Factors Associated With Advance Care Planning by Race

Racial differences in the effect of verbal and nonverbal memory on concrete planning for future care needs among older adults: a multi-group structural equation modeling approach

Concrete planning for future care needs may positively impact older adults' subsequent mental health and quality of life. However, the cognitive factors that facilitate concrete planning among Black and White older adults are still poorly understood. We investigated whether there are significant differences between Black (n = 159) and White (n = 262) older adults in concrete planning and explored racial differences in the relationship between verbal and nonverbal episodic memory and concrete planning. Results revealed that Blacks showed lower engagement in concrete planning and lower scores than Whites on each verbal and nonverbal memory task. For Blacks, but not Whites, verbal memory and nonverbal memory performance predicted concrete planning with higher nonverbal memory relating to less concrete planning and higher verbal memory associated with more concrete planning. Our findings suggest racial differences exist in how episodic verbal and nonverbal memory affect concrete planning, a crucial factor for older adults' preparation for future care.

Turning It Over to God: African American Assisted Living Residents' End-of-Life Preferences and Advance Care Planning

OBJECTIVES

Assisted living (AL), a popular long-term care setting for older Americans, increasingly is a site for end-of-life care. Although most residents prefer AL to be their final home, relatively little is known about end-of-life preferences and advance care planning, especially among African American residents. Our research addresses this knowledge gap.

METHODS

Informed by grounded theory, we present an analysis of qualitative data collected over 2 years in a 100-bed AL community catering to African American residents. Data consisted of field notes from participant observation conducted during 310 site visits and 818 observation hours, in-depth interviews with 25 residents, and a review of their AL records.

RESULTS

Residents varied in their end-of-life preferences and advance care planning, but united in the belief that God was in control. We identified "Turning it over to God" as an explanatory framework for understanding how this group negotiated end-of-life preferences and advance care planning. Individual-level resident factors (e.g., age, pain, and function) and factors reflecting broader cultural and societal influences, including health literacy and care experiences, were influential.

DISCUSSION

Contradictions arose from turning it over to God, including those between care preferences, planning, and anticipated or actual end-of-life outcomes.

Advanced Care Planning for Hospitalized Patients Following Clinician Notification of Patient Mortality by a Machine Learning Algorithm

Importance

Goal-concordant care is an ongoing challenge in hospital settings. Identification of high mortality risk within 30 days may call attention to the need to have serious illness conversations, including the documentation of patient goals of care.

Objective

To examine goals of care discussions (GOCDs) in a community hospital setting with patients identified as having a high risk of mortality by a machine learning mortality prediction algorithm.

Design, Setting, and Participants

This cohort study took place at community hospitals within 1 health care system. Participants included adult patients with a high risk of 30-day mortality who were admitted to 1 of 4 hospitals between January 2 and July 15, 2021. Patient encounters of inpatients in the intervention hospital where physicians were notified of the computed high risk mortality score were compared with patient encounters of inpatients in 3 community hospitals without the intervention (ie, matched control).

Intervention

Physicians of patients with a high risk of mortality within 30 days received notification and were encouraged to arrange for GOCDs.

Main Outcomes and Measures

The primary outcome was the percentage change of documented GOCDs prior to discharge. Propensity-score matching was completed on a preintervention and postintervention period using age, sex, race, COVID-19 status, and machine learning-predicted mortality risk scores. A difference-in-difference analysis validated the results.

Results

Overall, 537 patients were included in this study with 201 in the preintervention period (94 in the intervention group; 104 in the control group) and 336 patients in the postintervention period. The intervention and control groups included 168 patients per group and were well-balanced in age (mean [SD], 79.3 [9.60] vs 79.6 [9.21] years; standardized mean difference [SMD], 0.03), sex (female, 85 [51%] vs 85 [51%]; SMD, 0), race (White patients, 145 [86%] vs 144 [86%]; SMD 0.006), and Charlson comorbidities (median [range], 8.00 [2.00-15.0] vs 9.00 [2.00 to 19.0]; SMD, 0.34). Patients in the intervention group from preintervention to postintervention period were associated with being 5 times more likely to have documented GOCDs (OR, 5.11 [95% CI, 1.93 to 13.42]; P = .001) by discharge compared with matched controls, and GOCD occurred significantly earlier in the hospitalization in the intervention patients as compared with matched controls (median, 4 [95% CI, 3 to 6] days vs 16 [95% CI, 15 to not applicable] days; P < .001). Similar findings were observed for Black patient and White patient subgroups.

Conclusions and Relevance

In this cohort study, patients whose physicians had knowledge of high-risk predictions from machine learning mortality algorithms were associated with being 5 times more likely to have documented GOCDs than matched controls. Additional external validation is needed to determine if similar interventions would be helpful at other institutions.

Preferences Elicited and Respected for Seriously Ill Veterans through Enhanced Decision-Making (PERSIVED): a protocol for an implementation study in the Veterans Health Administration

BACKGROUND

Empirical evidence supports the use of structured goals of care conversations and documentation of life-sustaining treatment (LST) preferences in durable, accessible, and actionable orders to improve the care for people living with serious illness. As the largest integrated healthcare system in the USA, the Veterans Health Administration (VA) provides an excellent environment to test implementation strategies that promote this evidence-based practice. The Preferences Elicited and Respected for Seriously Ill Veterans through Enhanced Decision-Making (PERSIVED) program seeks to improve care outcomes for seriously ill Veterans by supporting efforts to conduct goals of care conversations, systematically document LST preferences, and ensure timely and accurate communication about preferences across VA and non-VA settings.

METHODS

PERSIVED encompasses two separate but related implementation projects that support the same evidence-based practice. Project 1 will enroll 12 VA Home Based Primary Care (HBPC) programs and Project 2 will enroll six VA Community Nursing Home (CNH) programs. Both projects begin with a pre-implementation phase during which data from diverse stakeholders are gathered to identify barriers and facilitators to adoption of the LST evidence-based practice. This baseline assessment is used to tailor quality improvement activities using audit with feedback and implementation facilitation during the implementation phase. Site champions serve as the lynchpin between the PERSIVED project team and site personnel. PERSIVED teams support site champions through monthly coaching sessions. At the end of implementation, baseline site process maps are updated to reflect new steps and procedures to ensure timely conversations and documentation of treatment preferences. During the sustainability phase, intense engagement with champions ends, at which point champions work independently to maintain and improve processes and outcomes. Ongoing process evaluation, guided by the RE-AIM framework, is used to monitor Reach, Adoption, Implementation, and Maintenance outcomes. Effectiveness will be assessed using several endorsed clinical metrics for seriously ill populations.

DISCUSSION

The PERSIVED program aims to prevent potentially burdensome LSTs by consistently eliciting and documenting values, goals, and treatment preferences of seriously ill Veterans. Working with clinical operational partners, we will apply our findings to HBPC and CNH programs throughout the national VA healthcare system during a future scale-out period.