Background: While it is generally recognized that oncology navigation programs can vary in structure, makeup, and domain of care, a common task expected of all navigators is identifying patients’ potential or existing barriers to cancer care. This is an important aspect of evaluating the impact of navigation in meeting patient needs.
Objectives: This study examined barriers to care identified in an oncology navigation program and their association with diagnosis-to-treatment time (DxTT) and navigator time spent (NTS) for patients diagnosed with cancer or a cancer recurrence.
Methods: Data were collected over 6 months in 2020-2021 from 633 adult cancer patients from an outpatient oncology navigation program across 3 clinics in suburban Colorado. An observational, correlational quantitative design was used to retrospectively investigate 79 individual patient barriers identified by oncology nurse navigators across 8 barrier categories and their association with DxTT and NTS.
Results: The mean number of barriers identified per patient was 3.06. DxTT was significantly associated with patient age (P=.03); type of cancer (P<.001); the individual barriers of Hearing Loss (P=.043), Treatment Decisions (P=.001), and Worry (P=.007); and the barrier category of Risks (P=.045).
Conversely, NTS was significantly associated with gender (P=.039); type of cancer (P<.001); the individual barriers of Pain (P=.001), Transportation (P=0), Time Off From Work/School (P=.013), Anxiety (P=0), Fear (P=.001), and Worry (P=.021); and the barrier categories of Physical Concerns (P=.023), Social Concerns (P=.001), Home/Family Concerns (P=.013), Emotional Concerns (P<.001), and Total Barriers (P<.001).
Conclusions: NTS had a greater number of significant associations with individual and category barriers to care than did DxTT, demonstrating these barriers’ substantial influence on navigator effort. Patient demographic elements and individual barriers may be useful for navigation programs to consider when assigning caseloads and anticipating NTS with patients.
These results can also be used to further identify patients at risk for delays in starting treatment.
Taken together, these elements may contribute to a professional acuity tool for oncology navigation and suggest further research is indicated on the impact of individual barriers to cancer care.
ONNs provide multidimensional care for oncology patients that promotes timely access to care, improved communication, and continuity of cancer care services across multiple providers in a healthcare system.
Since its inception, oncology navigation has had the goal of guiding patients through the continuum of cancer care in support of their individual care choices. Oncology nurse navigators (ONNs) provide multidimensional care for oncology patients that promotes timely access to care, improved communication, and continuity of cancer care services across multiple providers in a healthcare system.1 A critical component of the navigation process involves getting to know the patient to identify and mitigate any barriers to receiving the desired care. Oncology navigators vary in background and training, with some having nursing degrees and others with a degree in social work. In some programs, the navigators have no formal training, are former cancer patients, or are described as financial navigators. Because of varied navigator backgrounds, tasks performed are vastly different. For example, a registered nurse may be expected to assess a patient for medical risk factors likely to prevent them from proceeding with or completing cancer treatment. Such an assessment by a patient navigator (nonclinical) without educational training and clinical knowledge and experience would not be possible.
Navigation programs not only differ in tasks assigned to navigators but also in the number of team members, or where the navigator functions within the continuum of care. For example, one program may only be built to ensure patients with abnormal breast imaging can complete their workup, while patients with other types of cancer may not interact with a navigator. The variance in the type of navigation programs and services creates difficulty in developing standard definitions, workflows, and metrics for programs to demonstrate effectiveness. As Sein et al state, “Navigation programs are diverse, and the lack of standardized metrics to evaluate the impact of navigation on patient quality outcomes has made it difficult to measure programmatic success.”2
In 2017, the Academy of Oncology Nurse & Patient Navigators (AONN+) published 35 evidence-based navigation standard metrics that navigation programs are suggested to track.3 An exploratory study approved by an institutional review board (IRB) followed that examined 10 of the identified metrics in additional detail.4 Results from this study indicated the following 5 core metrics were applicable across 8 varied study sites: (1) barriers to care, (2) navigation caseload, (3) psychosocial distress screening, (4) social support referrals, and (5) navigator competencies. As a result, these 5 identified metrics were suggested as those directly influenced by oncology navigators and which did apply to any program.4 The outcome confirmed the important focus on barriers to care for any navigation program seeking to define its impact in meeting patient needs. To add to this body of knowledge, the current study focused specifically on barriers to oncology treatment as they relate to oncology navigation outcomes of care.
The Oncology Navigation Standards of Professional Practice released in 2022 clearly reinforce the expectation that all navigators should be identifying barriers to care and making appropriate referrals based on barriers identified.5 Since barriers to care are both an important task of the navigation role and trackable across navigation programs, investigating specific barriers to identify at-risk patients or forecast required navigation time is warranted. Findings from Ramachandran et al examining nearly 4000 patient cases revealed that women who were identified as having barriers to care had a longer time from initial abnormal breast or cervical finding to completion of cancer screening.6 There was no clear pattern to the impact based on type of barrier; simply, the presence of an identified barrier to care was associated with a less timely resolution. A similar study also showed an association with barrier presence and a longer time from initial abnormal finding to completion of workup for women with abnormal breast or cervical screenings.7 Both studies had defined lists and descriptions of barriers that were created from literature reviews and study team feedback and from which navigators could choose.6,7
Time from diagnosis to treatment is identified as a high-priority metric in the published AONN+ standardized metrics.3,4 Time from diagnosis to treatment may be impacted by many things, and delays in treatment start have been associated with higher cancer mortality.8,9 With some variability in findings, multiple studies have documented shorter time from diagnosis to treatment start when navigation is involved with a patient case. Freund et al found both improved time from initial finding to diagnostic resolution and from initial diagnosis to treatment start for patients working with a navigator after 91 days of care; however, no benefit was seen in the first 90 days of navigator involvement.10 Results suggested navigation is most beneficial for those where the greatest delays are seen and who are at risk of being lost to follow-up. This study included patients with breast, cervical, prostate, and colon cancer only. In a study that examined patients diagnosed with breast cancer, it was found that patients who received navigation services started treatment earlier (23 days) than those who were not navigated (33 days).11
Examining the results from the AONN+ metrics trial, it was noted that patients who received navigation support started treatment 11 days earlier on average than those who were not navigated.4 Conversely, another study found patients with prostate cancer who were connected with navigation support experienced a longer time from diagnosis to treatment. The authors theorized this finding reflected delays in shared decision-making for this specific population.12
Knowing the beneficial impact of navigation involvement on patient outcomes and that navigators will routinely be documenting barriers to care, it is possible that investigating barriers to care may help estimate required navigation time and staffing. Hendren et al found that 3 individual barriers to care—being unemployed, of minority status, and unmarried—were associated with higher NTS.13 Likewise, another study revealed that certain identified barriers were associated with increased NTS: financial barriers (169 minutes), transportation (74 minutes), end-of-life issues (65 minutes), arrangement for dependent care (60 minutes), scheduling of appointments (34 minutes), and assistance with activities of daily living (24 minutes).14,13
The purpose of the current study was to examine barriers to care identified in an oncology navigation program, their association with time from diagnosis to treatment start, as well as NTS for cancer patients.
The purpose of the current study was to examine barriers to care identified in an oncology navigation program, their association with time from diagnosis to treatment start, as well as NTS for cancer patients. These variables were 2 standardized core metrics identified by AONN+, with the number of barriers to care validated as a metric trackable by navigation programs of varied setups4 and time from diagnosis to treatment identified as a high-priority metric.3 If barriers to care were associated with an increase in time from diagnosis to treatment, it would be prudent for navigation programs to ensure they assess barriers and maintain adequate referral sources to mitigate those barriers. NTS was also investigated since it was previously identified as an area with potential associations with barriers. For this study, NTS included time chart reviewing and preparing to meet with the patient, time spent with patient or family (in person, by phone, or via email), and time spent coordinating care for the patient. Finally, in light of existing literature that omitted some cancer types, this study included all cancer types so the results could be reasonably extrapolated to navigation programs of varied setup.
An observational, correlational quantitative design was used to retrospectively investigate patient barriers identified by ONNs and their association with time from diagnosis to treatment start and NTS for patients diagnosed with cancer or a cancer recurrence. The study was reviewed and exempted by the responsible IRB, and all procedures followed were in accordance with applicable ethical standards.
Initially, it was noted that inconsistencies existed regarding how barriers were entered in the electronic medical record. As a result, an in-service training was conducted in July 2020 for the ONN team to review each barrier and methods to chart it accurately and consistently. For the next 4 weeks additional education sessions and chart reviews were conducted to ensure barriers were being charted consistently among the ONN team members. This process assisted in collecting more accurate and valid data. After consistency in charting barriers was ensured, a time frame from August 1, 2020, through January 31, 2021, was established for a retrospective analysis of navigation data.
Predetermined variables of the study included barriers to care, diagnosis-to-treatment time (DxTT), and NTS. The list of barriers based on the NCCN Distress Thermometer screening tool and used in the AONN+ IRB-approved metrics trial was utilized as this was the most complete list and had recently been used in the national study.4 A few additions were made to this list based on ONN feedback to include English as a Second Language, Non-English Speaking, and Lives >50 Miles Away. The navigation team was able to make referrals to social work, counseling, nutrition, financial case coordinators, or community resources to complete interventions addressing barriers to care identified; however, all time spent documented in this study was NTS. Seventy-nine individual patient barriers to care across 8 barrier categories, including a category for Total Barriers, were investigated. Age, Gender, and Type of Cancer were used as demographic variables. Full lists of variables are provided in Table 1 and 2.
The sample comprised retrospective chart data from 633 adult patients with cancer seen in an outpatient oncology navigation program across 3 clinics in suburban Colorado. The program manages over 2500 patients annually, is staffed by ONNs with a minimum of a BSN degree, and serves patients from northern Colorado, southern Wyoming, and western Nebraska. These patients had either a new diagnosis of cancer or a new diagnosis of recurrent cancer. Sample demographics are displayed in Table 3.
Data are summarized as mean (SD) or frequency (percentage) for continuous and categorical data, respectively. Linear regression models were used to explore the association between age and DxTT in days and NTS, with adjustment for cancer type and gender included. The association between total number of barriers for each type and DxTT in days/NTS used the same approach with linear regression modeling. P values <.05 were considered significant, and all analyses were completed using R v4.1.1.
Of the 633 patients, 314 were female and 319 were male. Average age was 65.5 years, and the mean number of barriers identified per patient was 3.06. The number of patients with each cancer type is provided in Table 3, with the cancer types of unknown/unlisted (n=12) and metastatic (n=2) not studied further due to small size and lack of description. Table 4 and 6 present the mean DxTT in days for each cancer type and the mean NTS in minutes for each cancer type, respectively.
DxTT was significantly associated with patient age (P=.03); type of cancer (P<.001); the individual barriers of Hearing Loss (P=.043), Treatment Decisions (P=.001), and Worry (P=.007); and the barrier category of Risks (P=.045). The associations of age and barrier categories with DxTT (adjusting as appropriate for age, cancer type, and gender) are displayed in Table 5.
NTS was significantly associated with gender (P=.039); type of cancer (P<.001); the individual barriers of Pain (P=.001), Transportation (P=0), Time Off From Work/School (P=.013), Anxiety (P=0), Fear (P=.001), and Worry (P=.021); and the barrier categories of Physical Concerns (P=.023), Social Concerns (P=.001), Home/Family Concerns (P=.013), Emotional Concerns (P<.001), and Total Barriers (P<.001). The associations of age and barrier categories with NTS (adjusting as appropriate for age, cancer type, and gender) are shown in Table 7.
Findings indicate the navigation program studied was effective; ONNs were identifying appropriate barriers, spending time with patients based on those barriers, and addressing them to reduce delays in treatment.
Findings indicate the navigation program studied was effective in that ONNs were identifying appropriate barriers, spending time with patients based on those barriers, and addressing them to reduce delays in treatment. The mean number of barriers (3.06) identified per patient is higher than the average of 2.2 barriers identified per patient noted in the AONN+ IRB-approved metrics trial.4 This variation may be due to differences in program setup as compared with national standards and/or the data collection occurring during the height of the COVID-19 pandemic.
On initial analysis, there was a statistically significant association (P=.003) between patient gender and DxTT: an average of 35.82 days (SD=26.02) for females and 44.11 days (SD=39.47) for males. When further examined, this difference was found to result from type of cancer (P<.001) rather than gender. As shown in Table 4, those with gynecologic cancers had the shortest duration of DxTT (21.86 days), while patients with genitourinary (GU) cancers had the lengthiest (57.34 days). This result is not unusual as there are some cancers where a biopsy is not performed due to risk to the patient. This means the “date of diagnosis” and “date of first treatment” are the same. Such situations could include removing an ovarian mass to make a diagnosis, removing a brain tumor due to symptoms, or performing abdominal surgery for relief of a colon blockage.
After adjusting for number of barriers, cancer type, gender, and age was found to be significantly associated with DxTT (Table 5). For each additional year of age, patients started treatment 0.223 days later (P=.03). Often, ONNs see cancers in younger patients at later stages or with a more aggressive biology,15,16 and therefore the care team works diligently to initiate treatment. However, it should not be overlooked that older patients appear to begin treatment later than their younger counterparts.
The barrier of Treatment Decisions was associated with a later treatment start. It is understandable that patients who are struggling to make decisions about treatment would start treatment later than others.
After adjusting for age, cancer type, and gender, there were 3 individual barriers and 1 barrier category significantly associated with DxTT. The individual barriers were Hearing Loss (earlier treatment start when present; 22.25 days vs 40.3 days; P=.043); Treatment Decisions (later treatment start when present; 50.35 days vs 36.7 days; P=.001); and Worry (earlier treatment start when present; 30.01 days vs 41.78 days; P=.007). When Hearing Loss or Worry was present, patients started treatment earlier. Conversely, the barrier of Treatment Decisions was associated with a later treatment start. It is understandable that patients who are struggling to make decisions about treatment would start treatment later than others. These findings could be due to the low number of patients who fit this category (eg, Hearing Loss, n=12), or that staff may be working to ensure patients with hearing loss are leaving the clinic with all needed appointments because later follow-up may be difficult. It is also possible staff is emphasizing scheduling care for patients who exhibit worry over their diagnosis and treatment in order to offer relief to the patient.
The sole barrier category significantly associated with DxTT was Risks, a miscellaneous category for barriers that did not fit elsewhere (Table 5). It is notable that for each identified barrier within this category, a patient started treatment 4.41 days earlier (P=.045). This timing may be due to patients in this category (eg, those with no insurance or those living far from their primary treatment site), for whom the care team provides targeted support. Staff may be asking for appointments together and requesting specific appointment times rather than scheduling the next available appointment. This approach can result in more streamlined appointments and schedules for patients.
Similar to DxTT, there was a statistically significant association (P=.039) between patient gender and NTS: an average of 189.51 minutes (SD=145.16) for females and 167.61 minutes (SD=119.97) for males. In addition, there was a significant association between type of cancer and NTS (P<.001), with ONNs reporting spending the most time with patients who have primary central nervous system cancers (277.40 minutes) and the least amount of time with sarcoma patients (117.5 minutes) (Table 6). Notably, at the study site, many patients with primary sarcoma are sent to a larger regional institution for treatment plan development, which would explain NTS for this sample.
After adjusting for age, cancer type, and gender, 6 of 79 individual barriers and 5 of 8 barrier categories were significantly associated with NTS. The individual barriers that significantly increased NTS when present were Pain (243.25 vs 172.79 minutes when absent; P=.001); Transportation (240.09 vs 172.84; P=0); Time Off From Work/School (212.45 vs 173.56; P=.013); Anxiety (231.87 vs 170.07; P=0); Fear (231.44 vs 171.78; P=.001); and Worry (212.02 vs 172.25, P=.021).
The 5 barrier categories significantly associated with NTS (Table 7) were Physical Concerns (P=.023), Social Concerns (P=.001), Home/Family Concerns (P=.013), Emotional Concerns (P<.001), and Total Barriers (P<.001). The increase in time was largest in the Home/Family Concerns category, as an additional 25.54 minutes were spent with the ONN for each barrier identified within this category. For each physical concern identified in a patient, an average of 6.87 extra minutes was spent; for each social and emotional concern, 18.99 and 18.14 additional minutes were spent, respectively; and for each barrier (Total Barriers), an average of 5.94 minutes was spent with an ONN irrespective of barrier category. These additional amounts of time needed with a navigator are not only significant and noteworthy findings but also underscore that navigator time must be proportional to identified barriers for effective and comprehensive navigation.
Taken together, DxTT had fewer significant associations with individual and category barriers than did NTS and was influenced more by demographic variables such as age. If one is seeking to identify patients at risk for starting treatment later, one should be examining demographic variables more than barriers. Conversely, a higher number of individual (6/79) and category (5/8) barriers were significantly impactful to NTS and demonstrate that barriers to care indeed have a substantial influence on navigator effort and caseload.
Currently, there is no validated acuity tool for ONNs to accurately describe their caseloads or identify patients who may be at higher risk for delays in care. Similarly, there is no widely accepted definition of acuity, although a common understanding includes patient characteristics or findings that can be used to estimate or predict the level of attention and service the patient will need from healthcare providers.17 Previously proposed acuity tools for navigation have included the number of barriers as a variable.18 In the initial stages of development of a national acuity tool for oncology navigation, it was postulated that barrier type may have more significance in the determination of acuity than the number of barriers.19 In addition, some studies have found links between the number of barriers identified with overall NTS.6,14 Results from this study confirm these prior findings and suggest demographic variables and identified barriers to care could be used in an acuity tool developed for navigation. NTS is also important to consider when developing an acuity tool since an important aspect of acuity is anticipating the amount of time required to meet patient needs. This study confirmed that the identification of certain barrier types could be used to indicate higher NTS. In addition, this study indicated that older patients appear to begin treatment later than their younger counterparts. If an acuity tool could also be used to identify patients at risk for delays in starting treatment, demographic variables including age should be considered, further allowing programs to provide adequate patient support based on their patient population and those patient needs.
The nurse navigator assignments in the study program are currently made by disease site, which allows navigators to become content experts, answer detailed patient questions, and identify additional areas in the disease-specific process where improvements may be possible. Although this program did not adjust how patients are assigned since the study affirmed the current process, these data suggest the program could adjust to also utilize NTS predictions and barriers present to assign patient loads. Other programs could consider repeating this study in their populations to likewise test their patient assignment processes.
Other recommendations for clinical practice and research include investigating different types of barriers, such as nurse navigator or patient navigator (nonclinical) training; investigating additional demographic variables, such as cultural factors, especially for DxTT; and comparing data from different program setups, including those that are nonclinical. While this research has shown that different barriers can impact timing of treatment and NTS, more research is warranted to further define and explore the influence of individual barriers to timely and effective cancer navigation and care.
Limitations of this study include self-reported NTS; data collection during the COVID-19 pandemic, which may have impacted patient volumes and interactions; and sampling from a suburban/rural region, which may be dissimilar to other geographic areas and programs. In addition, the number of some individual barriers identified was too small for full analysis. Strengths of this research include consistency of ONN team documentation, use of a comprehensive barrier list, a large sample of all cancer types across a broad region, and statistical subanalyses to adjust for selected variables.
This study delineates patient demographic elements and types of time-intensive barriers that could be useful for navigation programs to consider when assigning caseloads and anticipating NTS with patients.
This study, designed to examine barriers to care and their association with time to treatment and navigator effort, is a novel contribution to oncology navigation literature. It delineates patient demographic elements and types of time-intensive barriers that could be useful for navigation programs to consider when assigning caseloads and anticipating NTS with patients. These results can also be used to further identify patients at risk for delays in starting treatment. Taken together, these elements may contribute to a professional acuity tool for oncology navigation and suggest further research is indicated on the impact of individual barriers to cancer care.
The authors thank Kathy Brown, Quality RN, for her contributions to this research.
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