Literature Collection

Phenomenon: Despite the emergence of the integrated care (IC) model, IC is variably taught and is challenged by current siloed competency domains. This study aimed to define IC competencies spanning multiple competency domains. Approach: Iterative facilitated discussions were conducted at a half-day education retreat with 25 key informants including clinician educators and education scientists. Seven one-on-one semistructured interviews were subsequently conducted with different interprofessional providers in IC settings within a Canadian context. Data collection grounded in patient cases with a physical illness and concurrent mental illness (medical psychiatry) were used to elicit identification of complex patient needs and the key medical psychiatry knowledge and skills required to address these needs. A thematic analysis of transcripts was performed using constant comparison to iteratively identify themes. Findings: Participants described 4 broad competency domains necessary for expertise in IC: (a) extensive integrated knowledge of biopsychosocial aspects of disease, systems of care, and social determinants of care; (b) skills to establish a longitudinal alliance with the patient and functional relationships with colleagues; (c) constructing a comprehensive understanding of individual patients' complex needs and how these can be met within their health and social systems; and (d) the ability to effectively meet the patient's needs using IC models. These 4 domains were linked by an overarching philosophy of care encompassing key enabling attitudes such as proactively pursuing depth to understand patient and system complexity while maintaining a patient-centered approach. Insights: The study addresses how development of IC expertise can be fostered by integration of individual IC competency domains. The findings align with previous research suggesting that competencies from existing frameworks are being enacted jointly in expert capabilities to meet the complex needs of patients, in this case with comorbid physical and mental health concerns.

Electronic health care records offer big data to mine and analyze towards improving public health outcomes. The information extracted, specifically social network data, could help us understand the primary care referrals for patients experiencing alcohol use disorder and wield that knowledge to better inform the engagement of this patient population. Network exposure and affiliation exposure models are two metrics that can be utilized to analyze the influence of social networks. We developed a core software library that address the scalability issue of our previous work. Our library computed high volume, randomly generated network graphs that range from 500-10,000 nodes (~126,000-40 million edges). This C library can be integrated with our previous work to handle high volume network data. Future plans include providing support for variant network exposure models and interfaces towards big network data analytics.