Readmission is multifaceted, but patient driven, potentially avoidable and costly. When readmission rates have been investigated there are consistent features evident in the literature. Literature is generally focused on the efficacy of readmission rates as a quality of care metric (Kansagara et al., 2011), usually in a variety of settings and medical patient populations (Hansen et al., 2011; Leppin et al., 2014). Models have been devised to predict which patients might be readmitted, why patients might require readmission, and if at risk of readmission, which pre-discharge, post-discharge or in-patient interventions might reduce the risk. In the cardiac surgery domain, interest in readmission risk prediction coincided with interest in readmission as a quality-of-care metric and there is a plethora of literature from the United States examining readmission risk. In the Australian and European context there is minimal research focused on reasons for or risk of readmission for cardiac surgical candidates.

The utility of social, environmental and system factors that influence patients’ ability to enact self-care behaviours or access supports to avoid readmission is also under-investigated. Similarly, the impact of health literacy, patient participation and shared decision-making on rates of readmission is not well understood. Models are constructed using retrospective administrative data, real-time administrative data or less frequently prospective primary data collection. In data from the US there is some consistency in factors that increase readmission risk across cardiac settings and samples, but a key feature of models to date is their poor predictive ability. Models predict mortality with reasonable specificity and sensitivity, not readmission. Very few studies assess the influence of variables that are indicative of overall health and function. Frailty, social determinants of health and informal caregiver support are rarely evaluated (Kansagara et al., 2011). In addition, process factors such as the timeliness of post-discharge follow up, primary-care coordination and quality of inpatient care are not featured (Gallagher et al., 2020). It is postulated that patient activation, or patients’ capacity to access and enact post-discharge care crucial for optimal recovery, may influence rates of representation and readmission.

When strategies to reduce readmission rates are explored, interventions are generally in the form of a ‘care bundle’ or explicit actions implemented at a particular time point. Pre-discharge interventions commonly include patient education, medication reconciliation and scheduling of follow-up prior to discharge. Post-discharge interventions include follow-up phone calls, patient-activated hotlines, ambulatory care services and home visits (Hansen et al., 2011). There is also a range of bridging interventions designed to impact on transition from in-patient to primary care. These studies enable us to consider optimal strategies at specific trajectory time points, but systematic review has revealed poor study description, methodological flaws and the use of ‘care bundles’ make it difficult to determine the efficacy of any single intervention on readmission rate reduction (Hansen et al., 2011). Leppin et al. (2014) published a systematic review of interventions for the prevention of readmission that scrutinized features of interventions to determine effect on treatment burden and patients’ capacity to enact burdensome self-care. These authors concluded that all interventions work to some degree but interventions that support patient capacity for self-care in the transition from hospital to home are the most effective.

In the cardiac surgery domain, modelling studies have focused on factors that increase risk of readmission and reasons for readmission in American patients with varying levels of readmission risk. Most studies have focused on patients undergoing coronary artery bypass grafts (CABGS). Readmission rates vary widely from 39% in a large multicentre study of patients having surgery in the late 1980s (Steuer et al., 2002) to 6.3% in a low-risk single centre private sector cohort (Sun et al., 2008). In this relatively homogenous cohort common factors increasing readmission risk are increasing age, female gender, African American race, multiple comorbid conditions and postoperative complications (Hannan et al., 2011; Jarvinen et al., 2003; Vaccarino et al., 2003). Specific studies have also linked additional patient factors including obesity (Rockx et al., 2004), preoperative inflammatory markers (Brown et al., 2013) and diabetes (Stewart et al., 2000) to readmission risk. Few studies have investigated process factors such as ‘off pump’ surgery (Karolak et al., 2007) and ‘fast track’ early discharge (Gooi et al., 2007).

Early discharge has been linked to increased readmission rates into post-acute care settings and subsequent increased length of stay (LOS) in those settings (Bohmer et al., 2002; Cowper et al., 2007). Acute care facilitates avoid penalty by discharging patients early and admitting them to transitional or sub-acute care facilities (Lazar et al., 2001). Postoperative LOS is an independent predictor of readmission irrespective of postoperative complications and when LOS in the intensive care unit (ICU) is increased, readmission rates have been reported to be as high as 62% (Lagercrantz et al., 2010). Readmission rates do not differ in studies comparing centres with and without specialist cardiac services (Novick et al., 2007). Very few studies have explored social determinants of health but there has been interest in exploring the effect of mental health on outcome after cardiac surgery, including rates of readmission. Levels of stress, anxiety and depression (Oxlad et al., 2006; Tully et al., 2008) are reliable predictors of poor outcome and increased rates of readmission but are rarely included in models of prediction.

The most common reasons for readmission vary and include arrhythmias 2.4% (Efthymiou & O'Regan, 2011) to 23.1% (Sun et al., 2008), pneumonia or respiratory complications 0.5% (Efthymiou & O'Regan, 2011) to 18%, infection 1.3% (Efthymiou & O'Regan, 2011) to 20% (Cowper et al., 2007) and congestive heart failure 14% (Cowper et al., 2007). Additional reasons reported include constipation, hypotension (Efthymiou & O'Regan, 2011), chest discomfort (Fox et al., 2013), angina and pericardial effusion or tamponade (Sun et al., 2008). In the Australian context Murphy et al. (2008) found living alone was the only independent predictor of readmission in a single centre study of 181 patients of whom 14.4% were readmitted. Another single centre Australian study (Slamowicz et al., 2008) found substantial variability in readmission according to time, where readmission rates were 7% at 7 days, 15.2% at 30 days and 32.3% at 6 months post discharge. In more recent research Fox et al. (2013) found overall readmission was 26.9% when readmission (15%, 95% CI 10.5–13.7) and re-presentation (11.9%, 95% CI 13.5–16.5) were differentiated. When cohorts include other common types of cardiac surgery, readmission rates rise further. In a multicentre prospective Canadian study of 5158 patients over 10 years, 30-day readmission rates were 14.9%, 18.3% and 25% for isolated CABGS, isolated valve and combined CABGS and valve surgery, respectively (Iribarne et al., 2014).

Fewer interventions for readmission reduction in cardiac surgery cohorts have been investigated in contrast to those for general medical or surgical patients. An integrative review of preoperative education as a means of reducing readmission found educational materials, methods of needs assessment and specific teaching methods were under-investigated (Veronovici et al., 2014). Randomized controlled trials of models of care that incorporate a specialist nurse or nurse practitioner have shown no effect in terms of readmission reduction (Kalogianni et al., 2016; Sawatzky et al., 2013), despite reduced rates of anxiety and improvements in health-related quality of life. Discharge planning involves coordinating care to ensure a quality and safe transition from hospital to home (Bull et al., 2000). Inadequate discharge planning leaves patients ill-equipped to manage their care after hospitalization (Boughton & Halliday, 2009; Bull et al., 2000) and increases re-admission rates to hospital following discharge (Phillips et al., 2004; Shepperd et al., 2013). Effective discharge planning after cardiac surgery is crucial given the context of shortening LOS (Cowper et al., 2006) because an increased amount of care previously delivered in hospital is managed by patients and their families in their home environment (Bauer et al., 2009). However, that discharge planning needs to be co-designed, patient focused and informed by patients’ narrative.

Following cardiac surgery, the trajectory of recovery requires discharge planning to be organized for three distinct phases of rehabilitation: immediate, intermediate and ongoing. In the immediate phase of rehabilitation, the aim of quality discharge planning is a timely discharge from hospital. During the intermediate phase the aim of quality discharge planning is to reduce unplanned re-admission to hospital. The aim of quality discharge planning for the ongoing phase of rehabilitation is preparation of patients for long-term self-management of health (Shepperd et al., 2013). Robust evidence of the impact of patient participation in interventions for readmission reduction after cardiac surgery, is difficult to locate. In a qualitative study of providers and patients, a desire to participate in earlier conversations to allow time to develop plans for treatment and personal preferences, was expressed (Gainer et al., 2017). Patient participation is a quality indicator in healthcare and in the Australian setting ‘Partnering with Consumers’ is one of the eight National Standards for healthcare delivery. Patient participation is one aspect of care in the acute cardiac surgery context that has the potential to influence patient and surgical outcomes yet patients’ ability and willingness to participate is unclear (McTier et al., 2015). A key dimension of the patient quality and safety experience is being discharged from healthcare at the right time with the right plan (McElroy et al., 2016). Effective discharge planning should enhance patient understanding and engagement that in turn influences patients’ ability to participate in accessing and enacting post-discharge care crucial to avoid readmission. Before we can tailor discharge planning interventions to suit the needs of this patient cohort there needs to be evidence to establish whether improved self-efficacy enhances self-management and as such, the capacity to make decisions that reduce the likelihood of representing to hospital or requiring readmission.