Effectiveness of the Supportive and Palliative Care Review Kit (SPARK) for Cancer Patients in the Acute Hospital
Phase: NA
Conditions: Health Services Research
Sponsor: National Cancer Centre, Singapore

Title of study:  
Effectiveness of the Supportive and Palliative Care Review Kit (SPARK) for Cancer 
Patients in the Acute Hospital 
 
 
NCT ID: not assigned 
Date of document: 31 October 2017 
 

Study protocol 
 
Abstract 
 
 
Introduction  
There is a rising need for palliative care services in Singapore due to a rapidly ageing 
population and an increasing incidence of cancer. Current existing resources are 
inadequate – novel models of care are needed to expand access to palliative care without 
requiring significantly more specialist palliative care manpower.  
 
Oncologist-driven referrals to a palliative care consultation service is the norm worldwide, 
including Singapore. This results in variable access to palliative care due to differences in 
referral practices. Palliative care involvement is also often delayed. In this study, we 
propose to test Supportive and Palliative care Review Kit (SPARK) – a novel integrated 
model of care in which the palliative care team co-rounds with the medical oncology 
team.  
 
Specific Aims and Hypothesis  
This study aims to evaluate the impact of SPARK compared to usual care. We 
hypothesize that SPARK will result in more advanced cancer patients having access to 
palliative care, and at the same time operate at lower net cost. We also hypothesize that 
the improved efficiency of SPARK will result in shorter hospital length of stay for stage 4 
cancer patients.  
 
Methods  
A cluster randomized trial with step wedged design will be used to compare SPARK to 
usual care. Data will be collected on health services utilization and access to palliative 
care services. Net costs will also be compared between SPARK and usual care. Semi-
structured interviews with patients and healthcare professionals will be used to explore 
differences in experiences of healthcare provision between both models of care.  
 
Importance 
Singapore has a rising prevalence of cancer patients who require palliative care input, but 
only a minority are able to access it at present. If the SPARK model of care proves to be a 
scalable and cost-effective way of expanding access to palliative care, more cancer 
patients can benefit from palliative care. 
 
 
 
 
 
 

Specific aims and hypotheses 
The overall aim of this proposed study is to evaluate the impact of the Supportive and 
Palliative care Review Kit (SPARK) model of care compared to usual care on health services 
utilization, access to palliative care, net costs and the experiences of both patients and 
healthcare providers. SPARK is an integrated palliative care and medical oncology co-
rounding model of care for cancer patients in the acute hospital setting. We plan to develop 
and comprehensively assess this model using a prospective cluster randomized controlled 
trial and semi-structured interviews.  
The study specific aims and hypotheses are:  
Specific aim 1: To determine the effectiveness of SPARK on hospital length of stay (primary 
outcome) and other health service utilization metrics 
 
Hypothesis: The SPARK model of care will reduce hospital length of stay compared to usual 
care, due to earlier involvement of palliative care in the course of hospital admission and 
consequently swifter relief of problems that prompted the hospitalization. 
 
Other metrics of health service utilization will also be explored, including hospital-free days 
and visits to the Emergency Department in the last 6 months of life, and number of days to 
hospital readmission within 30 days. 
 
Specific aim 2: To assess the relationship between the SPARK model of care and access to 
palliative care services 
 
Hypothesis: The SPARK model will be associated with a greater proportion of cancer 
patients who receive any form of palliative care input during their hospital admission.  
 
 
 
 
 
 

Background and significance 
 
Palliative care, according to the World Health Organization, is an approach that improves the 
quality of life of advanced cancer patients and their families, through early identification, 
assessment and treatment of pain and other problems, physical, psychosocial and spiritual. (1) 
Concurrent palliative care and oncology care for advanced cancer patients results in improved 
quality of life, better symptom control and perhaps even longer survival. (2-7) International 
organizations recommend that palliative care should be integrated with oncology – the 
question that remains is how this integration should occur to optimize health outcomes. (7-9)  
 
According to published literature, only 5-11% of stage 4 cancer inpatients in the acute 
hospital setting receive palliative care. (10, 11) Predominant models of care rely on direct 
delivery of care by specialist palliative care healthcare professionals, and there is insufficient 
manpower to extend this labor-intensive service to more cancer patients. (12) Even in 
resource-rich United States, the estimated gap between current supply and hypothetical 
demand is 6,000-18,000 individual physicians. (13, 14) 
  
In Singapore, a rapidly ageing population and increasing incidence of cancer means a rising 
need for palliative care services that cannot be fully met by current models of care. (15-18) In 
the acute hospital setting, an unscheduled admission for a stage 4 cancer patient indicates that 
palliative care may be beneficial. (19) Our previous study showed that 84% of cancer 
inpatients had stage 4 cancer and that unscheduled admissions accounted for 82% of total 
admissions, yet only 21% received a palliative care review. (20) In recent years, anecdotal 
evidence suggests that home hospice services have been forced to restrict referrals and even 
close services temporarily to new referrals due to insufficient manpower. To extend palliative 
care services to more cancer patients, it will be inadequate to simply increase manpower 
within current service models – the need for large numbers of extra manpower cannot be met. 
 
Singapore urgently needs a model of care that can expand access to palliative care for more 
cancer patients without requiring a lot more palliative care manpower. Therefore, the focus of 
our research is not whether palliative care improves patient outcomes – that has already been 
affirmed in recent research. The overall research question addressed by this proposed study is 
whether the SPARK model of care is a better way of delivering integrated palliative care to 
cancer patients in the acute hospital setting. 
 
We propose a cluster randomized controlled trial to test the SPARK model of care for cancer 
inpatients in the acute hospital setting. This is novel in our institution because palliative care 
is currently delivered only as a separate consultation service – the most common model 
worldwide. (21) In the SPARK model, palliative care doctors and nurses are integrated with 
the oncology team, supporting them in their delivery of basic palliative care, seamlessly 
stepping in and out to deliver specialist palliative care directly to patients when needed. (22-
25) With this approach, it is anticipated that there will be more efficient collaborative 
working between the oncology and palliative teams. 
 
In the short term, our proposed study responds to an urgent need to expand access to 
palliative care for advanced cancer patients, in a model that does not require a large increase 
in specialist palliative care manpower.  In the long term, scalable and effective models of care 
need to be developed for the exponentially rising need for palliative care in Singapore and 
across the world.  
 

Methods 
 
Study setting 
The study will take place in Singapore General Hospital, a 1597-bedded acute hospital with 
more than 78,000 admissions per year. About 5,000 hospital admissions per year are cancer 
patients under the care of the Division of Medical Oncology (DMO), of which 80% are 
advanced cancer patients. This service is the largest public provider of cancer care in 
Singapore.  
 
Current model of service 
The current model of service is a consultation service with oncologist-driven referrals. A 
cancer patient admitted to hospital may have pain or other physical symptom problems such 
as breathlessness, or psychosocial and spiritual issues. The oncology team usually manages 
them first-line, and would refer to the palliative care team for further management only if the 
problems were unresolved after oncology management. Upon a referral from the oncologist, 
the palliative care team will review the patient and make suggestions for patient management, 
but the oncology team still takes primary responsibility for the patient’s care. In the current 
model, about 20% of cancer inpatients are referred to the palliative care team.  
 
The SPARK model of care 
In the SPARK model of care, a specialist palliative care physician and nurse join the DMO 
team as integrated members. The patient’s issues are managed collaboratively by the 
palliative care and oncology healthcare professionals. The form of palliative care provided 
will depend on the needs of individual patients and may include expert advice given to the 
oncology team, screening assessments for unidentified problems, or direct provision of 
palliative care for complex issues. It is anticipated that more patients admitted under the care 
of the integrated palliative oncology team will receive some varying degree of palliative care 
input as needed, whether directly or indirectly through support given to the oncology nurses 
and doctors.  
 
Study design 
Cluster-randomized trial with stepped-wedge design, unblinded due to practical challenges of 
blinding.  There will be 4 teams (clusters) within the DMO inpatient service, each comprising 
a team of senior consultant physicians, middle level and junior doctors, and oncology nurses. 
This follows current team structures. During the intervention period, a palliative care 
physician and nurse will join the team as integrated members.  
 
The study will be conducted over 20 months from January 2018 to August 2019, with each 
cluster crossing over from ‘usual care’ to ‘spark intervention’ at 4-month intervals. Each 
cluster will be randomly assigned a specific timing of crossing over.  
 
Table 1 
 
Jan-Apr 2018 
May-Aug 
2018 
Sep-Dec 2018 Jan-Apr 2019 
May-Aug 
2019 
Cluster 1 
control 
intervention 
intervention 
intervention 
intervention 
Cluster 2 
control 
control 
Intervention 
intervention 
intervention 
Cluster 3 
control 
control 
control 
Intervention 
intervention 
Cluster 4 
control 
control 
control 
control 
intervention 
 
Study patients 

All hospital admissions with stage 4 cancer under the care of the oncology team will be 
included.  
 
Data collection 
A waiver of individual patient consent will be sought for the collection of health services 
utilization data from the electronic health records, as this will present no more than minimal 
risk of harm to participants. Electronic health data will be kept confidential and stored in a 
secure location. All data will be anonymized prior to statistical analysis.  
 
Patient quality of life, measured by patient questionnaires, will not be measured because this 
was found to be infeasible in our pilot study. In the inpatient hospital setting, patients were 
physically ill with acute problems such as pain, leading to low recruitment and data 
completion rate. 
 
Specific aim 1: To analyze the effect of the SPARK model on hospital length of stay and 
health services utilization. The following data will be extracted from the electronic health 
records: dates of hospital admission and discharge for the index admission and all subsequent 
admissions, dates of all subsequent Emergency Department attendances, and date of referral 
to community palliative care services. 
 
Specific aim 2: To determine the access to palliative care. The receipt of different forms of 
palliative care from the palliative care doctor/nurse will be recorded. This may include direct 
care as well as indirect input e.g. specific advice regarding medication dose for that particular 
patient or suggestion of an appropriate source of support in the community for the patient. 
 
 
Data analysis 
Specific aims 1 and 2: Mixed-effect linear regression will be used to compare differences in 
(log-transformed) length of stay (primary outcome) and hospital-free days in the last 6 
months of life between ‘spark intervention’ and ‘usual care’ groups controlling for (team- and 
patient-level) clustering and time effect. (30) Similarly, mixed-effect logistic regression will 
be used to compare the proportion of patients who receive direct palliative care in the usual 
care model versus the SPARK model. A separate analysis will also be done for patients who 
receive any specific form of palliative care (direct or indirect).  
 
Specific aim 3: A net cost analysis of SPARK will be undertaken from the perspective of the 
restructured health system. It is possible that total labour costs are greater for SPARK but that 
costs per patient are lower because SPARK allows for seeing more patients. This difference, 
whether positive or negative, represents differences in direct labour costs. Because this is a 
new model of care, this cost will not be reflected in the billing data. However, the billing data 
can be used to quantify other differences that may result from SPARK, such as savings 
through reductions in length of stay. Using the billing data, we will again quantify total and 
per capita cost differences between SPARK and usual care. The net costs of SPARK will be 
quantified by adding per capita labour cost differences to per capita billing cost differences. If 
negative, then SPARK is cost saving. If positive, then SPARK is more expensive. 
 
Sample size calculation 
It is estimated that each of the 4 clusters will have an average of 300 stage 4 cancer patient 
admissions per 4-month period, giving a total of 6000 admissions over the 20-month study 
period.  

  
Based on results from our pilot study, we estimated the mean hospital length of stay to be 
about 6 days and the standard deviation to be 7. We estimated the intra-cluster coefficient 
arising from multiple admissions per person to be 0.2 and that each patient would have an 
average of 2 admissions during the study period. Therefore, the design effect arising from 
multiple admissions per person is 1.2 and 300 patient admissions would give an “effective 
sample size” of 250 admissions per 4-month period, or a total “effectiveness sample size” of 
5000 admissions over the study period. Assuming the coefficient of variation between 
clusters to be about 0.25, the sample size of totally 6000 admissions as aforementioned would 
provide 80% power at 5% two-sided type 1 error rate to detect a mean difference of 1 day in 
hospital length of stay in the stepped-wedge design in table 1. (31) 
 
 
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