Abstract
Background Most terminally ill cancer patients prefer to die at home, but a majority die in institutional settings. Research questions about this discrepancy have not been fully answered. This study applies artificial intelligence and machine learning techniques to explore the complex network of factors and the cause-effect relationships affecting the place of death, with the ultimate aim of developing policies favouring home-based end-of-life care. Methods A data mining algorithm (a classifier) and a causal probabilistic model (a credal network) for data analysis were developed with information derived from expert knowledge that was merged with data from 116 deceased cancer patients in southern Switzerland. This data set was obtained via a retrospective clinical chart review. Results Dependencies of disease and treatment-related decisions demonstrate an influence on the place of death of 13%. Anticancer treatment in advanced disease prevents or delays communication about the end of life between oncologists, patients and families. Unknown preferences for the place of death represent a great barrier to a home death. A further barrier is the limited availability of family caregivers for terminal home care. The family’s preference for the last place of care has a high impact on the place of death of 51%, while the influence of the patient’s preference is low, at 14%. Approximately one-third of family systems can be empowered by health care professionals to provide home care through open end-of-life communication and good symptom management. Such intervention has an influence on the place of death of 17%. If families express a convincing preference for home care, the involvement of a specialist palliative home care service can increase the probability of home deaths by 24%. Conclusion Concerning death at home, open communication about dying is essential. Furthermore, for the patient preference for home care to be respected, the family’s decision for the last place of care seems key. Early initiation of family-centred palliative care and provision of specialist palliative home care for patients who wish to die at home are suggested. Keywords Cancer; End-of-life care; Palliative Home care; Place of death; Communication; Family caregivers; Patient preference; Bayesian networks; Credal networks; Bayesian classifiers
Family Caregivers of Palliative Cancer Patients at Home: The puzzle of Pain Management
