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The American Journal of Managed Care October 2014
Quality of Care at Retail Clinics for 3 Common Conditions
William H. Shrank, MD, MSHS; Alexis A. Krumme, MS; Angela Y. Tong, MS; Claire M. Spettell, PhD; Olga S. Matlin, PhD; Andrew Sussman, MD; Troyen A. Brennan, MD, JD; and Niteesh K. Choudhry, MD, PhD
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A Comprehensive Hospital-Based Intervention to Reduce Readmissions for Chronically Ill Patients: A Randomized Controlled Trial
Ariel Linden, DrPH; and Susan W. Butterworth, PhD
Increasing Access to Specialty Care: Patient Discharges From a Gastroenterology Clinic
Delphine S. Tuot, MDCM, MAS; Justin L. Sewell, MD, MPH; Lukejohn Day, MD; Kiren Leeds, BA; and Alice Hm Chen, MD, MPH
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Kathleen T. Durant, PhD; Jack Newsom, ScD; Elizabeth Rubin, MPA; Jan Berger, MD, MJ; and Glenn Pomerantz, MD
The Duration of Office Visits in the United States, 1993 to 2010
Meredith K. Shaw; Scott A. Davis, MA; Alan B. Fleischer, Jr, MD; and Steven R. Feldman, MD, PhD
Evaluation of Collaborative Therapy Review to Improve Care of Heart Failure Patients
Harleen Singh, PharmD; Jessina C. McGregor, PhD; Sarah J. Nigro, PharmD; Amy Higginson, BS; and Greg C. Larsen, MD
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Gary Epstein-Lubow, MD; Rosa R. Baier, MPH; Kristen Butterfield, MPH; Rebekah Gardner, MD; Elizabeth Babalola, BA; Eric A. Coleman, MD, MPH; and Stefan Gravenstein, MD, MPH
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Eleftherios S. Xenos, MD, PhD; Jessica A. Lyden, BSc; Ryan L. Korosec, MBA, CPA; and Daniel L. Davenport, PhD
Using Electronic Health Record Clinical Decision Support Is Associated With Improved Quality of Care
Rebecca G. Mishuris, MD, MS; Jeffrey A. Linder, MD, MPH; David W. Bates, MD, MSc; and Asaf Bitton, MD, MPH
The Impact of Pay-for-Performance on Quality of Care for Minority Patients
Arnold M. Epstein, MD, MA; Ashish K. Jha, MD, MPH; and E. John Orav, PhD
Healthcare Utilization and Diabetes Management Programs: Indiana 2006-2010
Tilicia L. Mayo-Gamble, MA, MPH; and Hsien-Chang Lin, PhD
Predictors of High-Risk Prescribing Among Elderly Medicare Advantage Beneficiaries
Alicia L. Cooper, MPH, PhD; David D. Dore, PharmD, PhD; Lewis E. Kazis, ScD; Vincent Mor, PhD; and Amal N. Trivedi, MD, MPH

A Comprehensive Hospital-Based Intervention to Reduce Readmissions for Chronically Ill Patients: A Randomized Controlled Trial

Ariel Linden, DrPH; and Susan W. Butterworth, PhD
A hospital-based transitional care program for patients with heart failure or pulmonary disease failed to reduce 30- or 90-day readmissions or emergency department visits.
Objectives
Medicare penalizes hospitals with 30-day readmissions above their expected rates. Hospitals have responded by implementing transitional care interventions; however, there is limited evidence to inform the development of a successful intervention.

Study Design
Parallel-group, stratified, randomized controlled trial.

Methods
A total of 512 patients hospitalized at 2 community hospitals, with congestive heart failure (CHF) or chronic obstructive pulmonary disease (COPD), were randomly assigned to the intervention (n = 253) or usual care (n = 259). The intervention encompassed a 90-day hospital-based transitional care program. The primary end points were 30- and 90-day all-cause readmissions. Secondary measures included all-cause emergency department (ED) visits and mortality.

Results
On average, study participants were 67 years of age, 57% female, and 70% insured by Medicare. There was no statistical difference between treatment groups on 30-day readmission incidence rates (difference, 0.040; 95% CI, –0.047 to 0.127; P = .36), or 90-day readmission incidence rates (difference of 0.035; 95% CI –0.122 to 0.192; P = .66). Groups also did not differ in ED visit incidence rates at 30 or 90 days. The mortality rate among patients with CHF showed no difference between groups (risk ratio = 0.90; 95% CI, 0.40-2.05). However, for COPD, mortality at 90 days was lower in the intervention group than in the usual care group (risk ratio = 0.28; 95% CI, 0.10-0.83).

Conclusions
Stand-alone community hospitals may be unable to prevent readmissions despite the use of comprehensive, evidence-based intervention components that are within their control. Better collaboration between hospitals and community-based providers is needed to ensure continuity of care for discharged patients.

Trial Registration: ClinicalTrials.gov, Identifier: NCT01855022

Am J Manag Care. 2014;20(10):783-792
  • Our results suggest the need to continue experimenting with new interventions targeting readmissions, especially for severely ill patients. Our addition of interactive voice response and motivational interviewing–based health coaching to the transitional care model did not improve outcomes.
     
  • Our findings suggest that correcting improper use of the inhaler and increasing adherence to inhaled medications may reduce 90-day mortality for chronic obstructive pulmonary disease patients.
     
  • Hospitals, without collaborative relationships with community-based providers, may have limited ability to reduce readmissions, as they cannot ensure timely and continuous care for patients after discharge.
     
  • A challenging road lies ahead for stand-alone community hospitals seeking to decrease readmissions and avoid financial penalties.
CMS is implementing an array of policies intended to reduce readmissions because they are both costly and thought to be mostly avoidable.1 The CMS approach includes both penalties for hospitals if 30-day readmission rates for specified conditions exceed expected risk-adjusted rates, and support for demonstration projects aimed to enhance the patient’s transition from hospital to community.2 The commercial sector is also signaling a move in a similar direction. With the addition of all-cause readmission rates to the Healthcare Effectiveness Data and Information Set (HEDIS), health plans are positioned to create new incentives for hospitals to reduce readmissions.3

While hospitals have responded by implementing interventions to reduce 30-day readmissions, existing empirical evidence offers limited guidance on how to develop a successful program. Out of 43 studies in a recent systematic review of interventions to reduce readmissions, only 16 were deemed effective, and no single intervention component was consistently associated with effectiveness.4 Moreover, many of these interventions were developed and tested in academic settings or integrated delivery systems, leaving open the question of how even effective interventions would fare in stand-alone community-based hospitals. The lack of proven successful and generalizable models argues for continued innovation and experimentation with interventions designed to reduce readmissions.

In this study, we examined whether a comprehensive hospital-based transitional care intervention reduces readmissions for participants with congestive heart failure (CHF) and chronic obstructive pulmonary disease (COPD). We selected these conditions because they represent 2 of the top 3 readmitting diagnoses among Medicare beneficiaries.1 In addition, hospitals are currently penalized by CMS for 30-day readmissions among CHF patients, and COPD will be included as a measure in 2015. We hypothesized that rates of readmission would be lower for participants receiving the intervention, as would the secondary outcomes of emergency department (ED) visits and mortality rates.

METHODS

Trial Design and Setting


We conducted a parallel-group, stratified, randomized controlled trial at 2 independent, nonprofit hospitals: a 124-bed acute care facility located in Grants Pass, Oregon, and a 387-bed full-service tertiary facility in Medford, Oregon. The 2 hospitals serve 9 counties in southern Oregon and northern California (total population 574,214 in 2012) and had combined annual acute discharges of 21,430 in 2012. The Southern Oregon Institutional Review Board approved the study protocol and provided oversight for the duration of the trial.

Participants

Study participants were enrolled between June 2010 and November 2012, with follow-up activities provided until the end of January 2013. During the enrollment period, the hospital census report was reviewed daily at each hospital to identify hospitalized patients with a primary diagnosis of CHF or COPD using codes from the International Classification of Diseases, 9th Revision, Clinical Modification. Patients were eligible for inclusion if they: were at least 18 years of age; resided in any of the 9 counties served by the hospitals; were community-dwelling; had sufficient cognitive ability to communicate and reason (determined by study nurse); had access to a telephone; could communicate in English; and were not currently participating in another intervention aimed at reducing readmissions.

Randomization

Prior to study commencement, the principal investigator (AL) generated a randomization sequence to allocate participants to treatment arms using random permuted blocks.5 There were 4 strata (2 hospitals and 2 disease conditions) and 5 permuted blocks allocated in equal proportions, with a minimum size of 2 and maximum size of 10. The treatments were allocated in a 1:1 ratio, with 18 extra allocations provided to maintain the integrity of the final block in each stratum. The allocation sequence was concealed via sequentially numbered, opaque sealed envelopes. After participants signed the consent form and provided baseline information, the envelope was opened by study staff in their presence, simultaneously revealing the treatment allocation to both participant and study staff.

Intervention

The intervention included a comprehensive set of components commonly found in the transitional care model.6 Following the taxonomy proposed by Hansen et al,4 these components included: 1) pre-discharge components: patient education, discharge planning, medication reconciliation, follow-up appointment scheduled; 2) post discharge components: timely follow-up, follow-up telephone call, availability of patient hotline; and 3) bridging components: transition/health coach, patient-centered discharge instructions.

To enhance the innovative nature of the intervention, 2 post discharge components were added—motivational interviewing–based health coaching (MI) and symptom monitoring using interactive voice response (IVR). MI is a standardized, evidence-based health coaching approach described as a “collaborative, goal-oriented style of communication with particular attention to the language of change.”7 MI and IVR were selected because the literature supports their efficacy for: coaching in short duration interventions8; promoting activation for self-management9,10; addressing challenging behavior changes such as treatment compliance and smoking cessation11,12; and reducing hospitalizations for both CHF and COPD.4,13,14 (A complete description of intervention components is provided in Table 1.)

The MI component included 1 initial session from a study nurse during the index hospital stay to prepare participants to transition back to the community setting. A follow-up session was scheduled within 2 days of discharge, with additional sessions scheduled during the subsequent 90-day period based on the participant’s patient activation level, health literacy, severity of health condition, and preference. All sessions were delivered by 1 of 2 experienced nurses trained in administering transitional care and MI. The nurses were regularly monitored by an MI expert (SWB), using the Motivational Interviewing Treatment Integrity (MITI) tool, to ensure proficiency levels in MI associated with positive clinical outcomes.15 From multiple random checks, the average global clinician ratings for the 2 nurse-coaches were 3.9 and 4.2 out of 5 (with 3.5 or above indicating proficiency); their average percentage of using MI-adherent strategies were 91% and 99% (with 90% or above indicating proficiency).

The IVR component included daily symptom monitoring using an IVR system (Pharos Innovations, Northfield, Illinois) up to 30 days post discharge, with the 2 study nurses responding to symptom alerts within 24 hours.

Although the Transitional Care Model sometimes includes home visits, we did not include this in the intervention due to funding constraints and the lack of evidence that it is a compelling component. We also could not ensure provider continuity of care, given that the funding was restricted to hospital staff, and community-based providers were not obligated to participate in this hospital-based intervention.

Usual Care

Participants randomized to the control condition received usual care from the hospital, with transitional care typically consisting of brief patient education and discharge planning delivered in the traditional medical model.16

Data and Outcomes

The primary outcomes, 30- and 90-day all-cause readmissions, were created from the hospitals’ electronic medical record (EMR) system data. Secondary outcomes included: 30- and 90-day all-cause ED visits, also retrieved from the hospitals’ EMRs; and mortality rates at 90 days, obtained by linking patient Social Security numbers to the Social Security Death Index and searching obituaries in the regional newspapers. As suggested during the peer review process, we also created a composite rate variable that captured all unplanned returns to the hospital, composed of both readmissions and ED visits.

Self-reported sociodemographic characteristics, comorbidities, severity level of the primary condition (based on New York Heart Association [NYHA] functional classification for CHF and Global Initiative for Chronic Obstructive Lung Disease [GOLD] classification for COPD),17 and contact information of the participant’s community-based primary care provider (PCP) were collected during recruitment. Participants also completed the Patient Activation Measure (PAM) during recruitment (baseline) and by mail at 30- and 90-day time points. The PAM assesses knowledge, skills, beliefs, and behaviors needed to enable patients to manage their health and care.18,19 To assess the level of patient engagement with the novel components of our intervention (MI and IVR), we collected the following process measures: the number of MI sessions completed, IVR usage, and the relationship between IVR alert triggers and subsequent acute care utilization.

Statistical Analysis

Sample size calculations were estimated for over-dispersed Poisson observations,20 based on the hospitals’ prior 90-day readmission incidence rate of 0.51 per person. We calculated sample size separately for each condition to enable sufficient power to conduct subgroup analyses, resulting in sample sizes of 119 participants per group needed to detect a 0.23 incidence rate reduction of readmissions during a 90-day time period (2-tailed, α = 0.05, power = 0.80). The projected intervention effect size was based on successful randomized trials with similar emphases, follow-up periods, and sample sizes.21-24

All analyses were performed on the whole sample and separately by condition. Analyses were conducted from an intention-to-treat perspective. Baseline comparability of the intervention and usual care groups was evaluated using χ2 tests for categorical variables and t tests for continuous variables. We assessed the impact of the intervention on readmission incidence rates, ED visit incidence rates, and the composite rate using negative binomial regression. This model was chosen based on goodness of fit statistics that favored negative binomial over other count models.25 A count model is the most appropriate statistical approach given that some patients may experience multiple readmissions, and the data are expected to be highly skewed. Methods that recast the outcome as a binary variable, such as time-to-event analysis, introduce measurement bias by masking potential differences between study groups in their distribution of readmissions.

Differences in mortality rates between treatment and control groups were estimated using logistic regression and calculated as both risk differences and risk ratios, using the adjustment method proposed by Norton et al.26

 
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