Community-based care is an important area where innovative models of practice may assist patients to remain in their home rather than being admitted for institutional-based care. This is particularly important in addressing the growing number of older adults who wish to “age in place” (Special Senate Committee on Aging, 2009). As health human resource shortages continue, difficulty recruiting and retaining community-based providers are impacting the delivery of home care services (Zeytinoglu et al., 2009). Innovative use of technology may transform how healthcare workers communicate and practice together within home care settings; improve equitable health care delivery for clients and their families; and shape policy and planning of health human resources.
In response to the shortage of nurses available to provide palliative home care, the home care provider in South West Ontario, Canada, led an innovation team comprised of service provider organizations, a software / technology provider, and health care providers in the development and implementation of a new model of palliative home care – the eShift model of palliative home care (eShift) (Ralph et al. 2016; South West Community Care Access Centre, 2011). The eShift model combines communication and documentation technology with novel utilization of health human resources to meet patient needs for palliative care in the home. The core of the eShift model of care is the pairing of a remotely located directing registered nurse and personal support worker in the technician role in the home along with the eShift technology and supported by a large team of healthcare and technology providers. The aims of this study were to: 1) develop a theoretical framework explaining the structure, process and outcomes of the eShift model of care; 2) identify and measure the process and outcomes of care; and 3) conduct an economic analysis of the model.
Methods:
We conducted a three-year mixed methods study (Creswell & Clark, 2011) of the eShift model from pilot to full scale-up across the region. We conducted 29 individual interviews and five focus groups with 31 particpants for a total of 60 nurses, personal support workers, physicians, nurse practitioners, allied health, and decision-makers. A content analysis of the interviews was conducted using NVivo 10 qualitative software program which informed the development of a theoretical framework based on Donabedian’s structure, process, outcomes of care (Donabedian, 2005). We than began to test the model and developed an online survey using both standardized instruments and researcher developed questions to measure the structure, process and outcomes of the eShift model. The online survey was conducted with all nurses, personal support workers and care coordinators working in the eShift model (n= 143). Data were analyzed using SPSS 20 and descriptive and inferential statistics were generated.
Results:
This presentation will specifically focus on the findings related to perspectives on team within the technology-enable model of home care. The technology in this model was positioned as an enabling factor for team work within the eShift model. The focus on patient care practices enabled by the implemented technologies was thought to facilitate the transformative changes to palliative care in the home setting. While the core members of the eShift health team were the remotely located directing registered nurse and the personal support worker in the home, the larger health care team reflects the evolution of technology enabled palliative home care which includes the visiting nurse, primary care providers (physician, nurse practitioner), palliative care specialists, the care coordinator, and information technology support and design personnel. Participants described the importance of the collaborative relationship with the technology provider from early model development right through to implementation of the model of care The eShift model of care has been influential in transforming team work. The eShift model of care supported the development of a strong virtual relationship and coaching and mentoring between the directing registered nurse and the personal support workers. We used the Interprofessional Socialization and Valuing scale (King et al. 2010) to measure self-perceived ability to work with others. Mean scores for nurses (m-54.1; SD -7.29) and personal support workers (m- 55.8; SD-6.1) were high; findings also indicate high job satisfaction, high empowerment, and high work engagement.
Conclusion:
Overall the eShift model supported enhanced interprofessional collaboration through technology enabled communication and documentation systems. Findings suggest that the technology provider can play an important role in the interprofessional team; a finding absent in the current literature. Nurses and personal support workers reported high rates of interprofessional collaboration, mentoring, and feeling valued. Rather than hinder care processes, technology was seen as an enabler to communication among the team and contributed to enhanced relationships among and between providers of care.
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