Learning About 30-Day Readmissions From Patients With Repeated Hospitalizations | Page 2
Published Online: June 27, 2014
Jeanne T. Black, PhD, MBA
Although heart failure was the most prevalent chronic condition in the cohort, the 726 patients whose index stay had a principal diagnosis of heart failure comprised only 3.8% of all cohort patients. As shown in Table 3, 19.0% of these patients had 3 or more hospital stays in 6 months. However, an additional 216 patients (1.1% of the cohort) had an index stay for some other diagnosis and subsequently had a heart failure stay. A much larger proportion of these patients (57.9%) had 3 or more hospitalizations. For both groups of patients with at least 1 hospitalization for heart failure, the table shows that as the number of total hospitalizations increased, the proportion attributable primarily to heart failure decreased.
Examination of secondary diagnoses also revealed significant differences among the subgroups. The overall prevalence of mental health diagnoses coded from the medical record was 21.0%, but 43.5% of patients with very frequent readmissions had such diagnoses, compared with 34.7% of those with 3 to 5 hospital stays and 19.3% of those with 1 or 2 stays. The corresponding prevalence of substance abuse diagnoses was 27.6%, 9.9%, and 6.3%. Patients with very frequent readmissions also had a significantly higher prevalence of documented morbid obesity (9.0%) compared with the frequent readmission subgroup (6.0%) and the comparison subgroup (3.3%). A different pattern was observed in the prevalence of documented tobacco-use disorder, which was 6.0% in the comparison subgroup, 4.4% in the subgroup with 3 to 5 hospital stays, and 2.8% in the subgroup with very frequent readmissions. This finding was associated with the fact that a higher proportion of patients with an index stay for CAD had a documented diagnosis of tobacco-use disorder than did patients with other index diagnoses, and nearly all of the CAD patients were in the comparison subgroup. The proportion of patients documented as lacking housing using a diagnosis code was too small to report.
Patients in the frequent and very frequent readmissions subgroups also had a higher proportion of 30-day readmissions following their index stay. Table 4 shows the rate of readmission within 30 days after the index stay for all patients in the cohort who were discharged alive from their index stay, as well as for patients whose index admission diagnoses were heart failure, chronic pulmonary disease, or not attributable to any chronic condition. Although patients with only 1 or 2 hospital stays did have a higher 30-day readmission rate when their index stay was for heart failure, the 30-day readmission rate among patients with frequent readmissions was similar for patients with heart failure and for those whose index stay was not primarily associated with any chronic condition.
This analysis affirmed the common finding that a small proportion of patients accounts for a disproportionate share of resource use: in our analysis, 10% of a cohort of patients initially hospitalized with a medical diagnosis incurred 72% of the 30-day readmissions that occurred within 180 days of the initial discharge. Of the 3 conditions currently targeted by the HRRP, heart failure has received the most attention. Given the intense national focus on reducing 30-day readmissions among patients with heart failure, one might expect them to comprise a large share of such rehospitalizations. This is not the case. Jencks’ analysis of Medicare FFS claims in 2003-2004 showed that patients with an index DRG of heart failure accounted for 7.6% of 30-day readmissions.2 Our cohort included all adult patients, not just those with Medicare coverage, but the relative magnitude of the population with heart failure was consistent with Jencks’ findings. Heart failure was the most prevalent chronic condition in our cohort, but the more notable finding was that 87.3% of index hospital stays could not be categorized as any chronic condition based on the patient’s principal diagnosis. Although it had seemed reasonable to assume that most nonsurgical patients with frequent hospitalizations were suffering from a chronic condition, the principal diagnosis code for the inpatient stay did not perform well in identifying these conditions. Even when the principal diagnosis for every hospitalization was included, about half the patients with 3 or more hospitalizations remained uncategorized. Is it plausible that the hospitalizations of patients with multiple inpatient stays in 6 months were not associated with any major chronic condition? Probably not. More likely, this finding illustrates that the principal diagnosis obtained from administrative data reflects the acute manifestations and complications—or the side effects of treatment—that led to the hospitalization, rather than the underlying disease itself. This, in turn, results from a coding system based on individual body systems that is applied for reimbursement purposes.
This analysis has several implications for efforts to reduce readmissions. Overall, it suggests that intervention strategies should take into account patients’ readmission histories. Attempts to target patients by condition using inpatient MS-DRGs or ICD-9 codes are unlikely to be successful. Numerous efforts have been made to develop an algorithm to predict which patients are at highest risk of a single 30-day readmission. Most of the resulting models have had rather poor discriminative ability and have not been able to generate predictions in real time.15 This may be due in part to the heterogeneity of patients categorized on the basis of a single index discharge, as illustrated by this analysis. The Hospital Compare rate for 30-day readmissions following a heart failure discharge at the medical center combines patients with few readmissions, the majority of which are for heart failure, and patients with both multiple conditions and multiple hospitalizations. This analysis does suggest a simple way to identify which patients are at greatest risk of multiple 30-day readmissions: those who have already had 2 or more hospitalizations in the previous 6 months. The real challenge is not in predicting which patients are at highest risk, but in identifying which interventions are likely to be most effective for specific patients.
Many of the recommended approaches have been those that can be implemented by hospitals alone, such as improving inpatient education and discharge instructions, medication reconciliation, making a timely postdischarge follow-up physician appointment, prompt transmission of the discharge summary to the patient’s primary physician, and postdischarge phone calls to patients. Much of the evidence regarding the effectiveness of these strategies, however, lacks rigor and/or consists of single institutions’ descriptions of quality improvement projects.16-19 While these interventions have not achieved noticeable reductions in total readmissions, it is possible they may be sufficient for some patients who do not have a prior history of readmissions. This is an empirical question that has not been addressed, because nearly all assessments of readmissions have focused on the occurrence of a single 30-day readmission, without considering prior hospitalizations or readmissions that occur beyond the 30-day period.
A better strategy to reduce overall 30-day readmission rates may be to identify patients with multiple hospitalizations and stop their cycle of repeated readmissions. The best opportunity for doing this may be found with individuals in the subgroup that has a moderate number of readmissions. In the medical center, these patients were older, with a high proportion dually eligible for Medicare and Medicaid. They may benefit from the Patient Centered Medical Home approach to assure that their multiple medical conditions are addressed, including mental health issues; care is coordinated among specialists and across sites of care; polypharmacy is managed; and patients and caregivers are engaged as partners in their care. Other approaches to reducing readmissions in this subgroup are better coordination between hospitals and skilled nursing facilities,20 improved transitional care that incorporates elements of the integrated geriatric care model,21 and greater access to services that identify and address patients’ functional limitations.22
This analysis suggests that achieving lasting readmission reductions would be the most challenging and resource- intensive among the very frequent users. Patients in this subgroup may have multiple comorbidities that are difficult to manage. They are more likely to be members of minority groups, to be non–English-speaking, and to have Medicaid insurance, all of which makes accessing non-hospital care difficult. Thinking more holistically may help the people in both frequent readmission subgroups. This could include expanding the boundaries of traditional hospital social work services to address issues such as food insecurity and housing-related problems that increase the risk of morbidity and need for medical care services.23,24 There is also growing recognition that if clinicians encourage discussion of patient goals and educate patients and families about options, they may choose supportive care rather than repeated acute care interventions.25
The very frequent readmission group also includes individuals whose medical problems are complicated by substance abuse and/or mental illness. This is the highrisk population targeted by the Camden Coalition.26 Reducing readmissions and improving quality of life should be possible for many of these patients, but it will require hospitals to do the hard work of initiating and sustaining relationships with other community-based organizations.7,17 There is also the possibility that appropriate interventions may not yet exist for some patients with complex problems. New community resources may be required, such as partial hospitalization/day programs, rehabilitation programs, transitional living centers for medically fragile patients, etc. Finding the resources to make such community investments will be difficult, especially while the dominant fee-for-service reimbursement model means that savings from reducing hospital utilization flow to payers and not to providers or communities.
This study has a number of limitations. First, the data are drawn from a single large urban teaching hospital that serves as both a referral center and a community hospital. As a result, the findings may not be relevant to smaller, non-urban hospitals. Patients whose only hospitalizations were for surgical MS-DRGs were excluded, so “index” admissions in the cohort may have been preceded by other hospital stays within the prior 6 months. Only readmissions to the same medical center were captured. Therefore, total hospital utilization by patients with very frequent readmissions is likely to be understated; because a larger proportion lived outside of the medical center’s primary service area, they may have sought additional care from other hospitals near where they live. Like other analyses based on administrative data, this study could not investigate the association between readmissions and personal characteristics such as functional status, social support, food insecurity, access to transportation, or neighborhood resources, which may be even more important determinants of readmission patterns than patients’ basic demographic or diagnostic profiles.
Patients with frequent admissions represented a small proportion of all adult medical patients but accounted for the majority of 30-day readmissions. Therefore, a focus on these patients is an important component of efforts to reduce hospital readmissions. Patients with a pattern of repeated admissions differ from other patients in significant ways that suggest different approaches to care management, transitional care, and community-based services may be needed.
Author Affiliations: Cedars-Sinai Medical Center, Los Angeles, CA.
Source of Funding: None reported.
Author Disclosures: Dr Black reports employment with Cedars-Sinai Medical Center which, like all hospitals, is subject to the penalties of the Hospital Readmission Reduction Program.
Authorship Information: Concept and design; analysis and interpretation of data; drafting of the manuscript; critical revision of the manuscript for important intellectual content; statistical analysis.
Address correspondence to: Jeanne T. Black, PhD, Cedars-Sinai Medical Center, 6500 Wilshire Blvd, Suite 1220, Los Angeles, CA 90048. E-mail: Jeanne.Black@cshs.org.
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