Eligibility criteria included: (1) 18 years of age or older; (2) Medicare or Medicaid beneficiary, and (3) advanced or progressed solid tumor malignancy. Patients who were beneficiaries of commercial insurance were ineligible, unless the commercial insurance was secondary.
Eligible patients were identified by a variety of means, such as tumor board reports, appointment schedules, case reviews of the electronic medical record (EMR), and referrals from the primary oncology teams. The identification of potential candidates for the program was performed by an administrative member of the team with clinical experience. Once identified, one of the nurse care coordinators approached the patient, explained the program, and collected baseline data during that initial patient assessment of needs. Patients identified as clinically eligible but who were unable to be enrolled due to program capacity provided the concurrent control group.
The intervention included three primary components: routine electronic biopsychosocial screening, early access to specialty-level palliative care providers, and nurse care-coordination. We developed and used an electronic, tablet-based tool for obtaining patient-reported symptoms and concerns. Patients were screened at routine intervals, monthly during scheduled clinic visits or, if they were not being seen monthly, at mid-point between visits to their oncologist. They underwent a monthly, short screen which included the Edmonton Symptom Assessment Scale (ESAS), Distress Thermometer and Problem Checklist. Upon enrollment and at months 3, 6, 15, and 24, enrollees completed an extended screen which incorporated the monthly screen and additional measures, including the Functional Assessment of Cancer Therapy (FACT-G). The monthly screens were focused on identifying symptoms which warranted immediate attention, while the extended screen was done for the purpose of gathering more comprehensive data on overall wellbeing. Nurses reviewed the responses and directed clinically actionable concerns to the patient’s providers, including palliative care providers as appropriate.
A second component incorporated in the program was early referrals to specialty-level palliative care in the outpatient setting. The palliative care providers (MD or Nurse Practitioner) coordinated with the primary oncology team for both symptom management and advance care planning. The program’s original design was for all enrolled patients to see a palliative care provider. In the first year of the program, only about 60% of enrollees participated in an early consultation with palliative care. This rate was a function of supporting patient choice of whether or not to accept referral to palliative care providers, as well as an attempt to refer all patients in the program to early palliative care, even those with low symptom burden. As the program increased capacity, demonstrated capability and value to the primary oncology teams, and improved the enrollment process to focus increasingly on higher-risk patients, 98% of patients enrolled in the last year of the program participated in early palliative care consults.
The final primary component of the program was providing nurse care coordination for these complex patients. Nurses were aligned with patients in the same way that other supportive care team members (e.g. social workers) are aligned with disease teams within the cancer center. This design was to facilitate acceptance of and ease of communication by the project nurses with the oncology teams and increase the care coordinators’ familiarity with treatment protocols.
Nurse care coordinators on the project also served as the nursing support for the palliative care providers. Coordinators were responsible for tracking the biopsychosocial screening schedule of the patients and contacted the patients for assessment either by telephone or in person (if patient was coming to the cancer center). They connected the patient to any intervention or resources which the screening identified as a need. Nurses tracked admissions and emergency department (ED) visits within our hospital system for enrolled patients and facilitated appropriate follow-up. In doing so, they assured that the primary oncology team was notified of the admission or ED visit. If the patient had not yet been seen by a palliative care provider, they facilitated referral to either the in-patient or outpatient team, as appropriate. In addition, the nurse care coordinators provided clinical follow-up for issues generated by the supportive oncology/ palliative care interdisciplinary team.
The program, through its development of new infrastructure for electronic patient biopsychosocial screening, expansion of specialty level palliative care providers, and use of nurse care coordinators, gradually became integrated into the fabric of the cancer center. The project team members participated in work to sustain and spread the program within the organization. This included multiple educational sessions for staff, a midpoint survey of oncologists to assess their satisfaction with the program and solicit input on how to improve it, and design and implementation of a comprehensive electronic medical record addition to document end of life and goals of care discussions.
Quality of life was measured using the Functional Assessment of Cancer Therapy- General (FACT-G) which is a 27-item screen validated in patients with any form of cancer which measures the four primary QOL domains: Physical Well-Being, Social/Family Well-Being, Emotional Well-Being and Functional Well-Being . The FACT-G asks patients to report upon how they felt in the past seven days on a five-point Likert scale from “Not at All” to “Very Much.” For our use the tool was built within a website and either given to patients on a tablet to fill out or read by the nurse coordinators to the patients over the phone and the nurses entered the responses. The FACT-G was the measure of quality of life incorporated as part of the Extended Screen, collected at baseline and months 3, 6, 15, and 24 of enrollment.
Aggressiveness of Care was measured with multiple metrics, categorized into end-of-life measures and active treatment. End-of-life measures included death in the hospital, death in the ICU, ICU admission in the last thirty days of life, hospitalization in the last thirty days of life, emergency department visit in the last 30 days of life, chemotherapy in the last fourteen days of life, rates of hospice admission, hospice length of stay, and healthcare costs within the last thirty and ninety days of life. Active treatment measures included normalized rates, on a per member per month basis, of inpatient admissions (emergent, urgent, and total), total patient days, patient days in excess of CMS MSDRG geometric length of stay, emergency department visits and total cost. These indicators have been commonly used to reflect aggressiveness of care at the end of life [15, 16].
Health care utilization data were extracted from an internal financial decision support system providing detailed billing data for claims paid to wholly-owned University Hospitals’ entities. This source did not include access to oral pharmaceutical costs and acute healthcare utilization outside of the University Hospitals network. Full claims datasets from CMS for similar populations at UH suggest that the costs outside our wholly owned entities is about 10% of total claims. Hospice utilization data, including admission dates and length of stay, were obtained through a partnership with two hospice programs which combined represented 85% of hospice admissions from our system.
SPSS Version 24.0 was used for all analyses with p<.05 used as the criterion for statistical significance. Linear regressions were conducted for all cost analyses. Data met the assumptions for linear regression and no transformations were required. Estimated marginal means were used for comparison. Prior to conducting analyses, outlier cases whose average cost per member per month was >2.5 standard deviations above the total group mean were removed. This represented 13.2% of total cases (n=215 LINCC and n=113 control). Next, covariates were entered in Step 1 with the grouping variable (LINCC vs control) entered in Step 2. The following covariates were included in the analyses because there were statistically significant differences between groups (see Table 1): patient race, patient Medicaid status, cancer type, cancer stage, treatment status, weighted Hierarchical Condition Categories (HCC) system code, and number of palliative care visits. The HCC risk adjustment methodology is used by CMS to predict healthcare costs, establish Medicare Advantage rates, and to adjust performance calculations for value based models such as Accountable Care Organizations (ACO’s)..
For non-cost related analyses (hospice, palliative care) there were no cost outliers removed for analytic purposes. Chi-square analyses were employed for categorical analyses, Analysis of Variance analyses were employed for continuous analyses, and linear regression was used for analyses that employed covariates. In order to examine the association of palliative care with hospice use patterns, palliative care visits were dichotomized into 2 categories: 0-1 visits and > 1 visits. When examining the total number of palliative care visits per patient (which ranged from 0-25) the 75th percentile was 1.0 visit, thus guiding the decision to group palliative care visits.
For comparison of quality of life scores over time, using the Functional Assessment of Cancer Therapy – General form (FACT-G), Repeated Measures ANOVA was used.