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The American Journal of Managed Care October 2015
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The Impact of Kaua'i Care Transition Intervention on Hospital Readmission Rates
Fenfang Li, PhD; Jing Guo, PhD; Audrey Suga-Nakagawa, MPH; Ludvina K. Takahashi, BA; and June Renaud, BEd
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The Impact of Kaua'i Care Transition Intervention on Hospital Readmission Rates

Fenfang Li, PhD; Jing Guo, PhD; Audrey Suga-Nakagawa, MPH; Ludvina K. Takahashi, BA; and June Renaud, BEd
By enrolling selected high-risk elderly patients into the intervention, then empowering and educating them, this study successfully reduced hospital readmission rates.
Comparing patient characteristics between baseline and intervention period found no significant difference in the distribution of patients’ sex; discharge site; percentage of patients with arrhythmias, cellulitis, or COPD; mean age; or mean length of stay (Table 1). Nevertheless, a significant difference was observed in patients’ primary health insurance, with more patients insured with Medicare in the intervention period than in the baseline period (χ2 = 8.592; P = .035) (Table 1).

Septicemia was the top primary diagnosis, constituting 18% of the study sample, followed by pneumonia (10%), chronic heart diseases (5%), chronic bronchitis (4%), and cellulitis (3%). Among the 269 patients, a total of 14 patients had arrhythmias as the primary diagnosis, and among any of the first 3 diagnoses, 10 patients had cellulitis and 16 patients had COPD.

Readmission Rates at the Intervention Period and Baseline Period

A significant difference was observed in the 30-day, 60-day, and 1-year readmission rates between the intervention and baseline periods. Compared with baseline (fiscal year 2010), the 30-day readmission rate was reduced by 61.4%—from 12.5% at baseline to 4.8% during the intervention period (χ2 = 10.19; P = .001). The 60-day readmission rate was reduced by 53.6%—from 17.7% at baseline to 8.2% during the intervention period (χ2 = 11.09; P <.001). The 1-year readmission rate was reduced by 42.8%, from 28.1% at baseline to 16.0% during the intervention period (χ2 = 11.85; P <.001) (Figure).

Results from the Logistic Regression, Controlling for Patient Characteristics

Logistic regression revealed that the intervention significantly reduced the 30-day readmission rate, while controlling for patients’ primary health insurance, discharging sites, arrhythmias as primary admitting diagnosis, and presence of COPD or cellulitis in the first 3 admitting diagnoses (Table 2). Readmission within 30 days during the intervention period was one-third as likely as during the baseline period (odds ratio [OR], 0.34; P = .003). Patients with arrhythmias (OR, 2.96; P = .04) as the primary diagnosis, or cellulitis among the first 3 admitting diagnoses (OR, 3.27; P = .03), were 3 times more likely to be readmitted within 30-day period, compared with patients without such conditions. Neither patients’ discharge site nor primary insurance was a significant predictor for 30-day readmission.

Similar results were found in predicting the 60-day readmission rate. Readmission within 60 days during the intervention period was less than half as likely as during the baseline period (OR, 0.42; P <.01).  Patients with arrhythmias as the primary diagnosis (OR, 2.93; P = .03) and patients with cellulitis among the first 3 admitting diagnoses (OR, 2.68; P = .03) were 3 times more likely to be readmitted within a 60-day period. Neither patients’ discharge site nor primary insurance was a significant predictor for 60-day readmission.

 Readmission within a year was also significantly reduced by the intervention. During the intervention period, the 1-year readmission rate was only half of that during the baseline period (OR, 0.48; P <.001). Arrhythmias as the primary diagnosis and cellulitis among the first 3 admitting diagnoses were no longer significant predictors; however, COPD among the first 3 admitting diagnoses emerged as a significant predictor of readmission within a year (OR, 3.20; P <.01). Patients with COPD among the first 3 admitting diagnoses were 3 times more likely to be readmitted than patients without this condition.

Among the 58 patients referred to KCTI, 31 completed the Patient Activation Assessment (PAA), 48 completed the Overall Quality of Care Transition Score, and 16 completed the CTI 30-day follow-up survey. The PAA found that the KCTI program succeeded in improving patients’ medication management ability, healthcare follow-up, and the use of PHR. The overall quality of care transition was high, with mean scores of 3.4 to 3.5 out of a possible 4. The CTI 30-day follow-up survey revealed that the majority of the patients improved in their understanding and skills in medication management and in recognizing signs of worsening health conditions.

DISCUSSION
A recent systematic review found that published studies of transitional care interventions do not often include, in their randomized controlled trials, the older patients at highest risk of rehospitalization.23 A Web-based survey to examine hospitals’ use of specific practices to reduce readmissions reported that fewer than half of hospitals had partnered with community physicians, and fewer than a quarter had partnered with local hospitals to manage patients at high risk for readmission.24 Those might be among the reasons why some interventions worked, but others did not.

This study identified multiple diagnoses as risk factors of readmission at baseline to be targeted for the intervention—a unique approach compared with most studies in which a single diagnostic group was identified and targeted.5,9,12 Patient education and empowerment were other signature components of the KCTI program. KCTI referred patients to an array of home- and community-based programs that might further assist patients’ care at home. Such practices are in accordance with the literature, which advocates for integrated, coordinated, or guided care to address transitional care in older adults.25,26

This study was able to reduce readmission rates in a facility with a relatively low rate of readmissions to start (eg, a 12.5% 30-day readmission rate at baseline, compared with an average of 20% in the literature).1-3 Several mechanisms might explain the success of the KCTI program and the sustained effect of a 30-day intervention at 60 days and 1 year. First, re-referrals of patients readmitted within 30 days back to the KCTI program might have prevented potential 60-day or 1-year readmissions. Secondly, a historically strong working relationship between KAEA and the hospital helped to secure the executive leadership support and front-line staff buy-in to implement the intervention. Finally, as Kauai’s area agency on aging and the county’s designated aging and disability resource center—the 1-stop-shop for long-term care information and resources—KAEA can assess, counsel, and link individuals to other community services that will continue to help keep the patient safe and healthy at home. KAEA serves as a safety net for the frail, vulnerable elders who now have a vital community resource that they can turn to for additional services and information as their needs change.

Limitations

One study limitation is that there was no randomization at the patient level, and subsequently, there was no equivalent comparison or control group for the study, which is a threat to internal validity. This was mitigated, however, by the evaluation of patient characteristics between baseline and intervention period. The findings revealed that patient characteristics remained the same among most of the identified risk factors, such as percent of patients discharged to a location other than their homes and percent of patients with certain admitting diagnoses (eg, cellulitis, COPD, arrhythmias). The only difference identified was the distribution of various types of health insurance among the hospital patient population, with a slightly higher percentage of Medicare patients (40.9%), but a slightly lower percentage of HMSA/65C Plus patients (23.4%) during the intervention period compared with baseline (ie, 34.7% for Medicare and 34.7% for HMSA/65C Plus patients, respectively). Nevertheless, this fact actually reflected the strength of the study because more patients with fee-for-serve Medicare during the intervention would conceivably bias the findings toward the null hypothesis. In addition, the effectiveness of the intervention remained true even after controlling for patient’s primary health insurance in the logistic regression. Patient’s primary insurance was not found to be a significant predictor for hospital readmission.

CONCLUSIONS
Overall, this study demonstrates that a patient-centered intervention designed to address readmission rates can be successfully implemented in a small acute hospital setting and can indeed reduce readmissions. The KCTI interventions that were successfully provided to patients with identified risk factors—educating and empowering patients with better skills in managing their own health, and referring patients to home- and community-based programs that might assist their care at home—are worthy of further investigation and replication.

Acknowledgments

The authors want to thank and acknowledge Kaua’i Veterans Memorial Hospital (KVMH) and its team for graciously serving as their partner in the Kaua’i Care Transition Intervention project Project (KCTI) and providing the data for analysis. Special thanks to Jerry Walker, MD, former KVMH chief executive officer; Jennie Ahn, former KVMH social services staff; Jocelyn Barriga, KVMH social service staff; Rebecca O’Brien, KVMH quality management staff; and Jan Pascua, the KCTP coach.

Special thanks also go to Pamela Arnsberger, PhD, for her leading role in developing the study design and conducting baseline data analysis.

Author Affiliations: Department of Human Nutrition, Food and Animal Sciences (FL), Myron B. Thompson School of Social Work (JG), University of Hawaii at Manoa, Honolulu; ASN Consulting Services (AS-N), Honolulu, HI; County of Kaua’i Agency on Elderly Affairs (LKT, JR), Lihue, HI.

Source of Funding: US CMS Hospital Discharge Grant.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (FL, JG, AS-N, LKT, JR); acquisition of data (FL, LKT); analysis and interpretation of data (FL, JG); drafting of the manuscript (FL, JG); critical revision of the manuscript for important intellectual content (JG, AS-N, LKT, JR); statistical analysis (FL); obtaining funding (LKT, JR); administrative, technical, or logistic support (AS-N, LKT, JR); and supervision (AS-N, JR).

Address correspondence to: Fenfang Li, PhD, University of Hawaii, 1950 East West Rd, AgSci 302H, Honolulu, HI 96822. E-mail: fenfang@hawaii.edu.
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