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.

Efforts to develop an individualized treatment rule (ITR) to optimize major depressive disorder (MDD) treatment with antidepressant medication (ADM), psychotherapy, or combined ADM-psychotherapy have been hampered by small samples, small predictor sets, and suboptimal analysis methods. Analyses of large administrative databases designed to approximate experiments followed iteratively by pragmatic trials hold promise for resolving these problems. The current report presents a proof-of-concept study using electronic health records (EHR) of n = 43,470 outpatients beginning MDD treatment in Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) clinics, which offer access not only to ADMs but also psychotherapy and combined ADM-psychotherapy. EHR and geospatial databases were used to generate an extensive baseline predictor set (5,865 variables). The outcome was a composite measure of at least one serious negative event (suicide attempt, psychiatric emergency department visit, psychiatric hospitalization, suicide death) over the next 12 months. Best-practices methods were used to adjust for nonrandom treatment assignment and to estimate a preliminary ITR in a 70% training sample and to evaluate the ITR in the 30% test sample. Statistically significant aggregate variation was found in overall probability of the outcome related to baseline predictors (AU-ROC = 0.68, S.E. = 0.01), with test sample outcome prevalence of 32.6% among the 5% of patients having highest predicted risk compared to 7.1% in the remainder of the test sample. The ITR found that psychotherapy-only was the optimal treatment for 56.0% of patients (roughly 20% lower risk of the outcome than if receiving one of the other treatments) and that treatment type was unrelated to outcome risk among other patients. Change in aggregate treatment costs of implementing this ITR would be negligible, as 16.1% fewer patients would be prescribed ADMs and 2.9% more would receive psychotherapy. A pragmatic trial would be needed to confirm the accuracy of the ITR.