additional data or framing were:

1) Improving decision-making by patients

2) Encouraging being screened with either test

Four goals that supported excluding additional data or framing were:

3) Avoiding overwhelming or confusing patients

4) Shortening the presentation to maintain patient interest

5) Avoiding triggering irrational heuristics or biases

6) Excluding data that is not evidence-based

Balancing these goals required value judgments.  For example, although negative framing could improve understanding for some patients, it was not included since it could cause confusion or trigger the optimism bias.  This decision, like others, involved judging the value of the information (optional vs. essential), and the disvalue of confusing some patients or reducing uptake (outcomes and public health).

 

Conclusion: Selecting which quantitative information to include in DAs requires value judgments that are inherently debatable and should be recognized and discussed more widely.