A navigation program demonstrated decreased odds of repeat emergency department (ED) visits in patients with low baseline ED utilization and increased odds of follow-up primary care appointments.
Objectives: Our study examines the impact of an emergency department (ED) patient navigation program for patients in a Medicaid accountable care organization across 3 hospitals in a large health system. Our program engages community health workers to (1) promote primary care engagement, (2) facilitate care coordination, and (3) identify and address patients’ health-related social needs.
Study Design: Our study was a retrospective analysis of health care utilization and costs in the 30 days following the index ED visit, comparing individuals receiving ED navigation and matched controls. The primary outcome of interest was all-cause return ED visits, and our secondary outcomes were hospital admissions and completed primary care appointments.
Methods: Patients with ED visits who received navigation were matched to comparable patients with ED visits without an ED navigator interaction. Outcomes were analyzed using fixed effects logistic regression models adjusted for patient demographics, ED visit characteristics, and preceding utilization. Our primary outcome was odds of a return ED visit within 30 days, and our secondary outcomes were odds of a hospitalization within 30 days and odds of having primary care visit within 30 days.
Results: In our sample, there were 1117 ED visits by patients meeting our inclusion criteria with an ED navigator interaction, with 3351 matched controls. ED navigation was associated with 52% greater odds of a completed follow-up primary care appointment (odds ratio [OR], 1.52; 95% CI, 1.29-1.77). In patients with no ED visits in the preceding 6 months, ED navigation was associated with 32% decreased odds of repeat ED visits in the subsequent 30 days (OR, 0.68; 95% CI, 0.52-0.90). There was no statistically significant impact on return ED visits in those with higher baseline ED utilization.
Conclusions: Our program demonstrates that high-intensity, short-term patient navigation in the ED can help reduce ED visits in those with low baseline ED utilization and facilitate stronger connections with primary care.
Am J Manag Care. 2022;28(5):201-206. https://doi.org/10.37765/ajmc.2022.89140
In this study, patient navigation in the emergency department (ED) was associated with 52% greater odds of a completed follow-up primary care appointment. In patients with no ED visits in the preceding 6 months, navigation was associated with 32% decreased odds of repeat ED visits in the subsequent 30 days.
In the United States, approximately 33% of adults and 13% of children enrolled in Medicaid insurance programs report barriers to finding a doctor or delays in receiving care despite having a usual place of care.1 These barriers include lack of access to non–emergent care settings resulting from physical and economic barriers, as well as issues related to various social factors.2,3 Medicaid enrollees, in part because of these obstacles, have been shown to use the emergency department (ED) 6 to 7 times more often than privately insured patients.4
Traditionally, EDs and systems of emergency care have not been designed to actively address barriers to care, the social conditions underlying many acute presentations, or patterns of frequent utilization for low-acuity conditions. To address these challenges, the ED Navigator Program was created in March 2018 by Mass General Brigham (MGB; formerly Partners Healthcare), a large health system based in Boston, Massachusetts, that provides care for 130,000 Medicaid members and more than 700,000 patients overall enrolled in its affiliated accountable care organizations (ACOs).
The ED Navigator Program was launched at 3 of the 8 general acute care hospitals’ EDs within our health system—Brigham and Women’s Hospital, Massachusetts General Hospital, and North Shore Medical Center—which were selected by population health management (PHM) leadership. Supported by the system’s Medicaid ACO and PHM program, each hospital hired and embedded 1 ED navigator, a layperson with experience in the health care or social services sector, into the hospital’s ED. The ED navigators serve as part of the ED care team and are tasked specifically with (1) promoting primary care engagement by scheduling a post–ED discharge appointment with a primary care physician (PCP) and addressing barriers to PCP access, (2) coordinating care for patients already engaged in a PHM program and referring patient candidates to appropriate PHM programs, and (3) identifying patients’ health-related social needs and facilitating connections to community-based resources. The ED navigators generally approach low- or moderate-acuity patients in person during the discharge planning phase of their ED visit. Their patient interactions are deliberately brief, given the nature of ED flow. Therefore, to be most effective during their encounter, the ED navigators typically review the patient’s record, prepare resource materials, and develop an engagement approach based on motivational interviewing and trauma-informed care techniques before approaching the patient in the ED. ED navigators use an internally developed social needs assessment instrument and refer patients to resources using shared resource guides, which are continually updated. Selected resources include housing, food, transportation, and employment support programs. Examples of specific referrals include connection to local housing advocates, food pantries, or public transportation assistance programs. ED navigators also use a translation phone line to ensure that they are able to address the needs of patients, regardless of language. Once a patient has been discharged, the ED navigator follows up with the patient telephonically within 72 hours or coordinates follow-up with a member of the patient’s longitudinal care team.
Several other health systems across the country have implemented different ED-based care coordination models, showing variable results with respect to health outcomes, cost, and utilization.5 However, community health worker interventions based in the ED have demonstrated some of the most promising results to date. Memorial Hermann Health System in Houston, Texas, in a quasi-experimental pre-post study with a comparison group, demonstrated that state-certified, bilingual community health workers working with uninsured and Medicaid patient populations reduced ED visits and generated cost savings ranging from $331 to $1369 per patient after 12 months.6 In a descriptive analysis, Boston Medical Center demonstrated the ability of health promotion advocates in the ED to increase referrals to social support resources.7 Likewise, Erlanger Health System in Chattanooga, Tennessee, performed a randomized controlled trial with patient navigators working with high ED utilizers and demonstrated a decrease in ED visits and costs.8 Given the prior experience of ED-based care coordination and community health worker interventions, we sought to examine the efficacy of our ED Navigator Program in facilitating linkages to primary care and reducing ED visits.
For this analysis, we used clinical and administrative data for patients enrolled in the MGB Medicaid ACO during the period of June 1, 2018 (when the ED Navigator Program was first fully implemented), to October 31, 2019.
Using electronic health records (EHRs), we identified treat-and-release ED visits to the 3 hospital EDs in which the ED Navigator Program was implemented. We excluded ED visits that were high acuity, defined as having an emergency severity index (ESI) score of 1 or 2, or having been triaged to the “acute” pod of the ED. At the hospital that does not use ESI, patients in the acute pod are determined to have a potential illness requiring immediate evaluation. ED visits occurring on Saturday and Sunday were also excluded from the analysis, as the ED Navigator Program is currently available only Monday through Friday. Overnight visits occurring during the week are often followed up by phone the following day, so these were included in the evaluation. We matched these data to Medicaid claims data and removed ED visits that we were unable to match, including ED visits occurring during date spans when patients were not aligned to the ACO or when Medicaid coverage lapsed, or ED visits for substance use services, which were not shared with the ACO. ED visits with multiple records or claims from the same day were collapsed into a single event. We further excluded ED visits for persons with fewer than 3 months of claims data available in the 6 months preceding the ED visit or fewer than 1 month of claims data in the month following the ED visit (eAppendix A [eAppendices available at ajmc.com]). We then determined the number of ED visits, hospitalizations, and PCP visits in the preceding 6 months, also based on claims.
From these data, we identified all ED visits in which ED navigation occurred, limiting the data to the first ED visit with navigation for patients who had multiple encounters with the ED navigator. We developed a matched comparison population using the ED visits from patients who never used the ED navigator and selected 3 matched comparison patients for every intervention patient matched on the number of ED visits (0, 1, 2-3, or ≥ 4), inpatient stays (0, 1, or ≥ 2), and PCP visits (0, 1, or ≥ 2) in the 6 months prior to the ED visit. Only 1 episode per patient was selected. We obtained patient demographics from our system’s EHR data and Medicaid claims data.
For the evaluation, we conducted a retrospective matched analysis of health care utilization in the 30 days following the index ED visit, comparing persons who had navigation with those who did not. Our primary outcome of interest was any return ED visit (both treat-and-release ED visits and those resulting in a hospital admission) during the 30 days following the index ED visit. Our secondary outcomes of interest were any hospital admission or completed primary care appointment in the 30 days following the ED visit. We initially intended to explore missed appointments as a secondary outcome, but rates of missed appointments in both groups were markedly low, so we were unable to pursue this analysis. Thirty days was selected as the period of interest because it was felt to represent a period of time long enough for us to be able to observe an impact from the program but short enough that any observed effect could be reasonably attributable to this brief, limited intervention. This time period was also consistent with existing literature regarding ED return visits.9-11
These binary outcomes were analyzed using fixed effects logistic regression models adjusted for patient age, sex, race, ethnicity, primary language, employment, behavioral health history, ED visit characteristics, hospital, and baseline utilization matching criteria in our logistic regression analysis of return ED visits and follow-up PCP visits within 30 days (eAppendix B). In our logistic regression analysis of hospital admissions within 30 days, we adjusted only for age, sex, hospital, baseline utilization, ED visit characteristics, overnight ED visit, acuity, and history of mood disorder, which each showed independent correlation with the dependent variable so as to avoid overfitting the model, given the small number of 30-day admissions in the sample. ED visit characteristics include acuity level (defined by ESI or triage area), overnight visit (defined as arrival and discharge occurring between 10 pm and 7 am), ambulance arrival, and arrival as outside transfer. We used predetermined definitions based on diagnoses codes in claims data to incorporate indicators for patients’ socioeconomic status or related risk factors. Pediatric status was adjusted for using an age cutoff of 20 years, which is the age used at the included hospitals to triage patients to the separate pediatric section of the ED. Included covariates were selected a priori by the study team in consultation with ED providers, ED navigators, and program leaders to account for factors that may be associated with selection by the ED navigators or those that may be independently associated with our primary and secondary outcomes.
We used fixed effects for each of the 3 included hospitals to adjust for index ED visit hospital. A subgroup analysis was performed, stratifying patients on baseline levels of ED utilization to explore the potentially variable effect of the program on each of these populations. All included variables and subgroup analyses were defined by the study group a priori.
Our analysis was conducted as part of a routine program evaluation using a data repository approved by the MGB Institutional Review Board for retrospective program evaluation.
There were a total of 22,557 treat-and-release ED visits by MGB Medicaid ACO patients during the study period to the hospital EDs included in the ED Navigator Program. Of these, 12,113 visits met our criteria for inclusion and were able to be matched to claims data: 1315 with an associated ED navigator encounter and 10,798 potential control visits.
After matching, our final sample included 1117 intervention patients and 3351 comparison patients. ED visits with an ED navigator encounter were more likely to involve patients who were older, female, Black, married, and employed, and who also had documented anxiety and mood disorders (Table 1 [part A and part B]). Intervention and comparison patients had similar claims availability in the prior 6 months (5.8 months among intervention patients and 5.8 months among comparison patients).
Primary Outcome: 30-Day Return ED Visit
Overall, we were unable to detect a statistically significant difference in the odds of returning to the ED within 30 days for patients who received ED navigation compared with those who did not (odds ratio [OR], 0.88; 95% CI, 0.71-1.08) (Table 2). However, among individuals with no ED visits in the preceding 6 months, the ED Navigator Program was associated with lower odds of subsequent 30-day ED presentation, with an OR of 0.68 (95% CI, 0.52-0.90) (Table 3). However, the results were not statistically significant for individuals with 1, 2 to 3, or more than 3 visits in the preceding 6 months (adjusted ORs, 0.99 [95% CI, 0.64-1.52]; 1.15 [95% CI, 0.72-1.82]; and 1.21 [95% CI, 0.58-2.55], respectively).
With regard to PCP follow-up, after adjusting for included variables, patients who received ED navigation were significantly more likely to have a PCP visit within 30 days (OR, 1.52; 95% CI, 1.29-1.77). No significant difference was observed with regard to 30-day hospital admission (OR, 0.77; 95% CI, 0.47-1.27).
In our analysis, we found that ED navigator encounters were significantly associated with a reduction in return ED visits for patients with no prior visits in the preceding 6 months, as well as increased likelihood of completing a follow-up primary care appointment in the 30 days after the index ED visit for all patients. However, we were unable to identify a statistically significant difference in subsequent ED visits and hospital admission in the ED navigator group overall.
This study demonstrated that ED navigator encounters were significantly associated with increased rates of follow-up with primary care among a Medicaid ACO patient population. Specifically, with regard to primary care, patients with an ED navigator encounter had a statistically significant 52% increase in the odds of having a follow-up primary care appointment in the 30 days following their index ED visit. Notably, primary care practices in MGB already routinely engage in attempting to schedule follow-up primary care visits for patients seen in the ED. Therefore, the increased success of ED navigators in doing so suggests added incremental value in “capturing” patients at the point of care to reengage in long-term management.
With regard to acute care utilization, we found that the effect of the ED Navigator Program on reducing ED utilization was most pronounced among ED-naïve patients (ie, those with no prior ED visits in the preceding 6 months). In contrast, there was no significant effect on utilization on more frequent ED utilizers. This could be because of several factors. First, ED-naïve visitors may be less aware of the services provided in an ED vs urgent care or primary care and, once educated about other options by ED navigators, opt to use other forms of care for access. Secondly, those with higher baseline utilization are more likely individuals with ingrained patterns of ED utilization and care-seeking behaviors, as well as chronic conditions prompting recurrent presentation. These include both chronic health and mental health conditions, low health literacy, and a multitude of social factors, such as those related to housing or transportation.12-14 A brief 1-time encounter would be less likely to change well-established patterns of behavior or to definitively address the underlying social or medical problems precipitating frequent ED utilization.
Based on the results of this analysis, the MGB ED Navigator Program will continue to target and support patients with lower baseline levels of ED utilization. Additional research is needed to characterize patient and program factors that may affect the overall impact of the program. To better support patients with higher ED utilization patterns, we are collecting internal data to segment this population into distinct risk groups and to correspondingly create targeted referral pathways to longitudinal population health or care management programs. In addition, the magnitude of the difference in admission rates post intervention is notable, although no statistically significant difference was observed, which could potentially be due to small numbers of admissions in both groups. Additional research could aim to explore this potential relationship further with larger sample sizes.
Historically, care coordination programs have focused on patients with high levels of utilization, but this approach has been recently called into question.15 The results of the ED navigator intervention add to the existing literature by reflecting the potential value in targeting patients with low health care utilization but “rising risk” as well. In addition to redirecting care to more cost-effective environments in the short term, these interventions may also preempt eventual frequent utilization behavior and promote health in the long term by facilitating connections to primary care and to resources related to social determinants of health. They also illustrate that a brief 15-minute intervention can help change a patient’s overall care trajectory—at least in the short term—in contrast to more costly and time-intensive long-term programs.
The limitations of this study include generalizability, as this program and analysis are limited to a single health system and Medicaid ACO patients (not inclusive of patients who are dual eligible). The success of this program relies on being able to refer patients to existing programs and resources, which may not be available at more resource-constrained ACOs or institutions located in states with less of a robust social safety net than Massachusetts. A limitation of the program itself is that it is currently only available on weekdays. This programmatic limitation may affect the study because it is possible that the characteristics of patients visiting the ED on weekends may differ from those of patients visiting the ED during weekdays. Similarly, patients with low-acuity diagnoses seen overnight, who receive an intervention via phone, may have a different experience with the program than those who receive the face-to-face intervention.
With regard to methodology limitations, this study is a retrospective cohort analysis and not a randomized controlled trial. Therefore, it is possible that other factors may have confounded the results of our analysis. However, we accounted for this by matching patients based on baseline utilization and adjusting for potential confounders, including patient demographics, ED visit characteristics, health status, and socioeconomic status and related risk factors. Our adjustment for socioeconomic status was in part accomplished through the use of diagnosis codes in claims data. However, it is important to note that these socioeconomic diagnosis codes are infrequently used, and therefore, there is potential for misclassification.
Additionally, we were unable to match all visits recorded in our EHR with their associated claims. This is potentially because of patients churning in and out of Medicaid or our ACO (whose visits may have occurred in a brief period of lost eligibility); the exclusion of substance abuse visit claims from the data shared with our ACO; and, to a lesser extent, discrepancies in the ED dates of service as recoded in Epic and claims data (eg, as an ED encounter may span multiple days, it is possible that the date of ED arrival would not match a specific claim’s date of service). Regardless of these reasons, there would be no reason to believe that there would be any relationship between unmatched ED visits and our outcomes, unaccounted for by other included variables, or other systematic patterns of their distribution that may have biased our results, with the possible exception of substance use disorders (claims information regarding substance use disorder is not provided by the state to ACOs for privacy reasons). Patients in both the case and control groups had to have claims matched to ED records. Therefore, we do not believe this would have likely significantly biased our results. Although we did adjust our analysis for chart-documented history of substance use disorder and alcohol use disorder, because of this exception, these results may not necessarily be generalized to ED visits for these conditions. Similarly, given potential causes of inability to match records, it is possible that our findings cannot be generalized to patients who may rapidly churn in and out of an ACO.
Our analysis suggests that care coordination through the use of community health workers embedded as navigators in the ED with high-intensity, short-term interactions can help reduce ED visits among individuals with low levels of baseline ED utilization and facilitate stronger connections with primary care in a Medicaid ACO population. Our model highlights that short-term care coordination programs are successful, particularly for patients with lower levels of baseline ED utilization, and can ultimately promote primary care engagement, as well as assistance with health-related social needs. We believe that these results can provide important insights to health systems as they consider cost-effective program options for care management to strengthen primary care engagement and address the social determinants of health.
Salina Bakshi, MD, MPH, and Lucas C. Carlson, MD, MPH, contributed equally to this work and are listed as co–first authors.
The authors would like to acknowledge the contribution of Maryann Vienneau, Erin Maher, Alex Sheff, John Orav, Christin Price, Trancy Escobar, Katherine McLaughlin, Charline Gay, Elizabeth Fonseca, Kristen Risley, Deidra Smith-Horton, Laurie Isidro, Victoria Lo, Lindsay Jubelt, and Gregg Meyer to the development, implementation, and evaluation of this program. They would also like to recognize the ED navigators themselves for their dedication to their clients and regular efforts to support them across the continuum of care.
Author Affiliations: Population Health Management, Mass General Brigham (SB, LCC, JG, PW, KH, CV, AOF), Boston, MA; Department of Medicine (SB, PW) and Department of Emergency Medicine (BJY), Massachusetts General Hospital, Harvard Medical School, Boston, MA; Department of Emergency Medicine (LCC) and Department of Medicine (AOF), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA; The Mongan Institute, Massachusetts General Hospital (CV), Boston, MA.
Source of Funding: Funding for Medicaid accountable care organizations in part provided by state of Massachusetts.
Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (SB, LCC, KH, BJY, CV, AOF); acquisition of data (JG, KH, CV, AOF); analysis and interpretation of data (LCC, JG, PW, BJY, CV, AOF); drafting of the manuscript (SB, LCC, JG, PW, CV, AOF); critical revision of the manuscript for important intellectual content (SB, LCC, JG, PW, BJY, CV, AOF); statistical analysis (LCC, JG, CV); provision of patients or study materials (BJY, AOF); administrative, technical, or logistic support (SB, LCC, PW, KH); and supervision (SB, AOF).
Address Correspondence to: Priscilla Wang, MD, Massachusetts General Hospital, 15 Parkman St, Wang Ambulatory Care Center 634, Boston, MA 02114. Email: email@example.com.
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