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The American Journal of Managed Care January 2016
Does Distance Modify the Effect of Self-Testing in Oral Anticoagulation?
Adam J. Rose, MD, MSc; Ciaran S. Phibbs, PhD; Lauren Uyeda, MA; Pon Su, MS; Robert Edson, MA; Mei-Chiung Shih, PhD; Alan Jacobson, MD; and David B. Matchar, MD
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Bruce W. Sherman, MD, and Carol Addy, MD, MMSc
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Impact of a Scalable Care Transitions Program for Readmission Avoidance
Brent Hamar, DDS, MPH; Elizabeth Y. Rula, PhD; Aaron R. Wells, PhD; Carter Coberley, PhD; James E. Pope, MD; and Daniel Varga, MD
The Introduction of Generic Risperidone in Medicare Part D
Vicki Fung, PhD; Mary Price, MA; Alisa B. Busch, MD, MS; Mary Beth Landrum, PhD; Bruce Fireman, MA; Andrew A. Nierenberg, MD; Joseph P. Newhouse, PhD; and John Hsu, MD, MBA, MSCE
Effects of Continuity of Care on Emergency Department Utilization in Children With Asthma
Shu-Tzu Huang, MS; Shiao-Chi Wu, PhD; Yen-Ni Hung, PhD; and I-Po Lin, PhD
Outcomes Trends for Acute Myocardial Infarction, Congestive Heart Failure, and Pneumonia, 2005-2009
Chapy Venkatesan, MD; Alita Mishra, MD; Amanda Morgan, MD; Maria Stepanova, PhD; Linda Henry, PhD; and Zobair M. Younossi, MD
Factors Related to Continuing Care and Interruption of P4P Program Participation in Patients With Diabetes
Suh-May Yen, MD, PhD; Pei-Tseng Kung, ScD; Yi-Jing Sheen, MD, MHA, Li-Ting Chiu, MHA; Xing-Ci Xu, MHA; and Wen-Chen Tsai, DrPH
Oral Anticoagulant Discontinuation in Patients With Nonvalvular Atrial Fibrillation
Sumesh Kachroo, PhD; Melissa Hamilton, MPH; Xianchen Liu, MD, PhD; Xianying Pan, MS; Diana Brixner, PhD; Nassir Marrouche, MD; and Joseph Biskupiak, PhD, MBA
Value-Based Insurance Designs in the Treatment of Mental Health Disorders
Alesia Ferguson, PhD; Christopher Yates, BA; and J. Mick Tilford, PhD

Impact of a Scalable Care Transitions Program for Readmission Avoidance

Brent Hamar, DDS, MPH; Elizabeth Y. Rula, PhD; Aaron R. Wells, PhD; Carter Coberley, PhD; James E. Pope, MD; and Daniel Varga, MD
The 30-day readmission risk was reduced 25% by a collaborative program model employing discharge planning and telephonic follow-up for high-risk patients with CMS penalty diagnoses.
The quantitative measure to evaluate the extent of imbalance and heterogeneity between the study groups was the L1 metric, a nonparametric measure generated in the CEM process that quantifies imbalance by comparing relative frequencies of the 2 groups across each of the strata.31 Values of L1 close to 0 indicate a higher quality match with minimal imbalance, whereas an L1 value of 1 indicates complete dissimilarity or disproportionality between the groups (no overlap between groups in the strata assignment). Matching variables are optimized with respect to providing a low L1 with high retention of treatment group members. For additional confirmation of intergroup comparability, a Wald test was estimated to determine if the independent variables in multivariate modeling provided similar explanatory contribution to the dependent variable (readmissions) for both study groups. A small Wald statistic and a large P value indicate sufficient similarity between groups to obtain a reliable estimate of intervention effect.

Zero-inflated Poisson multivariate models were used to estimate intervention effects on all readmissions and on 30-day readmissions while accounting for potential confounders. These control variables included age (continuous), evaluation window (time from index admission to end of study period), CEM-generated weights, and disease status for COPD, HF, pneumonia, AMI, diabetes, chronic kidney disease, and coronary artery disease, as well as the status for each of these conditions, as documented during the index admission. Adjusted incidence rate ratios (IRRs; relative risk) were produced from the 0-inflated Poisson model by taking the exponential of the intervention variable coefficient, using the comparison study group as the reference. Poisson multivariate count regression models with least squares means statements were used to calculate adjusted daily readmission rates for each study group. Adjusted daily readmission rates were then converted to annualized readmission rates per 1000. Multivariate logistic regression was used to estimate adjusted odds ratios (ORs) and 95% CIs of intervention effects on the likelihood of having versus not having at least 1 readmission using the same control variables. Data manipulation and analysis were performed using SAS version 9.2 (SAS Institute, Cary, North Carolina).

Descriptive characteristics of the full population of admitted patients meeting study eligibility requirements and the matched treatment and comparison study groups are displayed in Table 1. CEM matching resulted in the pruning of 12 (2.1%) treatment members and 726 (17.9%) comparison members from the initially eligible populations. Although some improvement in balance was achieved through this pruning, adjustment using CEM weights resulted in near equivalence between the groups across all matching variables. The mean evaluation window (days from index admission to end of the 6-month study period) was also similar for the treatment and comparison groups at 88.9 and 81.8 days, respectively. Remaining differences, including evaluation window, were controlled for using additional covariates in statistical modeling of outcomes.

Further verification of comparability between the study groups was provided by L1 and Wald statistics. Table 2 displays an L1 postmatch statistic of 4.09 E-16 indicating minimal imbalance between study groups and a dramatic improvement in balance from the pre-match L1. The summary Wald test statistic and large associated P value (Wald = 0.0026; P = 1.0) provided confirmation of intergroup comparability with respect to all modeled covariates.

Adjusted IRRs showed significantly lower rates of all readmissions (P = .0060) and 30-day readmissions (P = .0107) in the treatment group relative to the comparison group (Table 3). The treatment group displayed 22% fewer overall readmissions and 25% fewer 30-day readmissions in contrast to the comparison group. As a reference point for the relative rates, the adjusted annualized readmission rate per 1000 patients was 603.0 for the treatment group and 771.4 for the comparison group.

Logistic analysis results indicated that treatment group members were significantly less likely to have had any readmission or a 30-day readmission during the study period, with adjusted odds being 0.47 and 0.56, respectively, relative to comparison group members (Table 4). Changing the reference group, the adjusted odds of having 1 or more readmissions during the study period was 2.1 times higher in the comparison group relative to the treatment group (adjusted OR, 2.1; 95% CI, 1.5-2.9). Results for 30-day readmissions were of similar magnitude (adjusted OR, 1.8; 95% CI, 1.3-2.5).

Across the board, results indicate that the CTS intervention significantly reduced hospital readmissions for THR patients with readmission-sensitive conditions. Participation in the CTS program was significantly associated with lower readmission incidence, both for 30-day readmissions and all readmissions occurring over the 6-month study period. Additionally, admitted patients who did not participate in CTS had approximately twice the odds of having a 30-day readmission or any readmission during the study period relative to the treatment group.

The estimated reduction in readmissions for the entire 6-month period demonstrates the sustained intervention impact, extending beyond the 30-day window that is often the bar for efforts aimed at preventing the immediate ramifications of failures to provide effective discharge instructions and planning. The measured impact of CTS is beyond that of more global hospital initiatives to reduce readmissions from which all patients benefit. The CTS model also intends to maximize cost efficiency and scalability through sophisticated prediction of high-risk cases for selective supplemental support and by outsourcing program operations, data management, predictive modeling, and postdischarge telephonic follow-up.

The CTS program builds on a remote telephonic intervention, also delivered by Healthways, that one study found was associated with a 23% lower likelihood of readmission within 30 days relative to the comparison group.32 CTS represents an enhancement of this telephonic model, one that allows for early intervention and a direct connection to providers. For example, in the prior study, Harrison et al highlighted the delayed delivery of patient discharge information and, consequently, delayed initiation of telephonic follow-up—problems that were due to claims-based identification without any direct data feed from the hospital or communication with providers. Initiating the intervention within a hospital environment with access to timely patient information ensures continuity of patient support across different settings, allows a relationship to be established with the patient prior to discharge, and permits rapid follow-up after discharge. Congruent with Kripalani et al, who concluded that single-component interventions are less impactful,33 the effect of CTS exceeded what was reported for the described patient-centric telephonic-only model while also using a cost-efficient approach.32

In addition to prior research supporting the benefit of telephonic follow-up, there is foundational evidence for other aspects of the CTS program reducing readmissions. A randomized study of nurse and pharmacist support to reinforce the discharge plan and review medications found a 0.695 IRR for 30-day readmissions compared with controls.23 A randomized trial of an intensive transitional care model delivered by advanced practice nurses to patients 65 years and older reported a significantly lower 24-week readmission rate for treatment relative to controls (20.3% vs 37.1%).34 Coleman et al reported positive results from the Care Transitions Intervention using both a quasi-experimental approach35 and a randomized trial.24 The trial, on admitted patients 65 years and older, showed significant reductions in treatment group readmission rates, with 30- and 90-day readmission adjusted ORs of 0.59 and 0.64, respectively. Similar findings have been published more recently on the real-world effectiveness of the Care Transitions Intervention using a quasi-experimental design.36

Comparison of the present study with published randomized trial results indicates the CTS program has performed comparably in readmission avoidance. The 30-day readmission incidence rate ratio of 0.75 for the CTS program was comparable with the 0.695 IRR reported by Jack et al,23 as was the CTS 30-day readmission OR of 0.56 compared with the Coleman et al24 trial result of 0.59. This is especially significant when taking into account that the CTS program was evaluated under “real-world effectiveness” conditions, whereas randomized trials are conducted in a more controlled setting.

The need for real-world effectiveness studies on readmissions has been acknowledged in the literature. Voss et al write, “Patients who agree to participate in randomized controlled trials are a select subset of that population, limiting the generalizability of these observations.”36 Although Coleman’s Care Transition Intervention has been tested extensively, both in randomized trials24,37 and effectiveness studies,35,36 there is value to testing alternative approaches to improve quality of healthcare solutions. A given solution is not necessarily applicable or practical in all environments. The use of a predictive model to selectively deliver this program to patients at higher risk, then outsourcing telephonic follow-up, makes this evaluated program potentially more operationally appealing than programs that are delivered less selectively and that require greater hospital staff time in order to intervene with a larger number of people and to follow up after discharge.

The retrospective design presents a limitation to the study, as it necessitated the creation of a comparison group from a convenience sample as opposed to prospective selection and randomization. Further, generalizability of study results is unknown, but the multisite design strengthens the likelihood that results would translate to other hospitals. Future work should evaluate additional diseases and conditions beyond the penalty diagnoses evaluated here, as well as program effectiveness over a longer duration and in different institutions, demographic groups, and geographic regions.

The changing architecture of healthcare reimbursement is requiring hospitals to quickly find solutions to improve quality metrics. In the first year of the Hospital Readmissions Reduction Program, 2200 hospitals received cumulative penalties of $280 million.10 Given that reported readmission outcomes are publicly available on the CMS Hospital Compare website, the revenue impact of negative press may be as detrimental as the reimbursement penalties. These changes in the healthcare market highlight the importance of the current study results, which indicate that CTS offers an efficient and effective solution for health systems, hospitals, and large provider groups that are seeking support in reducing readmissions among high-risk patients.

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