Boyd Gilman, PhD

Medicare, Medicaid, and commercial plans have all explored ways to improve outcomes for patients with high costs and complex medical and social needs. The purpose of this study was to test the effectiveness of a high-intensity care management program that the Rutgers University Center for State Health Policy (CSHP) implemented as an adaptation of a promising model developed by the Camden Coalition of Healthcare Providers.

We estimated the impact of the program on 6 utilization and spending outcomes for a subgroup of beneficiaries enrolled in Medicare fee-for-service (n = 149) and a matched comparison group (n = 1130).

 We used Medicare claims for all analyses. We used propensity score matching to construct a comparison group of beneficiaries with baseline characteristics similar to those of program participants. We employed regression models to test the relationship between program enrollment and outcomes over a 12-month period while controlling for baseline characteristics.

 A test of joint significance across all outcomes showed that the CSHP program reduced service use and spending in aggregate (

 = .012), although estimates for most of the individual measures were not statistically significant. Participants had 37% fewer unplanned readmissions (

 = .086) than did comparison beneficiaries. Although we did not find statistically significant results for the other 5 outcomes, the CIs for these outcomes spanned substantively large effects.

 Although these findings are mixed, they suggest that adaptations of the Camden model hold promise for reducing short-term service use and spending for Medicare super-utilizers.

An independent evaluation of a high-intensity care management program implemented by provider groups in 4 states shows that the model holds promise for efforts to reduce short-term service use and spending based on an analysis of 149 Medicare patients with complex medical and social needs. These findings may help managed care decision-makers to: 

Reduce spending among super-utilizers through the use of mobile interdisciplinary care teams

Address social determinants of health that contribute to high spending

Adapt community-based care management models to suit local contexts

Team-based care models designed to address the needs of super-utilizers—defined as patients with frequent hospital use and complex health issues&mdash;are proliferating in response to the cost of caring for such patients using traditional approaches.

 Medicare, Medicaid, and commercial insurers have all explored ways to improve quality and cost outcomes for super-utilizers.

 Arguably, the most recognized is the “hotspotting” model pioneered by the Camden Coalition of Healthcare Providers in New Jersey, first brought to national attention by an article in 

 In the Camden model, interdisciplinary mobile care teams provide high-intensity care management and care coordination to individuals with multiple chronic conditions. The model differs from many other care management models in its focus on both medical and social determinants of health among people with high service use.

So far, interest in programs to help super-utilizers has outpaced the available evidence on their effectiveness. Results from an early evaluation of the Camden model were promising: The Camden Coalition reported reductions in emergency department (ED) visits, inpatient admissions, and average charges per month among participants in a pilot program.

 However, the study included only 36 participants and used a pre-post design that did not control for regression to the mean or for confounding factors that might influence outcomes. Regression to the mean is particularly important in interventions in which patients are selected because of their recent service use; some decline in service use post enrollment would be expected without any intervention.

 No independent rigorous evaluation of the Camden model has been published to date, although a randomized controlled trial is ongoing.

 Evaluations of similar care management models have yielded mixed findings over time.

Recognizing the need for evidence, the Center for Medicare & Medicaid Innovation awarded a $14.3-million Health Care Innovation Award to the Rutgers University Center for State Health Policy (CSHP) to support adoption of the Camden model by provider groups in 4 states: (1) an independent physician association in San Diego, California; (2) a nonprofit community health center in Aurora, Colorado; (3) a nonprofit health system with 2 hospitals in Kansas City, Missouri; and (4) a nonprofit operator of 2 federally qualified health centers in Allentown, Pennsylvania. These provider groups targeted super-utilizers living in poor neighborhoods within their service areas.

Researchers affiliated with the Aurora site provided suggestive evidence on the program’s impacts on a group of primarily Medicaid-eligible beneficiaries. They found that it reduced ED visits and hospitalizations and increased primary care use among program participants in Colorado.

 However, the study’s comparison group included patients who declined to participate in the program, potentially biasing results.

In this article, we present evidence from an independent evaluation of the CSHP program across all 4 sites. Our evaluation included prespecified hypotheses that the program would (1) reduce total admissions, particularly readmissions, and (2) reduce Medicare parts A and B spending.

 We used rigorous tests that account for regression to the mean with a well-matched comparison group. This study provides a unique perspective on how the original Camden model can be adapted to a variety of settings and provider groups and adds to the available evidence on care management programs for super-utilizers.

The 4 CSHP sites enrolled 1068 participants between January 2013 and June 2015. Initially, all 4 sites used the same utilization-based criteria as the Camden model—2 or more hospital admissions in the past 6 months&mdash;to identify potential participants. Two sites amended these thresholds early in the award period to expand the pool of potential participants: One site targeted individuals with 2 or more hospital admissions in the prior 6 months or 3 or more admissions in the prior 12 months, and the other targeted individuals with 3 or more hospital events (admissions or ED visits) in the prior 6 months. Among those who met the utilization-based criteria, the sites excluded patients whose conditions, such as cancer or serious behavioral issues, could not be managed with existing program resources.

All 4 sites received technical assistance from the Camden Coalition to guide implementation of the intervention. The sites implemented the same basic set of activities, including development of individualized care plans, integrated care management services through mobile care teams, and education to improve patients’ ability to manage their medical and social needs. Care teams, which included some combination of nurses, community health workers, social workers, medical assistants, and/or behavioral health providers, provided education about the importance of using primary and specialty care instead of, or as a follow-up to, emergency and hospital care. They also addressed participants’ nonmedical needs, including enrollment in social services (such as housing and Social Security disability benefits) and behavioral health service programs.

On average, teams contacted participants 10 times per month, spending roughly 6 hours with each participant per month. The majority of that time (87%) was spent in person, at the participants’ homes and medical appointments, and the remainder was on the phone. Participants remained in the program for an average of 4.2 months, ideally until their health and social circumstances had stabilized and the program could graduate them. By June 2015, the program had graduated 673 participants, although some continued to receive occasional support from the care teams. Additional detail on the intervention is available in a CMS evaluation report.

Although the program’s 1068 participants had public, private, or no insurance, our study population is limited to 149 participants with Medicare fee-for-service (FFS) coverage for whom we could obtain service use and spending data for a 12-month period prior to program enrollment. At the time that we conducted the evaluation, these data were only available for Medicare FFS beneficiaries. Using Medicare claims, we selected 1130 comparison Medicare FFS beneficiaries who met the health and utilization criteria for CSHP program eligibility and were similar to the treatment group beneficiaries on other characteristics. For each potential comparison beneficiary, we created a pseudo-enrollment date to approximate the date that the beneficiary would have enrolled in the intervention if he or she had been in the treatment group. We used propensity scores to match potential comparison beneficiaries to treatment group beneficiaries using geography, baseline (preintervention) measures of service use and spending, chronic conditions, and other demographic and health-related variables (

We used data from the Medicare enrollment database and claims. We defined outcomes quarterly for the 12 months before and after each beneficiary’s enrollment date, so each beneficiary had outcomes in 4 baseline quarters and up to 4 postenrollment quarters in the analysis sample. We estimated impacts on 6 outcomes that the program was designed to address, which we specified prior to the evaluation: all-cause inpatient admissions, inpatient admissions for ambulatory care—sensitive conditions (ACSCs), 30-day unplanned readmissions, outpatient ED visits, Medicare parts A and B spending, and inpatient spending.

We estimated impacts as the regression-adjusted difference between comparison and treatment group beneficiaries in each of the outcomes during the year after enrollment. We used an intent-to-treat design and thus included some patients who dropped out of the program prior to graduation. The regressions adjusted for the same variables used in matching (Table 1

). We used 2-sided tests of significance with a 

 <.05 threshold because we were concerned about both type II and type I errors given the small size of the treatment population. This threshold has been applied in other evaluations of care management programs when policy makers needed to identify promising care models even if additional evidence was necessary before those models could be deemed to have significant impacts at the standard threshold of 

 To examine the aggregate impact of the program on service use and spending, we also conducted a test of joint significance across all outcomes. We used bootstrapping to estimate standard errors in all tests; this method accounted for clustering of outcomes at the individual level.

The characteristics of the treatment group were consistent with CSHP’s target population: individuals with multiple chronic conditions (7.7 on average) who were frequent users of hospital care (Table 1

). Service use and spending were roughly 10 times and 7 times the Medicare FFS average, respectively. The treatment group was also distinct from the average Medicare beneficiary in terms of demographic characteristics and reason for Medicare eligibility. They were younger and more likely to live in high-poverty areas, be entitled to Medicare due to disability or end-stage renal disease, and have dual Medicare and Medicaid coverage.

The observable characteristics of the matched comparison group were very similar to those of the treatment group at the start of the intervention. Groups were well balanced on all variables (Table 1

) and had similar trends in the outcome variables over the 4 quarters leading up to the intervention (

The treatment group had 0.126 (37%) fewer unplanned readmissions on average per quarter than comparison beneficiaries (

 = .086; 90% CI, —0.247 to –0.004]). The point estimates for admissions (–16%), ACSC admissions (–14%), and total spending (–9%) were all large and in the favorable direction, whereas the estimates for ED visits (5%) and inpatient spending (–1%) were close to zero. None of these 5 estimates was individually statistically significant (

). A joint test indicated that, as a group, the estimates for all 6 prespecified outcomes were significantly different from zero (

CSHP’s intervention contributes to a growing body of evidence on the use of care management programs to improve patient outcomes. Our results indicate that the program substantially reduced readmissions (by an estimated 37%) and suggest that it may have reduced all-cause admissions, ACSC admissions, and Medicare parts A and B spending. Many other care management interventions have not achieved this type of success.

 However, the CSHP program shares several distinctive features with the relatively few other models that have succeeded in reducing hospital admissions and/or readmissions, including targeting very high-risk patients and providing frequent in-person contact.

Another distinguishing feature of the CSHP program was its attention to social determinants of health. There is a growing understanding that social determinants affect health and service use, but payers and providers need more evidence on interventions that effectively address these issues, as they are particularly difficult to attend to during medical encounters.

 The results of this study suggest that adaptations of the Camden model are promising for these purposes and are feasible to implement in various settings.

Similar to other evaluations of comprehensive care management programs, our study’s main limitation was low sample size, in part because we were limited to studying Medicare beneficiaries, who accounted for only 26% of all enrollees in the CSHP program. The low sample size reduced the likelihood of finding a statistically significant impact on outcomes and prevented further investigation into whether effects might have differed for certain subpopulations (eg, by implementation site). Findings by Capp et al of significant reductions in ED visits at the Colorado site, which primarily enrolled Medicaid beneficiaries, suggest that this could be the case.

 There may also be some selection criteria for program enrollment that we could not fully replicate in claims data when selecting the comparison group. Finally, we could not compare program savings with costs because we estimated savings for only a fraction of participants and the costs were spread over all participants. Although it is possible that the costs of the program exceed the short-term savings to Medicare, any future cost-benefit analysis should take into account long-term costs and benefits of the program.

This study assessed whether, under CMS’s innovation award, the CSHP community-based care management program could improve the outcomes of super-utilizers across 4 implementation sites. Findings on this adaptation of a well-known super-utilizer program provide insight into the ability to replicate program impacts in different settings. In aggregate, there is sufficient evidence to suggest that the CSHP program reduced service use and parts A and B spending among Medicare beneficiaries. Although these results are promising, the Camden model will need further testing at larger scales before it is possible to make stronger conclusions about its impacts.&ensp;

The authors are grateful for programming and technical support from Mark Ezzo, Andrew McGuirk, Sandi Nelson, and Frank Yoon. They also thank the Center for State Health Policy staff (Margaret Koller, Joel Cantor, Jolene Chou) and staff at all of the implementation sites for giving them detailed program data and valuable perspectives on their implementation experience.

Mathematica Policy Research, Princeton, NJ (PS, CNS, KK, LM), Ann Arbor, MI (KWVB), Washington, DC (GP, LB), Cambridge, MA (BG), and Chicago, IL (KS); Centers for Medicare & Medicaid Services (TD), Baltimore, MD.

 Evaluation funded by CMS, Center for Medicare & Medicaid Innovation, contract number HHSM-500-2010-000261/HHSM-500-T0015. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of HHS or any of its agencies.

 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.

 Concept and design (PS, KWVB, TD, GP, BG, LB, KK, KS, LM); acquisition of data (PS, CNS, KWVB, GP, LM); analysis and interpretation of data (PS, CNS, KWVB, GP, BG, LB, KK, KS, LM); drafting of the manuscript (PS, CNS, KWVB, TD, GP); critical revision of the manuscript for important intellectual content (PS, CNS, KWVB, TD, GP, LB, KK, KS, LM); statistical analysis (PS, GP, KK); provision of patients or study materials (GP); obtaining funding (GP, LM); administrative, technical, or logistic support (TD, KS, LM); and supervision (GP, BG, LM).

 Purvi Sevak, PhD, Mathematica Policy Research, 600 Alexander Park, Princeton, NJ 08540. Email: psevak@mathematica-mpr.com.

1. Vaida B. For super-utilizers, integrated care offers a new path. 

. 2017;36(3):394-397. doi: 10.1377/hlthaff.2017.0112.

2. Mann C. Targeting Medicaid super-utilizers to decrease costs and improve quality. Medicaid website. medicaid.gov/federal-policy-guidance/downloads/cib-07-24-2013.pdf. Published July 24, 2013. Accessed December 13, 2016.

3. Bodenheimer T. Strategies to reduce costs and improve care for high-utilizing Medicaid patients: reflections on pioneering programs. Center for Health Care Strategies, Inc website. chcs.org/media/HighUtilizerReport_102413_Final3.pdf. Published October 2013. Accessed December 13, 2016.

4. Hong CS, Siegel AL, Ferris TG. Caring for high-need, high-cost patients: what makes for a successful care management program? The Commonwealth Fund website. commonwealthfund.org/~/media/files/publications/issue-brief/2014/aug/1764_hong_caring_for_high_need_high_cost_patients_ccm_ib.pdf. Published August 7, 2014. Accessed December 13, 2016.

5. Programs focusing on high-need, high-cost populations. Center for Health Care Strategies, Inc website. chcs.org/resource/programs-focusing-high-need-high-cost-populations. Published April 2016. Accessed December 13, 2016.

Articles

A community-based care management program for high-risk patients reduced hospital readmissions and also likely reduced admissions and Medicare parts A and B spending.

Effects of a Community-Based Care Management Model for Super-Utilizers

 To evaluate impacts of a telephonic transitional care program on service use and spending for Medicare fee-for-service beneficiaries at a rural hospital.

 Patients discharged from Atlantic General Hospital (AGH) with an AGH primary care provider were assigned a nurse care coordinator for 30 days. The nurse reviewed the patient’s conditions, assessed needs for transition support, conducted weekly telephone calls (beginning 24-72 hours after discharge) to monitor adherence to treatment plans, and scheduled follow-up appointments. Using claims data, we evaluated impacts on service use and spending using a difference-in-differences design with a matched comparison group.

 The intervention reduced Medicare spending in the 6-month period after discharge by 30.8%, or $1333 per beneficiary per month (90% CI, —$2078 to –$589), which was partly driven by a 39.4% reduction in spending for inpatient claims (difference, –$729; 90% CI, –$1234 to –$225). There were no statistically significant changes in the 14-day ambulatory care follow-up rate, 30-day unplanned readmission rate, number of inpatient admissions, or number of emergency department visits, although this may be due to modest statistical power to detect effects.

 The estimated $5.4 million in savings from this intervention more than offset the costs of the $1.1 million funding for the award. Although other studies have found that care transitions programs can improve outcomes, this study was unique in the size of the impacts relative to the low-touch intervention and the location in a small rural healthcare system.

A telephonic transitional care program at a rural hospital reduced postdischarge Medicare spending and inpatient spending for Medicare fee-for-service beneficiaries.

Healthcare decision makers seek to identify ways to deliver better healthcare at lower costs to beneficiaries with high healthcare needs, including patients recently discharged from a hospital.

Small rural community-based hospitals have the ability to reduce posthospitalization spending and healthcare use.

The estimated $5.4 million in savings from this transitional care program well exceeded CMS’ $1.1 million costs for the award.

This promising program model merits further testing.

CMS seeks to identify ways to deliver better healthcare, improve health, and lower costs for beneficiaries of its programs, particularly those with the highest healthcare needs.

 One promising approach toward achieving this goal is transitional care, which encompasses a range of services provided to patients as they transfer across settings or levels of care to improve outcomes and avoid preventable hospitalizations, readmissions, and emergency department (ED) visits.

 Transitional care interventions vary in the populations they target, the services they provide, the types of providers delivering services, and the duration of support. Intervention components typically include patient or caregiver education, discharge planning, scheduling postdischarge appointments, monitoring a patient’s condition and adherence to the discharge plan, medication reconciliation, and coordination among health professionals involved in the transition.

Prior studies have found that care transitions programs can improve patients’ outcomes.

 However, there is limited evidence on which interventions work best in different settings,

 and transitional care interventions at stand-alone community hospitals might not always achieve their goals.

 Further, lower rates of follow-up care and greater risk of ED visits for postdischarge Medicare beneficiaries in rural settings, compared with urban beneficiaries, highlight the need for policies that increase follow-up care in rural settings.

 Testing of transitional care programs in rural settings is needed.

This study examined how a telephonic transitional care intervention for patients discharged from the hospital affected service use and Medicare spending in a small rural healthcare system.

Using an observational cohort design, we evaluated the intervention’s effects on service use and spending among Medicare fee-for-service (FFS) beneficiaries using difference-in-differences (DID) design with a matched comparison group.

Atlantic General Hospital (AGH) is a private, not-for-profit, community-based healthcare delivery system with a 62-bed hospital and 7 primary care practices. AGH is located in Worcester County, Maryland, a largely rural county and a federally designated medically underserved area. Although the hospital is located in a resort area, most of AGH’s primary care patients live there year-round. AGH program administrators note that many residents are older than 65 years and have low levels of literacy.

In July 2012, AGH received $1.1 million in Health Care Innovation Award (HCIA) funding from CMS’ Center for Medicare & Medicaid Innovation (CMMI) to implement a patient-centered medical home model that included a care transitions program.

 The program aimed to reduce 30-day readmissions and healthcare costs and targeted patients discharged from AGH who had any diagnosis and an AGH primary care provider (PCP). The program employed 1 full-time nurse care coordinator with extensive clinical and case management experience who managed a caseload of 40 to 50 patients at any given time.

The nurse monitored the hospital’s daily census to identify eligible patients. Using AGH’s electronic health record system, the nurse reviewed patient information, including reason for the hospital stay, recent primary care visits, and discharge instructions, and notified the patient’s AGH PCP of the admission. The nurse visited patients in the hospital to describe the program and identify postdischarge needs. She later called patients at home within 24 to 72 hours of discharge to enroll them in the program. (Participation was voluntary; 10% of patients opted out or could not be reached by phone after 3 tries.) During the initial call, the nurse reviewed the patient’s conditions, reconciled medications and identified barriers to medication compliance, identified immediate needs for support and barriers to self-care, and scheduled follow-up appointments with the AGH PCP. Thereafter, the nurse called participants weekly to monitor their conditions and compliance with postdischarge treatment plans. Participants with unstable conditions based on the nurse’s clinical judgment or who needed additional support received more frequent calls to address emerging needs in a timely manner. In rare cases, the nurse contacted the participant’s PCP regarding urgent needs and coordinated additional office visits or referrals. Patients left the transitional care program within 30 days after discharge from the hospital.

The treatment group for our analysis included 638 Medicare FFS patients who had an AGH PCP and were discharged from AGH during the HCIA funding period (February 2013-May 2015). We defined the treatment group using intent-to-treat criteria and thus included some patients who did not participate in the program because they declined to participate or could not be reached by the care transitions care coordinator after 3 attempts. (Data indicate that 396 of the treatment group members, or 62%, were actually enrolled in the intervention.) 

) provide additional details on sample selection, data availability, and sample sizes. The intervention also targeted other patients, including those enrolled in Medicaid, Medicare managed care, or commercial insurance, but data limitations precluded them from being included in the study.

Each treatment beneficiary was matched to 1 to 4 comparison beneficiaries. The comparison group was selected using exact matching and propensity score matching techniques,

 and it included 2232 FFS Medicare beneficiaries who were discharged during the same time frame from either Peninsula Regional Medical Center (PRMC) or AGH but did not have an AGH provider (so the beneficiaries were not contacted by the nurse care coordinator). PRMC is a larger hospital than AGH, but it was selected as a comparison because it is located just 30 miles from AGH in Salisbury, Maryland, a city of about 30,000; participated in Maryland’s global payment model, like AGH; and did not implement the care transitions component.

To support the DID analyses, we also measured outcomes for 226 patients with an AGH PCP who were discharged from AGH in a 1-year period before the intervention began (July 2011-June 2012) and 1008 matched comparison beneficiaries from the same time frame. The pre- and postintervention cohorts included different patients—a potential study limitation.

Using Medicare FFS parts A and B claims data, we measured 5 outcomes: (1) the percentage of beneficiaries with an ambulatory care follow-up visit with a primary care or specialist physician within 14 days of the discharge that qualified the patient for the treatment or comparison group, (2) the percentage of beneficiaries with an unplanned readmission within 30 days of discharge, (3) the average number of all-cause readmissions within 6 months after discharge, (4) the average number of outpatient ED visits within 6 months after discharge, and (5) average Medicare parts A and B spending in the 6 months after discharge. These 5 confirmatory outcomes were prespecified based on being of the highest interest to CMMI. We later added 2 exploratory outcomes: average inpatient and noninpatient Medicare spending.

Impact estimates measured the differences in postdischarge outcomes between the treatment group patients during the intervention period and matched comparison beneficiaries, minus the differences in postdischarge outcomes between treatment group patients discharged in a 1-year period before the intervention began and matched comparison beneficiaries.

We used linear regression models to implement the DID framework, adjusting for patient-level covariates. The covariates included patients’ demographics, chronic conditions, and service use and spending 0 to 3 months and 4 to 12 months before enrollment, as well as indicators for each matched set and treatment status. The DID estimate was the coefficient for an interaction of a beneficiary’s treatment status with an indicator for being in the postintervention cohort. Weighted regression models were estimated to account for many-to-one matching, and inference was based on bootstrap standard errors. See eAppendix A for additional details.

Some baseline characteristics of the 638 Medicare FFS beneficiaries in the postintervention treatment group, such as gender and age, were similar to benchmarks for the national Medicare population, but other characteristics indicate that the treatment group had more healthcare needs than the general population (

). The Hierarchical Condition Category risk score for the treatment group was 2.47, indicating that the group could be expected to have Medicare spending more than double the national average over the next year.

 The prevalence of chronic obstructive pulmonary disease, chronic kidney disease, and congestive heart failure in the treatment group was more than twice the national average. Treatment group members also had high service use and spending. The treatment group beneficiaries had, on average, 1092 hospitalizations and 406 ED visits per 1000 beneficiaries, and their Medicare spending averaged $6603 per month in the quarter before enrollment.

In both the pre- and postintervention cohorts, the treatment and comparison beneficiaries were well matched on individual-level characteristics at baseline, including demographics, health status, chronic conditions, reason for the hospitalization leading to eligibility for enrollment in the care transitions program, and health service use and spending 1 year before discharge (eAppendix C).

, the follow-up ambulatory care visit rate in the 14 days following discharge was 73.5%, 5.9 percentage points higher than the regression-adjusted rate for the comparison group. This DID estimate was not statistically significant (90% CI, —1.6 to 13.4; 

The treatment group’s 30-day unplanned readmission rate following discharge was 11.6%, 1.9 percentage points higher than the comparison group’s after regression adjustment. This was a large difference (18.9%), but the large standard error means that the unfavorable impact was estimated imprecisely (90% CI, —3.6 to 7.3; 

The treatment group averaged 229 all-cause inpatient admissions per 1000 beneficiaries per quarter over the first 2 quarters following the beneficiary’s qualifying discharge, which was estimated to be 72 admissions fewer than the comparison group (90% CI, —149 to 4; 

= .121), a statistically insignificant difference of about 24%.

The treatment group rate of outpatient ED visits within 6 months after discharge was similar to the comparison group rate (after regression adjustment); however, the DID was not estimated precisely (difference, —19; 90% CI, –111 to 73; 

Medicare parts A and B spending for the treatment group averaged $2992 per beneficiary per month over the first 2 quarters following the beneficiary’s discharge, which was estimated to be $1333 lower than regression-adjusted spending for the comparison group. This DID estimate is statistically significant (90% CI, —$2078 to –$589; 

 = .003) and large (31% lower than the adjusted comparison group’s spending). The treatment group’s spending was higher than the comparison group’s during the preintervention period, but lower in the intervention period, leading to the large DID estimate.

Decreases in spending for inpatient claims accounted for 55% of the reduction in total spending. Specifically, regression-adjusted inpatient spending was $729 lower than the comparison group’s spending (90% CI, —$1234 to –$225; 

 = .017), whereas spending for noninpatient claims was $604 lower (90% CI, —$968 to –$239; 

This telephonic intervention decreased Medicare parts A and B spending substantially (by nearly one-third) during the first 6 months after beneficiaries’ enrollment, driven in part by a decrease in inpatient spending. AGH expected cost reductions to occur through decreases in the readmission rate and the number of ED visits, and it set a goal to obtain a 20% reduction for these 2 measures. However, tests for these outcomes, as well as for a decrease in the number of admissions and an increase in the rate of ambulatory care follow-up within 14 days of discharge, did not yield statistically significant results. The lack of observed effects may be due to imprecision in the estimates; AGH’s expected impacts fall within the 90% CIs.

Although other studies have found that care transitions programs can improve outcomes, this study was unique in both the size of the impacts relative to the low-touch (and low-cost) telephonic intervention and the location in a small rural healthcare system—a setting not often represented in such studies. The estimated $5.4 million in savings from the transitional care component well exceeded the $1.1 million HCIA award. The effects coincided with successful implementation of the program, including process improvements throughout the program to accommodate patients’ needs. Thus, this promising program model merits further testing, and hospitals looking to implement a care transitions program, particularly in a rural setting, might consider implementing AGH’s program model.&ensp;

Many individuals contributed to this study. The authors would specifically like to acknowledge the important contributions to this report made by Andrew McGuirk, Sandi Nelson, Ken Peckham, and Lauren Vollmer of Mathematica Policy Research, who skillfully processed and helped analyze Medicare claims and enrollment data.

 Mathematica Policy Research (KK, LMB, RS, GGP, BG, LB, KAS, SDH, LM), Ann Arbor, MI, Cambridge, MA, Princeton, NJ, and Washington, DC; Center for Medicare & Medicaid Innovation (CMMI), CMS (TJD), Baltimore, MD.

 Evaluation funded by CMS, CMMI, contract number HHSM-500-2010-000261/HHSM-500-T0015. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of HHS or any of its agencies.

 Concept and design (KK, LMB, GGP, BG, LB, KAS, TJD, LM); acquisition of data (RS); analysis and interpretation of data (KK, LMB, RS, GGP, BG, LB, KAS, SDH); drafting of the manuscript (KK, LMB, RS, GGP, SDH); critical revision of the manuscript for important intellectual content (KK, LMB, GGP, SDH, TJD, LM); statistical analysis (KK); obtaining funding (BG, LM); administrative, technical, or logistic support (KK, GGP, BG, LB, KAS, TJD, LM); and supervision (KK, GGP, LM).

 Keith Kranker, PhD, Mathematica Policy Research, PO Box 2393, Princeton, NJ 08543-2393. Email: kkranker@mathematica-mpr.com.

1. Health Care Innovation Challenge: cooperative agreement: initial announcement. CMS website. innovation.cms.gov/Files/x/Health-Care-Innovation-Challenge-Funding-Opportunity-Announcement.pdf. Published November 14, 2011. Accessed April 2, 2018.

2. Coleman EA, Boult C; American Geriatrics Society Health Care Systems Committee. Improving the quality of transitional care for persons with complex care needs. 

. 2003;51(4):556-557. doi: 10.1046/j.1532-5415.2003.51186.x.

3. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30-day rehospitalization: a systematic review. 

. 2011;155(8):520-528. doi: 10.7326/0003-4819-155-8-201110180-00008.

4. Peikes D, Lester RS, Gilman B, Brown R. The effects of transitional care models on re-admissions: a review of the current evidence. 

5. Feltner C, Jones CD, Cené CW, et al. Transitional care interventions to prevent readmissions for persons with heart failure: a systematic review and meta-analysis. 

. 2014;160(11):774-784. doi: 10.7326/M14-0083.

A telephonic transitional care program at a rural hospital reduced postdischarge Medicare spending by 31% and reduced inpatient spending for Medicare fee-for-service beneficiaries.

Boyd Gilman, PhD

Articles

Effects of a Community-Based Care Management Model for Super-Utilizers

Rural Hospital Transitional Care Program Reduces Medicare Spending