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Growing literature suggests that a significant proportion of rehospitalizations could be prevented if systems were put in place aimed at identifying and addressing some of the underlying issues that cause them. This article highlights key risk factors for unplanned rehospitalizations and illustrates a project that has successfully addressed many of the underlying issues that contribute to them.
The study illustrated herein took place at an inner-city academic teaching hospital.
Proactively identifying patient-, clinician-, and system-associated barriers to successful discharge transitions is critical for effective transitions of care for patients leaving the hospital setting. This process represents a culture change, requires a multidisciplinary approach to care, and mandates clear delineation of roles and responsibilities in the process, with ultimate and clear process ownership being defined. With such steps in place in a system of care, it is reasonable to expect a reduction in preventable rehospitalizations.
The ability to predict which patients are at high risk for rehospitalizations is an inexact science. The scientific literature has begun to introduce methods of modeling this prediction (Bowles & Cater, 2003; Coleman, Min, Chomiak, & Kramer, 2004; Lagoe, Noetscher, & Murphy, 2001). These models, however, are cumbersome and limited when applied to a general inpatient population. Several, largely retrospective, studies have examined clinical, demographic, and logistical risk factors for rehospitalization and posthospitalization adverse events, some of which may lead to rehospitalization. Caution should be exercised in applying the demographic risks because the studies were often done in small and specific populations.
Some of the major clinical parameters identified include high-risk medication use (antibiotics, gluco-corticoids, anticoagulants, narcotics, antiepileptic medications, antipsychotics, antidepressants, and hypoglycemic agents; Budnitz et al., 2006; Budnitz, Shehab, Kegler, & Richards, 2007; Forster et al., 2004; Forster, Murff, Peterson, Gandhi, & Bates, 2005; van Walraven & Forster, 2007), polypharmacy (five or more medications; Campbell, Seymour, Primrose, & ACMEPLUS Project, 2004), and specific clinical conditions (e.g., advanced chronic obstructive pulmonary disease, diabetes, heart failure, stroke, and depression; Bula, Wietlisbach, Burnand, & Yersin, 2001; Coleman et al., 1998; Gwadry-Sridhar, Flintoft, Lee, Lee, & Guyatt, 2004; Kartha et al., 2007; Ng et al., 2007; Phillips et al., 2004; Strunin, Stone, & Jack, 2007).
The demographic and logistical factors identified include prior hospitalization (Billings, Dixon, Mijanovich, & Wennberg, 2006; Coleman et al., 1998; Comette et al., 2005; Rodriguez-Artalejo, Guallar-Castillon, & Herrera, 2006; Smith et al., 2000; Soeken, Prescott, Herron, & Creasia, 1991), postdischarge follow-up appointment time provided prior to discharge, low income, low-educational level (Forsythe et al., 2006), low literacy (Baker et al., 2002), reduced social network indicators (Rodriguez-Artalejo et al., 2006), and unmarried/widowed individuals (Forsythe et al., 2006).
Adding to the importance of understanding and improving discharge transitions, the Centers for Medicare & Medicaid Services (CMS, 2008) has indicated that it will incorporate reporting rehospitalization rates as a part of its hospital quality measure reporting program starting in 2009. Of note, the literature does question whether rehospitalization is a valid marker of poor healthcare delivery, given the multifaceted etiologies for its occurrence (Ashton & Wray, 1996).
Nonetheless, it is clear that some rehospitalizations are preventable. Estimates of how many are preventable vary widely depending on patient population and definitions used for preventability (Oddone et al., 1996). In addition, it is important to recognize that hospitalizations can be upsetting, disruptive (Heiskell & Pasneu, 1991), and costly to patients and are quite costly to healthcare systems (HCUPnet, n.d.). Understanding the root causes of unplanned hospitalizations is critical for strategizing about reducing these adverse events.
In an effort to clarify the etiologies for unplanned readmissions to the hospital, we undertook an intensive study of the discharge process using several methods such as process mapping, failure mode–effect analysis, qualitative analysis, root–cause analysis, and quantitative analysis to identify the key processes related to rehospitalization, which were then classified into one of three buckets (Greenwald, Denham, & Jack, 2007):
These pilot data served as the foundation for Project RED (ReEngineered Discharge, n.d.), which was a randomized controlled trial of usual care for adult medical inpatients being discharged home as compared with a “RED” discharge. The discharge intervention was based on core principles derived from the pilot analyses, including
The RED intervention had three components:
Although final analyses of the data from Project RED are still underway, interim analysis data highlight several important aspects:
It is clear from these data, and those from others evaluating discharge interventions (Coleman, Parry, Chalmers, & Min, 2006; Dudas, Bookwalter, Kerr, & Pantilat, 2001; Einstadter, Cebul, & Franta, 1996; Naylor et al., 1994; Schnipper et al., 2006; van Walraven, Seth, Austin, & Laupacis, 2002; Weinberger, Oddone, & Henderson, 1996), that no system will completely eliminate rehospitalizations. Nonetheless, if even a small fraction of the more than 39 million discharges in 2006 (for a total cost of more than $943 billion; HCUPnet, n.d.) could be prevented, the healthcare economic impact would be profound and some patients would be saved from unnecessary rehospitalizations. The RED intervention was determined to be highly cost-effective even after considering the nursing time needed to deliver the program. The RED research team is now developing an automated health information technology system that will significantly reduce the amount of human time required. These results are due out in 2010.
The themes of RED, however, can be applied across all institutions and incorporated into local process improvement efforts around multidisciplinary transitions of care. Indeed, based largely on the work done by Project RED and the core principles it developed, the National Quality Forum (2006) adopted Safe Practice 11 regarding safe discharges. Thus, project teams will have this national imprimatur to motivate their organizations to advance their discharge transition process to meet this new safe practice.
Inpatient case managers, nurses, physicians, pharmacists, therapists, primary care physicians, emergency physicians, hospital administrators, and importantly, patients and their caregivers have an important opportunity to work together to achieve the goal of preventing unnecessary hospitalizations. Institutions must be knowledgeable about the discharge transitions literature, as well as basic process improvement skills, and be invested in improving their local system. Numerous online resources are now available to help systems begin this multidisciplinary process (Table 1). Healthcare teams invested in improving their institution’s discharge transition process must understand that culture change surrounding hospital systems improvement is often slow, but important. Healthcare dollars and, more important, patient care depends on it.
This study was funded by AHRQ-PIPS RFA-HS-05-012 “Partnerships in Implementing Patient Safety: Testing the Re-Engineered Hospital Discharge” (grant no. 1 U18 HS015905-01; principal investigator: B.W.J.).
Jeffrey L. Greenwald, MD, is Associate Professor of Medicine and Director of the Hospital Medicine Unit at Boston University School of Medicine and Boston Medical Center. In addition to being a coinvestigator on Project RED, Dr. Greenwald is also a coinvestigator on Project BOOST, a Hartford Foundation—funded project through the Society of Hospital Medicine looking to improve discharge transitions for older adults.
Brian W. Jack, MD, is Associate Professor of Family Medicine at Boston University School of Medicine. He is the principal investigator for the “Re-Engineered Hospital Discharge” program for which he received the Patient Care Award for Excellence in Patient Education Innovation and was the Patient Safety Investigator of the Month for the Agency for Health Research and Quality.
The authors have no conflict of interest.