Application of a Navigator Acuity Scale on Diverse/Underserved Populations

November 2020 Vol 11, No 11
Carla Strom, MLA
Wake Forest Baptist Comprehensive Cancer Center
Maria Alejandra Combs, JD, OPN-G
Wake Forest Baptist Comprehensive Cancer Center,
Winston Salem, NC
Emily Copus, MSW
Wake Forest Baptist Comprehensive Cancer Center
Alexis Daniels, MS
Wake Forest Baptist Comprehensive Cancer Center
Kelsey Shore, BA, CCRC
Wake Forest Baptist Health,
Winston Salem, NC
Karen Winkfield, MD, PhD
Wake Forest Baptist Comprehensive Cancer Center

Background: The use of acuity scales to assess cancer patient needs when undergoing treatment is gaining recognition as a way to optimize caseloads for patient navigators (PNs). We developed a novel approach to a non-nurse navigator acuity scale to systematically allocate navigation services in 3 medically underserved populations: Hispanic, African American (AA), and Rural. The role of these non-nurse population health navigators (PHNs) is to assist patients undergoing cancer treatment in a culturally sensitive and linguistically appropriate manner. The instrument is designed to preidentify the level of navigation services required to inform stratified care based on patient characteristics (PCs) and barriers.

Objective: Assess the application of the PHN acuity scale to diverse populations and identify areas where cultural adaptation may be required to tailor resources and/or interventions.

Methods: The level of navigational services (intensity) is based on acuity scores determined by PCs and barriers to care. Each individual characteristic or barrier is assigned a score of 0, 1, 2, or 3, with intensity levels increasing exponentially. The intersecting scores are combined to generate 4 intensity levels classified as no, low, medium, and high navigation. PCs include disease site, cancer phase, distress level, age, comorbidities, disabilities, treatment factors, and clinical trial participation. Patient barriers (PBs) include insurance status, impact area, travel distance, language/literacy, social supports, transportation, and treatment adherence. These are given scores ranging from 0 to 20+. Acuity scale data collected prospectively by the PNs during the first year of implementation (6/15/2019-6/15/2020) were abstracted for analysis.

Results: In this cohort (N = 198: Hispanic, n = 69; AA, n = 65; Rural, n = 64), most patients have medium to high overall intensity levels (medium 43.9%, high 11.1%). Rural patients tend to have the low intensity levels (62.5%), whereas 44.6% of AAs are at the medium level, and Hispanics have the largest percentage of high-intensity patients (11.6%). The average scores for PCs and PBs, the 2 components that comprise the overall intensity score, also varied by patient population, with AA having the highest average PC score and Rural having the lowest average PB score (AA 9.7, 7; Hispanic 8.7, 8.4; Rural 8.9, 4, PCs and PBs, respectively). The most common PC present in all populations that directly impacted intensity level was cancer type. The most common PB was a combined factor labeled Impact Access (IA) (low income/socioeconomic status [SES], work/school issues = 1; documentation status, legal issues = 2; homelessness, prisoner = 3). IA barriers were identified in 27.3%, 69.7%, and 17.2% of low-, medium-, and high-intensity patients, respectively. The specific IA barriers varied by population, with low income/SES and school/work issues being the most common for AA (26.3%) and Rural (79.7%), and documentation status/legal issues for Hispanics (59.1%).

Conclusion: These data confirm that the developed acuity scale is accurately reflecting the level of navigation required for the underserved populations being cared for but highlight differences based on the characteristics of each population. Future plans include tailoring the instrument based on key findings obtained during the first year of implementation to further refine future navigation processes.

Sources

Baldwin D, Jones M. Developing an acuity tool to optimize nurse navigation caseloads. Oncology Issues. 2018;33(2):17-25.

Brennan CW, Meng F, Meterko MM, D’Avolio LW. Feasibility of Automating Patient Acuity Measurement Using a Machine Learning Algorithm. Springer Publishing Company. https://connect.springerpub.com/content/sgrjnm/24/3/419.

Hopkins J, Mumber MP. Patient navigation through the cancer care continuum: an overview. J Oncol Pract. 2009;5:150-152.

Jones PA. What works: Measuring acuity on a medical-surgical unit. American Nurse. www.americannursetoday.com/works-measuring-acuity-medical- surgical-unit.

Strusowski T, Sein E, Johnston D, et al. Standardized Metrics Source Document. Academy of Oncology Nurse & Patient Navigators. https://aonnon line.org/images/AONN/metrics-landing-page/Source-Document-February- 2017.pdf.

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