Behavioral Nudges to Improve Palliative Care Utilization in Advanced Cancer
Phase: NA
Conditions: Cancer
Sponsor: Abramson Cancer Center at Penn Medicine

Behavioral Nudges to Improve Palliative Care Utilization in 
Advanced Cancer  
 
Study Protocol 
 
May 2022 
 
Version: 2023.01.20 
 
 
 
 
 

Outline 
1. Abstract 
2. Overall objectives 
3. Aims 
 
3.1 Primary outcome 
 
3.2 Secondary outcomes 
4. Background 
5. Study design 
 
5.1 Design 
 
5.2 Study duration 
 
5.3 Target population 
 
5.4 Accrual 
 
5.5 Key inclusion criteria 
 
5.6 Key exclusion criteria 
6. Subject recruitment 
7. Subject compensation 
8. Study procedures 
 
8.1 Consent 
 
8.2 Procedures 
9. Analysis plan 
10. Investigators 
11. Human research protection 
 
11.1 Data confidentiality 
 
11.2 Subject confidentiality 
 
11.3 Subject privacy 

 
11.4 Data disclosure 
 
11.5 Data safety and monitoring 
 
11.6 Risk/benefit  
 
 
11.6.1 Potential study risks 
 
 
11.6.2 Potential study benefits 
 
 
11.6.3 Risk/benefit assessment 
 
 

1. Abstract 
Patients with cancer often undergo costly therapy and acute care utilization that is discordant 
with their wishes, particularly at the end of life. Despite early palliative care consultations being 
an NCCN guideline-concordant practice in advanced cancer, palliative care referral rates for 
stage IV patients are low. In this project, we will evaluate a health system initiative that uses 
behavioral nudges to prompt palliative care referrals among outpatients with advanced cancer in 
terms of successful palliative care referrals and downstream quality of life outcomes. In 
partnership with the health system, this will be conducted as a 2-arm pragmatic cluster 
randomized trial.  
2. Overall objectives 
The primary objective of the study is to evaluate the impact of sending behavioral nudges to 
clinicians, as compared to usual practice, on completion of palliative care referrals among 
patients with advanced cancer. Our secondary objectives are to identify the impact of prompted 
palliative care referrals on quality of life, acute care utilization, and end-of-life utilization.  
3. Aims 
 
3.1 Primary outcome 
The primary outcome of the study is palliative care visits 
 
3.2 Secondary outcomes 
• 
Change in quality of life between 0 and 9 weeks (as measured by the Functional Assessment of 
Cancer Therapy – General (FACT-G) among patients who receive palliative care compared to 
matched controls) (Appendix A) 
• 
Quality of life at 9 weeks (as measured by the Functional Assessment of Cancer Therapy – 
General (FACT-G) among patients who receive palliative care compared to matched controls) 
 
3.3 Exploratory outcomes 
• 
Unplanned 30-day emergency room and hospitalization rates (among all patients) 
• 
Metrics of aggressive end-of-life care (systemic therapy within 14 days before death, no hospice 
enrollment prior to death; hospice enrollment <3 days before death) among decedents 
• 
Documented advance care planning or serious illness conversations in the outpatient medical 
record 
• Hospice referral and enrollment among all patients 
• Overall survival, defined as date from enrollment to death 
4. Background 
Patients with advanced cancer have poor quality of life and life expectancy.1 Palliative care is a 
medical specialty focusing on providing relief from the symptoms and stress of serious illnesses 
such as cancer.2 While palliative care referrals often occur in the inpatient setting for acutely ill 

individuals3, early outpatient specialty palliative care concurrent with cancer-directed treatment 
improves quality of life and survival, is an evidence-based practice, and is endorsed by national 
guidelines.4–8 Palliative care can be provided in the ambulatory, telemedicine, or home-based 
settings.3,6,9,10 However, nearly half of patients with advanced cancer do not receive palliative 
care prior to dying.11,12 Lack of standardized referral criteria and screening methods for palliative 
care contributes to underutilization. There is a high need for strategies to increase use of 
palliative care in oncology.13,14 
Clinicians underutilize palliative care, initiating referrals a median of 2 months before death.15 
This status quo bias, which predisposes clinicians to continue current practice even if not the 
optimal option, may lead to delayed or missed palliative care referrals.16 Additionally, optimism 
bias, the cognitive bias that causes clinicians to believe that their own patients are at lesser risk 
of negative outcomes, may cause clinicians to underestimate a patient’s mortality risk or 
symptom burden, thus delaying palliative care referral.17 Finally, overconfidence bias, the 
propensity to overestimate one’s desired behaviors when it is not objectively reasonable, may 
lead clinicians to incorrectly believe they are initiating more palliative care referrals than their 
peers.18  
Overcoming suboptimal clinician decision-making biases are key to increasing palliative care 
referrals. Principles from behavioral economics can inform “nudges” that change how clinicians 
receive information and make choices such as palliative care referral.19 Default, opt-out nudges 
that make the optimal choice the path of least resistance can mitigate clinicians’ status quo bias.19 
Reframing clinicians’ prognoses by providing data-driven life expectancy assessments may 
combat optimism bias.17  
Early palliative care intervention can improve quality of life for patients with advanced cancer. 
Outpatient palliative care is available at 98% of NCI-designated cancer centers and 63% of non-
NCI centers.20 Early outpatient palliative care concurrent with cancer-directed treatment 
improves quality of life, reduces symptom burden, and decreases rates of aggressive end of life 
care.4,5 Nevertheless, only a minority of patients who qualify for palliative care receive it.   
 
--References-- 
1.  
Prigerson HG, Bao Y, Shah MA, et al. Chemotherapy Use, Performance Status, and Quality of 
Life at the End of Life. JAMA Oncol. 2015;1(6):778-784. doi:10.1001/jamaoncol.2015.2378 
2.  
Palliative Care Definition | What is Palliative Care. Accessed January 17, 2021. 
https://www.capc.org/about/palliative-care/ 
3.  
Parikh RB, Kirch RA, Smith TJ, Temel JS. Early specialty palliative care--translating data in 
oncology into practice. N Engl J Med. 2013;369(24):2347-2351. doi:10.1056/NEJMsb1305469 
4.  
Temel JS, Greer JA, Muzikansky A, et al. Early palliative care for patients with metastatic non-
small-cell lung cancer. The New England Journal of Medicine. 2010;363(8):733-742. 
doi:10.1056/NEJMoa1000678 

5.  
Zimmermann C, Swami N, Krzyzanowska M, et al. Early palliative care for patients with 
advanced cancer: a cluster-randomised controlled trial. Lancet (London, England). 2014;383(9930):1721-
1730. doi:10.1016/S0140-6736(13)62416-2 
6.  
Brumley R, Enguidanos S, Jamison P, et al. Increased satisfaction with care and lower costs: 
results of a randomized trial of in-home palliative care. J Am Geriatr Soc. 2007;55(7):993-1000. 
doi:10.1111/j.1532-5415.2007.01234.x 
7.  
Ferrell BR, Temel JS, Temin S, et al. Integration of Palliative Care Into Standard Oncology Care: 
American Society of Clinical Oncology Clinical Practice Guideline Update. JCO. 2016;35(1):96-112. 
doi:10.1200/JCO.2016.70.1474 
8.  
Levy M, Smith T, Alvarez-Perez A, et al. Palliative Care Version 1.2016. J Natl Compr Canc 
Netw. 2016;14(1):82-113. doi:10.6004/jnccn.2016.0009 
9.  
Seow H, Tanuseputro P, Barbera L, et al. Development and Validation of a Prognostic Survival 
Model With Patient-Reported Outcomes for Patients With Cancer. JAMA Netw Open. 
2020;3(4):e201768-e201768. doi:10.1001/jamanetworkopen.2020.1768 
10.  
Wang SE, Liu I-LA, Lee JS, et al. End-of-Life Care in Patients Exposed to Home-Based 
Palliative Care vs Hospice Only. J Am Geriatr Soc. 2019;67(6):1226-1233. doi:10.1111/jgs.15844 
11.  
Narang AK, Wright AA, Nicholas LH. Trends in advance care planning in cancer patients: 
Results from a national, longitudinal survey. JAMA Oncol. 2015;1(5):601-608. 
doi:10.1001/jamaoncol.2015.1976 
12.  
Dying in America: Improving Quality and Honoring Individual Preferences Near the End of Life. 
Institute of Medicine. Accessed April 23, 2019. http://nationalacademies.org/hmd/Reports/2014/Dying-
In-America-Improving-Quality-and-Honoring-Individual-Preferences-Near-the-End-of-Life.aspx 
13.  
Hui D, Mori M, Watanabe SM, et al. Referral criteria for outpatient specialty palliative cancer 
care: an international consensus. Lancet Oncol. 2016;17(12):e552-e559. doi:10.1016/S1470-
2045(16)30577-0 
14.  
Caraceni A, Lo Dico S, Zecca E, et al. Outpatient palliative care and thoracic medical oncology: 
Referral criteria and clinical care pathways. Lung Cancer. 2020;139:13-17. 
doi:10.1016/j.lungcan.2019.10.003 
15.  
Cheng W-W, Willey J, Palmer JL, Zhang T, Bruera E. Interval between palliative care referral 
and death among patients treated at a comprehensive cancer center. J Palliat Med. 2005;8(5):1025-1032. 
doi:10.1089/jpm.2005.8.1025 
16.  
Kahneman D, Tversky A. Prospect Theory: An Analysis of Decision under Risk. Econometrica. 
1979;47(2):263-291. doi:10.2307/1914185 
17.  
Ingersoll LT, Alexander SC, Ladwig S, Anderson W, Norton SA, Gramling R. The contagion of 
optimism: The relationship between patient optimism and palliative care clinician overestimation of 
survival among hospitalized patients with advanced cancer. Psycho-Oncology. 2019;28(6):1286-1292. 
doi:10.1002/pon.5080 
18.  
Croskerry P, Norman G. Overconfidence in Clinical Decision Making. The American Journal of 
Medicine. 2008;121(5):S24-S29. doi:10.1016/j.amjmed.2008.02.001 
19.  
Halpern SD, Ubel PA, Asch DA. Harnessing the power of default options to improve health care. 
N Engl J Med. 2007;357(13):1340-1344. doi:10.1056/NEJMsb071595 
20.  
Hui D, De La Rosa A, Bruera E. State of Integration of Palliative Care at National Cancer 
Institute – Designated and Nondesignated Cancer Centers. JAMA Oncol. 2020;6(8):1292-1295. 
doi:10.1001/jamaoncol.2020.1471 
 
 
5. Study design 
 
5.1 Design 

This is a 2-arm pragmatic randomized trial among approximately 250 outpatients with advanced 
cancer to assess response to behavioral nudges to refer to palliative care. The setting will be 
outpatient oncology at the Ann B. Barshinger Cancer Institute at Lancaster General Health. 
Eligible clinicians will be randomized in clusters by overlapping clinical team (pod) to receive 
default EPIC-based prompts for palliative care referral among advanced cancer patients (Arm 1) 
vs no intervention/usual practice (Arm 2). These nudges have been shown to positively influence 
clinician behavior and are often used to drive value-based oncology care; however, they have never 
been studied in the context of palliative care referral. The primary outcome is completion of a 
palliative care referral within 12 weeks of enrollment.  
 
Following the intervention, brief REDCap questionnaires will be sent to all clinicians who 
participated in the trial to explore perceptions of the study intervention.  This survey should take 
no longer than five minutes for clinicians to complete and will assess their feedback on the overall 
intervention in addition to specific components including automated identification of patients, 
receiving text and email notifications, and identifying the appropriate patients.  The survey will 
also collect basic demographic information (age, gender, practice site, and comfort with SICs) 
(Appendix 3).  
 
 
5.2 Study duration 
The study is expected to begin in April 2022 and take 9 months (3 months for enrollment + 6 
months total follow up) to complete. Our analysis will take place from December 2022 to March 
2023.  
 
5.3 Target population 
Outpatients with advanced cancer at the Ann B. Barshinger Cancer Institute at Lancaster General 
Health who are eligible for palliative care (see below).  
 
5.4 Accrual 
Patients will accrue to the trial according to their clinician pod in advance of a repeat patient 
visit. Any eligible patient cared for by an oncology outpatient clinician will be considered 
eligible.  
 
5.5 Key inclusion criteria 
Patients who meet the following criteria will be eligible for the study.  
• Have a functional telephone number 
• Receive ongoing care from hematology/oncology services within the Ann B. Barshinger 
Cancer Institute (ABBCI) at Lancaster General Health. 
5.6 Key exclusion criteria 
 
• Patients appearing for a new patient visit 

• Patients who have previously received palliative care 
• Patients who are enrolled in an ongoing clinical trial of a therapeutic agent 
• Patients who receive primary oncologic care within another institution 
6. Subject recruitment 
Eligible patients will be identified by a Clinical Research Coordinator in advance of a repeat 
patient visit with an eligible clinician. 
7. Subject compensation 
No compensation will be offered to clinicians in the intervention.   
Patients who are administered quality of life surveys and complete a FACT-G survey at 9 weeks 
or earlier will be offered $50 at the 9-week timepoint. 
8. Study procedures 
 
8.1 Consent 
A waiver of informed consent is requested for the delivery of the palliative care nudge.  This is a 
health system initiative that will be implemented and the study is to evaluate that initiative.  
Therefore, physicians and their patients will not be consented as this is the standard of practice 
per the health system initiative and national cancer guidelines.  Without a waiver of the consent, 
the initiative would still be implemented by the health system, but the study would be infeasible.  
There are several additional reasons why we feel a waiver of consent should be granted.  First, it 
is not feasible to consent every physician and as mentioned this initiative would occur with or 
without the study of it.  Second, if members of the control group were consented, this alone 
could change their behavior.  This could potentially disrupt the design of the study and making 
interpretation of the findings challenging.  Third, physicians are not being forced to refer to 
palliative care for their patients.  Instead, they are receiving behavioral nudges, including default 
EPIC-based nudges. This is no different than standard of care in which a physician would review 
the same information and decide to have a palliative care consultation. The initiative is simply a 
reminder for the physician and makes their standard of care process easier to conduct. Finally, as 
part of a previous quality improvement initiative, we interviewed nearly 40 clinicians after a trial 
testing a clinician nudge to perform serious illness conversations. We found no evidence of harm 
and found that serious illness conversations were considered standard of care for patients with 
cancer.  
Patients who are selected for quality of life surveys will be consented over the phone for 
administration of FACT-G surveys at baseline (prior to hospital discharge) and at 3, 6, 9, and 18 
weeks after enrollment. A copy of the informed consent form is available (Appendix B). Those 
who elect not to participate will be asked to grant permission (or not) for the study team to 

collect limited data from them. An abbreviated Study Decline Consent form will be utilized for 
this purpose (Appendix C). 
 
8.2 Procedures 
Baseline data on oncologists and their patients at Lancaster General Health will be obtained from 
Penn Data Store and Clarity (Epic’s data reporting database). Physician data includes 
demographic information (e.g. sex, type of medical degree, etc.) and may be also obtained from 
publicly available databases or websites online. Baseline data collected on patients will include 
age, gender, zip code of residence, type of cancer, Elixhauser comorbidity index, ECOG 
performance status, whether the patient is on active systemic therapy, and understanding of 
palliative care. This will be collected via the electronic health record and an in-person enrollment 
questionnaire (Appendix D) upon enrollment into the trial.  Those who elect not to participate, 
but consent to limited data collection, will be asked to fill out a similar questionnaire including 
reason(s) for not wanting to participate in a de-identified manner (Appendix E). 
Prior to the trial, staff will randomize each clinician pod in a 1:1 fashion to either intervention or 
usual care. The Principal Investigator (RBP) will be blinded to arm assignment. In both 
intervention and control arms, research coordinator staff will identify patients with advanced 
cancer who are actively cared for within the Ann B. Barshinger Cancer Institute system. They 
will then regularly review the electronic health record for eligible clinicians to identify patients 
with an upcoming repeat patient visit who are eligible for palliative care. Patients who are 
eligible will have a solid malignancy and at least one of the following five conditions: (1) 
Documented advanced (stage IV) solid malignancy; (2) Known central nervous system 
metastasis, (3) Eastern Cooperative Oncology Group performance status > 2, Karnofsky 
Performance Status < 50, (4) uncontrolled symptoms such as pain, escalating opioids, 
nausea/vomiting, distress, dyspnea, delirium, or (5) hospitalization relevant to cancer or 
treatment. 
 
For patients whose clinician pods are randomized to the intervention arm, the care team pod will 
receive an EPIC in-basket message indicating that the patient is eligible for Palliative Care and 
that a default referral order has been pended for that patient. Clinicians will be given an opportunity 
to respond if they do not want their patient to be referred to palliative care. For clinicians who do 
not respond, after the allotted time, the Clinical Research Coordinator will reach out to the patient 
to introduce palliative care and ask if ABBCI staff can schedule an appointment. For non-English 
speaking patients, an interpreter will assist research staff. For clinicians who respond no, the 
pended order will be removed and no patient contact by the research team will occur.  
 
For eligible patients whose clinician pods have been randomized to the control arm, no intervention 
will be provided. Clinicians will refer patients to palliative care based on their usual practice.  
 
Among patients in the intervention arm for whom palliative care referral is placed, who accept for 
a scheduler to call them to make an appointment, a trained research coordinator will approach 40 

patients as a convenience sample to administer a baseline FACT-G survey (Appendix A). 40 
randomly selected patients, matched on categories including age category and type of cancer, will 
be selected from the pods not receiving the intervention. For patients that choose to enroll in the 
survey component, a baseline FACT-G survey will be administered prior to discharge in-person 
via paper or electronically. Surveys will be repeated at 3, 6, 9, and 18-week intervals by either 
telephone or in-person at an in-person visit. For patients that complete the 9-week survey or any 
survey prior, a $50 gift card will be given at the 9-week time point. Patients who miss their visits 
with palliative care will be contacted by the coordinator to identify why (Appendix F) and 
reschedule the appointment. 
 
9. Analysis plan 
We will use REDcap, a HIPAA-compliant and secure database tool, to track all components of the 
study. Descriptive statistics will be used to estimate the frequencies, means, and standard 
deviations of the study variables. Differences between study groups in baseline characteristics and 
clinical outcomes will be assessed with the use of two-sided Fisher's exact tests and chi-square 
tests for categorical variables and independent-samples Student's t-tests for continuous variables. 
The unit of analysis will be the patient.  
 
The primary study endpoint is a binary patient-level variable indicating completion of a palliative 
care visit within 24 weeks of the clinician behavioral nudge. This analysis will be conducted as a 
time-to-event analysis among all eligible advanced cancer patients. Cox proportional hazard 
regression analyses, adjusted for baseline covariates, will be used to examine the effect of nudges 
on completion of palliative care consultation for advanced cancer patients. Competing risk caused 
by patients who die within 24 weeks will be considered as censored in the analysis. Covariates 
will include patient and cancer-related variables.  Patients will be followed for 24 weeks or until 
death. Using a longitudinal multilevel mixed-effects model with clustering by discharging 
clinician (intraclass correlation coefficient 0.5 based on prior randomized trial); a two-sided type 
I error rate of 0.05; and a baseline palliative care visit rate of 45% (standard deviation 10%), we 
expect that a sample size of 250 patients will give us 80% power to detect a meaningful 34 
percentage point response in palliative care visits, considering the presence of a competing risk of 
6-month mortality at 20%.  
 
One secondary outcome will be (1) change in quality of life between 0 and 9 weeks (as measured 
by the Functional Assessment of Cancer Therapy – General (FACT-G) among patients who 
receive palliative care compared to matched controls. Multivariate linear regression analyses, 
adjusted for baseline scores, will be used to examine the effect of palliative care on completion of 
palliative care consultation for advance cancer patients. For intention-to-treat analyses, we will use 
the conservative method of carrying the most recent quality-of-life values forward to account for 
all missing data, including data that are missing owing to death. 
 
Other secondary outcomes will include (1) change in quality of life between 0 and 18 weeks (as 
measured by the Functional Assessment of Cancer Therapy – General (FACT-G) among patients 
who receive palliative care compared to matched controls; (2) unplanned 30-day emergency room 
and hospitalization rates (among all patients); (3) metrics of aggressive end-of-life care among 

decedents (chemotherapy within 14 days before death, no hospice enrollment prior to death; 
hospice enrollment <3 days before death); (4) documented advance care planning or serious illness 
conversations in the outpatient medical record. 
 
For the exploratory outcome of overall survival, survival time will be calculated from the date of 
enrollment to the date of death with the use of the Kaplan–Meier method. Data from patients who 
are alive at the last follow-up will be censored on that date. A Cox proportional-hazards model 
will be used to assess the effect of triggered palliative care on survival, with adjustment for baseline 
covariates.   
 
  
10. Investigators 
Ravi B. Parikh, MD, MPP (Principal Investigator) is Assistant Professor of Medicine and Health 
Policy and Innovation Faculty at the Penn Center for Cancer Care Innovation (PC3I). Justin E. 
Bekelman, MD (Mentor, Co-Investigator) is Professor of Radiation Oncology, Medicine and 
Health Policy and the Director of PC3I. Pallavi Kumar, MD, MPH (Co-Investigator), is Assistant 
Professor of Clinical Medicine and Director of Oncology Palliative Care at the Abramson Cancer 
Center. Niharika Ganta, MD (Co-Investigator) is Assistant Professor of Clinical Medicine 
and Interim Chief of Palliative Care at the University of Pennsylvania. Jinbo Chen, PhD (Co-
Investigator) is Professor of Biostatistics and Director of the Statistical Center for Translational 
Research in Medicine. Ramy Sedhom (Co-Investigator) is Assistant Professor of Clinical 
Medicine and Innovation Faculty at the Penn Center for Cancer Innovation. All are faculty at the 
Perelman School of Medicine at the University of Pennsylvania.  
Bethann Scarborough, MD (Co-Investigator) is the Palliative Care Program Lead Physician at 
the Ann B. Barshinger Cancer Institute at Penn Medicine Lancaster General Health. Shanthi 
Sivendran, MD (Co-Investigator) is the Division Chief Hematology/Oncology at Penn Medicine 
Lancaster General Health. 
11. Human research protection 
 
11.1 Data confidentiality 
Computer-based files will only be made available to personnel involved in the study through the 
use of access privileges and passwords. Wherever feasible, identifiers will be removed from 
study-related information. Precautions are already in place to ensure the data are secure by using 
passwords and HIPAA-compliant encryption. 
 
11.2 Subject confidentiality 
Data on physicians and patients will be obtained from Epic, Penn Data Store and chart review. 
Any information that is obtained will be used only for research purposes and to inform the 
behavioral nudges described above.  Information on individual patients will only be disclosed 

within the study team. All study staff will be reminded of the confidential nature of the data 
collected and contained in these databases. 
Data regarding provider performance of palliative care referral are already shared among the 
health system and some providers and will continue to be shared as part of the trial. General 
panel-level statistics regarding acute care utilization at the end of life provider’s deceased patient 
panel will be shared to outpatient providers as part of the intervention but is planned to occur 
regardless of trial approval as part of quality improvement efforts. 
Data will be stored, managed, and analyzed on a secure, encrypted server behind the University 
of Pennsylvania Health System (UPHS) firewall. The primary investigator (Dr. Parikh) and 
statistical analyst will be blinded to the randomization schema and which groups are receiving 
the intervention. All study personnel that will use this data are listed on the IRB application and 
have completed training in HIPAA standards and the CITI human subjects research. Data access 
will be password protected. Whenever possible, data will be de-identified for analysis. 
 
11.3 Subject privacy 
All efforts will be made by study staff to ensure subject privacy.  Data will be evaluated in a de-
identified manner whenever possible. We will require time and date of appointment and zip code 
data of trial participants to define our exposure period and link to area-level socioeconomic data 
from the American Community Survey.  
 
11.4 Data disclosure 
Information on physicians and patients will not be disclosed to anyone outside of the study team. 
 
11.5 Data safety and monitoring 
The investigators will provide oversight for the study evaluation of this health system initiative.  
Providers will use their clinical judgment to determine the appropriateness of referring to 
palliative care with patients, in accordance with standard of care.  
11.6 Risk/benefit  
 
 
11.6.1 Potential study risks 
The potential risks associated with this study are minimal.  Breach of data is a potential risk that 
will be mitigated by using HIPAA compliant and secure data platforms for the nudge 
interventions (name of list platform and platform used to share info w/ MAs) and evaluation 
(PMACS server). As noted above, substantial data demonstrates that palliative care referrals 
improve patient goal-concordant care without any identified harms (despite concerns that 
palliative care may increase psychosocial distress, the opposite has been found), so the negative 
impact on patients is minimal. The FACT-G survey has been used in many trials measuring 

quality of life for patients with advanced cancer and no detrimental effect on patient well-being 
has ever been found.  
 
 
11.6.2 Potential study benefits 
As described in the literature, patients may have improved quality of life and better goal-
concordant care when exposed to palliative care, especially earlier in their disease course. An 
intervention that prompts providers to refer to palliative care for patients with advanced cancer 
may increase the likelihood that patients get seen by palliative care. However, it is possible that 
patients will receive no benefit from this study. 
 
 
11.6.3 Risk/benefit assessment 
The risk/benefit ratio is highly favorable given the potential benefit from eligible patients having 
a palliative care consultation benefitting from better goal-concordant care and that efforts have 
been put into place to minimize the risk of breach of data. 
 
APPENDICES 
1. Sample Provider Communication 
Sample timeline of provider nudge communication 
7 days prior to identified patient’s repeat patient visit (RPV): notification of referral sent to care 
team 
3 days prior to RPV: opt-out period ends 
Sample provider email content 
Dear Dr. XXX: 
ABBCI has identified that your patient [INSERT NAME] may benefit from a palliative care 
consultation. If you do not choose to opt out, we will reach out to the patient on your behalf to 
introduce palliative care and place a referral if the patient accepts.  
Please prepare your patient for this palliative care appointment at your next visit. 
If you wish to opt your patient out, please reply to this message with .optout  
Please note that not opting out within 4 days indicates a default order. 
Thank you,  
Bethann Scarborough, MD 

Shanthi Sivendran, MD 
 
2. Sample Patient Communication 
Sample timeline of patient communication 
3 days prior to RPV: Study team contacts patient with introduction to palliative care 
Sample patient email content 
My name is XX, and I’m calling from ABBCI. Dr. [NAME] wants to include the palliative care 
team as a partner in your cancer care and schedule an appointment for you. You benefit when 
palliative care is brought into your cancer care team because they provide an extra layer of 
support to you and your caregivers throughout treatment.  
What questions do you have for me? 
Our scheduler will call you to coordinate an appointment if that is okay with you. 
 
3. Sample Physician REDCap Questionnaire  
Thank you for participating in this questionnaire, which should take no more than 5 minutes. 
You have been asked to participate in this study because you were a part of an initiative starting 
May 2021. As you might recall, this intervention consisted of nudges for Palliative Care referral. 
 
Please rate your agreement with the statements using the following:  
(1) Strongly disagree 
(2) Disagree 
(3) Neutral 
(4) Agree 
(5) Strongly agree 
 
 
1 
2 
3 
4 
5 
Sentiment Overall 
I was satisfied with the overall experience of being involved 
in the study. 
 
 
 
 
 
Participating in the study helped me to increase Palliative 
Care referrals for patients. 
 
 
 
 
 
Identification of Patients 
It was helpful to receive names of patients who may be 
appropriate for Palliative Care referrals. 
 
 
 
 
 
The nudges accurately identified patients who were 
appropriate for Palliative Care referrals. 
 
 
 
 
 

The nudges motivated me to refer more patients to Palliative 
Care. 
 
 
 
 
 
Nudges 
I would have been more satisfied with different system of 
delivery than EPIC Inbox messages. 
 
 
 
 
 
The EPIC Inbox messages interrupted my clinical workflow 
in a negative way 
 
 
 
 
 
I opted-out of palliative care visits more often than not 
 
 
 
 
 
Experience 
The intervention led to more timely palliative care visits for 
my patients 
 
 
 
 
 
 
Please describe how the study could be improved. 
[free text response] 
 
Please tell us if you have any additional thoughts about your experience in this study. 
[free text response] 
 
Demographics 
What is your age? 
 
What is your gender? 
Female 
Male 
Other 
 
 
Please select your 
comfort level with 
having a Serious 
Illness Conversation 
Very  
uncomfortable 
Uncomfortable Neutral Comfortable Very  
comfortable 
What do you consider 
to be optimal timing 
for Palliative Care? 
At time of 
diagnosis 
Within 3 
months 
At 
times 
of PC 
need 
Following 
progression 
of cancer 
therapy 
When out 
of treatment 
options