Why do older people with multi-morbidity experience unplanned hospital admissions from the community: a root cause analysis (BMC Health Services Research)

Background: Increasing demand for hospital services by older people is a major concern for Australian health care providers. To date there has been little in-depth research that encompasses contextual and systems factors contributing to hospital admissions. The objective of this study was to determine the reasons why older patients experienced unplanned hospital admissions to a major public hospital. Methods: A retrospective qualitative study using a Root Cause Analysis (RCA) methodology was conducted in a major public hospital in Adelaide, South Australia and surrounding community. Community dwelling older people admitted to the hospital who were well enough to give informed consent and be interviewed were invited to take part in the study. With patients consent, family members, general practitioners (GPs) and specialists were also interviewed and patient hospital records reviewed. Using a purposive sampling technique to obtain maximum variability, thirty-six older people (aged 70 years and older) participated in the study. GPs (n = 17), family members (n = 14), and other healthcare providers (n = 12) involved in their care were also interviewed. Cases were then analysed according to a standardized protocol to determine the root cause of admission. Root causes were then assigned to broader categories using thematic analysis. Results: The root causes of unplanned admissions were identified and categorised into six causal groups: a consequence of minimal care, progression of disease, home care accessibility, high complexity, clinical error, and delayed care-seeking by the patient. Conclusions: RCA can be effectively applied to determine the causes of unplanned hospital admissions although the process is time consuming. Four categories of admission (minimal care, clinical error, home care access, delayed care-seeking) were deemed potentially preventable. This methodology and classification approach may assist in designing interventions to prevent future hospitalisations in this high-risk population.