Literature Collection

BACKGROUND: People living with MLTCs attending primary care often have unmet social care needs (SCNs), which can be challenging to identify and address. Artificial intelligence (AI) derived clusters could help to identify patients at risk of SCNs. Evidence is needed on views about the use of AI-derived clusters, to inform acceptable and meaningful implementation within interventions. METHOD: Qualitative semi-structured interviews (online and telephone), including a description of AI-derived clusters and a tailored vignette, with 24 people living with MLTCs and 20 people involved in the care of MLTCs (carers and health care professionals). Interviews were analysed using Reflexive and Codebook Thematic Analysis. RESULTS: Primary care was viewed as an appropriate place to have conversations about SCNs. However, participants felt health care professionals lack capacity to have these conversations and to identify support. AI was perceived as a tool that could potentially increase capacity but only when supplemented with effective, clinical conversations. Interventions harnessing AI should be brief, be easy to use and remain relevant over time, to ensure no additional burden on clinical capacity. Interventions must allow flexibility to be used by multidisciplinary teams within primary care, frame messages positively and facilitate conversations that remain patient centered. CONCLUSION: Our findings suggest that implementing AI-derived clusters to identify and support SCNs in primary care is perceived as valuable and can be used as a tool to inform and prioritse effective clinical conversations. But concerns must be addressed, including how AI-derived clusters can be used in a way that considers personal context.

The authors describe a real-world application of virtually integrated primary and behavioral health care implemented within an accountable care organization (ACO) system. Cost-of-care data from before and after a 6-month intervention were analyzed for 121 Medicaid and Child Health Plan Plus ACO members. The intervention was associated with a significant shift in the distribution of health care costs, from inpatient and emergency care to outpatient and preventive care. The program demonstrates a flexible and replicable approach to integration that can help expand effective primary care.

The authors describe a real-world application of virtually integrated primary and behavioral health care implemented within an accountable care organization (ACO) system. Cost-of-care data from before and after a 6-month intervention were analyzed for 121 Medicaid and Child Health Plan Plus ACO members. The intervention was associated with a significant shift in the distribution of health care costs, from inpatient and emergency care to outpatient and preventive care. The program demonstrates a flexible and replicable approach to integration that can help expand effective primary care.

INTRODUCTION: The RAINBOW randomized clinical trial validated the efficacy of an integrated collaborative care intervention for obesity and depression in primary care, although the effect was modest. To inform intervention optimization, this study investigated within-treatment variability in participant engagement and progress. METHODS: Data were collected in 2014-2017 and analyzed post hoc in 2018. Cluster analysis evaluated patterns of change in weekly self-monitored weight from week 6 up to week 52 and depression scores on the Patient Health Questionnaire-9 (PHQ-9) from up to 15 individual sessions during the 12-month intervention. Chi-square tests and ANOVA compared weight loss and depression outcomes objectively measured by blinded assessors to validate differences among categories of treatment engagement and progress defined based on cluster analysis results. RESULTS: Among 204 intervention participants (50.9 [SD, 12.2] years, 71% female, 72% non-Hispanic White, BMI 36.7 [6.9], PHQ-9 14.1 [3.2]), 31% (n = 63) had poor engagement, on average completing self-monitored weight in <3 of 46 weeks and <5 of 15 sessions. Among them, 50 (79%) discontinued the intervention by session 6 (week 8). Engaged participants (n = 141; 69%) self-monitored weight for 11-22 weeks, attended almost all 15 sessions, but showed variable treatment progress based on patterns of change in self-monitored weight and PHQ-9 scores over 12 months. Three patterns of weight change (%) represented minimal weight loss (n = 50, linear β1 = -0.06, quadratic β2 = 0.001), moderate weight loss (n = 61, β1 = -0.28, β2 = 0.002), and substantial weight loss (n = 12, β1 = -0.53, β2 = 0.005). Three patterns of change in PHQ-9 scores represented moderate depression without treatment progress (n = 40, intercept β0 = 11.05, β1 = -0.11, β2 = 0.002), moderate depression with treatment progress (n = 20, β0 = 12.90, β1 = -0.42, β2 = 0.006), and milder depression with treatment progress (n = 81, β0 = 7.41, β1 = -0.23, β2 = 0.003). The patterns diverged within 6-8 weeks and persisted throughout the intervention. Objectively measured weight loss and depression outcomes were significantly worse among participants with poor engagement or poor progress on either weight or PHQ-9 than those showing progress on both. CONCLUSIONS: Participants demonstrating poor engagement or poor progress could be identified early during the intervention and were more likely to fail treatment at the end of the intervention. This insight could inform individualized and timely optimization to enhance treatment efficacy. TRIAL REGISTRATION: ClinicalTrials.gov# NCT02246413.