Barriers to COVID-19 Vaccination in a Troop of Fleet Antiterrorism Security Team Marines: Observational Study.

BACKGROUND: In 2019, the World Health Organization declared the reluctance to vaccinate despite the availability of vaccination services as one of the top 10 threats to global health. In early 2021, self-reported reluctance to vaccinate among military personnel might have been considered a significant threat to national security. Having a choice architecture that made COVID-19 vaccination optional rather than required for military personnel could have inadvertently undermined military readiness if vaccination uptake did not reach an acceptable threshold. OBJECTIVE: The purpose of this observational study was to examine Marines' self-reported reasons for planning to decline the COVID-19 vaccine to understand their barriers to vaccination. METHODS: As the vaccination became available to 1 company of Fleet Antiterrorism Security Team (FAST) Marines in early 2021, company command required those planning to decline vaccination to write an essay with up to 5 reasons for their choice. These essays provided the data for this study. Qualitative descriptive analysis with elements from grounded theory was used to thematically categorize FAST Marines' written reasons for planning to decline the COVID-19 vaccine into a codebook describing 8 key behavioral determinants. Interrater agreement among 2 qualitatively trained researchers was very good (κ=0.81). RESULTS: A troop of 47 Marines provided 235 reasons why they planned to decline the COVID-19 vaccine. The most frequent reasons were difficulty understanding health information (105/235, 45%), low estimates of risk (33/235, 14%), and fear of physical discomfort (29/235, 12%). Resulting interventions directly targeted Marines' self-reported reasons by reducing barriers (eg, normalized getting the vaccine), increasing vaccine benefits (eg, improved access to base gyms and recreational facilities), and increasing nonvaccine friction (eg, required in writing 5 reasons for declining the vaccine). CONCLUSIONS: Understanding the barriers military personnel experience toward COVID-19 vaccination remains critical as vaccine acquisition and availability continue to protect military personnel. Insights from subpopulations like FAST Marines can enhance our ability to identify barriers and appropriate intervention techniques to influence COVID-19 vaccination behaviors.

Leveraging behavioral science and artificial intelligence to support mental health in the workplace: a pilot study.

Introduction: Many American employers seek to alleviate employee mental health symptoms through resources like employee assistance programs (EAPs), yet these programs are often underutilized. This pilot study explores the design of a behavioral science-based email campaign targeting engagement with stress management and mental health resources via an EAP, among employees of a large home builder in the Southeastern US. Methods: Behavioral designers created a behavioral science intervention using a multi-step design approach and evidence based behavioral strategies. For this pilot intervention, employees received either a treatment message [i.e., behavioral science message assembled and delivered via the behavioral reinforcement learning (BRL) agent] or a control message (i.e., a single generic, supportive message with a stock photo) with a call to action to utilize their EAP. Results: A total of 773 employees received emails over the course of 1 year. Engagement was high, with an 80% email open rate. Over 170 employees (22%, 159 treatment and 14 control) clicked the CTA and logged into the EAP site at least once. Discussion: This pilot study suggests that using behavioral science and artificial intelligence can improve employee usage of EAP, specifically with the intention of exploring mental health and stress management resources, compared to benchmark rates of 5% per year.

Responses to a COVID-19 vaccination intervention: Qualitative analysis of 17K unsolicited SMS replies.

The development of effective interventions for COVID-19 vaccination has proven challenging given the unique and evolving determinants of that behavior. A tailored intervention to drive vaccination uptake through machine learning-enabled personalization of behavior change messages unexpectedly yielded a high volume of real-time short message service (SMS) feedback from recipients. A qualitative analysis of those replies contributes to a better understanding of the barriers to COVID-19 vaccination and demographic variations in determinants, supporting design improvements for vaccination interventions. OBJECTIVE: The purpose of this study was to examine unsolicited replies to a text message intervention for COVID-19 vaccination to understand the types of barriers experienced and any relationships between recipient demographics, intervention content, and reply type. METHOD: We categorized SMS replies into 22 overall themes. Interrater agreement was very good (all κpooled > 0.62). Chi-square analyses were used to understand demographic variations in reply types and which messaging types were most related to reply types. RESULTS: In total, 10,948 people receiving intervention text messages sent 17,090 replies. Most frequent reply types were "already vaccinated" (31.1%), attempts to unsubscribe (25.4%), and "will not get vaccinated" (12.7%). Within "already vaccinated" and "will not get vaccinated" replies, significant differences were observed in the demographics of those replying against expected base rates, all p > .001. Of those stating they would not vaccinate, 34% of the replies involved mis-/disinformation, suggesting that a determinant of vaccination involves nonvalidated COVID-19 beliefs. CONCLUSIONS: Insights from unsolicited replies can enhance our ability to identify appropriate intervention techniques to influence COVID-19 vaccination behaviors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Patient Experience of the Future is Personalized: Using Technology to Scale an N of 1 Approach.

Personalized experiences are more effective at creating sustained behavior change. Digitally enabled personalized outreach can improve patient's experience by providing relevant, meaningful calls to action at a time when labor-intensive human-to-human personalization is challenged by systemic health staffing shortages. Strategic use of digital tools to engage patients and supplement human-to-human care scale personalization to the benefit of patient and provider experience. Specifically, digital personalization can support: Identification of patients eligible for a procedure, service, or outreachEngaging patients with a personalized call to actionAugmenting care through the use of digital tools, andMonitoring patient progress over time to ensure continued support.The technology to support a more personalized patient experience includes infrastructure to consolidate rich data, an intelligence capability to identify candidates for each call to action, and an engagement layer that presents patients with personalized output. Steps to develop and execute a personalization strategy are provided.

Feasibility of a Reinforcement Learning-Enabled Digital Health Intervention to Promote Mammograms: Retrospective, Single-Arm, Observational Study.

BACKGROUND: Preventive screenings such as mammograms promote health and detect disease. However, mammogram attendance lags clinical guidelines, with roughly one-quarter of women not completing their recommended mammograms. A scalable digital health intervention leveraging behavioral science and reinforcement learning and delivered via email was implemented in a US health system to promote uptake of recommended mammograms among patients who were 1 or more years overdue for the screening (ie, 2 or more years from last mammogram). OBJECTIVE: The aim of this study was to establish the feasibility of a reinforcement learning-enabled mammography digital health intervention delivered via email. The research aims included understanding the intervention's reach and ability to elicit behavioral outcomes of scheduling and attending mammograms, as well as understanding reach and behavioral outcomes for women of different ages, races, educational attainment levels, and household incomes. METHODS: The digital health intervention was implemented in a large Catholic health system in the Midwestern United States and targeted the system's existing patients who had not received a recommended mammogram in 2 or more years. From August 2020 to July 2022, 139,164 eligible women received behavioral science-based email messages assembled and delivered by a reinforcement learning model to encourage clinically recommended mammograms. Target outcome behaviors included scheduling and ultimately attending the mammogram appointment. RESULTS: In total, 139,164 women received at least one intervention email during the study period, and 81.52% engaged with at least one email. Deliverability of emails exceeded 98%. Among message recipients, 24.99% scheduled mammograms and 22.02% attended mammograms (88.08% attendance rate among women who scheduled appointments). Results indicate no practical differences in the frequency at which people engage with the intervention or take action following a message based on their age, race, educational attainment, or household income, suggesting the intervention may equitably drive mammography across diverse populations. CONCLUSIONS: The reinforcement learning-enabled email intervention is feasible to implement in a health system to engage patients who are overdue for their mammograms to schedule and attend a recommended screening. In this feasibility study, the intervention was associated with scheduling and attending mammograms for patients who were significantly overdue for recommended screening. Moreover, the intervention showed proportionate reach across demographic subpopulations. This suggests that the intervention may be effective at engaging patients of many different backgrounds who are overdue for screening. Future research will establish the effectiveness of this type of intervention compared to typical health system outreach to patients who have not had recommended screenings as well as identify ways to enhance its reach and impact.

An Economic Impact Model for Estimating the Value to Health Systems of a Digital Intervention for Diabetes Primary Care: Development and Usefulness Study.

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.

Personalized Digital Health Communications to Increase COVID-19 Vaccination in Underserved Populations: A Double Diamond Approach to Behavioral Design.

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.

Working to reduce the effects of discrimination: Identity management strategies in organizations.

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.