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Improving Diabetic Patient Transition to Home Healthcare: Leading Risk Factors for 30-Day Readmission
Hsueh-Fen Chen, PhD; Taiye Popoola, MBBS, MPH; Kavita Radhakrishnan, PhD, RN; Sumihiro Suzuki, PhD; and Sharon Homan, PhD
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Improving Diabetic Patient Transition to Home Healthcare: Leading Risk Factors for 30-Day Readmission

Hsueh-Fen Chen, PhD; Taiye Popoola, MBBS, MPH; Kavita Radhakrishnan, PhD, RN; Sumihiro Suzuki, PhD; and Sharon Homan, PhD
Home healthcare transition from hospitals for diabetic Medicare home healthcare beneficiaries can be improved by identifying risk factors for 30-day readmissions due to ambulatory care—sensitive conditions.
Based on the conceptual framework discussed previously, the intensity of home healthcare visits and external environment are also related to 30-day readmissions due to ACSCs. The intensity of home healthcare visits was defined as the log-transformed number of home healthcare visits per week.13,20 The external environment included the quality of care from hospitals and home health agencies, and the characteristics of counties where patients resided. An indicator variable was created as a proxy variable for poor quality of hospital care if a readmission adjustment factor for a hospital from the 2013 HRRP file was less than 1; namely, a hospital received a financial penalty due to having excess 30-day readmissions for heart failure, pneumonia, and AMI.22  Previous studies have found that hospital-based agencies have better-quality performance than non–hospital-based home health agencies.13,29 An indicator variable was created from POS if home health agencies were hospital-based.

The characteristics at the county level extracted from AHRF included the number of primary care physicians per 1000 population, the number of acute care hospital beds per 1000 population, per capita income at the county level, and whether or not a county is rural. Table 1 provides the operational definition of variables and data sources.

Statistical Analysis

We assessed multicollinearity among the independent variables. The variance inflation factor ranged from 1.00 to 3.26, indicating that multicollinearity was not problematic. Readmission times were censored at 30 days when readmission did not occur in the 30-day window. A Cox proportional hazards regression model was used to model time-to-readmission. Estimated adjusted hazard ratios (HRs) and 95% CIs are reported.

RESULTS

Table 2 presents the descriptive analysis for the study variables. The study sample consisted of 120,208 home healthcare episodes within 14 days of hospital discharge. The 30-day ACSC-related readmission rate is approximately 6% in our study sample. Approximately 20% of home healthcare episodes (23,743 of 120,208) had 30-day readmissions. Among these readmissions, 7441 admissions were ACSCs, accounting for approximately $62 million in Medicare payments; they consumed about 43,000 inpatient days (Table 3). Heart failure alone accounted for about 48% of ACSC hospitalizations and $28 million of the Medicare expenses. ACSCs related to diabetes, including short-term and long-term diabetes complications, uncontrolled diabetes, and lower-extremity amputation among patients with diabetes, were observed in about 22% of all ACSC hospitalizations. Readmission rates due to urinary tract infection, bacteria pneumonia, and COPD or asthma in older adults were about 8%, 7%, and 7% of ACSC hospitalizations, respectively.

Table 4 presents the HRs and 95% CIs from the Cox regression model. Several factors were positively associated with 30-day readmissions due to ACSCs (P <.05). Statistically significant factors included age (75-84 years), race (African American), health conditions (congestive heart failure, peripheral vascular disease, COPD, renal failure, deficiency anemia, fluid and electrolyte diseases, depression and/or anxiety), requirement of assistance in medication management, and the presence of a pressure or stasis ulcer. Patients were less likely to experience 30-day readmissions due to ACSCs if they had comorbid conditions of valvular disease, hypertension, obesity, and psychosis. The following factors were not statistically associated with 30-day readmissions due to ACSCs: age (85 years or older), race (Hispanic or other race), being a female, being dual eligible, living alone, ADL functions, and other health conditions (pulmonary circulation disease, coagulopathy, paralysis, other neurological disorders, hypothyroidism, metastatic cancer, solid tumor without metastasis, rheumatoid arthritis, and weight loss).

Increases in intensity of home healthcare visits were associated with high 30-day readmissions due to ACSCs. Expectedly, patients who were discharged from the hospitals that received Medicare payment reduction had higher odds of being readmitted due to ACSCs than their counterparts. Hospital-based home health agencies and county characteristics were not significantly associated with ACSC admissions within 30 days of hospital discharge.

DISCUSSION

One in 5 (20%) diabetic Medicare home healthcare episodes had 30-day readmission in our study—a rate comparable to the national 30-day readmission rate among all Medicare hospital stays.30 Approximately 1 in 3 (7441/23,743) readmissions is preventable. Reduction in preventable 30-day readmissions requires care collaboration among hospital coordinators, physicians, home healthcare professionals, patients, and their families and caregivers. Awareness of the leading readmission risk factors can inform the decisions and type of care provided by the collaborating clinicians and home healthcare providers.

The risk factors identified in our study serve as sentinel conditions in the care transition for our study population. Among all risk factors, COPD and renal failure are the strongest predictors for 30-day ACSC-related readmission. The findings in our study aid physicians and home healthcare professionals with prioritizing care and providing timely and effective interventions to reduce potentially avoidable readmissions. Additionally, for patients and their families and caregivers, engagement is a critical part of chronic disease management. Rather than solely focusing on diabetes treatment, education, and support, home health nurses can utilize the findings in our study to develop a multi-comorbidity approach for teaching patients and their families and caregivers to manage their comorbid conditions.

Healthcare professionals currently rely on their professional judgments to decide what messages should be communicated during the coordinated care transition. As a result, variations in communication occur across different levels of providers, which may harm patients. Being informed about key risk factors for readmission can reduce the variations in information exchange among clinicians and home healthcare providers. This is especially critical under the CMS Electronic Health Records Incentive Programs, which requires providers to deliver an electronic summary care record for each transition of care and referral.31 Our research model yields a “checklist” of significant risk factors that can be used to enhance the meaningful use of the electronic health record.

Our study findings indicated that increases in intensity of home healthcare visits were associated with high 30-day readmissions due to ACSCs. This finding is consistent with a previous study of Medicare home healthcare patients with heart failure.13 The intensity of visits is dependent on patients’ clinical conditions; thus, it is likely that patients who are at high risk of 30-day readmissions due to ACSCs are likely to receive high intensity of visits. One study based on the data from 1 home health agency found that early home healthcare visits reduced rehospitalization for heart failure patients, although the generalizability of the findings is limited.32 CMS requires home healthcare professionals to visit patients within 48 hours after receiving physicians’ referrals; however, some patients may require home healthcare visits on the same date of hospital discharge or high-intensive visits in the first few days after hospital discharge. Future studies based on large data that focus on the association between 30-day readmissions and the timing of the first home healthcare visit or the intensity of the home healthcare visit at different time periods within the first 30 days after hospital discharge are recommended.

We also estimated the risk associated with several community factors at the county level, (eg, per capita income). None of these factors were statistically significant. Previous studies found that ACSCs were associated with socioeconomic status and community characteristics when using data at the individual or zip code level,17,18,33,34 but found insignificance when using data at the county level. 35,36 It is likely that the measures for the community factors at the county level may not be as sensitive as the ones at the individual level or small geographic areas. Future studies based on individual level or small geographic areas for the community characteristic measures are suggested.

Limitations

There are data constraints and study limitations. First, measures of patients’ vital signs and laboratory results, and the quality of home healthcare visits, which all likely affect 30-day readmissions due to ACSCs, were not available in the database. Second, because the study was based on Medicare FFS beneficiaries, generalizability is limited to this population. Third, previous studies found that the number of hospitalizations in the previous 6 months of the index admission was critical.1,18 The present study only used data for 1 year, which did not allow us to track the number of previous hospitalizations for our study sample. Fourth, one should be cautious about the variable of hospital penalty extracted from the HRRP in our study. Evidence has shown that community factors and patients’ socioeconomic status affect the risk of 30-day readmission, which are not taken into account in the HRRP.37,38 Finally, a clinical decision rule has been used to quantify the probability of a patient’s outcome.39 Future studies applying clinical decision rules to quantify a risk score that flags high-risk patients are imperative.

CONCLUSIONS

Despite these limitations, our findings have important policy implications. The Medicare Payment Advisory Commission has reported the high variation in quality of care among home health agencies.1 Instead of solely targeting hospitals under the HRRP, a policy that addresses the excess preventable 30-day readmissions among home health agencies after taking community factors and patients’ socioeconomic status into account is imperative and could be effective in reducing overall 30-day readmissions. Patients with chronic obstructive pulmonary disease or renal failure had a 40% higher risk of 30-day ACSC-related readmissions than their counterparts. Knowing the risk factors identified above, hospital providers can improve care planning and transition of care to the home healthcare providers.

Acknowledgments

The authors thank Carol Kominski, PhD, who provided editing of the final version of the manuscript.

Author Affiliations: Department of Health Management and Policy (HFC) and Department of Biostatistics and Epidemiology (SS, SH), School of Public Health, University of North Texas Health Science Center, Fort Worth, TX; Department of Health Policy and Management, School of Medicine, University of Kansas Medical Center (TP), Kansas City, KS; School of Nursing, University of Texas – Austin (KR), Austin, TX.

Source of Funding: This study was supported by the Junior Faculty Seed Fund at the University of North Texas Health Science Center (UNTHSC). The views expressed in this publication are the views of the authors and do not necessarily reflect the views of UNTHSC.

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 (HFC); acquisition of data (HFC); analysis and interpretation of data (HFC, TP, KR, SS); drafting of the manuscript (HFC, KR, SH); critical revision of the manuscript for important intellectual content (HFC, TP, KR, SS, SH); statistical analysis (HFC, SS, SH); provision of patients or study materials (HFC); obtaining funding (HFC); administrative, technical, or logistic support (HFC, TP); and supervision (HFC).

Address correspondence to: Hsueh-Fen Chen, PhD, Department of Health Management and Policy, University of North Texas Health Science Center, 3500 Camp Bowie Blvd, EAD 601S, Fort Worth, TX 76107. E-mail: Hsueh-Fen.Chen@unthsc.edu.
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