Development of an Acuity Scale for Population Health Navigation

November 2019 Vol 10, No 11 —October 25, 2019
Carla Strom, MLA
Wake Forest Baptist Comprehensive Cancer Center
Maria Alejandra Combs, JD, OPN-G
Wake Forest Baptist Comprehensive Cancer Center,
Winston Salem, NC
Kelsey Shore, BA, CCRC
Wake Forest Baptist Health,
Winston Salem, NC
Karen Winkfield, MD, PhD
Wake Forest Baptist Comprehensive Cancer Center

Background: The complexities of cancer care can be reduced for patients and families through patient navigation. However, since resources are often limited, institutions need to identify the best way to allocate navigation services and identify the level of care needed by each patient. We developed a navigator acuity scale for non-nurse population health navigation (PHN), with a specific focus on providing support to traditionally underserved populations. The purpose of the instrument is to identify the level of navigation services required and stratify care based on characteristics and barriers patients face.

Objective: To develop a culturally sensitive instrument that measures intensity of need in order to determine the amount of navigation services needed for new, underserved cancer patients to be provided by a navigator.

Methods: A retrospective chart review was performed to identify a list of patient characteristics and barriers identified from 3 years of cancer navigation experience in the Hispanic population. Patient characteristics include: disease site, age, disability, comorbidities, treatment factors, and clinical trial participation. Barriers to care include: insurance status, rural/urban designation, distance traveled, language, health literacy, transportation, and treatment adherence. These parameters were utilized to design an acuity scale to estimate the level of navigation intensity. The levels of intensity are classified as no navigation and low, medium, and high navigation. The scale was applied to two cohorts. Feasibility of implementation and scale validity were assessed in a retrospective cohort of patients navigated by the Hispanic patient navigator (HPN) from 12/1/18-2/28/19. The acuity score was applied and then compared to the initial estimate of level of navigation needed and the services rendered, as reported by the HPN. Once the acuity scale was validated in the retrospective cohort, it was implemented within the population health navigation program and applied prospectively to a new cohort of patients navigated by the HPN from 3/1/19-5/31/19.

Results: In the retrospective cohort (n = 20) most patients navigated by the HPN required a low level of assistance (no navigation, n = 5; low, n = 8; medium, n = 5; high, n = 2). When compared to the initial estimate of time the HPN documented would be required, 75% of the scores (n = 15) were consistent, but there was a 25% rate of discordance (n = 5) of patients who were thought by the HPN to require less navigation but ranked higher on the acuity scale. A review of services indicated the acuity scale was more accurate than HPN estimation. The prospective cohort (n = 15) revealed a more diverse level of patient need (no navigation, n = 5; low, n = 3; medium, n = 3; high, n = 4). Comparisons in the prospective group revealed a 93% rate of concordance (n = 14), yielding an 18% increase in congruence following instrument implementation.

Conclusion: The acuity scale developed has high correlation with the navigator-reported level of assistance but is more accurate at reflecting patient needs. Implementation of the acuity scale will be applied to the rural and African-American patient navigator programs to further determine feasibility and inter-rater and subpopulation reliability. Future plans include integration of the tool into the Electronic Medical Record and utilization to adjust navigator workload by allowing PHNs to prioritize patient interactions.

Sources

  1. Baldwin D, Jones M. Developing an acuity tool to optimize nurse navigation caseloads. Oncology Issues. 2018;33(2):17-25.
  2. Brennan CW, Meng F, Meterko MM, et al. Feasibility of automating patient acuity measurement using a machine learning algorithm. J Nurs Meas. 2016;24:419-427.
  3. Hopkins J, Mumber MP. Patient navigation through the cancer care continuum: an overview. J Oncol Pract. 2009;5:150-152.
  4. Jones PA. What works: measuring acuity on a medical-surgical unit. American Nurse Today. www.americannursetoday.com/works-measuring-acuity-medical-surgical-unit. 2015.
  5. Strusowski T, Sein E, Johnston D, et al. Standardized Metrics Source Document. https://aonnonline.org/images/articles/standardized_metrics/Metrics-Source-Document.pdf. 2017.
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