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

Abstract

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

Background

Patients with cancer often experience physical and emotional distress, utilize unplanned acute care, and undergo medical interventions that are discordant with their wishes at the end of life [1,2,3,4,5,6]. Serious illness conversations (SICs) that elicit patients’ values, goals, and care preferences, particularly earlier in the disease trajectory, are an evidence-based practice that improves patient mood and quality of life [7,8,9,10,11,12] and are recommended by national guidelines, including those of the American Society of Clinical Oncology and National Academies of Medicine [13,14,15]. However, most patients with cancer die without a documented SIC, contributing to unwarranted and unwanted aggressive care at the end of life [7]. Moreover, SICs are implemented inequitably. Those more likely to experience social and health inequities, such as Black or African American patients, are less likely than non-Hispanic White patients to have SICs [16,17,18,19,20,21]. Current implementation strategies [22] to promote SICs—including the Serious Illness Care Program (SICP) developed by Ariadne Labs [23]—focus primarily on clinician education and training and have only marginally increased the timeliness and frequency of SICs [11, 12]. While core elements of this program are scalable, such as its structured conversation guide for clinicians, SIC completion in routine practice remains low. For example, even after formal SICP training, medical oncology clinicians at our large academic cancer center documented SICs for fewer than 5% of patients with cancer seen in medical oncology practices [24].

Efforts to implement SICs can be enhanced using approaches from behavioral economics, a discipline that encompasses a set of principles, theories, and strategies derived from economics, cognitive psychology, and social psychology to understand human decision-making. Behavioral economics posits that individuals deploy common mental heuristics that may account for behavior that is “non-rational” (i.e., not consistent with maximizing individual utility). Clinician heuristics that may undermine the initiation of SICs include optimism bias, or the belief that one’s own patient is unlikely to experience a negative event; uncertainty about prognosis and optimal SIC timing; and fear that bringing up end-of-life issues may be distressing to patients [25, 26]. Due to optimism bias, clinicians routinely overestimate the life expectancy of patients with advanced cancer, particularly in the era of novel and personalized therapeutics [27,28,29], and delay SICs until too late in the disease course. Delaying SICs reinforces a social norm that SICs are not part of routine oncology care and are not appropriate until late in the disease course, often after unplanned acute care utilization [15, 30,31,32]. Such norms are powerful behavioral determinants: individuals desire to conform to an approved behavior (an injunctive norm) and the behavior of others (a descriptive norm). These norms may be influenced by broader factors such as structural racism—most notably, beliefs that non-White patients may be less receptive to end-of-life conversations—that contribute to inequitable access to SICs [33]. Scant research has evaluated strategies designed to harness norms directly and align clinicians and patients toward engaging in SICs [34].

Implementation strategies informed by behavioral economics are well-suited to address these barriers, which fundamentally imply the need for strategies directed towards clinicians and patients. By intentionally modifying the way choices are framed, nudges can counter behavioral pathways that might thwart behavior change or harness those pathways to redirect decisions toward better outcomes, while preserving principles of autonomy and choice [24, 35,36,37]. Nudges to clinicians have been shown to increase rates of evidence-based behaviors such as influenza vaccination [38,39,40]. We demonstrated the effectiveness of a clinician-directed nudge designed to counteract optimism bias and aid clinicians in identifying patients at high risk of predicted 180-day mortality based on a validated machine-learning prognostic algorithm (i.e., those most likely to benefit from SICs). This strategy led to a near fourfold increase in SIC documentation for high-risk patients, with particular improvement seen among non-White patients, and is now in routine use across sites at our large academic cancer center [25]. However, clinicians still did not have SICs for over half of high-risk patients, illustrating gaps that remain with a clinician-directed implementation strategy alone [25].

As a signature project of our National Cancer Institute-funded Penn Implementation Science Center in Cancer Control (P50 CA244690), this study will evaluate the effect of nudges to clinicians, nudges to patients, or nudges to both, on SIC completion. The clinician nudge will build on our prior approach to clinician nudges by adding performance feedback via peer comparison. This addition will serve to remind clinicians of their performance on SIC documentation by providing both an injunctive norm (institutional guidelines) and a descriptive norm (displaying the behavior of peers). The patient nudge will be a digital communication designed to normalize SICs as a routine part of cancer care and to prime patients (and, in turn, their clinicians) to have earlier SICs. Priming is a type of nudge that frames information to activate one’s self-efficacy and willingness to engage in behavior change [41] and may also signal an injunctive norm for both clinicians and patients. A prior study with patients that had serious illnesses (including some with cancer) demonstrated promise to this approach [42]. The clinician and patient nudges will be compared with usual care at our institution, which consists of a basic clinician-directed nudge identifying high-risk patients and providing performance feedback without peer comparison; importantly, this arm will serve as an active control. We hypothesize that providing a clinician nudge that counteracts optimism bias and targets injunctive and descriptive norms via peer comparison, and a patient nudge that normalizes and primes patients towards serious illness communication, will lead to increased rates of SICs for patients with cancer at risk of short-term mortality [35, 43, 44].

Methods

Study aims

The main objective of this four-arm cluster randomized pragmatic trial is to test the independent effects of multilevel behavioral economic implementation strategies involving nudges to clinicians, nudges to patients, or nudges to both, as compared to usual care, on SIC completion for patients predicted to be at high risk of 6-month mortality (aim 1). We hypothesize that each of the implementation strategy arms will increase SIC rates compared to usual care and that the combination arm (nudges to both clinicians and patients) will be most effective. We will also conduct a quantitative evaluation using secondary data to identify moderators of implementation effects on SIC completion, evaluating for heterogeneity of effects by patient race/ethnicity, income, and geographic location, as well as by clinician and practice factors (aim 2). Finally, we will investigate potential mechanisms by which the nudges increase SICs using qualitative data collected from clinicians and patients (aim 3).

Study design

We will conduct a four-arm cluster randomized pragmatic trial to test the effect of nudges to clinicians, nudges to patients, or nudges to both, compared to usual care, on SIC completion. Using a 2 × 2 factorial design, eligible clinicians and patients will be randomized independently, yielding four study arms (Table 1). Clinician clusters will be randomized to receive a weekly nudge delivered via email and text message containing (a) identification of patients at high risk of predicted 6-month mortality and (b) performance feedback on SIC documentation compared to peers, versus usual care. Patients will be randomized to receive a nudge delivered via email and text message containing (a) a normalizing message and (b) a short questionnaire designed to prime patients to have earlier SICs, versus usual care. Usual care will consist of a basic clinician-directed nudge identifying high-risk patients and providing performance feedback without peer comparison, which will serve as an active control. The study arms are described in more detail below.

Table 1 Randomized 2 × 2 factorial design yielding 4 independent study arms

The study duration for clinicians will be approximately 12 months, during which they will receive nudges according to the assigned study arm. The study duration for patients will be approximately 6 months per participant, defined by an initial index clinical encounter (marking study entry and the beginning of the nudge exposure period according to the assigned study arm) and by a 6-month follow-up period over which study outcomes will be ascertained (Fig. 1).

Fig. 1
figure1

CONSORT diagram

Participants and setting

The study participants will include approximately 5500 high-risk patients with cancer cared for by approximately 166 medical and gynecologic oncology clinicians across the following four hospitals and six community practice sites within the Penn Medicine Abramson Cancer Center (ACC): Hospital of the University of Pennsylvania, Pennsylvania Hospital, Penn Presbyterian Medical Center, Chester County Hospital, Valley Forge Medical Center, Radnor Medical Center, Cherry Hill Medical Center, Voorhees Medical Center, Sewell Medical Center, and Regional Hematology Oncology Associates. These entities include approximately 200 oncologists and annually serve >15,000 new cancer patients per year (52% female, 42% Hispanic, 16.2% Black, 21% Medicaid/uninsured).

The clinician sample will include eligible medical oncologists, gynecologic oncologists, and advanced practice providers (APPs, i.e., physician assistants and nurse practitioners) caring for patients with cancer at participating sites. Eligible clinician participants will provide care at least 1 half-day clinic session per week for adult (age > 18 years) patients with solid, hematologic, or gynecologic malignancies at a participating site. Clinicians providing exclusively survivorship, genetics, benign hematology, leukemia, or bone marrow transplant care will be excluded from the clinician sample. We justify these exclusions by (1) the lack of high-risk patients in survivorship, genetics, and benign hematology clinics and (2) the suboptimal algorithm performance among patients with leukemia or after bone marrow transplantation. All eligible clinicians will have the opportunity to undergo SICP training, which is required training for medical and gynecology clinicians at our health system. Table 2 details study inclusion and exclusion criteria.

Table 2 Participant inclusion and exclusion criteria

The patient sample will include those receiving care for solid, hematologic, or gynecologic malignancies from an eligible clinician at a participating site, who have at least one scheduled outpatient clinical encounter either in person or via telemedicine during the study period (“index clinical encounter”). We will exclude patients with a previously documented SIC within 6 months of enrollment or with a non-valid mobile phone number, representing approximately 5% of the cohort. Patients will accrue as they are seen in follow-up by an eligible clinician at a participating practice site.

This study was approved by the University of Pennsylvania Institutional Review Board and by the other sites under a reliance agreement. Since this is a pragmatic trial focused on improving implementation of evidence-based practices with minimal risk to patients, we received a waiver of participant informed consent for clinicians and patients for aims 1 and 2. For aim 3, potential participants will provide informed consent prior to data collection.

Study procedures and implementation strategies

Eligible clinicians and patients will be identified using the criteria listed above. Eligible clinicians will be randomized in clusters at the start of the study and will receive nudges identifying high-risk patients with or without peer comparisons for the duration of the study period. Clinicians will be clustered with APPs with whom they work, and vice versa, and these non-overlapping clusters will serve as the clinician unit of randomization and analysis. Clusters of clinicians, once identified, will be randomized to study arms by small-block permutation.

Eligible patients will be randomized in advance of a qualifying index clinical encounter and will accrue to the study accordingly. Patients will be exposed to implementation strategies based on their assigned study arm, which will depend on their own randomization (determined at the time of the index clinical encounter) and that of their clinician (determined at study initiation). Randomization will therefore employ a hybrid model of cluster randomization of clinician groups and independent randomization of patients.

We employed rapid cycle approaches to finalize the content, messaging, and design to optimize and refine implementation strategies prior to trial launch. These rapid cycle iterations included design meetings with behavioral economics experts (co-authors DAA and AMB), in-depth discussion with oncology clinicians who subspecialize in a variety of cancer subtypes and who were recruited from ten practice sites, and focus group discussions with members of a patient and caregiver cancer advisory group. We then engaged in multiple rounds of usability testing with both clinicians and patients, resulting in the final patient and clinician nudges as described below.

Nudge to clinician only

The clinician nudge will be delivered weekly via email with text message reminders sent on the mornings of clinic sessions. The weekly emails will identify patients scheduled for outpatient visits in the following week who are at high risk of predicted 180-day mortality based on a validated machine-learning prognostic algorithm [45, 46]. The emails will also contain clinician performance feedback on SIC documentation over the past 4 weeks with graphical comparison to peer clinicians who specialize in their specific disease group (e.g., thoracic, genitourinary, etc.) and/or who practice at their practice site. The text messages sent on the mornings of clinic sessions will alert clinicians to the appointment time and initials of high-risk patients scheduled to be seen during the clinic that day. Figure 2 shows the sample content of the clinician messaging.

Fig. 2
figure2

Clinician nudge

Nudge to patient only

The patient nudge will be delivered via text message and email in advance of the index clinical encounter. It will consist of a normalizing message prompting patients to complete a brief electronic questionnaire designed to prime patients towards having a SIC and will be re-sent a maximum of two times at monthly intervals only for those patients who neither fill out the priming questionnaire nor have a documented SIC during follow-up. Patients will have the opportunity to opt out of receiving any further text messages at any point. Patient-reported data from the priming questionnaire will be shared with the appropriate clinical team in real-time via the electronic medical record. Figure 3 shows the sample content of the patient messaging.

Fig. 3
figure3

Patient nudge

Nudges to both clinician and patient

Both strategies described above will be used in combination.

Usual care

Usual care at our institution changed as a result of our prior study, discussed above, showing the effectiveness of a clinician-directed nudge to promote SICs [25]. Accordingly, usual care in this trial will consist of weekly emails and text messages identifying high-risk patients, as well as clinician performance feedback without peer comparison. This approach has the advantage of incorporating an active control arm and will also enable us to understand the effect of clinician performance feedback with versus without peer comparison. Figure 2 shows the sample content of the clinician nudge used in usual care.

Measures

The primary and secondary outcomes are summarized in Table 3. The primary outcome is SIC documentation among high-risk patients only (as identified by the machine-learning algorithm), measured as the presence or absence of a standardized SIC note in the electronic medical record during a participant’s 6-month follow-up period after the index clinical encounter. SIC documentation by clinicians will serve as a proxy measure for completed SICs, but is also an important outcome by itself as documentation facilitates centralized communication of patient goals and wishes to the health care team. The secondary outcomes are SIC documentation, among all patients (regardless of algorithm-predicted mortality risk); outpatient palliative care referral, among high-risk patients only; and aggressive end-of-life care (composite of any of the following three criteria: chemotherapy within 14 days before death, hospitalization within 30 days before death, or admission to hospice 3 days or less before death), among high-risk patients who die. These secondary outcomes will each be measured at the patient level as a binary outcome, ascertained over a participant’s 6-month follow-up period.

Table 3 Primary and secondary outcomes

We will additionally collect quantitative data on potential moderators of implementation effects and any health inequities that arise during implementation [47], using data collected routinely through the electronic medical record, publicly available data from the US Census, and data from the clinician survey. These will include patient-level variables (age, sex, race/ethnicity, cancer type, cancer stage, and health insurance), clinician-level variables (years in practice and patient panel size), practice-level variables (community vs. hospital-based setting, urban vs. non-urban location, and payer mix), and neighborhood-level variables (linked at the patient- and practice-level, including median income and educational attainment). The baseline clinician survey will assess distal constructs (e.g., organizational learning) and proximal constructs (e.g., perceived self-efficacy to engage in SICs), given findings that these constructs are important for implementation [48].

Finally, following active trial participation, semi-structured interviews will assess contextual factors across the five domains of the Consolidated Framework for Implementation Research (CFIR) to understand patient and clinician experiences with and responses to the nudges. Among clinicians, a core interview guide comprised of semi-structured questions will be used to assess multilevel organizational or system factors and processes related to implementation of SICs. Specific questions will probe clinicians about key barriers and facilitators of the nudges. Among patients, questions will probe participants about reactions to the nudge and experiences at the provider and systems levels (e.g., perceptions of acceptability, appropriateness); additionally, in line with our health equity focus [49, 50], there will be questions about social and structural factors that may contribute to health inequities such as experiences of racism, discrimination, medical mistrust, perceived health care access, and language barriers. In addition to semi-structured interview questions, all clinicians and patients interviewed will complete a brief questionnaire that assesses demographics and beliefs and behaviors related to SICs.

Sample size and power

We are seeking to detect the main effects of nudges and their interaction with at least 80% power using a two-sided alpha of 0.05. We anticipate enrolling 66 physician-APP clusters who will together care for an estimated 5500 high-risk patients over the study period, yielding approximately 83 high-risk patients per cluster on average. We have generated power calculations using various patient enrollment and cluster correlation assumptions (Table 4). We calculated power by simulation, generating exponential time to event under a variety of assumptions. Patients are clustered within clinician units. Within-cluster correlation was imposed by drawing correlated, normally distributed random numbers, transformed to exponential using copula methods.

Table 4 Power simulations

Assuming conservatively that we enroll 70 patients per cluster with a within-cluster correlation of 0.1, we will have >80% power to detect a true hazard ratio (HR) of effect of 1.6 for the clinician nudge, HR 1.25 for the patient nudge, and hazard ratio of ratios (HRR) of 1.6 for the interaction. Assuming 70 patients per cluster and a within-cluster correlation of 0.3, we will have >80% power to detect a true hazard ratio of effect of HR 2.0 for the clinician nudge, HR 1.3 for the patient nudge, and HRR 1.8 for the interaction. These estimates improve slightly if we assume enrollment of 90 patients per cluster, allowing us to detect similar effect sizes with more power, as shown in Table 4.

Statistical methods

For aim 1, the primary outcome will be modeled in a time-to-event analysis using a Cox proportional hazards model, with cluster-correlated robust standard errors to account for patient clustering within clinician clusters. SIC documentation, as defined above, will serve as the primary event outcome. Days between the index clinical encounter and SIC documentation will serve as the time variable. Nudge exposure will be determined based on randomization of the clinician cluster and randomization of the patient. Cox regression will estimate hazard ratios for the main effects and a ratio of hazard ratios for the interaction of dual clinician and patient nudges. Significance will be determined using the z-score corresponding to each of the estimated effects. All enrolled patients will be included in the intention-to-treat analysis, and subjects will be censored at the time of last structured electronic health record activity or death, should they not be observed to have a documented SIC during the follow-up period. Covariates will be assessed across study arms and included in the Cox model if unbalanced across arms.

We will use our fitted Cox model to generate predicted probabilities of SIC documentation within 6 months, via marginal standardization, as well as median time to documented SIC across arms. The functional form of the model will be checked using cumulative martingale residuals, and the proportional hazards assumption will be checked using scaled Schoenfeld residuals. The secondary outcomes will be similarly modeled. All hypothesis tests will use a two-sided alpha of 0.05 as the threshold for statistical significance.

For aim 2, we will explore the heterogeneity of implementation effects on SIC documentation by including an interaction term between study arm and, separately, patient, clinician, and neighborhood factors. Evidence for effect modification will be judged based on the z-score corresponding to the ratio of hazard ratios (interaction term).

For aim 3, we will use convergent mixed-methods design and analysis to help identify for whom implementation strategies are most effective, including among patients more likely to experience social and health inequities, and to identify how strategies might work (i.e., mechanism of change) [51, 52]. Informed by CFIR, we will identify contextual conditions (e.g., inner setting) and implementation conditions (characteristics of specific implementation strategy and process) shaping response to patient and clinician nudges. The constant comparative method, guided by grounded theory [53, 54], will be used to deductively code a priori domains of interest (guided and organized by CFIR domains) and to inductively explore emergent findings and overarching themes. We will triangulate these qualitative data with other quantitative data collected in the trial (e.g., trial outcomes, structured questionnaire data). These coded data will serve as inputs to assess multilevel mechanisms shaping nudge effectiveness across our trial using qualitative comparative analysis.

Discussion

This randomized controlled trial will test the effect of nudges to clinicians, patients, or both, versus usual care, as implementation strategies to improve SIC completion among patients with cancer at high risk of mortality. By harnessing heuristics of both clinicians and patients and thereby addressing multilevel barriers to SIC engagement, it builds on our prior work demonstrating the moderate effectiveness of a clinician-directed nudge alone [25]. Through incorporation of this basic clinician nudge into an active control arm, this study is uniquely positioned to assess the added impact of clinician peer comparison within the clinician nudge, as well as the impact of a patient nudge designed to normalize SICs and prime patients (and clinicians) to participate in them.

We expect the study to yield essential insights into the effectiveness of patient-level nudges, clinician-level nudges, or both as implementation strategies to increase the uptake of evidence-based practices in cancer care, and to advance our understanding of the multilevel contextual factors and mechanisms that drive response to these strategies. Specifically, we will advance the science of implementation by exploring patient, clinician, and inner-setting factors that contribute to response to patient and/or clinician nudges, including assessing effectiveness across a diverse group of patients and practice settings. We expect that this will lend critical insights into the external validity of these novel approaches. Furthermore, if successful, this study will provide a generalizable approach to integrate predictive analytics with nudges to engage both clinicians and patients in evidence-based conversations about treatment goals and end-of-life wishes. These results will lay the foundation for how care settings can ensure that patients with cancer and their clinicians engage in more frequent and earlier SICs and feel empowered to initiate optimally timed conversations. This may help drive goal-concordant care, improve quality of life, and reduce unwanted end-of-life care utilization. Furthermore, our study will enhance understanding of the extent to which clinician and/or patient nudges affect health inequities in cancer care delivery.

Our study has several limitations. First, this is a single-health system study and thus our approach, even if successful, may not generalize to other health systems—particularly institutions without robust electronic medical record infrastructure, clinician training on serious illness communication, or buy-in from administrative leaders and the clinical workforce. Despite this limitation, our study consists of a racially and ethnically diverse patient population, spanning academic and community oncology settings with a common electronic medical record infrastructure, and thus lessons should generalize to diverse settings. Second, our primary outcome of SIC documentation is not a robust measure of SIC quality or patient satisfaction. While SIC documentation has been used as an outcome in several supportive care studies [12, 25], future studies of nudges should assess patient-reported outcomes and other conversation quality metrics as their primary outcome; we will measure such outcomes in an exploratory fashion as part of aim 3.

If successful, future directions of this work include a large, multi-health system study to test the generalizability of patient and/or clinician nudges to improve SIC rates and a well-powered study to assess the impact of clinician and patient SIC nudges on end-of-life care utilization and outcomes.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to patient privacy but are available from the corresponding author on reasonable request.

Abbreviations

ACC:

Abramson Cancer Center of Penn Medicine

ACP:

Advanced Care Planning

APP(s):

Advanced practice provider(s)

HR :

Hazard ratio

HRR :

Hazard ratio of ratios

NCI:

National Cancer Institute

SIC(s):

Serious illness conversation(s)

References

  1. 1.

    Earle CC, Landrum MB, Souza JM, Neville BA, Weeks JC, Ayanian JZ. Aggressiveness of cancer care near the end of life: is it a quality-of-care issue? J Clin Oncol. 2008;26(23):3860–6. https://doi.org/10.1200/JCO.2007.15.8253.

    Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Emanuel EJ, Young-Xu Y, Levinsky NG, Gazelle G, Saynina O, Ash AS. Chemotherapy use among Medicare beneficiaries at the end of life. Ann Intern Med. 2003;138(8):639–43. https://doi.org/10.7326/0003-4819-138-8-200304150-00011.

    Article  PubMed  Google Scholar 

  3. 3.

    Wright AA, Keating NL, Ayanian JZ, Chrischilles EA, Kahn KL, Ritchie CS, et al. Family perspectives on aggressive cancer care near the end of life. JAMA. 2016;315(3):284–92. https://doi.org/10.1001/jama.2015.18604.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Wright AA, Keating NL, Balboni TA, Matulonis UA, Block SD, Prigerson HG. Place of death: correlations with quality of life of patients with cancer and predictors of bereaved caregivers’ mental health. J Clin Oncol. 2010;28(29):4457–64. https://doi.org/10.1200/JCO.2009.26.3863.

    Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Wen F-H, Chen J-S, Su P-J, et al. Terminally ill cancer patients’ concordance between preferred life-sustaining treatment states in their last six months of life and received life-sustaining treatment states in their last month: an observational study. J Pain Symptom Manage. 2018;56(4):509–518.e3. https://doi.org/10.1016/j.jpainsymman.2018.07.003.

    Article  PubMed  Google Scholar 

  6. 6.

    Earle CC, Neville BA, Landrum MB, Ayanian JZ, Block SD, Weeks JC. Trends in the aggressiveness of cancer care near the end of life. J Clin Oncol. 2004;22(2):315–21. https://doi.org/10.1200/JCO.2004.08.136.

    Article  PubMed  Google Scholar 

  7. 7.

    Wright AA, Zhang B, Ray A, Mack JW, Trice E, Balboni T, et al. Associations between end-of-life discussions, patient mental health, medical care near death, and caregiver bereavement adjustment. JAMA. 2008;300(14):1665–73. https://doi.org/10.1001/jama.300.14.1665.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Mack JW, Weeks JC, Wright AA, Block SD, Prigerson HG. End-of-life discussions, goal attainment, and distress at the end of life: predictors and outcomes of receipt of care consistent with preferences. J Clin Oncol. 2010;28(7):1203–8. https://doi.org/10.1200/JCO.2009.25.4672.

    Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Detering KM, Hancock AD, Reade MC, Silvester W. The impact of advance care planning on end of life care in elderly patients: randomised controlled trial. BMJ. 2010;340(mar23 1):c1345. https://doi.org/10.1136/bmj.c1345.

    Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Brinkman-Stoppelenburg A, Rietjens JAC, van der Heide A. The effects of advance care planning on end-of-life care: a systematic review. Palliat Med. 2014;28(8):1000–25. https://doi.org/10.1177/0269216314526272.

    Article  PubMed  Google Scholar 

  11. 11.

    Bernacki R, Paladino J, Neville BA, Hutchings M, Kavanagh J, Geerse OP, et al. Effect of the serious illness care program in outpatient oncology: a cluster randomized clinical trial. JAMA Intern Med. 2019;179(6):751–9. https://doi.org/10.1001/jamainternmed.2019.0077.

    Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Paladino J, Bernacki R, Neville BA, Kavanagh J, Miranda SP, Palmor M, et al. Evaluating an intervention to improve communication between oncology clinicians and patients with life-limiting cancer: a cluster randomized clinical trial of the serious illness care program. JAMA Oncol. 2019;5(6):801–9. https://doi.org/10.1001/jamaoncol.2019.0292.

    Article  PubMed  Google Scholar 

  13. 13.

    Bickel KE, McNiff K, Buss MK, Kamal A, Lupu D, Abernethy AP, et al. Defining high-quality palliative care in oncology practice: an American Society of Clinical Oncology/American Academy of Hospice and Palliative Medicine guidance statement. J Oncol Pract. 2016;12(9):e828–38. https://doi.org/10.1200/JOP.2016.010686.

    Article  PubMed  Google Scholar 

  14. 14.

    Bernacki RE, Block SD. American College of Physicians High Value Care Task Force. Communication about serious illness care goals: a review and synthesis of best practices. JAMA Intern Med. 2014;174(12):1994–2003. https://doi.org/10.1001/jamainternmed.2014.5271.

    Article  PubMed  Google Scholar 

  15. 15.

    Institute of Medicine. Dying in America: Improving Quality and Honoring Individual Preferences Near the End of Life. Washington, DC: The National Academies Press; 2015. https://doi.org/10.17226/18748.

  16. 16.

    Schubart JR, Levi BH, Bain MM, Farace E, Green MJ. Advance care planning among patients with advanced cancer. J Oncol Pract. 2019;15(1):e65–73. https://doi.org/10.1200/JOP.18.00044.

    Article  PubMed  Google Scholar 

  17. 17.

    Rao JK, Anderson LA, Lin F-C, Laux JP. Completion of advance directives among U.S. consumers. American Journal of Preventive Medicine. 2014;46(1):65–70. https://doi.org/10.1016/j.amepre.2013.09.008.

    Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Degenholtz HB, Arnold RA, Meisel A, Lave JR. Persistence of racial disparities in advance care plan documents among nursing home residents. Journal of the American Geriatrics Society. 2002;50(2):378–81. https://doi.org/10.1046/j.1532-5415.2002.50073.x.

    Article  PubMed  Google Scholar 

  19. 19.

    Kulkarni SP, Karliner LS, Auerbach AD, Pérez-Stable EJ. Physician use of advance care planning discussions in a diverse hospitalized population. J Immigrant Minority Health. 2011;13(3):620–4. https://doi.org/10.1007/s10903-010-9361-5.

    Article  Google Scholar 

  20. 20.

    Smith AK, McCarthy EP, Paulk E, et al. Racial and ethnic differences in advance care planning among patients with cancer: impact of terminal illness acknowledgment, religiousness, and treatment preferences. J Clin Oncol. 2008;26(25):4131–7. https://doi.org/10.1200/JCO.2007.14.8452.

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    LoPresti MA, Dement F, Gold HT. End-of-life care for people with cancer from ethnic minority groups: a systematic review. Am J Hosp Palliat Care. 2016;33(3):291–305. https://doi.org/10.1177/1049909114565658.

    Article  PubMed  Google Scholar 

  22. 22.

    Powell BJ, Waltz TJ, Chinman MJ, Damschroder LJ, Smith JL, Matthieu MM, et al. A refined compilation of implementation strategies: results from the Expert Recommendations for Implementing Change (ERIC) project. Implementation Science. 2015;10(1):21. https://doi.org/10.1186/s13012-015-0209-1.

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Serious Illness Care. Ariadne Labs. Accessed 22 June 2021. https://www.ariadnelabs.org/areas-of-work/serious-illness-care/

  24. 24.

    Takvorian SU, Ladage VP, Wileyto EP, Mace DS, Beidas RS, Shulman LN, et al. Association of behavioral nudges with high-value evidence-based prescribing in oncology. JAMA Oncol. 2020;6(7):1104–6. https://doi.org/10.1001/jamaoncol.2020.0746.

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Manz C, Parikh RB, Evans CN, et al. Effect of integrating machine learning mortality estimates with behavioral nudges to increase serious illness conversions among patients with cancer: a stepped-wedge cluster randomized trial. JCO. 2020;38(15_suppl):12002. https://doi.org/10.1200/JCO.2020.38.15_suppl.12002.

    Article  Google Scholar 

  26. 26.

    Chandar M, Brockstein B, Zunamon A, Silverman I, Dlouhy S, Ashlevitz K, et al. Perspectives of health-care providers toward advance care planning in patients with advanced cancer and congestive heart failure. Am J Hosp Palliat Care. 2017;34(5):423–9. https://doi.org/10.1177/1049909116636614.

    Article  PubMed  Google Scholar 

  27. 27.

    Christakis NA, Lamont EB. Extent and determinants of error in doctors’ prognoses in terminally ill patients: prospective cohort study. BMJ. 2000;320(7233):469–72. https://doi.org/10.1136/bmj.320.7233.469.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Sborov K, Giaretta S, Koong A, Aggarwal S, Aslakson R, Gensheimer MF, et al. Impact of accuracy of survival predictions on quality of end-of-life care among patients with metastatic cancer who receive radiation therapy. J Oncol Pract. 2019;15(3):e262–70. https://doi.org/10.1200/JOP.18.00516.

    Article  PubMed  Google Scholar 

  29. 29.

    LeBlanc TW, Temel JS, Helft PR. “How much time do I have?”: communicating prognosis in the era of exceptional responders. Am Soc Clin Oncol Educ Book. 2018;38(38):787–94. https://doi.org/10.1200/EDBK_201211.

    Article  PubMed  Google Scholar 

  30. 30.

    Lakin JR, Le E, Mourad M, Hollander H, Anderson WG. Incentivizing residents to document inpatient advance care planning. JAMA Intern Med. 2013;173(17):1652–4. https://doi.org/10.1001/jamainternmed.2013.8158.

    Article  PubMed  Google Scholar 

  31. 31.

    Gonzales MJ, Dobro J, Guilfoile K, Fisher K, Byock I. An employer health incentive plan for advance care planning and goal-aligned care. Population Health Management. 2018;21(4):285–90. https://doi.org/10.1089/pop.2017.0125.

    Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Barnato AE, Moore R, Moore CG, Kohatsu ND, Sudore RL. Financial incentives to increase advance care planning among Medicaid beneficiaries: lessons learned from two pragmatic randomized trials. J Pain Symptom Manage. 2017;54(1):85–95.e1. https://doi.org/10.1016/j.jpainsymman.2017.02.016.

    Article  PubMed  Google Scholar 

  33. 33.

    De Souza J, Gillett K, Froggatt K, Walshe C. Perspectives of elders and their adult children of Black and minority ethnic heritage on end-of-life conversations: a meta-ethnography. Palliat Med. 2020;34(2):195–208. https://doi.org/10.1177/0269216319887070.

    Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Morgan B, Tarbi E. Behavioral economics: applying defaults, social norms, and nudges to supercharge advance care planning interventions. J Pain Symptom Manage. 2019;58(4):e7–9. https://doi.org/10.1016/j.jpainsymman.2019.06.014.

    Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Meeker D, Linder JA, Fox CR, Friedberg MW, Persell SD, Goldstein NJ, et al. Effect of behavioral interventions on inappropriate antibiotic prescribing among primary care practices: a randomized clinical trial. JAMA. 2016;315(6):562–70. https://doi.org/10.1001/jama.2016.0275.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Patel MS, Day SC, Halpern SD, Hanson CW, Martinez JR, Honeywell S Jr, et al. Generic medication prescription rates after health system-wide redesign of default options within the electronic health record. JAMA Intern Med. 2016;176(6):847–8. https://doi.org/10.1001/jamainternmed.2016.1691.

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Patel MS, Volpp KG, Asch DA. Nudge units to improve the delivery of health care. N Engl J Med. 2018;378(3):214–6. https://doi.org/10.1056/NEJMp1712984.

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Yoong SL, Hall A, Stacey F, Grady A, Sutherland R, Wyse R, et al. Nudge strategies to improve healthcare providers’ implementation of evidence-based guidelines, policies and practices: a systematic review of trials included within Cochrane systematic reviews. Implementation Sci. 2020;15(1):50. https://doi.org/10.1186/s13012-020-01011-0.

    Article  Google Scholar 

  39. 39.

    Kim RH, Day SC, Small DS, Snider CK, Rareshide CAL, Patel MS. Variations in influenza vaccination by clinic appointment time and an active choice intervention in the electronic health record to increase influenza vaccination. JAMA Network Open. 2018;1(5):e181770. https://doi.org/10.1001/jamanetworkopen.2018.1770.

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Patel MS, Kurtzman GW, Kannan S, Small DS, Morris A, Honeywell S Jr, et al. Effect of an automated patient dashboard using active choice and peer comparison performance feedback to physicians on statin prescribing: the PRESCRIBE cluster randomized clinical trial. JAMA Netw Open. 2018;1(3):e180818. https://doi.org/10.1001/jamanetworkopen.2018.0818.

    Article  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Cohn A, Maréchal MA. Priming in economics. Social Science Research Network. 2016. https://doi.org/10.2139/ssrn.2775289.

  42. 42.

    Curtis JR, Downey L, Back AL, Nielsen EL, Paul S, Lahdya AZ, et al. Effect of a patient and clinician communication-priming intervention on patient-reported goals-of-care discussions between patients with serious illness and clinicians: a randomized clinical trial. JAMA Intern Med. 2018;178(7):930. https://doi.org/10.1001/jamainternmed.2018.2317.

    Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Kiefe CI, Allison JJ, Williams OD, Person SD, Weaver MT, Weissman NW. Improving quality improvement using achievable benchmarks for physician feedback: a randomized controlled trial. JAMA. 2001;285(22):2871–9. https://doi.org/10.1001/jama.285.22.2871.

    CAS  Article  PubMed  Google Scholar 

  44. 44.

    Linder JA, Meeker D, Fox CR, Friedberg MW, Persell SD, Goldstein NJ, et al. Effects of behavioral interventions on inappropriate antibiotic prescribing in primary care 12 months after stopping interventions. JAMA. 2017;318(14):1391–2. https://doi.org/10.1001/jama.2017.11152.

    Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Parikh RB, Manz C, Chivers C, Regli SH, Braun J, Draugelis ME, et al. Machine learning approaches to predict 6-month mortality among patients with cancer. JAMA Netw Open. 2019;2(10):e1915997. https://doi.org/10.1001/jamanetworkopen.2019.15997.

    Article  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Manz CR, Chen J, Liu M, Chivers C, Regli SH, Braun J, et al. Validation of a machine learning algorithm to predict 180-day mortality for outpatients with cancer. JAMA Oncol. 2020;6(11):1723–30. https://doi.org/10.1001/jamaoncol.2020.4331.

    Article  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Shelton RC, Chambers DA, Glasgow RE. An extension of RE-AIM to enhance sustainability: addressing dynamic context and promoting health equity over time. Front Public Health. 2020;0. doi:https://doi.org/10.3389/fpubh.2020.00134, 8

  48. 48.

    Maslach C, Jackson SE. The measurement of experienced burnout. Journal of Organizational Behavior. 1981;2(2):99–113. https://doi.org/10.1002/job.4030020205.

    Article  Google Scholar 

  49. 49.

    Shelton RC, Adsul P, Oh A. Recommendations for addressing structural racism in implementation science: a call to the field. Ethnicity & Disease. 2021;31(Suppl):357-364. doi:https://doi.org/10.18865/ed.31.S1.357

  50. 50.

    Woodward EN, Matthieu MM, Uchendu US, Rogal S, Kirchner JE. The health equity implementation framework: proposal and preliminary study of hepatitis C virus treatment. Implementation Science. 2019;14(1):26. https://doi.org/10.1186/s13012-019-0861-y.

    Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Lewis CC, Klasnja P, Powell BJ, Lyon AR, Tuzzio L, Jones S, et al. From classification to causality: advancing understanding of mechanisms of change in implementation science. Front Public Health. 2018;6. https://doi.org/10.3389/fpubh.2018.00136.

  52. 52.

    Creswell JW, Plano Clark VL. Designing and conducting mixed methods research. 3rd ed. Inc: Sage Publications; 2018.

    Google Scholar 

  53. 53.

    Glaser BG, Strauss AL. The discovery of grounded theory: strategies for qualitative research. Aldine Pub Co; 1967, .

    Google Scholar 

  54. 54.

    Creswell JW. Qualitative inquiry & research design: choosing among five approaches. 3rd ed. Inc: Sage Publications; 2013.

    Google Scholar 

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Acknowledgements

We are grateful for the support and insights provided by all investigators and research staff. We also wish to thank our technology solution partners, including Michael Draugelis and Jason Lubken, in partnership with co-author Corey Chivers, at Penn Medicine’s Predictive Healthcare as well as Catherine Reale, Michael Kopinsky, Neda Khan, Sadie Friday, Aaron Leitner, and Christianne Sevinc at Way to Health. Finally, we are grateful for the foundational research upon which this manuscript rests, such as contributions to the science of nudges by Dr. Mitesh Patel of Ascension Health and creation of the Serious Illness Conversation clinician guide and patient worksheet by Ariadne Labs at Brigham & Women’s Hospital and the Harvard T.H. Chan School of Public Health.

Funding support

Dr. Takvorian acknowledges funding support from grant P50 CA244690 and K12 CA076931 of the National Cancer Institute.

Funding

The authors are solely responsible for the design and conduct of this study, study analyses, and the drafting of this paper. The funder—The National Cancer Institute (NCI P50 CA244690)—played no role in the study design, collection, analysis, and interpretation of data; the writing of the report; and the decision to submit the article for publication. National Cancer Institute P50CA244690.

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Affiliations

Authors

Contributions

ST, JB, and RP led the design of the study and preparation of this paper. RB, RS, and JB obtained the funding for this study. AC and TS helped develop the study protocol and manage the study. PG oversees clinical informatics. CC oversees data science integration. DA and AB oversaw the rapid cycle approach. JB, LNS, and CS lead the implementation lab. AL and KZ support the implementation lab and overall study conduct. KR leads the mixed-methods work. KC and RS support assessment of health equity. EPW is the study biostatistician. SW developed the study database and helped with informatics. LNS, LMS, PK, and NO helped with intervention design. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Samuel U. Takvorian.

Ethics declarations

Ethics approval and consent to participate

This study is approved by the University of Pennsylvania IRB (protocol #844816) and is registered on clinicaltrials.gov under the title “Effect of Behavioral Nudges on Serious Illness Conversation Documentation (SPP2)” and registration number NCT04867850.

Consent for publication

Not applicable

Competing interests

Dr. Takvorian has provided consultation to Genentech and receives funding support from Pfizer unrelated to this research. Dr. Schnoll has provided consultation to Pfizer, GlaxoSmithKline, and Curaleaf and has received free medication and placebo from Pfizer for past studies. Dr. Asch is a partner at VAL Health. Dr. Beidas receives royalties from Oxford University Press. She has provided consultation to the Camden Coalition of Healthcare Providers. She currently consults to United Behavioral Health and serves on the Clinical and Scientific Advisory Board for Optum Behavioral Health. Dr. Bekelman reports grants from Pfizer, grants from UnitedHealth Group, grants from Embedded Healthcare, grants from Blue Cross Blue Shield of North Carolina, personal fees from UnitedHealthcare, personal fees from CMS, personal fees from NCCN, personal fees from Optum, personal fees from CVS Health, and personal fees from Astrazeneca (indirect via NCCN), outside the submitted work.

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Supplementary Information

Additional file 1.

CONSORT Checklist-SIC Protocol. Title: CONSORT 2010 checklist of information to include when reporting a randomized trial. Description: CONSORT checklist

Additional file 2.

Full Protocol-SIC Protocol. Title: Social and Behavioral Sciences Human Research Protocol. Description: IRB-approved study protocol

Additional file 3.

Funding Letter-SIC Protocol. Title: Notice of Award. Description: NCI funding letter

Additional file 4.

IRB Approval Letter-SIC Protocol. Title: IRB Amendment: Notice of Approval. Description: IRB approval letter

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Takvorian, S.U., Bekelman, J., Beidas, R.S. et al. Behavioral economic implementation strategies to improve serious illness communication between clinicians and high-risk patients with cancer: protocol for a cluster randomized pragmatic trial. Implementation Sci 16, 90 (2021). https://doi.org/10.1186/s13012-021-01156-6

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Keywords

  • Serious illness conversation
  • Advanced care planning
  • End-of-life cancer care
  • Pragmatic trials