Previous research has found that perspective taking improves attitudes towards outgroups. We find that taking the perspective of an outgroup member not only improves attitudes towards outgroups, but also reduces prejudice and discriminatory behavior against other specific individual members of that outgroup. Experiment 1 demonstrates that perspective-taking improves liking towards another member of the outgroup, while experiment 2 finds that the improved liking does not generalize to all outgroups, only the group to which the target of empathy belongs. Finally, experiment 3 shows that perspective taking also increases helping behavior towards another member of the outgroup. Moreover, we find evidence that perspective taking improves intergroup attitudes through the induction of empathy.

Despite efforts to dispel discrimination, workplace discrimination still occurs. We introduce two classes of identity management strategies individuals use to mitigate the negative consequences of discrimination: identity switching (i.e., deemphasizing target identities and recategorizing to a more positively valued identity) and identity redefinition (i.e., stereotype reassociation and regeneration). Organizations adopting a color-blind approach may make it more difficult for individuals to use identity switching because the policies deemphasize differences in social identities. In contrast, organizations adopting a multicultural approach may make it more difficult for individuals to use identity redefinition. Multicultural approaches, applied superficially, may celebrate group differences that might actually reinforce culturally dominant stereotypes. We explore the likelihood that individuals will adopt each strategy given these organizational approaches to diversity. We outline steps organizations can take to reduce the need for identity management strategies and to facilitate identity management when necessary.

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The COVID-19 pandemic exacerbated pre-existing health disparities. People of historically underserved communities, including racial and ethnic minority groups and people with lower incomes and educational attainments, experienced disproportionate premature mortality, access to healthcare, and vaccination acceptance and adoption. At the same time, the pandemic increased reliance on digital devices, offering a unique opportunity to leverage digital communication channels to address health inequities, particularly related to COVID-19 vaccination. We offer a real-world, systematic approach to designing personalized behavior change email and text messaging interventions that address individual barriers with evidence-based behavioral science inclusive of underserved populations. Integrating design processes such as the Double Diamond model with evidence-based behavioral science intervention development offers a unique opportunity to create equitable interventions. Further, leveraging behavior change artificial intelligence (AI) capabilities allows for both personalizing and automating that personalization to address barriers to COVID-19 vaccination at scale. The result is an intervention whose broad component library meets the needs of a diverse population and whose technology can deliver the right components for each individual.

Background Diabetes is associated with significant long-term costs for both patients and health systems. Regular primary care visits aligned with American Diabetes Association guidelines could help mitigate those costs while generating near-term revenue for health systems. Digital interventions prompting primary care visits among unengaged patients could provide significant economic value back to the health system as well as individual patients, but only few economic models have been put forth to understand this value. Objective Our objective is to establish a data-based method to estimate the economic impact to a health system of interventions promoting primary care visits for people with diabetes who have been historically unengaged with their care. The model was built with a focus on a specific digital health intervention, Precision Nudging, but can be used to quantify the value of other interventions driving primary care usage among patients with diabetes. Methods We developed an economic model to estimate the financial value of a primary care visit of a patient with diabetes to the health system. This model requires segmenting patients with diabetes according to their level of blood sugar control as measured by their most recent hemoglobin A1c value to understand how frequently they should be visiting a primary care provider. The model also accounts for the payer mix among the population with diabetes, documenting the percentage of insurance coverage through a commercial plan, Medicare, or Medicaid, as these influence the reimbursement rates for the services. Then, the model takes into consideration the population base rates of comorbid conditions for patients with diabetes and the associated current procedural terminology codes to understand what a provider can bill as well as the expected inpatient revenue from a subset of patients likely to require hospitalization based on the national hospitalization rates for people with diabetes. Physician reimbursement is subtracted from the total. Finally, the model also accounts for the level of patient engagement with the intervention to ensure a realistic estimate of the impact. Results We present a model to prospectively estimate the economic impact of a digital health intervention to encourage patients with documented diabetes diagnoses to attend primary care visits. The model leverages both publicly available and health system data to calculate the per appointment value (revenue) to the health system. The model offers a method to understand and test the financial impact of Precision Nudging or other primary care–focused diabetes interventions inclusive of costs driven by comorbid conditions. Conclusions The proposed economic model can help health systems understand and evaluate the estimated economic benefits of interventions focused on primary care and prevention for patients with diabetes as well as help intervention developers determine pricing for their product.