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The American Journal of Managed Care February 2018
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Community Navigators Reduce Hospital Utilization in Super-Utilizers
Michael P. Thompson, PhD; Pradeep S.B. Podila, MS, MHA; Chip Clay, MDiv, BCC; Joy Sharp, BS; Sandra Bailey-DeLeeuw, MSHS; Armika J. Berkley, MPH; Bobby G. Baker, DMin, BCC; and Teresa M. Waters, PhD
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Community Navigators Reduce Hospital Utilization in Super-Utilizers

Michael P. Thompson, PhD; Pradeep S.B. Podila, MS, MHA; Chip Clay, MDiv, BCC; Joy Sharp, BS; Sandra Bailey-DeLeeuw, MSHS; Armika J. Berkley, MPH; Bobby G. Baker, DMin, BCC; and Teresa M. Waters, PhD
Super-utilizers place a significant clinical and financial burden on the healthcare system. The authors investigated the effectiveness of community navigators in reducing hospital utilization and costs.

Objectives: Super-utilizers place a significant burden on the healthcare system. Blending the roles of patient navigators and community health workers may address the clinical and social needs of these patients. This study evaluated the effectiveness of community navigators in reducing hospital utilization and costs among super-utilizers from a low-income area in Memphis, Tennessee.

Study Design: Controlled pre-post (difference-in-differences [DID]) design using Methodist Le Bonheur Healthcare electronic health records from 2013 to 2016.

Methods: Data were abstracted for 1 year pre- and post intervention for super-utilizers working with a community navigator (n = 159) and a control group of similar super-utilizers (n = 280). We compared utilization (hospital encounters, total hospital days, days between encounters, 30-day readmissions) and costs before and after working with a navigator for the intervention group with utilization and costs in a control group not working with a navigator and compared relative changes using a DID approach.

Results: Utilization and cost outcomes for intervention and control groups declined significantly from the pre- to postintervention periods. Relative to the control group, super-utilizers working with community navigators had an additional 13% reduction in hospital encounters (95% CI, –19% to –6%), 8% reduction in total hospital days (95% CI, –14% to –2%), and 9% increase in days between encounters (95% CI, 4%-15%). The intervention group also had additional reductions in 30-day readmissions (–18%; 95% CI, –44% to 22%) and costs (–$4903; 95% CI, –$13,579 to $3774), but these were not statistically significant.

Conclusions: Community navigators can reduce subsequent hospital utilization in super-utilizers. Expansions of this model should examine the model’s effectiveness in other populations and outcomes.

Am J Manag Care. 2018;24(2):70-76
Takeaway Points
  • Blending the roles of patient navigators and community health workers may address the complex clinical and social needs of super-utilizers, who have high healthcare utilization  rates and expenditures and place a significant burden on the healthcare system.
  • Working with super-utilizers from low-income neighborhoods, community navigators made significant reductions in the super-utilizers’ hospital encounters and hospital days, and these super-utilizers experienced longer gaps between subsequent hospital encounters, all compared with a control group not working with navigators.
  • The community navigator model is a clinically impactful and potentially cost-saving approach to improving the health and reducing the clinical and financial burden of super-utilizers.
“Super-utilizers” is a term referring to patients who have high rates of healthcare utilization and high medical expenditures and place a heavy burden on the healthcare system.1 Although super-utilizers make up roughly 5% of all patients, they account for half of all expenditures.2 Super-utilizers typically have serious and often multiple comorbidities and are hospitalized more frequently for uncontrolled chronic conditions, such as congestive heart failure, chronic lung conditions, and diabetes, compared with typical patients.3,4 They are also more likely to suffer from poor mental health and substance abuse problems.5-7

Interventions to improve the health of super-utilizers and reduce their healthcare utilization need to address the diverse medical, behavioral, and social needs of these patients in both inpatient and outpatient settings. Patient navigators have emerged as a potential solution for low-income minority patients in cancer care.8 They are intended to aid patients in overcoming barriers to obtaining timely and quality healthcare and to guide patients through the continuum of care.9,10 Although navigators for patients with cancer have been shown to improve screening rates and cancer stage at diagnosis, the evidence supporting similar programs for underserved patients with multiple chronic conditions is limited.11

More recently, community health workers (CHWs) have become popular as a cost-efficient solution to help high-risk vulnerable patients navigate the healthcare system. Evidence has shown that CHWs can be successful in connecting patients to ambulatory care and reducing subsequent hospital utilization following discharge.12-15 CHWs differ from patient navigators in that they are typically trusted lay members of the community who liaise between health and social services to provide aid and support consistent with patients’ values and needs.16-18 Combining the roles of patient navigators and CHWs may maximize the efficiency and effectiveness of programs intended to aid vulnerable populations, such as super-utilizers.

The Familiar Faces Program

Memphis, Tennessee, is the poorest metropolitan area of at least 1 million citizens in the nation, with an overall poverty rate of 20.3%, which is largely due to disproportionate poverty rates among blacks (29.9%) and Hispanics (34.9%) compared with whites (10.4%).19 As part of the ongoing efforts to improve healthcare in Memphis, the Methodist Le Bonheur Healthcare (MLH) system developed its Familiar Faces program to address the needs of super-utilizers in one of the poorest zip codes (38109) in the Memphis area. Residents of 38109 have been shown to contribute to a significant proportion of healthcare utilization and costs in the greater Memphis area.20 A previous study of super-utilizers with multiple chronic conditions in Memphis found that many individuals lacked sufficient outpatient care and admissions were often related to uncontrolled chronic diseases.21

The Familiar Faces program worked to address the needs of the community by blending the roles of CHWs and patient navigators into a single role of community navigator. Similar to CHWs, the community navigators employed by the Familiar Faces program are members of the community, with some having medical training and others having nonmedical professional experience. In addition to connecting clients to health and social resources in their community, the community navigators focus on building trust between the client and navigator and subsequently with other healthcare entities and social systems in the community. However, unlike CHWs, the community navigators are employed and embedded within the hospital system and receive the training often provided to patient navigators, such as background education on the role of patient navigation, motivational interviewing, ethical decision making, health literacy, client communication strategies, professional conduct and boundaries, and client resources.

To be eligible for the Familiar Faces program, an individual had to have 11 or more MLH hospital encounters originating in the emergency department (ED) during a predetermined 1-year screening period (ie, be a super-utilizer) and be a resident of the 38109 zip code. There were 2 cohorts of Familiar Faces clients served by the program, with additional cohorts currently in progress. Clients in cohort 1 (n = 84) were identified between May 1, 2012, and April 30, 2013, and began receiving services from the first community navigator in January 2014 (Figure). Clients in cohort 2 (n = 75) were identified between January 1, 2014, and December 31, 2014, and began receiving services on January 1, 2015, from a second community navigator.

Upon the start date for each cohort, when an eligible Familiar Faces client had an encounter at an MLH hospital, the electronic health record (EHR) system notified the community navigator via text message to meet the client in the ED or in the hospital if they were admitted for observation or to an inpatient setting. The community navigator then engaged with the client to create a partnership by offering their services. If the client accepted, they became part of the Familiar Faces cohort. If they declined, the community navigator would continue to receive text messages each time they returned to the ED, and they would again engage with the client about their services. Ultimately, only 3 clients refused services when approached by the community navigators during the intervention period.

The goal of the client–community navigator partnership was to identify underlying causes for frequent ED encounters and to develop a plan to change clients’ health behaviors. The services offered by the community navigators included linking clients to community resources, helping to identify and eliminate barriers to health, coordinating care, tailoring health information to client needs, and motivating them to make healthy choices. This partnership was designed to last for 1 year following the initial engagement of the client, at which point the clients would ideally be prepared to manage their own care moving forward.

The purpose of this study was to ascertain the effectiveness of community navigators in reducing hospital utilization and costs in super-utilizers. To determine the impact of the program, we compared MLH hospital utilization and costs of Familiar Faces clients in the year before and after they worked with a community navigator. To control for typical patterns in utilization and costs in super-utilizers, we compared the changes in utilization and costs observed in Familiar Faces clients with similar data for super-utilizers in contiguous zip codes.


Data Sources, Elements, and Extraction

Clinical data were obtained from the MLH EHR system (Cerner Corporation; Kansas City, Missouri) and total costs were obtained from the CostFlex System (CostFlex Systems, Inc; Mobile, Alabama). Data queries were constructed in both systems to extract and match clinical and cost data for Familiar Faces clients based on unique medical record and encounter identifiers. To act as comparison groups, during the same screening periods as those establishing cohorts 1 and 2, we abstracted clinical and cost data for all individuals who met the criteria for a super-utilizer (n = 280) from contiguous zip codes: 38106 (n = 83), 38114 (n = 61), 38116 (n = 111), and 38126 (n = 25). Comparison group individuals for cohorts 1 and 2 were selected without replacement (ie, are unique patients). We did not abstract comparison group individuals from the 38109 zip code who were either already enrolled in cohorts 1 or 2 or were being engaged in enrollment into future cohorts. Table 1 describes characteristics of the 38109 zip code, the contiguous zip codes, and national averages.


From the clinical data, we abstracted patient demographics (age, gender, and race), insurance status (Medicaid, Medicare, or private insurance), and self-reported primary care provider (PCP) category (having a usual PCP, using a community clinic, or having no PCP). Individuals with no PCP information were categorized as having no PCP. We also created a Charlson Comorbidity Index (CCI) score for each patient and categorized them as having no or mild comorbidity (0-2), moderate comorbidity (3-4), or severe comorbidity (≥5). We also adjusted for the raw CCI score, and our results did not change substantively.

We used clinical data to assess the number of MLH hospital encounters for each patient for 1 year pre- and post intervention; hospital encounters were then stratified by whether the patient was ultimately discharged from the ED or admitted for observation or an inpatient stay. We also assessed 30-day readmissions, total hospital days, average days between hospital encounters, and total costs of care for the pre- and postintervention time periods.

Statistical Analysis

Chi-square and t tests were used to test for significant differences in baseline categorical and continuous variables, respectively, between intervention groups. We then calculated average utilization rates for the intervention and control groups during the pre-intervention and postintervention periods. Using a difference-in-differences (DID) approach, we estimated the relative difference in utilization rates from the pre- to postintervention period for the intervention (difference 1) and control groups (difference 2) and the relative difference between the 2 groups (DID = difference 1 / difference 2). Using a generalized linear model, the DID estimate is the coefficient on the interaction term between intervention status and pre- versus postintervention indicator. We employed a log-link in the generalized linear model to estimate relative rates for utilization outcomes. We also considered a Poisson regression model, and our results did not change substantively. Relative changes are presented as relative rate reductions ([1 – relative rate] × 100%) or relative rate increases ([relative rate – 1] × 100%) if the relative rate was less than or greater than 1, respectively.

We estimated the average annual costs per patient incurred by the intervention and control groups pre- and post intervention. Similarly, we used a DID approach to estimate the pre- versus post­intervention differences in costs for the intervention Familiar Faces clients (difference 1) and control group (difference 2) and the absolute difference in costs between the 2 groups (DID estimator = difference 1 – difference 2). Because the cost data were skewed, we explored generalized linear models with a log-link and other models for count data, such as Poisson and negative binomial regression models. Our results using these alternative model specifications did not change our findings substantively.

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