NURS 503 Week 3 Behavioral Economics Paper on Supports and Barriers for Behavior Change Example

Why a Default Beats a Lecture: Behavioral Economics, HIV Testing, and the Supports and Barriers to Changing What Patients and Clinicians Do

Most adults say they would want to know if they had HIV, and HIV tests are fast, inexpensive and covered by most insurance. Yet many people who are offered a test decline it, and many more are never offered one. Classical economics would explain the gap with missing information: people decline because they do not understand the benefits. Behavioral economics offers a different explanation. People make decisions with limited attention, react strongly to how choices are framed, put off unpleasant actions and tend to accept whatever happens if they do nothing. This paper applies those ideas to HIV testing, the behavior behind the leading health indicator examined in Week 2, and identifies the supports and barriers that determine whether a test happens.

Core Ideas of Behavioral Economics

Behavioral economics combines psychology with economics to describe how people actually decide. Several of its ideas apply directly to health. People are boundedly rational: they rely on shortcuts because attention and time are limited. They show present bias, giving more weight to discomfort today than to benefits later. They are loss averse; Kahneman and Tversky (1979) showed in their prospect theory that losses loom larger than equivalent gains and that the way an outcome is framed changes the choice. They also show status quo bias, staying with the current option or the default.

From these observations came the idea of choice architecture, the recognition that every setting in which people choose is designed somehow, and that design can make good choices easier without removing any option. Thaler and Sunstein (2021) call such design features nudges. Health systems have begun to apply these ideas deliberately; Patel et al. (2018) describe nudge units in health systems that change defaults and the presentation of choices in the electronic health record to improve care at low cost. The population health text likewise treats behavior change as a central task of population health management (Nash et al., 2016).

Supports for Behavior Change

Defaults

The strongest support for testing is a default that makes testing the normal course. In a randomized trial among 8,715 patients aged 13 to 64 in an urban emergency department, Montoy et al. (2018) assigned patients to opt-in, active-choice or opt-out offers of an HIV test. Compared with opt-in testing, active choice, in which patients were asked to say yes or no, increased acceptance by 11.5 percentage points, and opt-out testing, in which the test was presented as routine unless declined, increased acceptance by 23.9 percentage points. The same test, offered by the same staff in the same department, was accepted far more often when saying nothing meant yes.

Small Incentives

The same trial tested cash incentives. Offers of $1 did not change acceptance, but $5 and $10 increased it by 10.5 and 15 percentage points. When incentives were combined with defaults, the effects were less than additive (Montoy et al., 2018). Incentives therefore work, but a default achieved a larger effect at no cost per test, which matters for a program screening thousands of patients.

Framing and Social Norms

How an offer is worded can reduce the perceived loss of agreeing. Presenting the test as something offered to every patient frames it as routine rather than as a judgment about the patient's behavior, which also reduces the stigma attached to being asked. Saying that most patients accept the test uses social norms to make acceptance feel ordinary.

Clinician Defaults

Clinicians respond to defaults too. When an electronic record includes the HIV test in a standard order set, or prompts the triage nurse to offer it to every eligible patient, testing becomes the path of least resistance for staff as well as patients (Patel et al., 2018).

Barriers to Behavior Change

Stigma and Fear

HIV remains stigmatized, and a test can feel like an accusation or the first step toward a frightening result. Loss aversion makes the possibility of bad news loom large, and present bias favors avoiding that fear today over the benefit of earlier treatment later.

Hassle and Time

Small frictions matter more than their size suggests. A separate consent form, a second needle stick or a long wait for results each lower acceptance. In the emergency department, staff worry that testing slows care. A pragmatic trial in four emergency departments found that routine opt-out screening was associated with an average increase of about 1.4 minutes in wait time and a small rise in patients leaving before treatment was complete, while length of stay for admitted patients did not increase (Jones et al., 2025). The barrier is real but small, and it can be reduced with blood drawn alongside other tests.

Clinician Habits and Beliefs

Many clinicians still test only patients they judge to be at risk, a habit that feels efficient but misses people who do not disclose risk. Others believe the emergency department is the wrong place for screening or fear having to deliver a positive result. These beliefs act as a default of their own: unless the system makes testing routine, it does not happen.

Cost and Coverage

Although screening is covered for most insured people, patients without insurance or with high deductibles may fear a bill, and that uncertainty itself discourages testing. Ambiguity aversion, the tendency to avoid choices whose costs are unknown, means that even a small and uncertain charge can deter more people than a known one. Stating plainly at the point of the offer that the test costs the patient nothing, where that is true, removes the ambiguity. Programs serving uninsured patients can apply for public funding for testing so that the statement is accurate for everyone.

Applying the Ideas to Young Adults

The Week 2 paper showed that young people have the lowest awareness of their HIV status, so it is worth asking how these ideas apply to them in particular. Young adults are less likely to have a regular source of care, which means fewer occasions on which a default could operate; for many, an emergency department, an urgent care clinic or a student health service is the only point of contact. Present bias may be especially strong at this age, when long-term health risks feel distant, and social norms carry great weight. Testing strategies for this group should therefore place routine opt-out offers in the settings young people actually use, frame testing as something everyone their age does, and reduce friction with rapid results or results delivered through a patient portal or text message.

Incentives may also matter more for young adults with little income. The finding that $5 and $10 offers raised acceptance among emergency department patients (Montoy et al., 2018) suggests that modest incentives could support community testing events aimed at young people, where an opt-out default is not possible because no other care is being delivered. Any such program should be evaluated, since the effect of incentives varies by setting and population.

Recommendations

Four design choices follow from this analysis. First, make HIV testing an opt-out default in emergency and primary care, with a clear statement that patients may decline. Second, build the offer into nurse triage and standard order sets so that clinicians' defaults match patients'. Third, use routine framing and normative language in scripts and signage, and train staff to offer the test the same way to every patient. Fourth, reduce hassle by drawing blood for the test with other laboratory work and by guaranteeing that results reach patients without a separate visit. Small incentives may help in outreach settings where no default exists. Each choice should be tested on one unit or clinic first, with acceptance rates tracked weekly, before it spreads.

The Ethics of Nudging

Nudges raise a fair question about manipulation. Thaler and Sunstein (2021) argue that nudges are justified when they steer people toward choices they would endorse on reflection, leave every option available and are transparent. Opt-out HIV testing meets those conditions when patients are clearly told they may decline. A Christian view of persons supports that balance. Scripture affirms that people are responsible for their choices, "choose this day whom you will serve" (English Standard Version Bible, 2001/2016, Joshua 24:15), which argues against hiding or removing options, and it also calls believers to seek others' good. Designing systems that make healthy choices easier, while honoring each person's freedom to decline, respects both.

Conclusion

Behavioral economics explains why information alone has not closed the gap in knowledge of HIV status and points to practical supports: defaults, small incentives, routine framing and clinician prompts. The barriers, stigma, fear, hassle, clinician habits and cost, are equally predictable and can be designed around. For the intervention project in this course, the most promising strategy is a routine opt-out default supported by staff scripts and reliable delivery of results.

References

English Standard Version Bible. (2016). Crossway. (Original work published 2001)

Jones, A. T., Haukoos, J., Hopkins, E., Lyons, M. S., Rothman, R. E., White, D. A. E., Hsieh, Y. H., Joseph, K. M., Murray, E., & Rowan, S. E. (2025). Effect of routine opt-out HIV screening on emergency department operational metrics: Results from the pragmatic randomized HIV TESTED trial. Annals of Emergency Medicine, 86(3), 240-251. https://doi.org/10.1016/j.annemergmed.2025.04.011

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. https://doi.org/10.2307/1914185

Montoy, J. C. C., Dow, W. H., & Kaplan, B. C. (2018). Cash incentives versus defaults for HIV testing: A randomized clinical trial. PLOS ONE, 13(7), Article e0199833. https://doi.org/10.1371/journal.pone.0199833

Nash, D. B., Fabius, R. J., Skoufalos, A., Clarke, J. L., & Horowitz, M. R. (2016). Population health: Creating a culture of wellness (2nd ed.). Jones & Bartlett Learning.

Patel, M. S., Volpp, K. G., & Asch, D. A. (2018). Nudge units to improve the delivery of health care. The New England Journal of Medicine, 378(3), 214-216. https://doi.org/10.1056/NEJMp1712984

Thaler, R. H., & Sunstein, C. R. (2021). Nudge: The final edition. Penguin Books.