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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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David Auerbach, PhD, MS; Ateev Mehrotra, MD, MPH; Peter Hussey, PhD; Peter J. Huckfeldt, PhD; Abby Alpert, PhD; Christopher Lau, PhD; and Victoria Shier, MA
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Helaine E. Resnick, PhD, MPH; and Michael E. Chernew, PhD

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.
ABSTRACT
Objectives:
To identify risk factors of 30-day readmissions due to ambulatory care–sensitive conditions (ACSCs) for diabetic Medicare home healthcare beneficiaries in order to improve transition from hospital-based care to home healthcare.

Study Design: We analyzed diabetic Medicare beneficiaries who received home healthcare within 14 days of hospital discharges in 2009. The unit of analysis is the home health episode for post acute care.

Methods: The conceptual framework was guided by Andersen’s Behavioral Model of Health Services. Data sources included: Medicare Beneficiary Summary File, Medicare Provider Analysis Review, Outcome Assessment Information Set, Home Health Agency Research Identifiable File, Hospital Readmissions Reduction Program Supplemental Data File, Provider of Services File, and Area Health Resources File. The dependent variable was time to first 30-day ACSC-related readmission. Proportional hazards regression was used for the statistical analyses.

Results: The 30-day ACSC-related readmission rate was approximately 6% in our study sample, costing the Medicare program about $62 million. Predictors of readmissions due to ACSCs within 30 days of hospital discharge were: being aged 75 to 84 years, being an African American, requiring assistance in medication management, and having 1 or more of the following clinical conditions: congestive heart failure, peripheral vascular disease, chronic obstructive pulmonary disease, renal failure, deficiency anemia, fluid and electrolyte diseases, depression and/or anxiety, and pressure or stasis ulcer. Patients with chronic obstructive pulmonary disease or renal failure had a 40% higher risk of 30-day ACSC-related readmissions than their counterparts.

Conclusions: Knowing the risk factors identified above, hospital providers can improve care planning and transition of care to the home healthcare providers.

Am J Manag Care. 2015;21(6):440-450
Take-Away Points
Identifying risk factors for 30-day readmissions due to ambulatory care–sensitive conditions (ACSCs) among patients with diabetes can inform healthcare providers about opportunities to improve home healthcare transition.
  • Thirty-one percent of 30-day readmissions were due to ACSCs, costing the Medicare program about $62 million in 2009.
  • Patients with the following conditions were at high risk for readmission: heart failure, peripheral vascular disease, chronic obstructive pulmonary disease, renal failure, deficiency anemia, fluid and electrolyte diseases, depression/anxiety, pressure/ stasis ulcer, and incapability of managing medication.
  • Identified risk factors could be used to prioritize care in the transition from hospital- based care to home healthcare.
There is a rising demand for post acute home healthcare among Medicare beneficiaries. This demand is associated with an unprecedented growth in the number of Medicare home healthcare episodes preceded by a hospitalization or skilled nursing home stay, increasing from 1.9 million episodes in 2001 to 2.3 million episodes in 2011.1 The transition of care is required when Medicare home healthcare beneficiaries are discharged from hospitals. This transition involves multiple levels and types of healthcare professionals. Previous studies have shown transitional care programs and discharge planning to be cost-effective in reducing 30-day readmissions for the elderly and Medicaid patients2-5; however, none of these studies specifically focused on Medicare home healthcare beneficiaries who are discharged from hospitals.

Medicare home healthcare beneficiaries are homebound due to physical or psychiatric limitations and require intermittentskilled care. On average, Medicare home healthcare beneficiaries have 4.2 diagnosed conditions, and 84% of them have at least 1 limitation in the activities of daily living (ADL).6 Conducting a smooth and safe care transition is typically challenging for healthcare professionals coordinating care for home healthcare beneficiaries with complex clinical conditions. Currently, there are no guidelines regarding what information should be exchanged during the coordinated care transition; consequently, gaps in care transition due to poor or insufficient communication between hospital coordinators, physicians, and home healthcare professionals, as well as poor documentation of discharge planning, have been found.7-10 This poor communication causes discontinuities in care and may result in potential harm to patients.

Previous studies reveal that identifying patients at high risk of a 30-day readmission can be useful for coordinating care and make transitional care more effective.11,12 In a study of Medicare home healthcare patients with heart failure, Madigan and colleagues identified risk factors for 30-day readmissions due to ambulatory care–sensitive conditions (ACSCs).13 The authors included several risk factors such as severity of dyspnea and ADL functions from the Outcome Assessment Information Set (OASIS) that provides clinical conditions for home healthcare patients. They also included a risk factor—the number of previous hospitalizations before the index hospitalization—from the Medicare Provider Analysis Review (MedPAR) that provides clinical conditions during hospitalization. The study found that the number of previous hospitalizations was the strongest predictor among all other predictors for 30-day readmissions due to ACSCs.

Dharmarajan and colleagues examined the timing of 30-day readmission for heart failure, pneumonia, and acute myocardial infarction (AMI) for Medicare patients. They found that among patients with 30-day readmissions, approximately 13% to 19%, and 61% to 68% of them were readmitted within 3 days and 15 days of hospital discharge, respectively.14 The evidence discussed above suggests that patients’ clinical conditions during hospitalizations affect the risk of 30-day readmissions for home healthcare patients who are transferred from hospitals. However, studies that evaluate specific patient risk factors during hospitalizations for home healthcare patients are limited, which is a critical gap in the field of home healthcare knowledge.

The purpose of this study is to identify the risk factors for 30-day readmissions due to ACSCs for diabetic Medicare home healthcare beneficiaries. We focused on diabetes because nearly a third (31%) of Medicare home healthcare patients have diabetes, making this the most common diagnosis.6 Additionally, diabetes is one of the major chronic diseases driving the overall healthcare expenditure growth in the Medicare program, thus causing a significant economic burden in the United States.15,16 This study focuses on 30-day readmissions due to ACSCs, such as urinary tract infection and ketoacidosis, as these conditions are potentially avoidable if patients receive timely and effective interventions,17,18 which can be affected by the quality of coordinated care among hospital coordinators, physicians, and home healthcare professionals.

METHODS

Study Design and Data Sources


We analyzed Medicare beneficiaries with diabetes who received home healthcare within 14 days of hospital discharge in 2009. The unit of analysis is the home health episode for post acute care. Andersen’s Behavioral Model of Health Services was used as a framework for determining and estimating the key predictors of 30-day readmission, while adjusting for empirically determined predictor variables identified in our review of the literature.13,14,19-21 Andersen posited that healthcare utilizations, such as 30-day readmissions due to ACSCs, are affected by predisposing factors (age and gender), enabling conditions (whether patients live alone), healthcare needs (comorbid conditions and ADL), and the external environment (quality of hospital care and the number of primary care physicians per 1000 population), as well as the intensity of home healthcare visits that home healthcare patients receive.13,20

We conducted analyses of national data, fitting proportional hazard regression models to home healthcare episodes after hospital discharge in 2009. The Medicare Beneficiary Summary File was used to identify Medicare beneficiaries who were enrolled in the fee-for-services (FFS) program. The 100% MedPAR file provided inpatient data, such as dates for admission and discharge, as well as the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for primary and secondary diagnoses. The 100% OASIS B1 version provided patients’ conditions at the time patients were admitted to home healthcare. The 2013 Hospital Readmissions Reduction Program (HRRP) Supplemental Data File provided the readmission adjustment factor that determines the percentage of Medicare payment reduction that hospitals received in 2013 due to excess 30-day readmissions between July 1, 2008, and June 30, 2011.22 The Provider of Services (POS) file provided the characteristics of home health agencies. The Home Health Agency Research Identifiable File provided the number of home healthcare visits. Finally, the Area Health Resource File (AHRF) provided community characteristics at the county level.

Study Sample

We used the date of discharge from MedPAR and the date of the start of home healthcare from OASIS to identify home healthcare episodes within 14 days of hospital discharges, relative to diabetes conditions for our study sample. This 14-day criterion is commonly used in OASIS assessment and previous studies.1,13,23 The study sample also needed to meet the following criteria: being 65 years or older, enrolling in the FFS program in 2009, and being discharged from a hospital before December 1, 2009, in order to have a full 30 days of follow-up in 2009. Patients whose admission and discharge dates were the same were also excluded because these records are likely to be incorrect.

Medicare home healthcare patients, on average, had 4.2 diagnoses, and diabetes is a significant contributor to several chronic diseases, such as heart disease and renal disease.6,24 We used the series of ICD-9-CM codes for the primary and secondary diagnoses used by Jiang and colleagues for identifying diabetic hospital admissions as our index admission (refer to Jiang et al 2005 for the detailed ICD-9-CM codes).25 Using CMS measures,26 additional readmissions within 30 days of hospital discharge were not counted as 30-day readmissions or used as an index admission. However, the readmissions that occurred after 30 days from the index hospitalization were used as the index admission if their diagnoses codes met the ICD-9-CM codes discussed above.

Variables

The outcome variable of interest was the time-to-first readmission due to ACSCs within 30 days of hospital discharge. The ACSCs were extracted from MedPAR, by applying the Prevention Quality Indicator (PQI) software version 4.5 from the Agency for Healthcare Research and Quality (AHRQ).27 The ACSCs were diabetes with short-term complications, diabetes with long-term complications, chronic obstructive pulmonary disease (COPD) or asthma in older adults, hypertension, heart failure, dehydration, bacterial pneumonia, urinary tract infection, angina without procedure, uncontrolled diabetes, and lower-extremity amputation among patients with diabetes. Although perforated appendix is included in ACSCs, the incidence was low (fewer than 3 individual patients) and was therefore excluded from the analysis.

The risk factors were grouped into the Andersen Model categories of predisposing, enabling, and need for healthcare. Predisposing factors included age (75-84 years and 85 years or older, with 65-74 years as the reference category), gender (male as the reference category), and race/ethnicity (black, Hispanic, and other race, with white as the reference category). The enabling factors included whether patients were eligible for Medicare and Medicaid benefits (with non–dual eligible patients as the reference group) and whether patients lived alone (with “not living alone” as the reference category). The need for healthcare was measured by the clinical and functional conditions of the patient.

We included comorbid conditions defined by Elixhauser and colleagues28 (the reference category for each comorbidity is “no condition”). The comorbid conditions were extracted from MedPAR, by using the Comorbidity Software from AHRQ. Only comorbid conditions that at least 1% of study sample had as risk factors were included. A total of 20 comorbid conditions were thus selected: congestive heart failure, hypertension, pulmonary circulation disease, peripheral vascular disease, coagulopathy, COPD, renal failure, deficiency anemia, paralysis, other neurological disorders, hypothyroidism, metastatic cancer, solid tumor without metastasis, rheumatoid arthritis, obesity, weight loss, fluid and electrolyte disorders, psychoses, and depression.

In addition to comorbid conditions, several variables related to the needs for healthcare from OASIS were also included. We created indicator variables for whether the patient had pressure or stasis ulcers, felt anxious, or required assistance in medication management, and 4 categories of patient ADL functioning (need for assistance in: 1 to 3 ADL functions, 4 to 6 ADL functions, at least 7 ADL functions, or complete dependence in ADL functions, with patients who could independently perform all ADL functions in the reference group). Because about 57% of patients who had depression also had anxiety, we created an indicator variable to represent whether patients had depression and/or anxiety, instead of using depression and anxiety individually.

 
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