BACKGROUND
Rehabilitation therapies are effective for older persons in home-based settings, and have the potential to save money for the health system, while also improving the quality of life for older adults who may otherwise be hospitalized or institutionalized. Although there is evidence that home-based rehabilitation can improve functional outcomes in older adults, research has shown that many older home care clients do not receive the rehabilitation services they need. Despite the home care sector’s increasing importance within Ontario’s health care system, we have a limited understanding of the population that currently utilizes these services and how these services are allocated in the province. This dissertation project aims to enhance the understanding of this domain using a large provincial data repository of home care client information (RAI-HC information system).
METHODS
Using the Andersen-Newman Framework to guide this research from a conceptual standpoint, and combining it with the CRoss Industry Standard Process for Data Mining (CRISP-DM) as an organizational framework, this dissertation focuses on examining data collected on older long-stay home care clients. Prior to the data mining modeling procedures, knowledge of the rehabilitation services in home care was developed through a series of semi-structured interviews with key informants. The results of this qualitative study were then used to inform quantitative analyses that included creating rehabilitation service user profiles using the K-means clustering algorithm, and the development of predictive models of rehabilitation service provision using a Random forest algorithm and multilevel models.
RESULTS
Older home care clients who receive occupational therapy and physiotherapy in the Ontario Home Care System form a complex and heterogeneous client population. These services are often provided to clients following an acute event, yet many older adults who could benefit from therapy services for functional improvement and maintenance are not provided services due to limited resources. K-means clustering analyses resulted in the creation of seven profiles of rehab service users illustrating the multidimensional diversity of the service user population. Predictive models were able to identify client characteristics that are commonly associated with service provision. These models confirmed the large amount of regional variation found across the province and highlighted the differences between factors that lead to occupational therapy and physiotherapy service provision.
CONCLUSIONS
Using multiple methods to systematically examine rehabilitation services for long-stay clients, new insights into the current user population and the client characteristics related to service provision were obtained. Future research activities should focus on ways to use the regularly collected standardized data to identify older long-stay home care clients who would benefit most from the rehabilitation therapy services provided by the provincial home care system.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/7342 |
| Date | 24 January 2013 |
| Creators | Armstrong, Joshua J. |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
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