Key Considerations for an Evidence-Based Oncology Patient Navigation–Specific Acuity Tool: A Scoping Review

July 2019 Vol 10, No 7

Categories:

Patient Acuity
Danelle Johnston, MSN, RN, HON-ONN-CG, OCN
Chief Nursing Officer & Senior Director of Strategic Planning and Initiatives, The Lynx Group/AONN+
Sharon Gentry, RN, MSN, CBCN, CBEC, HON-ONN-CG
Program Director, AONN+
Breast Nurse Navigator
Novant Health Derrick L. Davis Cancer Center
Winston-Salem, North Carolina
Tricia Strusowski, RN, MS
Manager, Chartis Oncology Solutions, Decatur, GA

Tools that help to characterize patient acuity have been used in healthcare for decades and have proved successful as a means of determining staffing needs, improving patient care, and controlling costs.1 Acuity tools can be incorporated into oncology patient navigation programs to support and enhance the effectiveness of navigators through patient-centric evidence-based methods that may have the potential to decrease the overall cost of care.

Objective

The aim of this scoping review is to provide an evidence base that will aid in the development of an oncology patient navigation–specific acuity tool.

Methods

Search Strategy

We utilized the scoping review method2 to research and analyze articles related to patient acuity in an oncology setting published in the past 10 years. This method was chosen to broadly examine the key concepts of acuity, including definition, tools, and factors related to measurement. To develop an overview of existing evidence from a navigation standpoint, the review addressed the following research questions to reveal particular supporting attributes of acuity:

Question 1. What are the definitions of acuity, acuity tool, acuity system, barriers to care, and distress?

Question 2. How can barriers to care be categorized?

Question 3. Is there a relationship between barriers to care and acuity?

Question 4. Is there a relationship between distress and acuity?

Question 5. What acuity tools already exist?

Question 6. How can a patient acuity score relate to factors such as productivity, return on investment (ROI), staffing/caseload, effectiveness, and time management?

Question 7. Is there any support in the literature to demonstrate that any of the Academy of Oncology Nurse & Patient Navigators (AONN+) Standardized Metrics are patient acuity measures?

PubMed, MEDLINE (Ovid), and CINAHL databases were searched for literature published between January 1, 2008, and December 25, 2018, using the search terms shown in Table 1. In some cases (eg, definitions, existing acuity tools, factors related to acuity), we included relevant articles published outside of the dates covered by the search. To expand the number of results, we performed hand searches of reference lists for additional articles of relevance. Articles were limited to studies that focused on or were conducted in humans and were published in English.

keyconsiderations_table1.png

Data Extraction

The PRISMA model was used to evaluate the articles based on the 7 selected questions.3 Included articles were classified by evidence type and sample size and reviewed for relevant study findings and information. We determined the quality level of included articles based on how well they helped the research questions along with their limitations.

Results

Study Characteristics

The search criteria and limits described above yielded 1711 articles. After titles and abstracts were screened for relevance, 199 full-text articles were reviewed and 105 articles were identified for inclusion in the literature review (Figure). Nineteen (18.1%) of 105 articles addressed multiple research questions.

keyconsiderations_figure1.png

1. Definitions of Acuity, Acuity Tool, Acuity System, Barriers to Care, and Distress

Seventeen (16.2%) of 105 articles evaluated the definitions of acuity, acuity tool, acuity system, barriers to care, and distress. Two articles outside the dates covered by the search were included because they contributed differentiation between caseload versus workload.4,5 Acuity was defined in 6 articles; however, there is no universal definition for acuity. In 3 articles, a basic definition of acuity was discussed as being the amount of time spent caring for a patient.6-8 Brennan and Daly8 clarified the concept of acuity and touched upon the need to separate acuity from productivity. The authors proposed the following definition of acuity: “a measure of the severity of illness of the patient and the intensity of nursing care that patient requires.”

More complex definitions of acuity were found in the other 3 articles—these incorporate its subsequent effect on workload or case management4,9 or its ability to capture functional and health limitations.10 Two of the articles defined acuity tool/system/scale as systems for quan­tifying patient severity/acuity to determine caregiving staffing.5,11 Barriers to care, although discussed in several articles included in the review, was only formally defined in 1 article as being “associated with screening, late presentation to care, and lack of treatment, which in turn result in poor health outcomes and health disparities.”12 Eight of the articles defined distress, which was generally well characterized, as a multifactorial unpleasant emotion that may affect one’s ability to cope or manage disease.13-20 Barry and colleagues13 further stated that distress may be experienced with or without a psychiatric syndrome and “can affect a patient’s ability to cope with and manage disease which, in turn, may affect health outcomes.”

Huber and Craig4 encouraged the selection of valid tools and emphasized the need for case management to be “front loaded,” with most of the effort in the beginning of treatment; this level of effort is usually not required to be maintained over time. The front-loaded concept is demonstrated by the navigators’ experience with their patients and supports the idea of acuity. The authors also discussed “acuity differential,” which reflects the outcome of reduction in complexity of core case management factors due to case manager interventions. Reduction in complexity, and thus acuity, corresponds directly to reductions in client symptoms, inappropriate case environment, and ineffective provider use.4

2. Categorization of Barriers to Care

Thirty-one (29.5%) of 105 articles evaluated the categorization of barriers to care. Boehmer21 provides 4 categories related to barriers to care: (1) patient-level barriers, (2) patient-physician relationship, (3) provider characteristics, and (4) system factors. Similarly, many articles presented 3 major distinct categories of barriers to care: patient-related, provider-related, and system-related.22-25 These categories of barriers align with the National Comprehensive Cancer Network Distress Thermometer, which incorporates practical problems, family problems, and emotional problems in the determination of a problem list to measure distress.18 Carrillo and colleagues12 categorized barriers to healthcare access as structural, financial, and cognitive, discussed their interplay, and included evidence-based examples of health outcomes associated with each type of barrier. The authors noted that these barriers lead to decreased prevention, late presentation, and decreased care. In the discussion of barriers to care by Guadag­nolo and colleagues,26 factors contributing to health dis­parities were identified as: (1) patient-related (ie, mistrust, low health literacy, socioeconomic status), (2) physician-related (ie, lack of cultural competency), and (3) healthcare system–related (ie, funding status). In another article, barriers were classified as either financial or nonfinancial.27

Some articles provided further subcategorization of barriers (eg, dividing patient-related issues into psychosocial issues and communication issues28 or into communication and emotional barriers29). Other articles identified physical/geographic and logistical/transportation issues as a separate major category.30-35 Esparza36 categorized barriers into 7 groups: (1) geographic (rural or inner city health professional shortage area and lack of capacity), (2) cultural (patient’s and provider’s beliefs and behaviors toward one another, disease, or the medical system), (3) socioeconomic (insurance, inability to pay for services, lower education), (4) system/organizational (long wait times, limited hours, not access friendly), (5) linguistic and communication, (6) social (child care issues, lack of support system), and (7) racism and provider bias. Other articles also categorized barriers into more than 5 groups,37-41 but these additional distinctions may be more appropriate as subgroups of the larger 3 categories discussed above. Korber and colleagues38 listed barriers and enhancers to breast cancer treatment according to the following themes: (1) education and information (which have a critical role), (2) symptom management, (3) support, (4) treatment to meet individual needs, (5) importance of teamwork, (6) resource assistance, (7) coordination of care, (8) role definition, and (9) survivorship.

Other articles did not categorize barriers but instead described specific types of barriers, gave additional perspectives, or provided other interesting findings. Studies of minorities described several specific barriers to care, including difficulty coordinating services or providing therapeutic interventions,42 belief in alternative therapy,43 and issues related to privacy/trust.44,45 In a similar manner, other articles gave perspectives that focused exclusively on financial barriers46 and barriers to care from the navigator’s standpoint.47 Additional studies discussed differences in barriers to care among married and unmarried patients,48 barriers that are common in newly diagnosed patients,49 and variables that account for racial differences in time until treatment.50

3. Relationship Between Barriers to Care and Acuity

Seven (6.7%) of 105 articles evaluated the relationship between barriers to care and acuity. Patients with low acuity were found to need less help with services such as education, care coordination, travel, and follow-up.51 Unsurprisingly, the total number of barriers to care was found to predict increased navigation time39,52 and time to resolution.23,39 More specifically, barriers such as being unmarried and unemployed were associated with increased navigation assistance.49 Carroll and colleagues52 analyzed the time needed for managing common barriers to cancer treatment and found that each additional barrier resulted in a 16% increase in navigator time. This finding supports the concept that barriers do need to be weighted as to the acuity, with the greater the number, the greater the resulting acuity. As such, the development of an acuity tool should also address the need to measure barriers objectively using different weights for individual barriers versus relying on subjective measures.

Lin and colleagues53 analyzed factors that impact a patient navigator’s time and noted that some barriers (financial, transportation, and fear/feelings about cancer) required more time, but the amount of navigator time spent per patient was consistent. This finding supports the idea that barriers do not need to be weighted in an acuity tool. The type of barrier did not have much effect on timeliness of care. Therefore, the authors argued that navigation services should be directed to patients with the most need—those facing barriers and delays. This supports the use of acuity to drive the actions of the navigator.

In a systematic review of conceptual frameworks to inform medical complexity, Zullig and colleagues10 stated that “contextual factors, including interpersonal, organization, and community factors, may drive complexity for patients and moderate the association between complexity and outcomes.” The authors noted the potential interaction between contextual factors and cultural and language barriers, which may influence healthcare quality.

4. Relationship Between Distress and Acuity

Fourteen (13.3%) of 105 articles evaluated the relationship between distress and acuity. Two studies found that emotional distress was strongly associated with compromised performance status in cancer patients.54,55 However, in an adjusted analysis, Barry and colleagues13 found that even low-acuity patients, especially those who were unemployed or disabled, are at risk for distress. Johnson and colleagues15 noted that younger patients may be more vulnerable to distress due to added developmental stress (eg, starting a family, completing education, postponing life goals). Kim and colleagues54 had similar findings that showed the effects of emotional distress on performance decline were increased in younger patients (<45 years) compared with older patients (>65 years).

Examples of major sources of distress for oncology patients include practical, family, emotional, spiritual, and physical problems56 such as pain, balance/mobility issues, and fatigue.57 There is evidence in the literature to suggest that distressed patients tax the healthcare system and utilize services at a higher rate than nondistressed patients.15 However, somewhat surprisingly, a more recent study also found that patients with more severe melanomas required significantly less psycho-oncologic support than those with less severe melanomas.58

Distress levels seem to stem from personality factors such as resilience and trait anxiety, and both Harding14 and VanHoose and colleagues59 found that trait anxiety/worry was the strongest predictor of distress. Fortunately, patients have been found to report fewer symptoms of distress after just 3 or 4 visits with a navigator.60

Warmenhoven and colleagues61 found that depression in palliative care patients is associated with poor outcomes in the palliation of physical symptoms, treatment adherence, and prognosis. De La Cruz and colleagues31 examined the impact of depression on the intensity of patient navigation. Due to higher utilization of healthcare services generally needed for depression, the authors reported that this condition could require additional navigation support to improve outcomes. Although linked to distress, depression was not found to change navigation time in women.30

Evidence also suggests that a higher cancer stage or more functional impairment is associated with higher spousal distress and therefore poorer work productivity.62 Lastly, studies of different distress measures have found that scores on the Behavioral Health Status Index and the Distress Thermometer are highly correlated63; however, distress scales also seem to be highly correlated with physical symptoms burden,61 suggesting that current tools may not actually be good measures of psychological distress.

5. Existing Acuity Tools

Twenty-two (21.0%) of 105 articles evaluated existing acuity tools. Three articles were published before 2008 and were outside the dates covered by the search. Shaha and Bush5 stressed the value of classification to be “most valid and reliable when its main or sole purpose is to describe the relative intensity of patients.” In 2006, Balstad and Springer64 appeared to be the first to indicate the need for a tool that measures patient acuity in the hospital nursing case management setting and the importance of capturing the variability that exists within patient demands in nursing care. Huber and Craig4 introduced the concept of the outcome of patient management (navigation) with the greater impact on patients with large acuity differential.

Since then, several oncology acuity tools have been reported, including:

  • Mitchell Cancer Institute’s oncology nurse navigation acuity tool65
  • Billings Clinic’s patient navigation acuity scale66
  • Oncology Acuity Tool67
  • Acuity Scale for Oncology Outpatient Infusion Room68
  • Cleveland Clinic Cancer Center’s Medical
  • Oncology Acuity of Care Rating System69
  • Lehigh Valley Health Network’s acuity scale70
  • Patient Navigator Acuity Tool71
  • Acuity-based staffing tool for chemotherapy
  • infusion department72
  • Warren Grant Magnuson Clinical Center’s patient intensity tool73

In their acuity-based staffing tool, Vortherms and colleagues72 provided a worksheet for assignment of patient acuity points, specifying acuity criterion and predetermined maximum acuity points.

In addition, the following nononcology-specific tools have been reported:

  • Patient Acuity Case Management Evaluation Tool4,64
  • BluCuity Scale74
  • Caseload Intensity Tool75
  • Patient Classification System76
  • Kilgore Heart Failure Case Management Acuity Tool77
  • Permanente Online Interactive Network Tool78
  • Intermountain Health Care’s acuity tool5
  • Association of UK University Hospitals’ tool79
  • Patient Centred Assessment Method80

6. Factors Related to Acuity

Thirty-one (29.5%) of 105 articles evaluated factors related to acuity such as productivity, ROI, staffing/caseload, effectiveness, and time management. One article outside the dates covered by the search contributed concepts of outcomes that are related to changes case managers make in their clients’ care. Huber and Craig4 developed groundwork showing that acuity differentials had a more direct relationship with actual care management activities than did the outcome measures such as ROI, hours billed, case life, and savings. Typically, as the number of barriers to care increases for a patient, so does the patient’s acuity score.81 Both barriers and acuity tend to be positively correlated with navigation time,81 suggesting that high-acuity patients used more resources per patient than low- or medium-acuity patients.65 Patient severity or condition instability has also been associated with the need for more intensive therapies, monitoring, and interventions.67 This evidence suggests that determining patient acuity is the best way to identify optimal patient caseloads for navigators, and that navigators who have patients with a higher acuity level should ideally have a lesser volume of patients.82

According to the American Nurses Association, an unanticipated outcome of implementing a patient classification and acuity system is the multidisciplinary collaboration involved and the opportunities resulting from this process.11 Chapman and colleagues83 underscore the importance of developing an acuity tool for community nursing in the context of its vital role in helping patients remain independent and manage their long-term conditions in conjunction with person-centered, preventive, and coordinated care. Although staffing models that consider a patient’s condition, severity, or acuity require evaluation of these factors throughout the day to be implemented,84 acuity-based systems can, in the long run, help hospitals use existing staff more efficiently.69,85,86 One example of a way to estimate workload is to multiply the volume of tumor by the patient’s acuity level.66

Several important factors need to be considered when determining nurse caseload, including a patient’s physical care requirements, health educational needs, psychosocial needs, and social support.87,88 Johnson and colleagues15 as well as Kim and colleagues54 noted that younger individuals’ distress is high due to family issues, whereas older individuals’ distress is due to decline in mobility. Nurse competency can also make a difference when it comes to staff assignment decisions.89 Currently, there are quite a few classification-based nurse staffing tools for caseload described in the literature.

Baldwin and Jones65 describe 11 factors of the Mitchell Cancer Institute Oncology Nurse Navigation Acuity Tool that influence acuity: (1) staging at diagnosis; (2) receiving multiple treatment modalities; (3) type of chemotherapy (multiagent vs single agent vs oral agents); (4) treatment status (new vs active treatment); (5) Eastern Cooperative Oncology Group performance status; (6) comorbidities; (7) hospitalizations; (8) colostomy, ileostomy, tracheostomy, feeding tube; (9) noncompliance with treatment; (10) family support; and (11) Patient Health Questionnaire 2/9 depression screening.

Mascarenhas and colleagues90 examined the correlation between caseload and acuity to determine if there were potential factors affecting productivity in oncology nurse navigation. The authors observed that head and neck and genitourinary patients presented the highest average acuity with the lowest caseload.90 Additionally, Willhite81 found that barriers and acuity scores correlated with navigation time, whereas distress scores did not show this correlation. O’Keeffe91 elaborated on the psychosocial component of patient-related factors affecting nurse workload in terms of nursing tasks related to monitoring and intervention correlating with mental disabilities, end-of-life care, palliative care, and including personal and family dynamics. Lastly, in an analysis of resource use and Medicare costs during lay navigation for geriatric patients with cancer, Rocque and colleagues92 found that supportive care interventions decrease acuity.

7. AONN+ Standardized Metrics and Patient Acuity Measures

Seven (6.7%) of 105 articles helped to evaluate the level of support in the literature to demonstrate whether any of the AONN+ Standardized Metrics are patient acuity measures. The AONN+ Standardized Metrics include 35 evidence-based oncology metrics used to measure the impact of patient navigation programs based on patient experience, clinical outcomes, and ROI.93

The ultimate goal of cancer navigation programs is to provide high-quality care with improved clinical outcomes and a high ROI.93 Metrics are used to evaluate the success of a navigation program and can include assessments of patient experience, clinical outcomes, and business performance.94 Additionally, treatment adherence, barriers to treatment, and patient satisfaction with care and navigation services are also important patient-related outcome measures for navigation services.95 Research suggests that patient navigation metric domains include measures of quality of life, satisfaction with care, social support, distress, and caregiver burden.90 Palos and Hare96 discuss navigation metrics to measure patient and/or caregiver outcomes that were agreed upon at the American Cancer Society’s National Leadership Summit. Patient navigation metric domains were: (1) quality of life, (2) satisfaction with care, (3) social support, (4) distress, and (5) caregiver burden.

The 35 metrics of patient navigation have only recently been identified, and more research needs to be done to evaluate if and how they relate to patient acuity. Nevertheless, previous studies of patient navigation programs have found that a more advanced stage of cancer at initial diagnosis predicted shorter times to consultation97 and that physical symptoms, psychological distress, and depression symptoms are associated with longer hospital stays, whereas physical and anxiety symptoms are associated with a higher likelihood for readmission within 90 days.98 Lastly, it has also been found that health centers with higher proportions of poor patients had smaller caseload sizes, suggesting that poorer patients generate a higher workload for doctors, most likely due to their more significant comorbidity profile.99

Discussion

Limitations

As described in Table 2, the included articles presented several limitations for the scoping review. These limitations need to be thoroughly considered in the development of an acuity tool that can maximize the effectiveness and ROI of oncology patient navigation programs.

keyconsiderations_table2.png

Key Findings

In summary, our database search strategy yielded 1711 records from which 199 full-text articles were reviewed and narrowed down to 105 relevant articles for inclusion in this scoping review. Findings from our review and analysis of relevant articles indicate a high level of variability and complexity in how acuity and its related factors are defined and determined. There is a lack of consensus on the definition of acuity, and no standard categorization of barriers to care exists. The relationships between barriers to care and acuity and between distress and acuity have not been clearly established. Existing acuity tools present additional shortcomings in their application to an oncology patient navigation-specific tool. The current literature also fails to provide sufficient support to demonstrate whether any of the AONN+ Standardized Metrics are patient acuity measures.

Implications for Practice

The learning outcomes from this scoping review will help to inform the development of an oncology patient navigation-specific acuity tool. When finalized, the acuity tool will be used to help oncology navigators characterize the intensity of patient navigation workload, aid in the allocation of resources, and measure the effectiveness of navigation on patient outcomes. The acuity tool will support and enhance oncology navigators’ effectiveness through patient-centric evidence-based methods that may potentially decrease the overall cost of care.

Conclusions

The importance of acuity tools and their potential to improve patient outcomes are well recognized. Nevertheless, the results of this scoping review demonstrate a continuing need for additional research to better understand how oncology navigation acuity is defined and determined. Further study is also required to evaluate how AONN+ Standardized Metrics perform as patient acuity measures. Addressing these gaps will facilitate the development of an acuity tool to optimize the effectiveness of oncology patient navigator programs.

Acknowledgments

AONN+ gratefully acknowledges Astellas US, LLC, for its collaboration on the Oncology Navigation Acuity Initiative. The Acuity Tool may be used to help oncology navigators characterize the intensity of patient navigation workload, aid in the allocation of resources, and measure the effectiveness of navigation on patient outcomes. This initiative is intended to support and enhance oncology navigators’ effectiveness through patient-centric evidence-based methods that may have the potential to decrease the overall cost of care. The authors would also like to acknowledge the Acuity Committee members for their expertise, dedication, and time on this very important project: Cheryl Bellomo, MSN, RN, OCN, ONN-CG; Nicole Erb; Barbara Hale, MSW, LCSW; Rita Kristy, MS; Rani Khetarpal; Wendy Latash, PhD; Nicole Messier, BSN, RN, OCN; Colleen Sullivan-Moore, RN, MS; and Lianna Willhite, BSN, RN, ONN-CG, CBCN.

Disclosures

Rita Kristy, MS, and Wendy Latash, PhD, are employees of Astellas US, LLC.

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