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The American Journal of Managed Care July 2019
Changing Demographics Among Populations Prescribed HCV Treatment, 2013-2017
Naoky Tsai, MD; Bruce Bacon, MD; Michael Curry, MD; Steven L. Flamm, MD; Scott Milligan, PhD; Nicole Wick, AS; Zobair Younossi, MD; and Nezam Afdhal, MD
Precision Medicines Need Precision Patient Assistance Programs
A. Mark Fendrick, MD; and Jason D. Buxbaum, MHSA
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Robert W. Dubois, MD, PhD
Real-Time Video Detection of Falls in Dementia Care Facility and Reduced Emergency Care
Glen L. Xiong, MD; Eleonore Bayen, MD, PhD; Shirley Nickels, BS; Raghav Subramaniam, MS, BS; Pulkit Agrawal, PhD; Julien Jacquemot, MSc, BSc; Alexandre M. Bayen, PhD; Bruce Miller, MD; and George Netscher, MS, BS
Impact of a Co-pay Accumulator Adjustment Program on Specialty Drug Adherence
Bruce W. Sherman, MD; Andrew J. Epstein, PhD; Brian Meissner, PharmD, PhD; and Manish Mittal, PhD
Heroin and Healthcare: Patient Characteristics and Healthcare Prior to Overdose
Michele K. Bohm, MPH; Lindsey Bridwell, MPH; Jon E. Zibbell, PhD; and Kun Zhang, PhD
Medicare’s Bundled Payment Model Did Not Change Skilled Nursing Facility Discharge Patterns
Jane M. Zhu, MD, MPP; Amol Navathe, MD, PhD; Yihao Yuan, MSc; Sarah Dykstra, BA; and Rachel M. Werner, MD, PhD
Number of Manufacturers and Generic Drug Pricing From 2005 to 2017
Inmaculada Hernandez, PharmD, PhD; Chester B. Good, MD, MPH; Walid F. Gellad, MD, MPH; Natasha Parekh, MD, MS; Meiqi He, MS; and William H. Shrank, MD, MSHS
Insurers’ Perspectives on MA Value-Based Insurance Design Model
Dmitry Khodyakov, PhD; Christine Buttorff, PhD; Kathryn Bouskill, PhD; Courtney Armstrong, MPH; Sai Ma, PhD; Erin Audrey Taylor, PhD; and Christine Eibner, PhD
Healthcare Network Analysis of Patients With Diabetes and Their Physicians
James Davis, PhD; Eunjung Lim, PhD; Deborah A. Taira, ScD; and John Chen, PhD
What Are the Potential Savings From Steering Patients to Lower-Priced Providers? A Static Analysis
Sunita M. Desai, PhD; Laura A. Hatfield, PhD; Andrew L. Hicks, MS; Michael E. Chernew, PhD; Ateev Mehrotra, MD, MPH; and Anna D. Sinaiko, PhD, MPP
Physician Satisfaction With Health Plans: Results From a National Survey
Natasha Parekh, MD, MS; Sheryl Savage; Amy Helwig, MD, MS; Patrick Alger, BS; Ilinca D. Metes, BS; Sandra McAnallen, MA, BSN; and William H. Shrank, MD, MSHS
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Evaluation of Interdisciplinary Geriatric Transitions of Care on Readmission Rates
Nada M. Farhat, PharmD; Sarah E. Vordenberg, PharmD, MPH; Vincent D. Marshall, MS; Theodore T. Suh, MD, PhD, MHS; and Tami L. Remington, PharmD

Evaluation of Interdisciplinary Geriatric Transitions of Care on Readmission Rates

Nada M. Farhat, PharmD; Sarah E. Vordenberg, PharmD, MPH; Vincent D. Marshall, MS; Theodore T. Suh, MD, PhD, MHS; and Tami L. Remington, PharmD
An interdisciplinary transitions of care service composed of nurse navigators, pharmacists, and medical providers reduced 30-day hospital readmissions among patients who received all components of the intervention.
ABSTRACT

Objectives: To evaluate the effect of an interdisciplinary transitions of care (TOC) service on readmission rates in a geriatric population.

Study Design: Single-center retrospective cohort study of adults 60 years or older discharged from an academic medical center.

Methods: From July 1, 2013, to February 21, 2016, a total of 4626 patients discharged from 1 hospital, including inpatient, emergency department, observation, and short-stay units, were included. Cases were scheduled for a TOC service with the interdisciplinary team. Controls received usual care at other sites. All-cause 14-, 30-, and 90-day readmission rates between propensity score–matched study groups were evaluated by intention-to-treat (ITT), per-protocol (PP), and as-treated methods.

Results: During the study period, 513 patients were scheduled for at least 1 component of the TOC intervention (ITT group). Of those patients, 215 completed all scheduled visits (PP group). Readmission rate after 30 days demonstrated no difference in the ITT group compared with the control group (12.8% vs 10.7%; P = .215), although it was significantly lower in the PP group in comparison with the control group (12.8% vs 7.9%; P = .042).

Conclusions: An interdisciplinary team based in a patient-centered medical home improved readmission rates for all patients who completed the intervention (PP group).

Am J Manag Care. 2019;25(7):e219-e223
Takeaway Points

Reduction in hospital readmissions is an important public health issue, especially in the older adult population. We studied the impact of an interdisciplinary transitions of care (TOC) service composed of nurse navigators, pharmacists, and medical providers on 14-, 30-, and 90-day hospital readmissions.
  • Older adults are an especially vulnerable patient population at high risk of hospital readmissions.
  • Reducing hospital readmission rates may improve insurance and hospital reimbursement.
  • An interdisciplinary TOC intervention may help healthcare and hospital administration allocate resources more effectively.
With hospital reimbursement tied to readmissions through the Hospital Readmissions Reduction Program and a national call to action to improve care coordination with the implementation of the Affordable Care Act, providing and coordinating effective care after hospitalization has become increasingly important.1 Approximately 20% of older adults who are discharged from the hospital setting are rehospitalized within 30 days of discharge, costing Medicare up to $17.4 billion annually.2,3

In an effort to reduce hospital readmission rates, CMS will continue to withhold payments for excess hospital readmissions; these withheld payments were projected to reach an all-time high of $528 million in 2017.4 Given the clinical and economic impact of these withheld payments, institutions nationwide are attempting to identify factors that could decrease avoidable rehospitalizations. To date, no single intervention has been shown to effectively reduce readmission rates. Additionally, studies in this area are heterogeneous in design and practice setting, limiting their external validity to apply their results to other practice models.5

Factors affecting hospital readmissions are multifactorial, although recent data have shown that 1 in 5 adult patients discharged from the hospital setting will experience an adverse event (AE) due to medical management within 3 weeks of discharge; more than half of such events were drug related and could potentially have been prevented or mitigated if identified sooner.6 The transitions of care (TOC) program presented in this study includes pharmacists as an integral component of care coordination to help identify and prevent AEs related to medication management.

Beyond optimizing medication use, improving care for geriatric patients using a patient-centered medical home (PCMH) has been supported by several organizations.7 Specific care coordination in a PCMH model allows for improved health, reduced cost of care, and reductions in health disparities.7 Preliminary results from a geriatric population in the Veterans Affairs health system examined an interdisciplinary team in a PCMH of patients with complex comorbidities. In this study, readmission rates were not assessed; however, the incidence of subspecialty clinic visits declined significantly as interdisciplinary geriatric care was transitioned into primary care.8 Our study aims to determine how readmission rates are affected by an interdisciplinary care model targeting older adults treated at a geriatrics PCMH.

A previous study of this clinic model was performed in 2012 and found that 1 readmission was avoided for every 18 patients completing the intervention.9 After this study, the team composition and clinical processes were changed in an effort to improve outcomes even further. Thus, the interdisciplinary team is now composed of nurse navigators, clinical pharmacists, and medical providers (board-certified geriatric medicine physicians and nurse practitioners). Additional changes included longer TOC appointment times, changes to pharmacists’ scheduling to increase the number of patients contacted, and nurse navigator phone calls to triage patient needs. These changes reduced no-show rates for provider visits and improved continuity of care because patients can potentially see a medical provider sooner and more often in this newer model.

METHODS

TOC Intervention

As part of the PCMH, the Turner Geriatric Clinic at the East Ann Arbor Health & Geriatric Center within Michigan Medicine (MM) functions with the interdisciplinary team; they work together to assist older adults discharged to home after an acute illness to prevent rehospitalization (eAppendix A [eAppendices available at ajmc.com]). Patients seen at this clinic include those discharged from emergency departments, observation or medical short-stay units, subacute rehabilitation facilities, or inpatient units. Each patient is scheduled for a pharmacist and provider follow-up appointment upon discharge. Nurse navigators receive a list of all discharged patients and use that to make calls to each patient. These 3 components make up the complete TOC intervention. All interactions are documented in the electronic health record (EHR) (eAppendix B).

Study Design

This was a single-center retrospective cohort study. Included were adults 60 years or older discharged from MM, including observation, short-stay unit, and inpatient admissions, between July 1, 2013, and February 21, 2016. We selected this date range to minimize overlap with new TOC services implemented at the health system level. Patients were required to have completed a primary care provider (PCP) visit within 3 years prior to the first hospitalization meeting inclusion criteria during the study period.

Cases had an established PCP at the Turner Geriatric Clinic, whereas controls had an established PCP at other PCMH clinics at MM. At the time of this study, there were no systemwide services targeting control patients after hospital discharge. Exclusion criteria included having an outside PCP, being discharged to subacute rehabilitation or nursing home facilities, and receiving only emergency department care, given that discharge resources in this setting vary from those in other units.

This study was approved by the Institutional Review Board at MM. Data were obtained internally through the Data Office for Clinical and Translational Research. The accuracy of our data was confirmed with a manual chart review demonstrating an error rate of less than 5% among a sample of patients. Data collected included patient demographics, comorbidities, number of medications at discharge, information related to the index hospitalization and any readmissions, and interdisciplinary team utilization (ie, nurse navigator, pharmacist, medical provider). We characterized comorbidities using the Charlson Comorbidity Index, in addition to the High-Risk Diagnoses for the Elderly Scale, a tool for mortality prediction in older hospitalized patients.10

Statistical Analysis

Data were analyzed with descriptive statistics, in addition to univariate and multivariate regressions. The descriptive analysis was based on sums and overall percentages for categorical variables and on means and SDs for continuous variables.

Multivariate logistic regression was used for predicting binary outcomes. For comparative analyses, we used 3 comparisons: (1) intention-to-treat (ITT) analysis, which included all patients scheduled for the TOC intervention, whether these visits were completed or not; (2) per-protocol (PP) analysis, which included only patients who completed all components of the TOC intervention; and (3) as-treated (AT) analysis, which compared patients completing the TOC intervention with those who did not complete the TOC intervention (patients who were scheduled for TOC intervention but did not complete all components were included in the control group).

Statistical power was estimated with Fisher’s exact test in the context of 2-sample comparisons with variable sample sizes. We chose population estimates of the effect size based on the previous study assessing this clinic model.9 Our initial anticipated treatment size was 364 people in our TOC intervention assuming a 6.7% absolute reduction in 30-day readmission, which was achieved in the previous clinic model.9 With a ratio of matching 1 TOC patient to 3 control patients, we determined that we would have 88% power to detect the difference.

Analyses were performed in R 3.4.0 in SAS 9.4 (SAS Institute; Cary, North Carolina) using the TWOSAMPLEFREQ procedure for Fisher’s exact test power estimation.

RESULTS

Over the duration of the study period, 513 patients were scheduled for at least 1 component of the intervention (ITT). Of those, 215 completed all components (PP and AT) (Figure). ITT, PP, and AT populations had no significant differences in baseline characteristics after matching, except that cases were slightly older than control groups (eAppendix C).

Readmission Outcomes

In the ITT analysis, there was no significant difference in readmission rates at 14, 30, and 90 days (Table). However, unadjusted readmission rates at 30 days were significantly lower for the PP population versus control group (12.8% vs 7.9%; odds ratio [OR], 0.58; 95% CI, 0.35-0.98; P = .042), as well as for the AT population versus control group (12.8% vs 7.9%; OR, 0.59; 95% CI, 0.35-0.98; P = .041) (Table). Additionally, for the PP population, 1 readmission was avoided for every 20 patients completing the intervention, with a 38% relative risk reduction. Furthermore, we performed a subgroup analysis with a breakdown by age and found no statistically significant differences in readmission rates based on age, although our study was not initially powered to look at this difference between age groups.

Time to Intervention

When comparing timing of interventions post discharge, patients who were readmitted within 30 days were contacted by a nurse navigator an average of 4.2 days after hospital discharge, compared with 2.1 days post discharge for patients who were not readmitted (OR, 1.36; 95% CI, 1.09-1.69; P <.05). In the PP group, the mean (SD) time to a visit with the nurse navigator was 2.3 (2.0) days, with the pharmacist was 5.6 (3.4) days, and with the medical provider was 10.1 (4.2) days.

DISCUSSION

Several recommendations have been proposed to improve care transitions for older adults. These include addressing factors that make transitions complex, engaging patients’ family and caregivers, tailoring home care to meet patient needs, designing recovery plans, and predicting and avoiding preventable readmissions.2 Our practice model aims to follow these recommendations to improve patient outcomes and prevent hospital readmissions.

This study adds to the body of TOC literature supporting the use of interdisciplinary teams in an outpatient PCMH working to reduce hospital readmissions. This model also includes pharmacists as part of the care team, making it a unique approach to help reduce rehospitalizations through more appropriate medication management.

 
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