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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.

Objectives: To evaluate the Care Transition Solution (CTS) as a means to improve quality through reduction of preventable hospital readmissions among patients with readmission-sensitive conditions subject to penalties imposed by the Affordable Care Act.

Study Design: A retrospective quasi-experimental evaluation of the impact of the CTS among admitted patients diagnosed with heart failure, acute myocardial infarction, chronic obstructive pulmonary disease, and/or pneumonia (CMS readmission-penalty diagnoses) in 14 acute care hospitals in Texas. The program, designed for scalable delivery, incorporated identification of high readmission–risk patients, assessment of individual needs, medication reconciliation, discharge planning, care coordination, and telephonic postdischarge follow-up.

Methods: The treatment group of program enrollees (N = 560) and the comparison group with no program contact (N = 3340) were matched on 8 coarsened demographic, diagnosis, and severity variables associated with readmission risk. Assessed outcomes included relative risk and odds of readmission within 30 days postdischarge and overall within the 6-month evaluation period. Zero-inflated Poisson multivariate models were used to estimate intervention effects controlling for matching-generated weights, age, disease status, and period of evaluation.

Results: Treatment group risk of readmission was 22% lower overall (incidence rate ratio [IRR], 0.78; P <.01) and 30-day readmission risk was 25% lower (IRR, 0.75; P = .01) relative to the comparison group. Odds of any or 30-day readmission were 0.47 (95% CI, 0.35-0.65) and 0.56 (95% CI, 0.41-0.77), respectively, for treatment relative to comparison.

Conclusions: Participation in the CTS resulted in significantly lower rates of readmissions among patients with readmission-sensitive conditions, offering a scalable and sustainable approach to reduce the number of preventable hospital readmissions.

Am J Manag Care. 2016;22(1):28-34
Take-Away Points
There is increasing pressure to reduce hospital readmission rates by improving quality while maximizing efficiency. The evaluated collaborative care transitions program provides a scalable approach to reduce readmissions. 
  • Scalable delivery of the Care Transitions Solution (CTS) is achieved through predictive early identification of patients at high risk of readmission and telephonic follow-up. 
  • Among program participants, the 30-day readmission risk was reduced by 25% and the odds of any readmission within 30 days were reduced by more than half, relative to matched nonparticipants. 
  • The CTS can be efficiently implemented by hospitals and is an effective system to reduce the number of preventable readmissions.
The changing healthcare landscape has amplified the need to improve healthcare quality while also lowering healthcare spending, which reached $2.8 trillion in the United States in 2012.1 A comparison of 12 countries showed the proportion of US gross domestic product spent on healthcare was more than 40% higher than in second-ranked France, yet measures of quality for US healthcare are often lower than in countries that spend far less. High rates of hospitalization for chronic conditions in the United States are one example; they have been attributed to low performance in care coordination and safety, and only average levels of care effectiveness and patient-centered care.2 Although these observations could reflect a dysfunctional system, they are more likely the result of a healthcare system increasingly strained by a surging prevalence of complex conditions and concomitant care needs.

A focus on reducing hospital readmissions—which contribute to overall hospitalization rates—is recognized as an opportunity to improve care while also reducing avoidable costs. A study of nearly 12 million Medicare hospital discharges in 2004 found that approximately 20% resulted in 30-day readmissions, with only one-tenth of those readmissions likely planned. The total costs of these unplanned readmissions were over $17 billion.3

Diagnoses that are particularly readmission-sensitive include heart failure (HF), acute myocardial infarction (AMI), and pneumonia. In one study, 30-day readmission rates for these diagnoses were 24.8%, 19.9%, and 18.3%, respectively, in a Medicare population.4 These conditions also account for a significant amount of hospitalization and cost in Medicaid, commercially insured, and uninsured US populations.5 Readmission rates are concerning for these and other serious conditions across all age and payer groups.6-9

In 2012, the Affordable Care Act established strong financial incentives for hospitals and physicians to reduce readmissions. The law required CMS to establish the Hospital Readmissions Reduction Program, which penalizes, through reduced reimbursement, those hospitals whose risk-adjusted readmission rates for HF, pneumonia, and AMI exceed the national average. In 2015, the program expanded to include chronic obstructive pulmonary disease (COPD) and elective hip or knee replacement as readmission penalty conditions.10,11 Commercial payers12-14 and state Medicaid agencies will likely follow with similar programs to raise quality and reduce readmissions.15,16

High rates of unplanned readmissions are a reflection of the fact that the US healthcare system has traditionally functioned in a fragmented and poorly coordinated fashion, often leaving discharged patients and their family members uneducated, confused, and unprepared for the ongoing management of conditions to avoid future adverse events.17-20 Inadequate hand-off of patient management among providers, poorly coordinated hospital discharge processes, discharge instructions for the patient that lack sufficient education and follow-up, and lack of medication reconciliation before and after hospitalization all amplify the likelihood that a patient will return to the hospital. Illustrating the consequences of these problems, the Medicare Payment Advisory Commission’s 2007 report to Congress estimated that 76% of 30-day readmissions are potentially preventable.21

Although broad efforts to address gaps in the discharge and hand-off process can benefit all hospitalized patients, complex cases at higher risk for readmission often require additional resource-intensive planning and follow-up processes. Full implementation of comprehensive approaches established in the literature22-24 for large populations or across multiple hospitals is a formidable initiative given resource constraints in the current healthcare environment. Thus, there is a need for scalable approaches delivering effective readmission prevention models that augment what hospitals, health plans, and providers are already doing to prevent unplanned readmissions.

Texas Health Resources (THR), a nonprofit, faith-based health system located in north Texas, is innovating through exploration and testing of scalable approaches to prevent readmissions. Specifically, THR—through partnership with Healthways, a global well-being improvement company—implemented the Healthways Care Transitions Solution (CTS) in 14 acute care hospitals. CTS uses predictive identification of admitted patients at high risk of readmission, then invokes a collaborative care model for discharge planning and follow-up that extends from the hospital to the home. The current study was conducted to assess the initial effectiveness of CTS participation in preventing readmissions among patients with readmission-sensitive conditions.

Study Design and Data Overview

A quasi-experimental retrospective cohort study was conducted to test the hypothesis that CTS program participation among admitted patients with readmission-sensitive conditions was associated with reduced readmission risk relative to a matched nonparticipant group. Data originated from 14 acute care hospitals in the Texas Health Resources network; specifically, admission-discharge-transfer data documenting patient admissions during the initial 6-month intervention time period of January 1 to July 1, 2013, were used for this study. Because this study was a retrospective analysis of a quality improvement initiative conducted anonymously, it did not require informed consent from participants and was exempt from institutional review board approval based on exclusion criteria outlined in the US Code of Federal Regulations (45 CFR §46.101).



Implementation of the CTS intervention was staggered over a 6-week period beginning in mid-January 2013; the program initiated at 2 to 3 hospitals each week. By March 2013, the CTS intervention program was fully functioning at all 14 sites. The CTS program was designed to deliver thorough and personalized patient education and discharge planning/preparation, provide regular individualized follow-up, and facilitate care coordination in order to avoid unnecessary visits/time spent in medical facilities or doctors’ offices, while encouraging appropriate medical care to avoid additional exacerbations of the patient’s condition.

Basic principles of the CTS program include the following: 1) Identification of patients at high risk of readmission using a predictive model, and clinical assessment to ensure alignment of resources with need; 2) Early engagement of admitted patients by a nurse transition coach to establish a strong relationship with the patient; 3) Detailed collection of contact information to facilitate postdischarge patient interactions; 4) Assessment of medical, psychosocial, functional, literacy, adherence, and support needs, and of capabilities such as functioning and self-efficacy, to tailor interactions accordingly; 5) Reconciliation of medications before hospitalization to medications after hospital discharge; 6) Provision and review of a patient-oriented Care Transition Record, with documented discharge plan; 7) Coordination of medical providers and service agencies for postdischarge patient care; and 8) Postdischarge telephonic follow-up (4 calls over 4 weeks) to track and support the patient’s recovery and ongoing self-management, and to encourage discharge plan adherence.

Study Population

The study-eligible population included all patients admitted to 1 of the 14 evaluated hospitals during the study period who were documented to have at least 1 of the following readmission-sensitive conditions that are also CMS readmission-penalty diagnoses: AMI, HF, pneumonia, or COPD. In accordance with intention-to-treat research design, all CTS enrollees were eligible for the treatment group, regardless of their level of participation or completion of the CTS program, to provide a realistic assessment of program effectiveness. The eligible population had an average age of 59.3 years (range = 18-96 years). Eligible comparison group members did not have any documented interaction with the CTS program. The staggered implementation across hospital sites provided a population of program-eligible patients admitted to sites where the program was not yet available, and although the comparison group was not constrained to patients admitted pre-implementation, the availability of these patients in the matching process diminished the potential for selection bias and availed a more equivalent group for comparison.

Drawing from this eligible population, comparable study groups were created using coarsened exact matching (CEM), which exactly matches individuals within a nonparametric framework into strata based on a set of shared characteristics (coarsened variables) chosen to explain selection bias and variance in the outcome. CEM typically requires removal of fewer cases for a given level of bias removal and thus is comparatively a more effective and efficient method for yielding an unbiased estimate of treatment effect.25,26 Matching variables included age group; gender; dichotomous indication of whether or not initial admission resulted from AMI, pneumonia, HF, or COPD; initial admission length-of-stay group; depression status27; stroke status28,29; hip fracture status30; and disease count (range = 0-5, counting discrete indications of COPD, HF, coronary artery disease, diabetes, and chronic kidney disease). After strata assignment, matched comparison members were assigned a weight specific to their stratum and representative of the relative proportion of members in that stratum; treatment group members were assigned a weight of 1.25 CEM weights were used as a covariate to adjust estimates in multivariate statistical modeling.

Outcomes Assessment

Study groups were compared on 2 metrics: readmissions within 30 days of prior admission and all readmissions occurring within the 6-month study time period. The initial (index) admission for each CTS group member was defined as the admission from which the member was enrolled in the CTS program. For each comparison group member, the index admission was the first admission record during the study period. A readmission was defined as a hospital admission occurring subsequent to the discharge date, documented for a previous admission during the study period, allowing for more than 1 readmission in the study period. Same-day readmissions, in which a discharge and subsequent admission occurred within 24 hours, were excluded as these cases generally denote transfers as opposed to readmissions. For each study member, hospital admission records were assessed from their index admission to the July 1, 2013, study end point.

Statistical Methods

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