Previous studies conducted by the investigators have shown that the Hello game
demonstrates successful advance care planning (ACP) engagement in general populations,
but has yet to be tailored to meet the unique needs of patients with advanced cancer and
their caregivers. Outlining their care preferences by engaging in ACP is an important
aspect of care according to patients with advanced cancer. However, only 55% of patients
with advanced cancer patients have participated in ACP. These patients have substantial
bio-psycho-social stressors that distinguish their ACP needs from others. Tailoring
established interventions that foster high quality conversations about medical treatment
preferences and end-of-life issues (such as the Hello game) is critically important for
this population given its unique needs. As evidenced by qualitative interviews with >200
participants, the Hello game creates a safe environment for sensitive conversations about
end-of-life issues and inspired sharing of rich perspectives, with no reported adverse
events, excessive burden, or negative emotional effects. That said, the intervention must
be adapted for patients with cancer, particularly those with advanced cancer and their
caregivers.
Additionally, while several effective ACP interventions exist (including Hello), how best
to disseminate these interventions has not been rigorously or systematically studied. In
other ongoing and previous studies, the investigators have demonstrated success in both
engaging individuals living in underrepresented communities in ACP and successfully
enrolling them in interventional research about ACP. The investigators credit these
successes to their unique intervention delivery approach called the Community Based
Delivery Model (CBDM). The CBDM overcomes key barriers to ACP (such as healthcare
distrust, resistance, and hesitancy to discuss end-of-life issues) by leveraging
established community connections to recruit participants to participate in ACP
interventions as well as research. In the CBDM, trusted community "hosts" (who are
leaders from local hospice organizations, senior centers, health agencies) invite
participants to attend an ACP event. They introduce the research team to the attendees
who may choose to participate in the ACP activity, the research, or both. Hosts are
provided with marketing materials and utilize their community network channels to
advertise the event. This model allows for research to be conducted more easily within
hard to reach and underserved communities such as Black, Hispanic and rural communities-
much like the most remote communities across the Penn State Cancer Institute's 28-county
catchment area.
Patients with cancer, however, are unique, and may require an alternative approach that
involves partnering with their oncology care team to introduce the concept of ACP and
encourage participation in ACP and research. Notably, there is evidence that patients are
more likely to engage in ACP when recommended by their physician, so how best to approach
ACP for cancer patients is unknown. A common approach to ACP intervention research is to
use a Healthcare Based Delivery Model (HBDM). In contrast to the CBDM, the HBDM is
positioned within the healthcare system (i.e., clinic-based recruitment) as the ACP
intervention is recommended by the patient's clinician (rather than through
community-based outreach groups). For this intervention delivery approach, research
assistants support interactions between clinicians (providers or nurses) to find
appropriate patients and garner interest in performing ACP. This model is commonly used
to recruit patients for clinical trials, including ACP interventions. For patients with
cancer, the HBDM may have some advantages over the CBDM, given the close bonds that form
between a patient and clinical care team as they interact frequently during active
treatments such as infusions and radiation that often span several hours and weeks.
Leveraging these therapeutic relationships may support greater acceptance of
opportunities to broach ACP than a community-based model, but this remains unknown.
