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The American Journal of Managed Care February 2015
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A Multidisciplinary Intervention for Reducing Readmissions Among Older Adults in a Patient-Centered Medical Home
Paul M. Stranges, PharmD; Vincent D. Marshall, MS; Paul C. Walker, PharmD; Karen E. Hall, MD, PhD; Diane K. Griffith, LMSW, ACSW; and Tami Remington, PharmD
How Pooling Fragmented Healthcare Encounter Data Affects Hospital Profiling
Amresh D. Hanchate, PhD; Arlene S. Ash, PhD; Ann Borzecki, MD, MPH; Hassen Abdulkerim, MS; Kelly L. Stolzmann, MS; Amy K. Rosen, PhD; Aaron S. Fink, MD; Mary Jo V. Pugh, PhD; Priti Shokeen, MS; and Michael Shwartz, PhD
Did Medicare Part D Reduce Disparities?
Julie Zissimopoulos, PhD; Geoffrey F. Joyce, PhD; Lauren M. Scarpati, MA; and Dana P. Goldman, PhD
Health Literacy and Cardiovascular Disease Risk Factors Among the Elderly: A Study From a Patient-Centered Medical Home
Anil Aranha, PhD; Pragnesh Patel, MD; Sidakpal Panaich, MD; and Lavoisier Cardozo, MD
Employers Should Disband Employee Weight Control Programs
Alfred Lewis, JD; Vikram Khanna, MHS; and Shana Montrose, MPH
Race/Ethnicity, Personal Health Record Access, and Quality of Care
Terhilda Garrido, MPH; Michael Kanter, MD; Di Meng, PhD; Marianne Turley, PhD; Jian Wang, MS; Valerie Sue, PhD; Luther Scott, MS
Leveraging Remote Behavioral Health Interventions to Improve Medical Outcomes and Reduce Costs
Reena L. Pande, MD, MSc; Michael Morris; Aimee Peters, LCSW; Claire M. Spettell, PhD; Richard Feifer, MD, MPH; William Gillis, PsyD
Decision Aids for Benign Prostatic Hyperplasia and Prostate Cancer
David Arterburn, MD, MPH; Robert Wellman, MS; Emily O. Westbrook, MHA; Tyler R. Ross, MA; David McCulloch, MD; Matt Handley, MD; Marc Lowe, MD; Chris Cable, MD; Steven B. Zeliadt, PhD; and Richard M. Hoffman, MD, MPH
Faster by a Power of 10: A PLAN for Accelerating National Adoption of Evidence-Based Practices
Natalie D. Erb, MPH; Maulik S. Joshi, DrPH; and Jonathan B. Perlin, MD, PhD, MSHA, FACP, FACMI
Differences in Emergency Colorectal Surgery in Medicaid and Uninsured Patients by Hospital Safety Net Status
Cathy J. Bradley, PhD; Bassam Dahman, PhD; and Lindsay M. Sabik, PhD
The Role of Behavioral Health Services in Accountable Care Organizations
Roger G. Kathol, MD; Kavita Patel, MD, MS; Lee Sacks, MD; Susan Sargent, MBA; and Stephen P. Melek, FSA, MAAA
Patients Who Self-Monitor Blood Glucose and Their Unused Testing Results
Richard W. Grant, MD, MPH; Elbert S. Huang, MD, MPH; Deborah J. Wexler, MD, MSc; Neda Laiteerapong, MD, MS; E. Margaret Warton, MPH; Howard H. Moffet, MPH; and Andrew J. Karter, PhD
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Leonardo Tamariz, MD, MPH; Ana Palacio, MD, MPH; Jennifer Denizard, RN; Yvonne Schulman, MD; and Gabriel Contreras, MD, MPH
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Maria B. Ospina, PhD; Liz Dennett, MLIS; Arianna Waye, PhD; Philip Jacobs, DPhil; and Angus H. Thompson, PhD
Emergency Department Use: A Reflection of Poor Primary Care Access?
Daniel Weisz, MD, MPA; Michael K. Gusmano, PhD; Grace Wong, MBA, MPH; and John Trombley II, MPP

A Multidisciplinary Intervention for Reducing Readmissions Among Older Adults in a Patient-Centered Medical Home

Paul M. Stranges, PharmD; Vincent D. Marshall, MS; Paul C. Walker, PharmD; Karen E. Hall, MD, PhD; Diane K. Griffith, LMSW, ACSW; and Tami Remington, PharmD
A collaborative practice model to reduce hospital readmissions from an outpatient environment.
Over the study period, 19,169 unique patients accounted for 31,436 hospitalizations, of which 18,530 qualified as index hospitalizations. A total of 831 TCP interventions were scheduled. After excluding scheduled TCP visits after ED, long-term care facility, or outside hospital stays (n = 131), and patients previously included in the study (n = 128), 572 patients remained in the intervention group. These patients were matched to usual-care patients, creating a total sample of 1144 index hospitalizations. The per protocol and as-treated intervention analyses excluded 356 incomplete TCP interventions. By definition, these patients are included in the as-treated analysis control group. Table 1 compares the baseline characteristics of the study groups. Prior to matching, patients scheduled for TCP tended to be older, have higher medication burdens, and have shorter lengths of stay compared with the usual care cohort. After matching, the usual care cohort was similar to the intervention group in age, gender, race, length of stay, and comorbidity. Patients in the intervention group had a significantly higher medications burden at discharge.

Primary Outcome

During the study period, patients scheduled for the intervention had a readmission rate of 21.1% (120 readmissions/ 572 index hospitalizations), compared with 16.4% among unmatched control patients (2952/17,958; P = .004). After matching, readmissions were not significantly different among study groups in the intention-to-treat analysis (21.1% in the TCP group vs 17.3% in the control group; P = .133) (Table 2). Only the per protocol and as-treated analyses revealed significant reductions in 30-day readmissions after completion of the TCP (P <.001 for both analyses). Compared with those never scheduled for TCP, 1 readmission was avoided for every 18 patients completing the intervention. Among the 356 patients scheduled but never completing TCP, 97 readmissions occurred.

Secondary Outcome

The mean ± SD time to readmission was not significantly different between the intervention and control groups in the intention-to-treat matched analysis (14 ± 9 vs 12 ± 9 days; log-rank χ2 = 2.117; df = 1; P = .146); however, significant delays in readmissions were observed in the per protocol (18 ± 9 vs 12 ± 9 days; log-rank χ2 = 5.871; df = 1; P = .015) and as-treated analyses (18 ± 9 vs 13 ± 9 days; log-rank χ2 = 12.978; df = 1; P <.001) (Figure). Almost 70% of readmissions were within 2 weeks among control groups, compared with 39% of patients receiving the intervention. The TCP contacted patients within 1 week of discharge in 43% of cases and within 2 weeks in 80% of cases.

After adjusting for multiple factors, patients in the intention-to-treat intervention group did not have a different odds of readmission compared with matched controls (odds ratio [OR], 0.923; 95% CI, 0.636-1.341). Only medication burden (OR, 1.03; 95% CI, 1.01-1.042) and number of HRDES diagnoses (OR, 1.268; 95% CI, 1.053-1.527) were associated with increased odds of readmission. However, as shown in the as-treated analysis (Table 3), those who did not receive or complete the TCP within 30 days of discharge had an increased odds of being readmitted within 30 days. Medication burden and number of HRDES diagnoses also remained significant predictors of readmission. Results of the per protocol analysis were similar to the as-treated groups. Adjusted ORs were all comparable to the unadjusted analysis. Based on results of the as-treated analysis, hospitalization cost avoidance was estimated to be $737,673 among the 345 completed interventions, or $2138 per intervention (Table 4). Cost avoidance was not observed in the intention-to-treat analysis.

DISCUSSION

These findings describe the potential impact of multidisciplinary transition-of-care interventions in a PCMH on readmission rates in a highly vulnerable patient population. Despite not finding significant reductions in readmissions in the primary analysis, this study adds to the body of transitions-of-care and PCMH literature, and important conclusions can be drawn from primary and secondary analyses.

Many transitional care interventions have shown benefits of close post discharge care coordination on readmission rates and healthcare utilization.7-14,32-37 However, most interventions have been hospital- or call center–based, and they do not include provider components similar to those of our community-based intervention. Anderson and colleagues33 found in a retrospective study that close discharge phone follow-up by a clinical pharmacist in a PCMH setting was associated with similar reductions in 30-day readmission rates as our per protocol analysis when compared with controls (10.6% vs 19.8%; P <.01), despite studying a younger population (mean age, 57 years). The intervention also decreased no-show rates at post discharge clinic appointments (33.8% vs 55.5%; P <.01). Further, Kirkham et al10 found close phone follow-up by pharmacists in a community-pharmacy setting, combined with inpatient medication delivery, also reduced readmissions (5% vs 9.5% without intervention; P <.05). When patients 65 years or older were compared (31% of the study population), those receiving usual care had significantly increased odds of readmission (adjusted OR, 6.05; 95% CI, 1.92-19.00). Lastly, Misky and colleagues35 found that early follow-up with a physician is associated with a trend in reduced 30-day all-cause readmissions (15.6% vs 27.3%; P = .05), especially readmissions for the same condition (3.1% vs 21.2%; P <.05). Despite positive results reported for individual care coordination activities, multiple systematic reviews and metaanalyses have found inconclusive evidence of the benefits of nonmultidisciplinary interventions or ones not taking place before, during, and after discharge reducing hospital admissions.4,5,15,38-40 Additionally, considerations must be made when comparing results from the current study to previously published literature.

Results of our intention-to-treat analysis could be influenced by the inclusion of patients who may have been excluded from other published studies, including those unable or unwilling to complete the intervention, those with altered mental status or inability to speak English, or those admitted for surgical procedures.7-10,33,34 Including these patients may have reduced participation in our intervention or biased our results by including sicker patients in the intervention group. Additionally, some studies have not excluded elective hospitalizations as 30-day readmissions.7,11,12,33,34 In our study, 254 elective hospitalizations were excluded as readmissions before matching, all among those never scheduled for the intervention.

As shown by the per protocol and as-treated analyses, our multidisciplinary intervention was effective at reducing readmissions only among those able to receive the service. There are many possible reasons for the 48% no-show rate for scheduled appointments. Among possible reasons: unawareness of or unavailability for the appointment, feeling too sick to come to a medical appointment so soon after hospitalization, transportation difficulties, refusal to see a medical provider other than their primary care provider, discharge to subacute rehabilitation facility, and rehospitalization. Process measures were analyzed to improve the efficiency of our intervention.

Reaching patients sooner was identified as an opportunity for improvement. We achieved our goal of calling within 2 to 4 days and seeing the patient in clinic within 7 days of discharge for only 21% of our patients. Among the 97 readmissions before any component of the TCP was completed, the average time to readmission was 8 ± 7 days. These observations led to process changes (which came after the study concluded) to accommodate those with discharges later in the week, with transportation difficulty, conflicting appointments, and conflicting preferences. First, all primary care providers began reserving time for TCP appointments daily, rather than scheduling patients only on select days with select providers. Second, pharmacists began making calls on a second day, allowing up to 12 more patients to be contacted per week. Third, a nurse navigator began assisting with scheduling and calling patients. These process improvements allow us to reach patients sooner and to increase continuity with primary care providers when possible.

Operational costs of the program are 0.2-0.4 full-time equivalents per member of the team. Appointments are scheduled while staff are already present performing primary care responsibilities, minimizing additional operating costs. Time spent per patient varies. Including chart review, coordinating with other care providers, intervention, and documentation, the pharmacist calls take on average 45 minutes per patient, and up to 24 patients were contacted per week. Social worker time ranges from 1 to 8 hours per patient per week. Medical evaluations are scheduled for 30- to 40-minute appointments, and can take an additional 30 minutes or longer to complete.

Cost avoidance of the program is dependent on the patient completing the intervention, only includes hospital costs, and does not factor in potential penalties avoided for excessive 30-day readmission rates, the opportunity cost of a hospital bed occupied, or revenue from billed services, all of which likely underestimate true cost savings. It also does not factor in costs of the program. This program was implemented in an established multidisciplinary PCMH, minimizing additional operating costs and aligning with the PCMH goals. The application of such an intervention may be difficult in institutions without such resources, structures, or incentives in place.

Limitations

The retrospective, nonrandomized design does not allow causality to be established, and may introduce selection bias. Matched analysis was performed but cannot account for all differences between groups. Given our study design, readmission rates may potentially be underreported. First, nearly one-fifth of 30-day readmissions among Medicare beneficiaries do not occur at the same hospital, possibly limiting the findings of this single-center study.41 Next, excluding individuals with multiple index hospitalizations may reduce capture of readmissions and limit comparison to other reported retrospective studies. The overall readmission rate in our matched study population was comparable to previously reported readmissions rates in this population22 and also similar when multiple index hospitalizations were included per patient (19.5%). Third, pertinent factors associated with risk of readmission among older adults—including socioeconomic status, prior hospital utilization, self-management skills, home support, and level of education—were not addressed.26,42-46 Last of all, patient satisfaction was not studied, but is important to consider when assessing the effectiveness of PCMH-based interventions.

CONCLUSIONS

 
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