Personalized care for complex lives: initial outcomes of a behaviorally-informed complex care intervention.

Background: 5% of patients account for the majority of healthcare spend, but standardized interventions for this complex population struggle to generate return on investment. The aim of this study is the development and proof of concept of an adaptive intervention to reduce cost and risk of readmission for medically high-risk individuals with any behavioral health diagnosis.

Methods: A behaviorally-oriented, personalized care service was delivered using a consultative, team-based approach including a physician, counselor, dietitian and social worker in collaboration with nurse care coordinators.

A Conceptual Framework for Building Individual and Team Capabilities to Provide Effective Longitudinal, Relationship-Based Clinical Case Management.

Individuals with complex, chronic diseases represent 5% of the population but consume 50% of the costs of care. These patients have , characterized by multiple chronic physical health conditions paired with a combination of behavioral health issues and/or unmet social needs. Unlike for most health problems, the problems faced by individuals with complex lives cannot be broken down into simpler parts to be solved independent from 1 another.

Depression and incident cardiovascular disease among patients with chronic kidney disease.

Background: Depression is associated with an increased risk of cardiovascular disease (CVD) and is prevalent among patients with chronic kidney disease (CKD). We aimed to identify the association of depression with incident CVD.

Methods: We studied patients with CKD stages 2-4 enrolled in the Chronic Renal Insufficiency Cohort (CRIC) and excluded participants with preexisting CVD.

The Unrecognized Impact of Anxiety in Complex and Costly Patients.

Strategies to reduce suffering and expense for complex and costly patients have met with limited success. This may be due to both the ongoing dependence on transactional relationships and the failure to recognize anxiety spectrum disorders as a primary driver of medical complexity. The authors describe an emerging current of thought regarding a universal approach to the conceptualization of anxiety disorders and extend it for application to medical complexity.

Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease.

Background: Deep brain stimulation (DBS) of the subthalamic region is an established therapy for advanced Parkinson's disease (PD). However, patients often require time-intensive post-operative management to balance their coupled stimulation and medication treatments. Given the large and complex parameter space associated with this task, we propose that clinical decision support systems (CDSS) based on machine learning algorithms could assist in treatment optimization.