Using an EPIC-Based Barriers to Care Assessment to Guide Care Navigation in a Large Oncology Service Line

Sharon Manne,¹ Jeanne Silva,¹ Dena O’Malley,² Shawna Hudson,² Ethan A. Halm,³ Jacintha Peram,¹ Justin D. Solleder,¹ Beth Handorf¹

¹Rutgers Cancer Institute, New Brunswick, NJ; ²Rutgers RWJ Medical School, Department of Family Medicine and Community Health, New Brunswick, NJ; ³Institute for Health, Health Care Policy and Aging Research, Robert Wood Johnson Medical School, New Brunswick, NJ

Background: Although there have been many oncology navigation programs implemented, little attention has been paid to characterizing barriers to care and sociodemographic and clinical factors associated with these barriers.

Objective: To describe a standardized access navigation program implemented for all cancer patients reaching out to an oncology call center providing navigation services to 13 oncology service lines for a large hospital system.

Method: Data from the navigation barriers assessments were merged with available sociodemographic and clinical data taken from the electronic medical record.

Results: More than half of patients reported up to 3 unique barriers to care in the first 3 navigation encounters, with 98% reporting at least 1. The most common barrier was learning needs, followed by psychosocial issues and obstacles transitioning the patient for treatment within the hospital system or to an outside organization. The most barriers over the first 3 navigation encounters were associated with being female, Black or African American race, Spanish or other unknown primary language, and not being married or partnered.

Conclusions: There is a subgroup of oncology patients with ongoing and challenging barriers to cancer care who may benefit from longer-term navigation services. Blacks, Spanish-speaking, and unmarried patients may require ongoing barriers assessments accompanied by navigation. The most common barriers are best handled by a variety of services and professionals, including and/or in addition to nurse navigators.

Over the past several decades, mortality for many cancers has improved. Unfortunately, these improvements have uncovered significant cancer disparities. For example, mortality rates among patients with lower income, less education, and minority race/ethnicity have increased.^1,2 One potential explanation for these disparities is that these individuals may encounter more barriers to accessing appropriate and timely care from initial diagnosis to cancer treatment and survivorship.³ To improve access to appropriate and timely care, there has been increased effort to identify and mitigate oncology care access barriers, which have been termed the social determinants of health (SDOH). SDOH typically incorporates an array of factors, including income, education, race, insurance status, housing and food insecurity, geographic factors such as census tract poverty level, residential segregation, neighborhood disadvantage, as well as neighborhood access to health services, personal safety, access to transportation, access to childcare, health literacy, and medical mistrust. An extensive literature documents the downstream consequences of SDOH on outcomes across all types of cancer.^4-14

Since the passage of the Patient Protection and Affordable Care Act in 2010, the United States has seen an increase in focus on assessing and addressing SDOH to improve population health. Professional organizations, including ASCO and the Centers for Medicare & Medicaid Services, have issued policy statements recommending assessment of SDOH.^15,16 However, increasing fragmentation in the US medical care system has compromised the ability to assess and address social determinants. The absence of clear medical pathways poses a barrier to cancer care. To address this challenge, patient navigation programs have been implemented in many oncology settings.¹⁷ Navigation is a patient-centered effort that helps patients overcome challenges by providing a liaison between the patient and their healthcare system.¹⁸ Services provided can vary, but the navigator’s role remains consistent: assessing and addressing barriers, improving care access, facilitating care coordination, providing patient education, providing guidance on resources to meet needs, and support throughout the cancer care experience.^19-22 Navigators can play a key role in care coordination by streamlining care for patients across diverse clinical settings. To this end, in 2017, the American College of Surgeons’ Commission on Cancer implemented a requirement for a community health needs assessment and patient navigation in its cancer program standards.²³ Similarly, the Academy of Oncology Nurse & Patient Navigators has developed oncology nurse navigator competencies, a toolkit for use in training and position development, and metrics.^24,25 As a result, there are a burgeoning number of oncology patient navigation programs spanning the cancer continuum, from prevention, screening, and treatment to survival^18,26-30 (see Chen et al³¹ for a review). Studies have demonstrated reductions in treatment delays, improved treatment adherence, increased patient satisfaction, reductions in racial disparities,^32-34 and improved cost-effectiveness,³⁵ but with less evidence supporting improvements in quality of life.³⁶ The vast majority of trials have focused on breast cancer.^37-39

In this study, we describe the development and implementation of an oncology navigation program developed by the Robert Wood Johnson Barnabas Health (RWJBH) oncology service line to assess and address SDOH and other barriers to care among newly diagnosed cancer patients across the entire RWJBH service line. There is a large literature focusing on the impact of navigation on screening uptake and improving timeliness of follow-up care after a screening abnormality,^21,40-42 and studies evaluating navigation outcomes among underserved or minority populations.^32-34,43 (see Lopez et al⁴² for a review). In addition, there is a relatively large literature documenting the beneficial impact of oncology navigation on outcomes among newly diagnosed cancer patients (eg, Chen et al,³¹ Lee, et al,⁴⁴ Bush et al,⁴⁵ Oh and Ahn⁴⁶). In terms of studies utilizing an assessment of SDOH access barriers followed by navigation, several studies have described navigation programs that conduct a barriers and navigation system assessment among various cancer patient populations.^47-49 One study⁴⁹ conducted a randomized controlled trial for underserved patients with any type of cancer seen in community settings. The intervention provided navigation in one arm and no navigation in the other and found that navigation to resolve barriers to cancer care resulted in higher levels of perceived care coordination, feeling more informed, involved, and prepared for their cancer treatment.

The advent of large, system-wide electronic medical records in oncology settings offer the unique opportunity to develop a standardized navigation program that links patients, navigators, and providers.

The advent of large, system-wide electronic medical records (EMRs) in oncology settings offer the unique opportunity to develop a standardized navigation program that links patients, navigators, and providers. In this study, we describe the development and implementation of a barriers to oncology care access assessment and navigation program using the hospital-wide EPIC-based EMR. This navigation program was developed by the leader of the patient navigation program at Rutgers Cancer Institute (JS). The program is implemented for all cancer patients who reach out to the oncology call center. The navigators provide services to 13 oncology service lines in the RWJBH system. The RWJBH navigation team is located at each of the organization’s 12 hospital cancer facilities, and 1 virtual program is located at the call center. The funding source was primarily RWJBH. For this analysis, 52 different navigators provided access navigation to the patients. Although there were other members of the team providing navigation to these patients, their work was not reflected in this dataset. The following access navigation procedures were implemented: all new patient calls for oncology care were referred to nonclinical access patient navigators, who implemented a semistructured interview that reviewed a set of barriers.

Patients were asked if they experienced each barrier, with an opportunity to provide additional input regarding each answer. Details on all the barriers listed in the dataset are provided in Table 1. The barriers assessment included additional needs that have not been included in traditional SDOH evaluations, such as psychosocial needs, care delivery issues, and cancer education needs. This broader approach was considered a strength of the navigation program because a broader evaluation fosters the identification of barriers to care at each navigation encounter and facilitates the identification of SDOH under the broader umbrella of provision of high-quality patient care. After the initial contact, patients were referred to the hospital where they were receiving care. Nurse navigators at each hospital managed the longitudinal navigation throughout the patient’s treatment journey. Navigators inquired about the other obstacles that were not previously endorsed and continued until the patient did not endorse any barriers or completed treatment. This workflow ensured that all team members operated within their professional scope, clinical and psychosocial concerns were managed by licensed nurses, and practical barriers were addressed by nonclinical navigators trained in resource coordination. Table 2 illustrates each navigator role and functions. This study had 2 aims. The first aim was to describe access barriers and navigation activities, and the second was to evaluate sociodemographic and medical factors associated with access barriers.

Methods

The study protocol (Pro2023002058) was reviewed and approved by the Rutgers Cancer Institute internal review board (IRB) in accordance with the US Federal Policy for the Protection of Human Subjects. After IRB approval, Rutgers Cancer Institute’s Clinical Research Data Warehouse (CRDW) team extracted the data from the EMR. Data were extracted from the onset of the navigation program July 1, 2022, through July 30, 2024. In addition to the initial barriers assessment and navigation practice data, additional navigation assessments were collected. Finally, from the EPIC chart, the CRDW collected demographic and clinical data, including sex, age, race/ethnicity, date of initial assessment, cancer type, and cancer stage. The Area Deprivation Index (ADI) was calculated as a measure of socioeconomic disadvantage in a region of interest. The ADI is based on a measure originally created by the Health Resources and Services Administration adapted and validated to the Census Block Group neighborhood level.^50-52 It includes 4 domains (income, education, employment, and housing quality) that were captured at block group level in 2015 to measure pre-COVID lockdown-related influences of neighborhood disadvantage. To calculate an ADI, census data block group data are ranked in percentiles from 1 to 100 (1=lowest disadvantage within the nation to 100=highest level of disadvantage).

Approach to Analyses

The number of navigation encounters ranged from 1 to 99. Almost half of the sample had 1 encounter (48.6%); 17.3% had 2 encounters, 8.8% had 3 encounters, 5.6% had 4 encounters, 3.7% had 5 encounters, and the remaining 15.9% had between 6 and 99 encounters. For our analysis, we chose the number of unique barriers endorsed during the first 3 navigation contacts as the outcome variable because almost two-thirds of the sample (74.5%) met this criterion. A unique barrier was defined as a new barrier identified during a follow-up encounter. Aim 1 analyses were descriptive and included frequencies of each barrier and the total number of unique barriers across the first 3 navigation encounters and subsequent navigation activities. Aim 2 analyses consisted of a multivariable generalized linear regression model to determine participant sociodemographic and medical factors associated with a higher number of unique barriers. As the outcome exhibited overdispersion, a negative binomial regression model was used. Linearity of continuous effects was checked using natural cubic splines, with model fit compared via the Akaike information criterion. For analytic purposes, language was categorized as English, Spanish, and other/unknown.

Results

Sample Characteristics and Descriptive Information for Barriers

Table 3 presents the sample characteristics. About 60% of the sample was female, and 53% were married. The average age was about 64 years. Race was 51.6% White, 15.2% Black, 7.3% Asian, 2% multiracial, 0.1% American Indian/Alaskan Native, and 21.8% identifying as “other.” The sample was primarily non-Hispanic (79.4%). The majority identified English as their primary language (84.1%). Approximately 25% resided in areas with higher levels of socioeconomic deprivation ADI ranging from 1-3). As described above, 52 navigators provided navigation services.

Table 4 presents the descriptive information for the unique barriers in the first 3 navigation contacts, and Figure 1 illustrates the top 15 barriers reported. The average number of barriers was 2.68, the median was 2, and the range was 0 to 13. Less than 10% of the sample reported no barriers across the first 3 navigation contacts. The most common barrier was learning needs (69.8%), which was defined as a lack of knowledge regarding the disease, disease process, treatments, and the resources available to the patient. The second most common barrier was psychosocial issues (55.8%), defined as psychosocial factors that negatively affect access to treatment and health outcomes, including deterioration of self-concept, disturbance of body image, sexual problems, difficulties in social relationships, and emotional distress. The third common barrier was institutional needs (34.5%), which was defined as obstacles in transitioning the patient for treatment, diagnostics, or scans within the RWJBH system or to an outside organization. The fourth most common barrier was treatment compliance, defined as failing to follow the recommended care plan. The least common barriers were religious issues (.03%), health equity identity (0.3%), and undocumented citizenship (0.1%). Figure 2 illustrates the total number of barriers identified in the first 3 navigation contacts.

Factors Associated With Total Barriers in the First 3 Navigation Contacts

Results of the regression are shown in Table 5. Results indicated that older age was significantly associated with fewer unique barriers over the first 3 navigation contacts (risk ratio [RR]=0.997 per year of age; 95% CI, 0.995-0.999; P=.009). Nonlinear effects of age were considered, but the model’s fit was best with a linear effect. More unique barriers over the first 3 navigation contacts were associated with female sex (RR=1.106; 95% CI, 1.042-1.175; P=.001), Black or African American race compared with White (reference category) (RR=1.101; 95% CI, 1.011-1.200; P=.028), Spanish as primary language (RR=1.402; 95% CI, 1.231-1.598; P<.001), other or unknown primary language (RR=1.152; 95% CI, 1.024-1.297; P=.019), and not being married or partnered (RR=1.082; 95% CI, 1.019-1.15; P=.01). No significant associations were observed for Hispanic ethnicity, ADI, or having cancer spread to secondary site(s).

Discussion

SDOH are well-established correlates of cancer outcomes. Each phase of cancer care is impacted by cancer care needs—beginning at diagnosis, then the transition to treatment, initiation and completion of treatment, and managing posttreatment care. If needs are identified, unmet social needs can be addressed by changing treatment plans and connecting patients to resources both within the healthcare organization and/or with community organizations. To mitigate unmet needs, oncology navigation programs have been developed over the past decade to curb resulting inequities in cancer outcomes.^{4,5,8,10,12,53} In this study, we described an EPIC-based oncology care navigation program implemented across a large oncology service line, as well as barriers and demographic and clinical correlates of these barriers. Our results indicated that more than half of the almost 3000 patients enrolled in our access navigation program over 2 years reported up to 3 unique barriers, with 98% reporting at least 1 barrier. Differential use of assessment instruments limits direct comparison across studies; however, the salience of psychosocial needs and barriers that interfere with treatment compliance were commonly endorsed in this and other studies. Psychosocial needs were endorsed by more than half of the patients, and treatment compliance was endorsed by 19.1%, consistent with previous research evaluating barriers to cancer care.^21,54-60 However, it is important to note that traditional social determinants, such as financial issues (5.9%), food insecurity (6.3%), housing and clothing hardships (6.3%), being underinsured or not insured (0.2%), and difficulty paying utilities (6.3%), were less common than reported in prior studies. For example, Zettler and colleagues⁶¹ reported that 80% of community oncologists reported financial insecurity as one of the top 3 barriers to care. Cotangco and colleagues⁶² found that gynecological oncology patients experienced housing insecurity (18%-19%), transportation challenges (19%-25%), and food insecurity (13%-16%). Nyakudarika and colleagues⁶³ reported higher levels of food insecurity (12.6%), difficulties paying utilities (9.6%), financial issues (13.3%), and support needs (20%) among gynecological cancer survivors. Some of this variability could be attributed to differences in how needs were defined, the number of barriers assessed, and sample characteristics (eg, more socially disadvantaged populations, English not their primary language, geography, etc).

A greater number of unique barriers was associated with being younger, female, Black, Spanish-speaking, speaking a primary language other than English, and being unpartnered. There is an extensive literature documenting the association of care barriers with younger age,^36,64-66 being Black,^67,68 a minority,⁶⁹ or speaking a non-English primary language.^36,66 Although there has been limited attention paid to marital status, Hendren and colleagues⁶⁹ reported more navigation time among unmarried patients.

This study has several strengths. One major strength is the availability of data from a standardized navigator-led assessment delivered to all patients referred to oncology care at 13 different cancer settings. These data included multiple navigation encounters for each patient, along with corresponding demographic and clinical data. A second strength is the assessment of an extensive and broad set of care barriers in addition to traditional SDOH. A third strength is the racially and ethnically diverse patient population diagnosed with a variety of cancer types, variability in primary language, and almost a quarter of our sample residing in socially deprived areas. Finally, the large sample size allowed for a more robust approach to evaluating the role of key variables, such as primary language and ADI. One limitation of our data is lack of reliable data from the EPIC database regarding insurance status and education. We were unable to assess insurance status because the health system’s data policy considered these data as identifiable information about the patient, and our data pull from EPIC could not contain identifiable patient information. With regard to education, this variable is assessed in some clinical settings in our health system, but because it was not widely assessed across the system, there were too much missing data and therefore we did not include it in the analyses.

Conclusions

Oncology care navigation fosters greater equity in the delivery of cancer care and is quickly becoming an important component of high-quality cancer care delivery. However, there are few standardized navigation assessment programs implemented across a large multisite oncology service line and housed in a database where longitudinal data can be pulled and evaluated to improve care delivery. RWJBH’s Access Navigation program is an EPIC-based navigation that can potentially serve as a model of oncology navigation. Our study results illustrated that almost all patients reported at least 1 barrier to cancer care, and one-third of the patients endorsed 4 or more unique barriers. Psychosocial needs were endorsed by more than half of the sample, emphasizing the importance of this issue and the need for early navigation to social services. Our findings suggest that there is a subgroup of patients with ongoing and challenging needs who require longer-term navigation services. Finally, females, Blacks, Spanish-speaking, and unmarried patients may require ongoing assessment and navigation. Because the most common barriers were learning needs, psychosocial issues, and obstacles transitioning the patient to treatment within the hospital system or to an outside organization, our results indicate that there is a significant need for patient education and psychosocial services in addition to the initial services offered by the access navigators. A more detailed evaluation of these barriers and the resources, expertise, and service coordination connected to the barrier response would improve the quality of care provided to patients.

Acknowledgments

We would like to acknowledge the Rutgers Cancer Institute’s Clinical Research Data Warehouse for their work on this project, including Marc Gregory, Kevin Meehan, Neema Patel, Feny Susilo, and Vlad Kholodovych. We would like to thank Dr David Foran for his generosity in providing support for this project. Finally, we wish to thank all the navigators whose work contributed to the data used in this project.

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