Advance Care Planning Among Patients With Advanced Cancer

Measuring decision aid effectiveness for end-of-life care: A systematic review

Objective

To systematically review research analyzing the effectiveness of decision aids for end-of-life care, including how researchers specifically measure decision aid success.

Methods

We conducted a systematic review synthesizing quantitative, qualitative, and mixed-methods study results using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Four databases were searched through February 18, 2023. Inclusion criteria required articles to evaluate end-of-life care decision aids. The review is registered under PROSPERO (#CRD42023408449).

Results

A total of 715 articles were initially identified, with 43 meeting the inclusion criteria. Outcome measures identified included decisional conflict, less aggressive care desired, knowledge improvements, communication improvements, tool satisfaction, patient anxiety and well-being, and less aggressive care action completed. The majority of studies reported positive outcomes especially when the decision aid development included International Patient Decision Aid Standards.

Conclusion

Research examining end of life care decision aid use consistently reports positive outcomes.

Innovation

This review presents data that can guide the next generation of decision aids for end-of-life care, namely using the International Patient Decision Aid Standards in developing tools and showing which tools are effective for helping to prevent the unnecessary suffering that can result when patients' dying preferences are unknown.

Acceptability and usability of the Planning Advance Care Together (PACT) website for improving patients' engagement in advance care planning

Objectives

Most prior advance care planning (ACP) interventions lack integration of the social context of patients' ACP process, which patients indicate is critically important. The current study developed the Planning Advance Care Together (PACT) website to foster inclusion of loved ones in the ACP process.

Methods

To provide feedback about the PACT website, patients with advanced cancer (N = 11), their caregivers (N = 11), and experts (N = 10) participated in semi-structured interviews. Patients and caregivers also completed standardized ratings of acceptability and usability.

Results

Overall, patient (n = 11) and caregiver (n = 11) ratings of acceptability and usability of the website exceeded benchmark cut-offs (≥24 on the Acceptability E-Scale and ≥ 68 on the System Usability Scale). Patients, caregivers, and experts liked the topic of ACP but felt that it could be emotionally challenging. They recommended focusing more on planning and less on end of life. They appreciated being able to include loved ones and recommended adding resources for caregivers.

Conclusions

Study findings support the preliminary usability and acceptability of the PACT website. Findings will be used to inform a modified prototype of the PACT website that is interactive and ready for field testing with patients with advanced cancer and their loved ones.

Innovation

We utilized a novel application of the shared mind framework to support patients with advanced cancer in engaging their loved ones in the ACP process.

Factors affecting advance care planning in older adults with cancer

INTRODUCTION

Advance care planning (ACP) has been widely endorsed and recommended for its many potential benefits, including improved end-of-life (EOL) care, enhanced satisfaction with care, and reduced anxiety and depression. However, little is known about the ACP completion rates and factors affecting ACP among older adults with cancer. This study's purpose was to examine biological, psychological, and social factors affecting ACP in this population.

MATERIALS AND METHODS

Data from the 2002 to 2016 waves of exit interviews from the national longitudinal Health and Retirement Study were analyzed. The sample included 1088 decedents, aged 55 and over, who had a diagnosis of cancer. The exit interviews were completed by a proxy respondent (usually the next of kin of the decedents). ACP outcomes included: having EOL care discussion, durable power of attorney (DPOA), and advance directives (ADs). Multiple logistic regression models were conducted to examine the relationships between predictor variables and each of the three ACP outcome variables.

RESULTS

Approximately 65% of the sample had ever discussed EOL care, 61.9% had an assigned DPOA, and 54.1% had ADs. Regression results showed that higher age, Black race, high school and above education, being widowed/never married, higher multimorbidity, and more limitations in activities of daily living and instrumental activities of daily living were significantly associated with the three ACP variables. Surprisingly, Black race was associated with higher odds of ever discussing EOL care and having ADs; high school and above education was associated with lower odds of all three ACP components.

DISCUSSION

The majority of participants in this study had discussed EOL care, had an assigned DPOA, and had ADs. However, most participants were White/Caucasian and had completed high school education. Future research that includes more diverse and minoritized participants is needed. Also, the contrasting association of Black race and higher educational status with ACP outcomes warrant further exploration in future studies.

Optimizing care in early phase cancer trials: The role of palliative care

Advancements in cancer treatment have led to improved survival rates, with early phase clinical trials (EPCTs) serving as important initial steps in evaluating novel therapies. Recent studies have shown that response rates in these trials have doubled in the last twenty years. Patients who enroll on EPCTs have advanced cancer and heightened symptomatology yet maintain a robust performance status that qualifies them for clinical trial participation. It is well established that many of these patients have needs that can be addressed by palliative care, including symptom management, value assessments, advance care planning, and psychosocial and spiritual support. Several small studies have aimed to identify the most beneficial palliative care intervention for this cohort of patients, ranging from formal clinic-based multidisciplinary palliative care interventions to home-based interventions. While outcomes have trended towards benefit for patients, especially pertaining to psychological well-being, most studies were not powered to detect additional benefits for improved physical symptom management, reduction in care utilization or increased length of time on trial. In this review, we discuss the unique palliative care needs of this population and what we can learn from results of past interventional studies. We advocate for a tailored palliative care approach that acknowledges the time toxicity experienced by patients enrolled in EPCTs and address challenges posed by shortages within the palliative care workforce.

Spending Analysis of Machine Learning-Based Communication Nudges in Oncology

BACKGROUND

Serious illness conversations (SICs) in the outpatient setting may improve mood and quality of life among patients with cancer and decrease aggressive end-of-life care. Interventions informed by behavioral economics may increase rates of SICs between oncology clinicians and patients, but the impact of these interventions on end-of-life spending is unknown.

METHODS

This study is a secondary analysis of a stepped-wedge cluster randomized, controlled trial that involved nine medical oncology practices and their high-risk patients at a large academic institution between June 2019 and April 2020. The study included 1187 patients who were identified by a machine-learning algorithm as high risk of 180-day mortality and who died by December 2020. The patients were randomly assigned to standard of care (controls) or to a behavioral intervention designed to increase clinician-initiated SICs. We abstracted spending - defined as inflation-adjusted costs for acute care (inpatient plus emergency room), office/outpatient care, intravenous systemic therapy, other therapy (e.g., radiation), long-term care, and hospice - from the institution's accounting system, and we captured spending at inpatient, outpatient, and pharmacy settings. To evaluate intervention impacts on spending, we used a two-part model, first using logistic regression to model zero versus nonzero spending and second using generalized linear mixed models with gamma distribution and log-link function to model daily mean spending in the last 180days of life. Models were adjusted for clinic and wedge fixed effects, and they were clustered at the oncologist level. For all patients with at least one SIC within 6 months of death, we also calculated their mean daily spending before and after SIC.

RESULTS

Median age at death was 68years (interquartile range, 15.5), 317 patients (27%) were Black or of ethnicities other than white, and 448 patients (38%) had an SIC. The intervention was associated with lower unadjusted mean daily spending in the last 6 months of life for the intervention group versus controls ($377.96 vs. $449.92; adjusted mean difference, -$75.33; 95% confidence interval, -$136.42 to -$14.23; P=0.02), translating to $13,747 total adjusted savings per decedent and $13 million in cumulative savings across all decedents in the intervention group. Compared with controls, patients in the intervention group incurred lower mean daily spending for systemic therapy (adjusted difference, -$44.59; P=0.001), office/outpatient care (-$9.62; P=0.001), and other therapy (-$8.65; P=0.04). The intervention was not associated with differences in end-of-life spending for acute care, long-term care, or hospice. Results were consistent for spending in the last 1 and 3 months of life and after adjusting for age, race, and ethnicity. For patients with SICs, mean daily spending decreased by $37.92 following the first SIC ($329.87 vs. $291.95).

CONCLUSIONS

A machine learning-based, behaviorally informed intervention to prompt SICs led to end-of-life savings among patients with cancer, driven by decreased systemic therapy and outpatient spending. (Funded by the Penn Center for Precision Medicine and the National Institutes of Health; ClinicalTrials.gov number, NCT03984773.).

Changing Life Plans: When to Engage Caregivers of Older Adults With Cancer in Advance Care Planning

Advance care planning (ACP) is a continuous process where individuals discuss and document their end-of-life preferences with trusted caregivers and health care providers. Caregivers are pivotal to include in ACP discussions because they assist loved ones to navigate serious medical illness. The purpose of this study was to examine caregivers' engagement in ACP decision making with their loved ones with cancer. A qualitative descriptive design was used, informed by Engel's biopsychosocial model, with a convenience sample of 14 caregivers in North Carolina. Virtual interviews were conducted using a semistructured interview guide. Using prevalence logic, the overarching theme of "Changing Life Plans" was explained by two subthemes, "Learning the Diagnosis" and "Keeping Them on Track." The timing and location of ACP conversations were important considerations. Over half of the participants (64%) had no knowledge or had misconceptions about ACP, and 5 had accurate knowledge. Nurses could develop partnerships with community leaders trained in palliative care principles to begin conversations early with community members. Advocacy groups might hold events, such as the Hello Game, in community settings to facilitate early ACP conversations.

Factors related to advance directives completion among cancer patients: a systematic review

INTRODUCTION

Advance directives (ADs) has recently been considered as an important component of palliative care for patients with advanced cancer and is a legally binding directive regarding a person's future medical care. It is used when a person is unable to participate in the decision-making process about their own care. Therefore, the present systematic review investigated the factors related to ADs from the perspective of cancer patients.

METHODS

A systematic review study was searched in four scientific databases: PubMed, Medline, Scopus, Web of Science, and ProQuest using with related keywords and without date restrictions. The quality of the studies was assessed using the Hawker criterion. The research papers were analyzed as directed content analysis based on the theory of planned behavior.

RESULTS

Out of 5900 research papers found, 22 were included in the study. The perspectives of 9061 cancer patients were investigated, of whom 4347 were men and 4714 were women. The mean ± SD of the patients' age was 62.04 ± 6.44. According to TPB, factors affecting ADs were categorized into four categories, including attitude, subjective norm, perceived behavioral control, and external factors affecting the model. The attitude category includes two subcategories: "Lack of knowledge of the ADs concept" and "Previous experience of the disease", the subjective norm category includes three subcategories: "Social support and interaction with family", "Respecting the patient's wishes" and "EOL care choices". Also, the category of perceived control behavior was categorized into two sub-categories: "Decision-making" and "Access to the healthcare system", as well as external factors affecting the model, including "socio-demographic characteristics".

CONCLUSION

The studies indicate that attention to EOL care and the wishes of patients regarding receiving medical care and preservation of human dignity, the importance of facilitating open communication between patients and their families, and different perspectives on providing information, communicating bad news and making decisions require culturally sensitive approaches. Finally, the training of cancer care professionals in the palliative care practice, promoting the participation of health care professionals in ADs activities and creating an AD-positive attitude should be strongly encouraged.

Development and refinement of a novel end-of-life planning website for patients with advanced cancer: a mixed methods approach

PURPOSE

Despite known benefits of planning for end-of-life, no digital tool exists to help patients with advanced cancer and their loved ones plan for death comprehensively. To address this unmet need, we developed a preliminary version of an innovative website to help patients with advanced cancer prepare for end-of-life tasks.

METHODS

Guided by the Obesity-Related Behavioral Intervention Trials (ORBIT) model for behavioral intervention development, patients with advanced cancer (n = 10) and their caregivers (n = 10) participated in a "Think Aloud" exercise and usability protocols to optimize the end-of-life planning website. The website was iteratively refined throughout the study in collaboration with the partnering company, Peacefully, Inc. Participants also completed the Acceptability E-Scale and System Usability Scale, with a priori benchmarks established for acceptability (scores of ≥ 24 on the Acceptability E-Scale) and usability (scores of ≥ 68 on the System Usability Scale).

RESULTS

Patients (N = 10) and caregivers (N = 10) completed usability testing. Patients were majority female (80%), White (100%), and had a mean age of 58 years. Caregivers (N = 10) were majority male (60%), spouse/partner (90%), White (90%), and had a mean age of 59 years. For patients, a priori hypotheses were met for both acceptability (mean score of 24.7, SD = 4.35) and usability (mean score of 73.8, SD = 6.15). For caregivers, acceptability was just below the cutoff (mean score of 22.9, SD = 4.07) and usability exceeded the cutoff (mean score of 70.0, SD = 8.42). Overall, patients and caregivers reported high levels of satisfaction and found the website helpful, with specific suggestions for changes (e.g., add more information about information security, improve text legibility).

CONCLUSIONS

The findings from this study will inform modifications to optimize an innovative website to support patients with advanced cancer to prepare holistically for end-of-life tasks.

The moderating effect of attitudes in the relationship between knowledge and self-efficacy in palliative care among nurses: A cross-sectional, correlational study

Provision of palliative care to patients with advanced chronic diseases or old populations is suboptimal, which results in unnecessary suffering of and burden to patients, caregivers, and society. Low self-efficacy in palliative care among nurses is a factor affecting suboptimal utilization of palliative care. Poor knowledge is a factor affecting low self-efficacy in palliative care of nurses. Attitudes may contribute to the relationship between knowledge and self-efficacy in palliative care, but these relationships have been rarely examined in nurses. This study aimed to determine whether nurses' attitudes moderate the relationship between knowledge and self-efficacy in palliative care. In a cross-sectional, correlational study, online or offline survey on self-efficacy, knowledge, attitudes, and covariates was conducted from 282 nurses in South Korea. PROCESS v4.1 for SPSS was used to address the study aim. Higher levels of knowledge (p = .048) and attitudes (p < .001), and the interaction term of knowledge and attitudes (p = .025) were significantly associated with higher levels of self-efficacy (F = 6.12, p < .001, R2 = .152), indicating the moderating effects of attitudes. The relationships between higher levels of knowledge and self-efficacy were significant only in nurses with highly and moderately positive attitudes (R2 change = .016, F = 5.11, p = .025), but not nurses with lack of positive attitudes. Our results supported the moderating role of nurses' attitudes in the relationship between knowledge and self-efficacy. To improve self-efficacy in palliative care in nurses, improvement in knowledge and facilitation of positive attitudes are needed.

Advance Care Planning conversations: What constitutes best practice and the way forward: Advance Care Planning-Gespräche: Was Best Practice ausmacht und wie es weitergehen kann

BACKGROUND

Advance Care Planning (ACP) conversations are a cornerstone of modern health care and need to be supported. However, research indicates that the uptake thereof is limited, regardless of various campaigns. ACP conversations are complex and specific elements thereof should be discussed at various timepoints during the illness trajectory.

OBJECTIVE

This narrative review delineates what ACP conversation should entail, and a way forward.

METHODS

A PEO (Population, Exposure, Outcome) search was performed using relevant keywords, and 615 articles were identified. Through screening and coding, this number was reduced to 24 articles. All the authors were involved in the final selection of the articles.

RESULTS

Various themes developed throughout the review which include timing early on in the disease trajectory; incorporating beliefs and culturally relevant contexts; conversations needing to be iterative and short; involving surrogates and family; applying various media formats.

DISCUSSION

ACP conversations are relevant. ACP is not static and needs to be dynamic as patients' illness trajectories and goals change. The care team needs to guard themselves against having ACP conversations to satisfy a metric and should instead be guided by the patient's expressed values and wishes. A system-wide operational plan will help alleviate common barriers in having appropriate ACP conversations.

Associations Between Patient-Perceived Cancer Curability and Advance Directive Completion

Background: Despite Advance Care Planning recommendations for patients with cancer, many lack Advance Directives (ADs). AD disparities persist among Black, Indigenous, or People of Color (BIPOC) patients. Based on a hypothesized correlation, we examined the association between patient-perceived cancer incurability and AD completion. Methods: This cross-sectional study obtained self-reported AD completion and incurability perception from routine care surveys. AD completion by incurability perception was estimated using modified Poisson regression. Subgroup analyses examined patients who were BIPOC, White, and had solid organ malignancies. Results: Our sample (N = 1209) was predominantly female (70%), White (73%) with early-stage disease (60%), and solid organ malignancies (82%). AD completion was 42%, and 40% of patients reported their cancer incurable. Patient-perceived incurability was not associated with increased AD completion (likelihood ratio 0.94, 95% confidence interval 0.78-1.13) in overall or subgroup analyses. Conclusion: Patient-perceived cancer incurability was not associated with AD completion, even accounting for race/ethnicity and cancer type.

Long-term Effect of Machine Learning-Triggered Behavioral Nudges on Serious Illness Conversations and End-of-Life Outcomes Among Patients With Cancer: A Randomized Clinical Trial

Importance

Serious illness conversations (SICs) between oncology clinicians and patients are associated with improved quality of life and may reduce aggressive end-of-life care. However, most patients with cancer die without a documented SIC.

Objective

To test the impact of behavioral nudges to clinicians to prompt SICs on the SIC rate and end-of-life outcomes among patients at high risk of death within 180 days (high-risk patients) as identified by a machine learning algorithm.

Design, Setting, and Participants

This prespecified 40-week analysis of a stepped-wedge randomized clinical trial conducted between June 17, 2019, and April 20, 2020 (including 16 weeks of intervention rollout and 24 weeks of follow-up), included 20 506 patients with cancer representing 41 021 encounters at 9 tertiary or community-based medical oncology clinics in a large academic health system. The current analyses were conducted from June 1, 2021, to May 31, 2022.

Intervention

High-risk patients were identified using a validated electronic health record machine learning algorithm to predict 6-month mortality. The intervention consisted of (1) weekly emails to clinicians comparing their SIC rates for all patients against peers' rates, (2) weekly lists of high-risk patients, and (3) opt-out text messages to prompt SICs before encounters with high-risk patients.

Main Outcomes and Measures

The primary outcome was SIC rates for all and high-risk patient encounters; secondary end-of-life outcomes among decedents included inpatient death, hospice enrollment and length of stay, and intensive care unit admission and systemic therapy close to death. Intention-to-treat analyses were adjusted for clinic and wedge fixed effects and clustered at the oncologist level.

Results

The study included 20 506 patients (mean [SD] age, 60.0 [14.0] years) and 41 021 patient encounters: 22 259 (54%) encounters with female patients, 28 907 (70.5%) with non-Hispanic White patients, and 5520 (13.5%) with high-risk patients; 1417 patients (6.9%) died by the end of follow-up. There were no meaningful differences in demographic characteristics in the control and intervention periods. Among high-risk patient encounters, the unadjusted SIC rates were 3.4% (59 of 1754 encounters) in the control period and 13.5% (510 of 3765 encounters) in the intervention period. In adjusted analyses, the intervention was associated with increased SICs for all patients (adjusted odds ratio, 2.09 [95% CI, 1.53-2.87]; P < .001) and decreased end-of-life systemic therapy (7.5% [72 of 957 patients] vs 10.4% [24 of 231 patients]; adjusted odds ratio, 0.25 [95% CI, 0.11-0.57]; P = .001) relative to controls, but there was no effect on hospice enrollment or length of stay, inpatient death, or end-of-life ICU use.

Conclusions and Relevance

In this randomized clinical trial, a machine learning-based behavioral intervention and behavioral nudges to clinicans led to an increase in SICs and reduction in end-of-life systemic therapy but no changes in other end-of-life outcomes among outpatients with cancer. These results suggest that machine learning and behavioral nudges can lead to long-lasting improvements in cancer care delivery.

Trial Registration

ClinicalTrials.gov Identifier: NCT03984773.

Factors associated with attitudes toward advance directives in nurses and comparisons of the levels between emergency nurses and palliative care nurses

AIM

Little is known about attitudes toward advance directives and factors associated with them among emergency and palliative care nurses who often or daily face end-of-life circumstances. Thus, we aimed to compare the levels of attitudes toward advance directives, communication skills, knowledge about end-of-life care (knowledge), and awareness of the concept of a good death (good death awareness) between emergency and palliative care nurses, and to examine factors associated with attitudes toward advance directives in the total sample.

METHODS

In this cross-sectional, correlational study, data were collected from 153 nurses (59 emergency and 94 palliative care nurses) at three tertiary hospitals using online or offline surveys and were analyzed using t-tests and multiple linear regression analysis.

RESULTS

The levels of attitudes, communication skills, knowledge, and good death awareness were moderate in both groups. Attitudes in emergency compared to palliative care nurses were less positive (46.78 vs. 48.38; p = .044), and knowledge was significantly lower (13.64 vs. 15.00; p = .004). Communication skills and good death awareness between the two groups were similar. In the total sample, emergency practice (B = -1.59, p = .024), and lower levels of good death awareness (B = 0.30, p < .001), communication skills (B = 0.18, p = .001), and education (B = -2.84, p = .015) were associated with less positive attitudes (F = 9.52, p < .001; R²  = 0.35).

CONCLUSIONS

The findings demonstrate the need for improvements in attitudes, knowledge, communication skills, and good death awareness in both groups, especially emergency nurses. Two modifiable targets of interventions to improve nurses' attitudes were also noted.

Verification of the Mandarin Chinese version of Advance Care Planning Engagement Survey Scale in community-dwelling older people

BACKGROUND

Advance care planning (ACP) has become increasingly critical for older people. The Advance Care Planning Engagement Survey (ACPES) develops targeted interventions for older people by measuring ACP-related behaviours, while previous studies focus only on advance directives. Moreover, while ACPES has English and Dutch versions, it does not yet have a Mandarin Chinese version (ACPES-MC) that can be used for Chinese populations.

OBJECTIVES

To translate the ACPES into Mandarin Chinese and test its reliability and validity in community-dwelling older people with chronic diseases.

METHODS

The English version of the ACPES was translated into Mandarin Chinese using Functional Assessment of Chronic Illness Therapy. Four communities were recruited in central China (N = 450) by convenience sampling. Internal consistency and construct validity were used to evaluate the reliability and validity of the ACPES-MC.

RESULTS

The ACPES-MC consists of 34 items across five domains, with good internal consistency (0.817), with each dimension ranging from 0.606 to 0.881; exploratory factor analysis was distributed to four different factors and the total variance explained was 63.537%; and confirmatory factor analysis results showed that χ²  = 3791.131 (p < .001), χ² /df = 1.106, CFI = 0.980, IFI = 0.980, NFI = 0.827 and RMR = 0.027, indicating a good model fit to previous factor structures.

CONCLUSIONS

The ACPES-MC is an effective and reliable tool that can measure the ACP-related behaviour stage of community-dwelling older people and evaluate the effect of ACP intervention.

IMPLICATIONS FOR PRACTICE

The ACPES-MC can be used in healthcare to identify potential ACP-related behaviours in community-dwelling older people with chronic diseases, including native and ethnic Chinese, evaluate their behaviour change stages and promote the application of ACP.

Attitudes and Intentions of Adult Patients With Cancer Toward Advance Directive: Direct and Indirect Relationships

BACKGROUND

Advance directives (ADs) are used to respect the will of patients experiencing a terminal illness regarding preferred medical treatment and to protect their rights. However, the AD completion rate is low.

OBJECTIVE

The aim of this study was to explore the factors influencing patient intentions toward AD.

METHODS

The Theory of Planned Behavior was used as the framework for this study. This study used a cross-sectional design using a face-to-face interview with structured questionnaires. A total of 230 patients with cancer were recruited. Path analysis was used to examine the hypotheses.

RESULTS

Demographic variables were not correlated with patients' attitudes toward AD. Patients' knowledge of AD ( β = .68, t = 16.15, P < .00) and recognition of important others' attitudes toward AD ( β = .30, t = 10.74, P < .00) were predictors of patients' attitudes toward AD. Patients' attitudes toward AD ( β = .27, t = 3.74, P < .00) and behavior control over AD ( β = .09, t = 1.99, P < .04) predicted patients' intentions toward AD.

CONCLUSION

Patients' knowledge of AD, the patients' important others' attitudes, and behavior control toward AD are predictively associated with the intention toward AD completion.

IMPLICATIONS FOR PRACTICE

Only when patients with cancer are provided an accessible approach for obtaining knowledge regarding AD and are given sufficient time and space can they and their significant others understand the meaning of AD and decide to complete one on their own terms.

What is the evidence for efficacy of advance care planning in improving patient outcomes? A systematic review of randomised controlled trials

Objectives

To conduct an up-to-date systematic review of all randomised controlled trials assessing efficacy of advance care planning (ACP) in improving patient outcomes, healthcare use/costs and documentation.

Design

Narrative synthesis conducted for randomised controlled trials. We searched electronic databases (MEDLINE/PubMed, Embase and Cochrane databases) for English-language randomised or cluster randomised controlled trials on 11 May 2020 and updated it on 12 May 2021 using the same search strategy. Two reviewers independently extracted data and assessed methodological quality. Disagreements were resolved by consensus or a third reviewer.

Results

We reviewed 132 eligible trials published between 1992 and May 2021; 64% were high-quality. We categorised study outcomes as patient (distal and proximal), healthcare use and process outcomes. There was mixed evidence that ACP interventions improved distal patient outcomes including end-of-life care consistent with preferences (25%; 3/12 with improvement), quality of life (0/14 studies), mental health (21%; 4/19) and home deaths (25%; 1/4), or that it reduced healthcare use/costs (18%; 4/22 studies). However, we found more consistent evidence that ACP interventions improve proximal patient outcomes including quality of patient–physician communication (68%; 13/19), preference for comfort care (70%; 16/23), decisional conflict (64%; 9/14) and patient-caregiver congruence in preference (82%; 18/22) and that it improved ACP documentation (a process outcome; 63%; 34/54).

Conclusion

This review provides the most comprehensive evidence to date regarding the efficacy of ACP on key patient outcomes and healthcare use/costs. Findings suggest a need to rethink the main purpose and outcomes of ACP.

PROSPERO registration number

CRD42020184080.

Impact of Behavioral Nudges on the Quality of Serious Illness Conversations Among Patients With Cancer: Secondary Analysis of a Randomized Controlled Trial

PURPOSE

Serious Illness Conversations (SICs) are structured conversations between clinicians and patients about prognosis, treatment goals, and end-of-life preferences. Although behavioral interventions may prompt earlier or more frequent SICs, their impact on the quality of SICs is unclear.

METHODS

This was a secondary analysis of a randomized clinical trial (NCT03984773) among 78 clinicians and 14,607 patients with cancer testing the impact of an automated mortality prediction with behavioral nudges to clinicians to prompt more SICs. We analyzed 318 randomly selected SICs matched 1:1 by clinicians (159 control and 159 intervention) to compare the quality of intervention vs. control conversations using a validated codebook. Comprehensiveness of SIC documentation was used as a measure of quality, with higher integer numbers of documented conversation domains corresponding to higher quality conversations. A conversation was classified as high-quality if its score was ≥ 8 of a maximum of 10. Using a noninferiority design, mixed effects regression models with clinician-level random effects were used to assess SIC quality in intervention vs. control groups, concluding noninferiority if the adjusted odds ratio (aOR) was not significantly < 0.9.

RESULTS

Baseline characteristics of the control and intervention groups were similar. Intervention SICs were noninferior to control conversations (aOR 0.99; 95% CI, 0.91 to 1.09). The intervention increased the likelihood of addressing patient-clinician relationship (aOR = 1.99; 95% CI, 1.23 to 3.27; P < .01) and decreased the likelihood of addressing family involvement (aOR = 0.56; 95% CI, 0.34 to 0.90; P < .05).

CONCLUSION

A behavioral intervention that increased SIC frequency did not decrease their quality. Behavioral prompts may increase SIC frequency without sacrificing quality.

Decision coaching for people making healthcare decisions

BACKGROUND

Decision coaching is non-directive support delivered by a healthcare provider to help patients prepare to actively participate in making a health decision. 'Healthcare providers' are considered to be all people who are engaged in actions whose primary intent is to protect and improve health (e.g. nurses, doctors, pharmacists, social workers, health support workers such as peer health workers). Little is known about the effectiveness of decision coaching.

OBJECTIVES

To determine the effects of decision coaching (I) for people facing healthcare decisions for themselves or a family member (P) compared to (C) usual care or evidence-based intervention only, on outcomes (O) related to preparation for decision making, decisional needs and potential adverse effects.

SEARCH METHODS

We searched the Cochrane Library (Wiley), Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE (Ovid), Embase (Ovid), PsycINFO (Ovid), CINAHL (Ebsco), Nursing and Allied Health Source (ProQuest), and Web of Science from database inception to June 2021.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) where the intervention was provided to adults or children preparing to make a treatment or screening healthcare decision for themselves or a family member. Decision coaching was defined as: a) delivered individually by a healthcare provider who is trained or using a protocol; and b) providing non-directive support and preparing an adult or child to participate in a healthcare decision. Comparisons included usual care or an alternate intervention. There were no language restrictions.

DATA COLLECTION AND ANALYSIS

Two authors independently screened citations, assessed risk of bias, and extracted data on characteristics of the intervention(s) and outcomes. Any disagreements were resolved by discussion to reach consensus. We used the standardised mean difference (SMD) with 95% confidence intervals (CI) as the measures of treatment effect and, where possible, synthesised results using a random-effects model. If more than one study measured the same outcome using different tools, we used a random-effects model to calculate the standardised mean difference (SMD) and 95% CI. We presented outcomes in summary of findings tables and applied GRADE methods to rate the certainty of the evidence.

MAIN RESULTS

Out of 12,984 citations screened, we included 28 studies of decision coaching interventions alone or in combination with evidence-based information, involving 5509 adult participants (aged 18 to 85 years; 64% female, 52% white, 33% African-American/Black; 68% post-secondary education). The studies evaluated decision coaching used for a range of healthcare decisions (e.g. treatment decisions for cancer, menopause, mental illness, advancing kidney disease; screening decisions for cancer, genetic testing). Four of the 28 studies included three comparator arms.  For decision coaching compared with usual care (n = 4 studies), we are uncertain if decision coaching compared with usual care improves any outcomes (i.e. preparation for decision making, decision self-confidence, knowledge, decision regret, anxiety) as the certainty of the evidence was very low.  For decision coaching compared with evidence-based information only (n = 4 studies), there is low certainty-evidence that participants exposed to decision coaching may have little or no change in knowledge (SMD -0.23, 95% CI: -0.50 to 0.04; 3 studies, 406 participants). There is low certainty-evidence that participants exposed to decision coaching may have little or no change in anxiety, compared with evidence-based information. We are uncertain if decision coaching compared with evidence-based information improves other outcomes (i.e. decision self-confidence, feeling uninformed) as the certainty of the evidence was very low. For decision coaching plus evidence-based information compared with usual care (n = 17 studies), there is low certainty-evidence that participants may have improved knowledge (SMD 9.3, 95% CI: 6.6 to 12.1; 5 studies, 1073 participants). We are uncertain if decision coaching plus evidence-based information compared with usual care improves other outcomes (i.e. preparation for decision making, decision self-confidence, feeling uninformed, unclear values, feeling unsupported, decision regret, anxiety) as the certainty of the evidence was very low. For decision coaching plus evidence-based information compared with evidence-based information only (n = 7 studies), we are uncertain if decision coaching plus evidence-based information compared with evidence-based information only improves any outcomes (i.e. feeling uninformed, unclear values, feeling unsupported, knowledge, anxiety) as the certainty of the evidence was very low.

AUTHORS' CONCLUSIONS

Decision coaching may improve participants' knowledge when used with evidence-based information. Our findings do not indicate any significant adverse effects (e.g. decision regret, anxiety) with the use of decision coaching. It is not possible to establish strong conclusions for other outcomes. It is unclear if decision coaching always needs to be paired with evidence-informed information. Further research is needed to establish the effectiveness of decision coaching for a broader range of outcomes.

Behavioral economic implementation strategies to improve serious illness communication between clinicians and high-risk patients with cancer: protocol for a cluster randomized pragmatic trial

BACKGROUND

Serious illness conversations (SICs) are an evidence-based approach to eliciting patients' values, goals, and care preferences that improve patient outcomes. However, most patients with cancer die without a documented SIC. Clinician-directed implementation strategies informed by behavioral economics ("nudges") that identify high-risk patients have shown promise in increasing SIC documentation among clinicians. It is unknown whether patient-directed nudges that normalize and prime patients towards SIC completion-either alone or in combination with clinician nudges that additionally compare performance relative to peers-may improve on this approach. Our objective is to test the effect of clinician- and patient-directed nudges as implementation strategies for increasing SIC completion among patients with cancer.

METHODS

We will conduct a 2 × 2 factorial, cluster randomized pragmatic trial to test the effect of nudges to clinicians, patients, or both, compared to usual care, on SIC completion. Participants will include 166 medical and gynecologic oncology clinicians practicing at ten sites within a large academic health system and their approximately 5500 patients at high risk of predicted 6-month mortality based on a validated machine-learning prognostic algorithm. Data will be obtained via the electronic medical record, clinician survey, and semi-structured interviews with clinicians and patients. The primary outcome will be time to SIC documentation among high-risk patients. Secondary outcomes will include time to SIC documentation among all patients (assessing spillover effects), palliative care referral among high-risk patients, and aggressive end-of-life care utilization (composite of chemotherapy within 14 days before death, hospitalization within 30 days before death, or admission to hospice within 3 days before death) among high-risk decedents. We will assess moderators of the effect of implementation strategies and conduct semi-structured interviews with a subset of clinicians and patients to assess contextual factors that shape the effectiveness of nudges with an eye towards health equity.

DISCUSSION

This will be the first pragmatic trial to evaluate clinician- and patient-directed nudges to promote SIC completion for patients with cancer. We expect the study to yield insights into the effectiveness of clinician and patient nudges as implementation strategies to improve SIC rates, and to uncover multilevel contextual factors that drive response to these strategies.

TRIAL REGISTRATION

ClinicalTrials.gov , NCT04867850 . Registered on April 30, 2021.

FUNDING

National Cancer Institute P50CA244690.

Examining the Association of Billed Advance Care Planning With End-of-Life Hospital Admissions Among Advanced Cancer Patients in Hospice

BACKGROUND

Advance care planning (ACP), or the consideration and communication of care preferences for the end-of-life (EOL), is a critical process for improving quality of care for patients with advanced cancer. The incorporation of billed service codes for ACP allows for new inquiries on the association between systematic ACP and improved EOL outcomes.

OBJECTIVE

Using the IBM MarketScan® Database, we conducted a retrospective medical claims analysis for patients with an advanced cancer diagnosis and referral to hospice between January 2016 and December 2017. We evaluated the association between billed ACP services and EOL hospital admissions in the final 30 days of life.

DESIGN

This is a cross-sectional retrospective cohort study.

PARTICIPANTS

A total of 3,705 patients met the study criteria.

MAIN MEASURES

ACP was measured via the presence of a billed ACP encounter (codes 99497 and 99498) prior to the last 30 days of life; hospital admissions included a dichotomous indicator for inpatient admission in the final 30 days of life.

KEY RESULTS

Controlling for key covariates, patients who received billed ACP were less likely to experience inpatient hospital admissions in the final 30 days of life compared to those not receiving billed ACP (OR: 0.34; p < 0.001).

CONCLUSION

The receipt of a billed ACP encounter is associated with reduced EOL hospital admissions in a population of patients with advanced cancer on hospice care. Strategies for consistent, anticipatory delivery of billable ACP services prior to hospice referral may prevent potentially undesired late-life hospital admissions.

Understanding of Health-related Decision-making Terminology Among Cancer Caregivers

Research on understanding health-related decision-making terminology among family caregivers of adults living with advanced cancer is lacking. The purpose of this study was to examine interpretations of the meaning of health-related decision-making terminology such as quality-of-life and end-of-life among caregivers of adults living with advanced cancer as a basis for improved understanding of caregiver decision support needs. Interviews were conducted with a purposive sub-sample of 10 caregivers of adults diagnosed with advanced cancer who completed a longitudinal, descriptive study (NRO14856) of factors influencing cancer care decisions. Audio transcripts were analyzed using qualitative descriptive methods. Caregivers described interpretations of the meaning and process of decision-making and decision-related distress. Caregivers were uncertain about the meaning of end-of-life-related terminology, and a placed high value on quality-of-life and faith/spirituality in the decision-making process. Improvements in information and decision support interventions are needed to better support caregivers and subsequently patients towards informed cancer care decisions.

Emergency Healthcare Providers' Knowledge about and Attitudes toward Advance Directives: A Cross-Sectional Study between Nurses and Emergency Medical Technicians at an Emergency Department

This study aimed to explore and compare knowledge levels about advance directives (ADs) and life-sustaining treatment (LST) plans in end-of-life patients between emergency nurses and emergency medical technicians (EMTs). Using a cross-sectional study design and convenience sampling, 96 nurses and 68 EMTs were recruited from 12 emergency medical centers. A survey on knowledge about and attitudes toward ADs was performed using both online and offline methods between November and December 2019. Emergency healthcare providers were conceptually knowledgeable regarding ADs and LST, although approximately half or fewer had knowledge about ADs (such as the legal process for preparation, family or healthcare providers’ role, and the healthcare proxy). The knowledge levels of nurses and EMTs were moderate. Nurses had significantly greater knowledge relative to EMTs about ADs and LST. Positive attitudes of emergency healthcare providers were also moderately low, with nurses having less positive views than EMTs. Significant differences regarding ADs were found, with younger emergency healthcare providers having fewer career years, no personal end-of-life experiences, and less need for ADs having less knowledge. Emergency healthcare providers’ knowledge about and attitudes toward ADs were moderately low, with EMTs demonstrating a greater knowledge deficit and nurses exhibiting lower positive attitudes. Younger and novice providers had lower knowledge, but younger providers had more positive attitudes, implying that professional education and training should begin early in their careers to enhance their confidence for emergency delivery of advanced care planning.

Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer: A Stepped-Wedge Cluster Randomized Clinical Trial

Importance

Serious illness conversations (SICs) are structured conversations between clinicians and patients about prognosis, treatment goals, and end-of-life preferences. Interventions that increase the rate of SICs between oncology clinicians and patients may improve goal-concordant care and patient outcomes.

Objective

To determine the effect of a clinician-directed intervention integrating machine learning mortality predictions with behavioral nudges on motivating clinician-patient SICs.

Design, Setting, and Participants

This stepped-wedge cluster randomized clinical trial was conducted across 20 weeks (from June 17 to November 1, 2019) at 9 medical oncology clinics (8 subspecialty oncology and 1 general oncology clinics) within a large academic health system in Pennsylvania. Clinicians at the 2 smallest subspecialty clinics were grouped together, resulting in 8 clinic groups randomly assigned to the 4 intervention wedge periods. Included participants in the intention-to-treat analyses were 78 oncology clinicians who received SIC training and their patients (N = 14 607) who had an outpatient oncology encounter during the study period.

Interventions

(1) Weekly emails to oncology clinicians with SIC performance feedback and peer comparisons; (2) a list of up to 6 high-risk patients (≥10% predicted risk of 180-day mortality) scheduled for the next week, estimated using a validated machine learning algorithm; and (3) opt-out text message prompts to clinicians on the patient's appointment day to consider an SIC. Clinicians in the control group received usual care consisting of weekly emails with cumulative SIC performance.

Main Outcomes and Measures

Percentage of patient encounters with an SIC in the intervention group vs the usual care (control) group.

Results

The sample consisted of 78 clinicians and 14 607 patients. The mean (SD) age of patients was 61.9 (14.2) years, 53.7% were female, and 70.4% were White. For all encounters, SICs were conducted among 1.3% in the control group and 4.6% in the intervention group, a significant difference (adjusted difference in percentage points, 3.3; 95% CI, 2.3-4.5; P < .001). Among 4124 high-risk patient encounters, SICs were conducted among 3.6% in the control group and 15.2% in the intervention group, a significant difference (adjusted difference in percentage points, 11.6; 95% CI, 8.2-12.5; P < .001).

Conclusions and Relevance

In this stepped-wedge cluster randomized clinical trial, an intervention that delivered machine learning mortality predictions with behavioral nudges to oncology clinicians significantly increased the rate of SICs among all patients and among patients with high mortality risk who were targeted by the intervention. Behavioral nudges combined with machine learning mortality predictions can positively influence clinician behavior and may be applied more broadly to improve care near the end of life.

Trial Registration

ClinicalTrials.gov Identifier: NCT03984773.

Do advance directive attitudes and perceived susceptibility and end-of-life life-sustaining treatment preferences between patients with heart failure and cancer differ?

There is limited evidence on the relationships of preference for end-of-life life-sustaining treatments [LSTs] and diagnostic contexts like heart failure [HF] or cancer, and patient attitudes toward and perceived susceptibility to use advance directives [ADs]. Thus, this study aimed to compare attitudes and perceived susceptibility between HF patients and community-dwelling patients with cancer, and examine the associations of these variables with their preference for each LST (cardiopulmonary resuscitation [CPR], ventilation support, hemodialysis, and hospice care). Secondary data were obtained from 36 outpatients with HF (mean age, 65.44 years; male, 69.4%) and 107 cancer patients (mean age, 67.39 years; male, 32.7%). More patients with HF preferred CPR than cancer patients (41.7% and 15.9%, χ2 = 8.88, P = 0.003). Attitudes and perceived susceptibility were similar between the two diagnostic cohorts. HF patients and those with more positive attitudes had greater odds of preferring CPR (odds ratio [OR] = 3.02, confidence interval [CI] = 1.19, 7.70) and hospice care (OR = 1.14, CI = 1.06, 1.23), respectively. HF diagnosis and AD attitudes increased the preference for CPR and hospice care, respectively. This suggests that it is important to gain positive attitudes toward ADs and consider diagnostic context to facilitate informed decision-making for LSTs.

Deconstructing the Complexities of Advance Care Planning Outcomes: What Do We Know and Where Do We Go? A Scoping Review

BACKGROUND/OBJECTIVES

Advance care planning (ACP) has shown benefit in some, but not all, studies. It is important to understand the utility of ACP. We conducted a scoping review to identify promising interventions and outcomes.

DESIGN

Scoping review.

MEASUREMENTS

We searched MEDLINE/PubMed, EMBASE, CINAHL, PsycINFO, and Web of Science for ACP randomized controlled trials from January 1, 2010, to March 3, 2020. We used standardized Preferred Reporting Items for Systematic Review and Meta-Analyses methods to chart study characteristics, including a standardized ACP Outcome Framework: Process (e.g., readiness), Action (e.g., communication), Quality of Care (e.g., satisfaction), Health Status (e.g., anxiety), and Healthcare Utilization. Differences between arms of P < .05 were deemed positive.

RESULTS

Of 1,464 articles, 69 met eligibility; 94% were rated high quality. There were variable definitions, age criteria (≥18 to ≥80 years), diseases (e.g., dementia and cancer), and settings (e.g., outpatient and inpatient). Interventions included facilitated discussions (42%), video only (20%), interactive, multimedia (17%), written only (12%), and clinician training (9%). For written only, 75% of primary outcomes were positive, as were 69% for multimedia programs; 67% for facilitated discussions, 59% for video only, and 57% for clinician training. Overall, 72% of Process and 86% of Action outcomes were positive. For Quality of Care, 88% of outcomes were positive for patient-surrogate/clinician congruence, 100% for patients/surrogate/clinician satisfaction with communication, and 75% for surrogate satisfaction with patients' care, but not for goal concordance. For Health Status outcomes, 100% were positive for reducing surrogate/clinician distress, but not for patient quality of life. Healthcare Utilization data were mixed.

CONCLUSION

ACP is complex, and trial characteristics were heterogeneous. Outcomes for all ACP interventions were predominantly positive, as were Process and Action outcomes. Although some Quality of Care and Health Status outcomes were mixed, increased patient/surrogate satisfaction with communication and care and decreased surrogate/clinician distress were positive. Further research is needed to appropriately tailor interventions and outcomes for local contexts, set appropriate expectations of ACP outcomes, and standardize across studies.

Integrating machine-generated mortality estimates and behavioral nudges to promote serious illness conversations for cancer patients: Design and methods for a stepped-wedge cluster randomized controlled trial

INTRODUCTION

Patients with cancer often receive care that is not aligned with their personal values and goals. Serious illness conversations (SICs) between clinicians and patients can help increase a patient's understanding of their prognosis, goals and values.

METHODS AND ANALYSIS

In this study, we describe the design of a stepped-wedge cluster randomized trial to evaluate the impact of an intervention that employs machine learning-based prognostic algorithms and behavioral nudges to prompt oncologists to have SICs with patients at high risk of short-term mortality. Data are collected on documented SICs, documented advance care planning discussions, and end-of-life care utilization (emergency room and inpatient admissions, chemotherapy and hospice utilization) for patients of all enrolled clinicians.

CONCLUSION

This trial represents a novel application of machine-generated mortality predictions combined with behavioral nudges in the routine care of outpatients with cancer. Findings from the trial may inform strategies to encourage early serious illness conversations and the application of mortality risk predictions in clinical settings.

TRIAL REGISTRATION

Clinicaltrials.gov Identifier: NCT03984773.

Modifiable Factors Associated with the Completion of Advance Treatment Directives in Hematologic Malignancy: A Patient-Caregiver Dyadic Analysis

Objective: The completion rates of advance treatment preferences in patients with hematologic malignancies are low. To improve these rates, the modifiable factors associated with completion need to be determined. This study aimed to examine the associations of patient attitudes toward, and knowledge about, advance directives (ADs) with the patient-caregiver dyadic completion of advance treatment directive surveys. Methods: Using a nonexperimental correlational design, 44 patient-caregiver dyads completed the questionnaires, including a Korean-Advance Directive model. Cohen's kappa coefficient and multiple logistic regression analyses examined the extent of dyadic agreement and patient factors for the dyadic completion of the advance treatment directive survey, respectively. Results: A minor group of patients (4.5%-11.4%) and caregivers (11.4%-18.2%) preferred aggressive end-of-life treatments, whereas more patients (47.7%) and caregivers (68.2%) supported hospice care. The only significant patient-caregiver dyadic concordance on treatment directives was for chemotherapy with a moderately high agreement (kappa = 0.60: 95% CI: 2.51-3.73). One score increase in AD knowledge and having a history of hematopoietic stem cell transplant (HSCT) increased the likelihood of dyadic completion of the treatment directive survey by 43% (p = 0.039) and 917% (p = 0.047), respectively. Conclusions: The patient-caregiver dyads in the setting of hematologic malignancy had a moderately high concordance with chemotherapy but were not associated with other treatment options. A higher level of AD knowledge and HSCT were associated with dyadic completion of the AD survey. Educational support is important to increase knowledge regarding ADs through ongoing palliative discussions among hematologic patients and their caregivers.

Machine Learning Approaches to Predict 6-Month Mortality Among Patients With Cancer

Importance

Machine learning algorithms could identify patients with cancer who are at risk of short-term mortality. However, it is unclear how different machine learning algorithms compare and whether they could prompt clinicians to have timely conversations about treatment and end-of-life preferences.

Objectives

To develop, validate, and compare machine learning algorithms that use structured electronic health record data before a clinic visit to predict mortality among patients with cancer.

Design, Setting, and Participants

Cohort study of 26 525 adult patients who had outpatient oncology or hematology/oncology encounters at a large academic cancer center and 10 affiliated community practices between February 1, 2016, and July 1, 2016. Patients were not required to receive cancer-directed treatment. Patients were observed for up to 500 days after the encounter. Data analysis took place between October 1, 2018, and September 1, 2019.

Exposures

Logistic regression, gradient boosting, and random forest algorithms.

Main Outcomes and Measures

Primary outcome was 180-day mortality from the index encounter; secondary outcome was 500-day mortality from the index encounter.

Results

Among 26 525 patients in the analysis, 1065 (4.0%) died within 180 days of the index encounter. Among those who died, the mean age was 67.3 (95% CI, 66.5-68.0) years, and 500 (47.0%) were women. Among those who were alive at 180 days, the mean age was 61.3 (95% CI, 61.1-61.5) years, and 15 922 (62.5%) were women. The population was randomly partitioned into training (18 567 [70.0%]) and validation (7958 [30.0%]) cohorts at the patient level, and a randomly selected encounter was included in either the training or validation set. At a prespecified alert rate of 0.02, positive predictive values were higher for the random forest (51.3%) and gradient boosting (49.4%) algorithms compared with the logistic regression algorithm (44.7%). There was no significant difference in discrimination among the random forest (area under the receiver operating characteristic curve [AUC], 0.88; 95% CI, 0.86-0.89), gradient boosting (AUC, 0.87; 95% CI, 0.85-0.89), and logistic regression (AUC, 0.86; 95% CI, 0.84-0.88) models (P for comparison = .02). In the random forest model, observed 180-day mortality was 51.3% (95% CI, 43.6%-58.8%) in the high-risk group vs 3.4% (95% CI, 3.0%-3.8%) in the low-risk group; at 500 days, observed mortality was 64.4% (95% CI, 56.7%-71.4%) in the high-risk group and 7.6% (7.0%-8.2%) in the low-risk group. In a survey of 15 oncology clinicians with a 52.1% response rate, 100 of 171 patients (58.8%) who had been flagged as having high risk by the gradient boosting algorithm were deemed appropriate for a conversation about treatment and end-of-life preferences in the upcoming week.

Conclusions and Relevance

In this cohort study, machine learning algorithms based on structured electronic health record data accurately identified patients with cancer at risk of short-term mortality. When the gradient boosting algorithm was applied in real time, clinicians believed that most patients who had been identified as having high risk were appropriate for a timely conversation about treatment and end-of-life preferences.