Postdischarge engagement of at-risk Medicaid beneficiaries in 6 health plans resulted in significant reductions in hospital readmissions at rates proportional to the frequency of engagement.
Objectives: To investigate the effect of managed care organization (MCO)-implemented postdischarge engagement, supported by other broadly focused interventions, on 30-day hospital readmissions in 6 at-risk Medicaid populations.
Study Design: Prospective cohort study.
Methods: One-year follow-up analysis of member claims data was performed following an intervention period from January 1, 2014, to December 31, 2014. Postdischarge engagement, supported by additional MCO-initiated interventions, was implemented to reduce 30-day hospital readmissions in Medicaid members having 1 or more dominant chronic conditions. Hospital readmission rates were calculated at baseline and at 1 year post intervention. Bivariable and multivariable generalized estimating equation analysis was used to quantify the likelihood of hospital readmissions.
Results: Following implementation, postdischarge engagement rates increased significantly, whereas provider follow-up rates remained unchanged. Increased member engagement resulted in statistically significant reductions in weighted readmission rates enterprise-wide (—10.1%; P <.01) and in 3 of 6 MCOs (—3.9% to –15.8%; P ≤.05) in 2014. Compared with nonparticipants, members who were successfully reached for postdischarge engagement displayed a 33% decrease in 30-day readmissions enterprise-wide (adjusted odds ratio, 0.67; 95% CI, 0.62-0.73) and a comparable decrease (—23% to –39%) in 5 of the 6 MCOs. In this context, greater frequency of postdischarge engagement was associated with proportionally decreased likelihood of readmissions.
Conclusions: Postdischarge engagement, against the backdrop of multifaceted MCO-implemented interventions, was associated with significantly reduced hospital readmissions in at-risk Medicaid subjects. Reduced likelihood of readmissions was observed at both the enterprise-wide and plan levels in a manner proportional to the frequency of engagement, a novel reported outcome for this population.
Am J Manag Care. 2018;24(7):e200-e206Takeaway Points
Postdischarge engagement, supported by broadly focused stakeholder education and encounters, is effective in reducing hospital readmissions in a frequency-sensitive manner.
Hospital readmissions signal gaps in the quality of care provided to patients. Of the 9 million Medicare patient hospitalizations per year,1 almost 1 in 5 are readmitted within a month of discharge and many more return to the emergency department.2 Many such readmissions are caused by inadequate discharge planning, poor care coordination between hospital and community clinicians, and the lack of effective longitudinal community-based care.
Best-practice recommendations to reduce readmissions have largely emerged from analyses conducted on commercial or Medicare fee-for-service populations, whereas relatively few analyses have been published on readmissions in the Medicaid managed care population.3 However, nonpregnant adult Medicaid patients experience readmission rates that are often higher than those experienced by Medicare beneficiaries. Readmission rates for adult (aged 45-64 years) Medicaid patients (22%)4 and those with heart failure (30%) are considerably higher than the corresponding rates in Medicare (16% and 25%, respectively).4,5 A retrospective analysis of Medicaid beneficiaries living in 19 states reported an average unadjusted 30-day readmission rate of 9.4% (range, 5.5%-11.9%).6 Hospital readmissions represent 12.5% of Medicaid payments for all hospitalizations, averaging $77 million per US state annually.6 Although value-based incentives, financing, and technical assistance can provide powerful drivers to minimize hospital readmissions, most attention in the literature to solving the readmission challenge has focused on Medicare and commercial populations.
However, with increased attention given to Medicaid expansion under the Affordable Care Act, effective approaches to minimize Medicaid hospital readmissions are increasingly being sought. In the pediatric Medicaid population, diseases of the respiratory system were the top cause of readmission, accounting for 21.2% of all readmissions,6 so a preponderance of Medicaid readmissions studies target pediatric asthma. A systematic review of 29 studies identified African American race, public or no insurance, previous admission, and complex chronic comorbidity as risk factors associated with pediatric asthma readmissions.7 The populations analyzed in 14 of the studies included (or were inferred to include) Medicaid beneficiaries.8-20 Three of the studies reported that the readmission rate of children with asthma insured by Medicaid was higher than that of comparable children with private insurance.11,16,19
Another systematic review retrieved 21 randomized clinical trials of transitional care interventions targeting chronically ill adults and further identified 9 interventions that demonstrated positive effects on hospital readmissions—related measures.21 Many of the successful interventions shared similar features, such as assigning a nurse as the clinical manager or leader of care and including in-person home visits to discharged patients. Five of the studies had interventions that could be described as discharge management plus follow-up,22-26 2 as coaching,27,28 and 1 each as disease/case management29 and telehealth.30 All but 1 of the interventions29 led to reductions in readmissions through at least 30 days after discharge. The majority of these interventions were performed on elderly populations, and only 1 study consisted of a sizable Medicaid cohort.22
In 2014, AmeriHealth Caritas Family of Companies, a Medicaid managed care organization (MCO), implemented multifaceted enterprise-wide initiatives—including enhanced postdischarge engagement—to reduce 30-day all-cause readmission rates for plan members having 1 or more dominant chronic conditions, or a single condition of moderate chronic asthma, during the 2013 baseline period. We evaluate the impact of these initiatives in reducing hospital readmissions over a 1-year period enterprise-wide and across 6 MCO affiliates in 4 states (Pennsylvania, Louisiana, South Carolina, and Nebraska) and the District of Columbia (DC).
Study Design and Inclusion Criteria
This was a 1-year follow-up prospective study. The study cohort included Medicaid members aged 5 to 64 years who had 1 or more dominant chronic conditions, or a single condition of moderate chronic asthma (selected for high prevalence and impactability in the Medicaid population), from January 1, 2013, to December 31, 2013. Medicaid—Medicare dual-eligible members (≥65 years) were not served during this time period. Subjects were members of 6 Medicaid MCOs serving 4 states (southeastern Pennsylvania [PA01], Lehigh/Capital–New West Pennsylvania [PA02], Louisiana [LA], South Carolina [SC], and Nebraska [NE]) and DC. Baseline index inpatient admissions were included from January 1, 2013, through December 31, 2013, with claims paid through March 31, 2014, or inpatient admission incurred before January 1, 2013, or discharged after December 1, 2012, with claims paid through March 31, 2014. Baseline 30-day all-cause inpatient readmissions were included if they were incurred between January 1, 2013, and December 31, 2013, with claims paid through March 31, 2014, and the admission date was less than 30 days from prior index inpatient discharge. For measurement year 2014, the same logic was followed, with the admit date or discharge date advanced 12 months forward.
Hospital Readmission Identification Logic
Hospital readmissions were extracted from claims data housed in AmeriHealth Caritas’ data management platform. Index admission was defined as an acute admission or maternity delivery. The outcome variable of 30-day all-cause readmission was based on acute admissions (but excluding maternity deliveries) incurred within 30 days after discharge of prior index admissions.
Categories of Interventions
A variety of initiatives targeting members, providers, and MCO associates were implemented to reduce 30-day all-cause hospital readmissions and expenditures in at-risk Medicaid members (Table 1), ranging from nonclinical interventions (eg, telephonic care management) to stakeholder communications (eg, member mailings, continuing medical education webinars for providers). Most interventions were general in nature, but several were member-specific and tiered to the member’s risk level (eg, telephonic vs in-person contact during postdischarge planning). Pharmacy-based initiatives were implemented to improve medication adherence in partnership with the pharmacy benefit manager PerformRx.
Members, especially those with the highest risk of readmission, were contacted following discharge, as were hospitals with the highest readmission rates to discuss ways to reduce member readmissions. Care management and care coordination decisions were made based on each member’s diagnosis, readmission risk factors, and available home supports. Individual health management barriers were identified by speaking with the patient and his or her family and provider. Interventions focused on ensuring that the member understood discharge instructions (including medication schedules) and on facilitating postdischarge provider visits.
Thirty-day all-cause readmission rates are expressed as utilization per 1000 members per year (PKPY) in each MCO.
readmission rate (in PKPY)
Readmissions per MCO × 1000
Member years per MCO
The percentage change of readmission rate per MCO is the quotient value of the difference in readmission rates between 2014 and 2013 divided by the readmission rate in 2013. The overall readmission rate (in PKPY) is the quotient value of the above formula summed for all 6 MCOs. The final rate is a weighted average of readmission rates across all 6 MCOs at baseline and the study period; plans having more participants contributed a proportionally greater rate to the overall average versus plans with fewer participants, better reflecting overall performance.
Also reported was the number of successful member calls within 30 days of the discharge date for the index admission (from January 1, 2014, to November 30, 2014) or within the days between the index discharge date and readmit date, whichever was shorter. A successful member call was only logged when an associate spoke directly with the member or his/her parent or guardian during the outreach, but not if the associate left a message or spoke to someone else at the residence.
A χ2 test was performed to compare differences in pre- versus postintervention engagement rates and postdischarge follow-up rates with providers within 7, 14, and 30 days of inpatient discharge in 2013 and 2014. The Poisson distribution with GENMOD procedure (PROC GENMOD) was used to test readmission rate differences pre- and post intervention (details in eAppendix [available at ajmc.com]).
The number of successful member calls was grouped as a categorical variable with 4 groups (0, 1, 2, ≥3 calls) and as a dichotomous variable (0 vs ≥1 calls). Chi-square tests and Cochran-Armitage trend tests were used to compare the association between the engagement (≥1 call vs no calls) and readmissions and between the number of successful calls (0, 1, 2, ≥3 calls) and readmissions.
To quantify the likelihood of readmission among members with a different number of successful calls in 2014, the index discharge became the study unit. Because some members encountered multiple discharges in the study period, the generalized estimating equations (GEE) model was employed to account for repeated measures for 1 subject. The model used a binomial likelihood function, a logit link function, and an independent correlation matrix. Empirical standard error estimates were used to estimate the standard errors. Odds ratios (ORs) and 95% CIs were then calculated as the exponentials of the parameters (OR = eβ). Two types of models with different independent variables were developed: 1 dichotomous (≥1 calls vs no call) and the other categorical (1, 2, ≥3 calls vs no call). For both models, bivariable GEE analysis was first performed to explore the unadjusted effect of successful calls on preventing readmission. Multivariable GEE analysis was then used to quantify the independent effect after controlling for sociodemographic variables like age, gender, race, aid category, and risk scores.
All analyses were performed using SAS EG 7.1 (SAS Institute; Cary, North Carolina). A significance level of P <.05 was deemed statistically significant for all comparisons.
Medicaid subjects (N = 149,748) in 6 MCOs had 1 or more dominant chronic conditions (range, 54.41%-75.39%) or a single condition of moderate chronic asthma (range, 24.61%-45.59%) in 2013 (Table 2). Clinical Classifications Software (CCS Grouper; Rockville, Maryland) clustered the top 10 diagnosis categories of the study group at baseline, accounting for more than 45% of all hospital readmissions enterprise-wide, which were (in descending order) sickle cell disease, diabetes with complication, congestive heart failure, complicated procedures, asthma, chronic obstructive pulmonary disease, septicemia, pneumonia, chest pain, and skin infection. The majority of subjects in all MCOs were female (range, 51.97%-58.42%), and a plurality were younger than 18 years (range, 27.93%-58.06%; primarily because of asthma). In other respects, the MCOs comprised a diverse population.
Postdischarge Engagement and Provider Follow-Up
The rates of MCO-initiated postdischarge engagement (eAppendix Table 1A) and provider follow-up (eAppendix Table 1B) were compared in 2013 and 2014. Postdischarge engagement rates within 7, 14, and 30 days after discharge increased significantly enterprise-wide from 2013 to 2014 (15.07 percentage points [from 10.18% to 25.25%], 18.90 percentage points [from 14.18% to 33.08%], and 21.37 percentage points [from 17.74% to 39.11%], respectively; P <.001 for all) and for all MCOs except 1 (P ≤.001 for all; except 30-day NE, P = .018). Conversely, provider follow-up rates remained statistically unchanged.
The hospital readmission rates following discharge were also compared in 2013 and 2014 (Figure 1; eAppendix Table 2). The enterprise-wide weighted 30-day hospital readmission rate significantly decreased from 41.30 PKPY to 37.11 PKPY (—10.1%; P <.01) between 2013 and 2014. Postdischarge hospital readmission rates decreased in every MCO (range, —3.9% to –32.2%), but the reduction was only statistically significant in 3 of the 6 MCOs (PA01, P = .03; LA and SC, both P <.01). The enterprise-wide weighted hospital readmission rate for members with asthma alone significantly decreased as well (—20.7%; P <.03).
Postdischarge Engagement Prevents Hospital Readmissions
To investigate the impact of successful postdischarge engagement on readmissions, hospital readmission rates of members who were successfully engaged post discharge at least once (≥1 successful call) were compared with unengaged members (no successful calls; Figure 2; eAppendix Table 3). Members who were successfully engaged post discharge had significantly lower readmission rates enterprise-wide than unengaged members (14.5% vs 19.1%; P <.001). Even following adjustment with confounding variables of age, gender, race, aid category, and risk scores, postdischarge engagement was correlated with a 33% decrease in hospital readmissions (adjusted OR, 0.67; 95% CI, 0.62-0.73). A comparable decrease was observed in every MCO (range, —23% to –39%; adjusted OR, 0.61-0.77), except for NE, the plan with the smallest sample size.
We next examined whether an increasing number of successful postdischarge calls was associated with a proportionately decreasing likelihood of hospital readmission (Figure 3; eAppendix Table 4). Enterprise-wide, members successfully engaged post discharge with 1, 2, and 3 or more contacts all had significantly lower readmission rates than unengaged members (16.3%, 15.4%, and 11.3% vs 19.1%, respectively; P <.001 for all). Even following adjustment with confounding variables of age, gender, race, aid category, and risk scores, increasing the number of postdischarge engagements from 1 to 2 to 3 or more successful contacts displayed decreasing likelihood of hospital readmissions (adjusted ORs, 0.80, 0.74, and 0.54, respectively). This observation was consistently detected in every MCO except for NE; however, the CI for 1 versus 0 and 2 versus 0 successful calls overlapped with the ordinate at 1.0 (indicating no change in OR) for 3 and 2 MCOs, respectively. In other words, 1 postdischarge engagement was insufficient to drive a decreased likelihood of readmission rates in 3 of the 6 MCOs (LA, SC, and DC), and even 2 postdischarge engagements were insufficient in 2 MCOs (LA and SC). By contrast, 3 postdischarge engagements were sufficient to decrease the likelihood of readmission rates in all 5 MCOs for which the OR could be calculated.
Ineffective transitions of care following hospital discharge are a major challenge in population health, where limited windows of opportunity exist to engage patients in follow-up care.31 Postdischarge engagement, supported by other broadly focused MCO-led nonclinical interventions, was examined for its impact on 30-day hospital readmissions in at-risk Medicaid subjects. Following implementation, postdischarge engagement rates increased significantly enterprise-wide (while provider follow-up rates remained unchanged), resulting in a statistically significant reduction in weighted readmission rates enterprise-wide, accompanied by a 4.8% reduction in associated expenditures. Members successfully engaged post discharge displayed decreased likelihood of readmission compared with those unengaged in 2014. Moreover, greater frequency of postdischarge engagement was associated with proportionally decreased likelihood of readmissions, a novel reported outcome for this population.
Although all 6 MCOs reported declines in readmission rates, 3 MCOs reported nonsignificant declines (P >.05). Two of these 3 MCOs had the lowest baseline readmission rates, while eliciting further reductions in postdischarge readmission rates with the highest magnitude declines among all MCOs (NE, −32.2%; DC, −19.9%). Therefore, the nonsignificance was largely due to their small population sizes relative to other MCOs (NE and DC had 1% and 5%, respectively, of the total study population). Moreover, the third “nonsignificant” MCO (PA02) showed a borderline P value of .09. Finally, inter-MCO differences in member sociodemographic profiles (racial composition, rurality, chronic disease-to-asthma ratio, and non-SSI status) and degree of implementation of the assorted postdischarge engagement processes (especially those dependent on the local health ecosystem) likely contributed to inter-MCO variations.
Reasons for this study’s successful outcomes are varied. First, postdischarge engagement by care managers established a link between member and MCO, providing a touchpoint for addressing postdischarge barriers. Postdischarge outreach addresses issues relating to medication possession and equipment/home care services, comprehension of discharge instructions, and reporting of signs and symptoms. The proportionally lower likelihood of hospital readmission with increasing telephonic engagements in some cohorts was striking. Care managers representing MCOs—associated by members with the quality of medical care they receive—frequently hear health-related complaints. At minimum, telephonic engagement fosters member awareness of their health-related needs. Care managers are trained to spontaneously identify potentially serious issues and assist in addressing them before they require emergency care, an impact greatly amplified when: (1) contacting members with chronic and comorbid conditions (more likely to be enrolled in care management) and (2) completing validated health-related assessments on members to uncover additional assessment-focused concerns (Glenn Hamilton, MD, personal communication, December 8, 2017). Finally, members develop a stronger relationship with care managers who contact them more regularly.
Lastly, nonclinical initiatives helped establish a framework for implementing and communicating postdischarge solutions among members, providers, and MCO associates. Many successful initiatives were later adopted into the MCO’s system of care. Overall provider engagement remained steady during most follow-up periods in most plans. Providers often lack dedicated staff for effective postdischarge engagement and may be unaware that a patient had a hospital admission. Conversely, MCOs primarily become aware of their members’ hospital admissions upon processing their medical claims to reimburse hospitals for services rendered, so improved MCO—provider communication regarding patient status alerts both parties of unanticipated acute hospitalizations, follow-up appointment status, and existing care gaps. MCOs can supply de facto “back-office support” in patient engagement to provider offices with limited resources. Successful MCO-led member engagement is an ongoing challenge across all health plan programs and requires creative solutions, like initial outreach via SMS texting. Thus, additional investigations regarding the effectiveness of postdischarge engagement and other nonclinical interventions are warranted.
Several limitations are apparent in this study. First, with the exception of postdischarge engagement, the individual efficacy of the remaining initiatives could not be determined. Second, despite a favorable overall rate, substantial variation in readmission rates exists among individual MCOs, consistent with reports in the Medicare population.32,33 To mitigate these effects, multivariable analyses were performed after controlling for sociodemographic confounding covariates at each MCO and enterprise-wide, with MCO population size also included as a covariate. Finally, a 1-year observational window cannot project whether postdischarge engagement maintains lower hospital admissions over the longer term. Not all nonclinical intervention components in this study were carried over in future years, so a uniform 2-year analysis was not possible under these circumstances. Additionally, member churn common to Medicaid MCOs dramatically reduced the number of members eligible for the study analysis, making any broad conclusions from a 2-year analysis suspect. Specifically, an attrition rate of 14% after the first year post analysis increased to 25% in the second year and 33% in the third year. In spite of these limitations, these preliminary results drawn from the first-year study were (and continue to be) important in advancing our care management processes and developing additional high-touch community-based interventions.
This study, reporting on the success of postdischarge engagement in managing hospital readmission among demographically diverse Medicaid managed care populations, provides an important addition to existing hospital readmission literature. To our knowledge, it is also the first reported instance of a proportional, frequency-sensitive association between increased postdischarge member engagement and decreased likelihood of readmissions in Medicaid, a trend observed both enterprise-wide and in individual plans. Therefore, health plans, like providers, can dramatically impact hospital readmission rates through effective postdischarge engagement and other multifaceted interventions aimed at members, providers, and associates.
The authors wish to thank the Pennsylvania Department of Human Services, the Louisiana Department of Health, the South Carolina Department of Health and Human Services, and the District of Columbia Department of Health for their continuing support.Author Affiliations: AmeriHealth Caritas (WG, DK, TPD, JJ, KEM, ADG), Philadelphia, PA.
Source of Funding: Work on this manuscript was supported by the AmeriHealth Caritas Family of Companies.
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 (WG, TPD, JJ, KEM, ADG); acquisition of data (WG); analysis and interpretation of data (WG, DK, TPD, KEM, ADG); drafting of the manuscript (DK); critical revision of the manuscript for important intellectual content (WG, DK, TPD, KEM, ADG); statistical analysis (WG); and supervision (TPD, JJ, KEM, ADG).
Address Correspondence to: Wanzhen Gao, PhD, AmeriHealth Caritas Family of Companies, 200 Stevens Dr, Philadelphia, PA 19113. Email: email@example.com.REFERENCES
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