Developing a decision-tool to help organizations design care patterns for people with complex care facilitating integrated care: the ICARE4OLD project

Introduction: Older adults receiving home care (HC) services or living in long-term care facilities (LTCF) often experience multimorbidity. Identifying subgroups of older people sharing the same types of chronic diseases and having similar levels of impairment can help organizations target interventions effectively. Decision-support tools can foster tailored care and integrated care for these persons, as they are based on the comprehensive assessment of all needs and capabilities a person has. The iCARE4OLD project aims at developing decision-support tools with machine learning techniques. These tools will assist physicians and caregivers to better plan healthcare trajectories for older people, using data collected with the interRAI instruments. Methods: Data from a large sample of older people receiving HC services or living in LTC homes in Canada, Italy, Finland, and New Zealand were used to run a Latent Class Analysis (LCA). This technique classified individuals according to their underlying disease patterns starting from a list of 19 chronic conditions. Results: The combined sample from all countries (N=102,000) showed a mean age of 80 years old and 65% were female. The LCA yielded a 5-class solution as the best model for all countries, for both HC and LTC, including five disease patterns. One of the models showed the following groups (1) Alzheimer/dementia; (2) psychiatric diseases; (3) cardio-pulmonary diseases ; (4) stroke/hemiplegia ; (5) other dementias. The groups of cardio-pulmonary disease pattern and the stroke/hemiplegia disease patterns showed the highest complexity, especially in Activities of Daily Living (ADLs), with 68.5% and 77.8% living with impairment. Conclusion: Our results showed that, by using a standardized assessment tool such as the interRAI, it is possible to identify homogeneous morbidity patterns in older patients receiving care in the community or long-term residential care. Prognostic machine learning algorithms are being developed and validated to better predict various health outcomes and to evaluate the modifying impact of pharmacological and non-pharmacological interventions. These algorithms will be the basis of an electronic decision support tool, which will be used by health professionals working in home care and nursing homes. This tool will show which care paths to be followed to achieve better health outcomes for older persons.