Leveraging Digital Monitoring for Major Depressive Disorder

EP: 1.Lower Costs, Better Outcomes: Managed Care Strategies for DM-BUP Adoption

EP: 2.From Data to Decisions: Enabling Early Adoption of Cost-Saving Depression Therapies

EP: 3.Reducing Costs for Alzheimer Disease With Better Tolerated Therapies

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EP: 4.Leveraging Digital Monitoring for Major Depressive Disorder

Zachary Contreras, Director of Pharmacy Benefits at Sharp Health Plan, discussed insights from AMCP Nexus regarding the management of patients with treatment-resistant major depressive disorder (MDD) initiating dextromethorphan-bupropion (DM-BUP).1 The majority of patients starting DM-BUP had previously received 2 or more antidepressant therapies, highlighting the complexity of this population and the need for earlier identification of treatment resistance to prevent escalating costs and poor clinical outcomes.

Contreras emphasized the potential of predictive and proactive screening tools to identify patients at high risk for treatment-resistant depression. Using patient-reported outcomes, electronic health records, and claims data, clinicians and payers can build predictive models to flag individuals likely to respond poorly to standard therapies. He noted that artificial intelligence (AI) could play a critical role by combining multiple data sources—such as prior antidepressant history, psychiatric comorbidities, and utilization patterns—to identify high-risk patients early, enabling timely intervention with therapies like DM-BUP. Continuous oversight from behavioral health and medical management teams remains essential to track patient progress and guide care decisions.

From an analytic standpoint, Contreras recommended approaches to ensure that increased costs are accurately attributed to treatment resistance rather than comorbidity burden. This includes comparing patients with treatment-resistant MDD to those with similar demographics and comorbidity profiles, running subgroup analyses for conditions such as bipolar disorder or schizophrenia, and classifying costs accordingly. These approaches help payers understand the true economic impact of treatment-resistant depression and support evidence-based decision-making.

Digital monitoring and patient-reported outcomes were highlighted as powerful tools for tracking early signs of treatment failure. Real-time symptom tracking via mobile surveys, wearable devices, or text-based check-ins can capture changes in mood, sleep, activity levels, and physiological markers. These data allow clinicians to intervene promptly, escalating treatment when necessary, improving patient outcomes, and potentially reducing downstream costs for payers.

Overall, Contreras underscored that combining predictive analytics, AI-driven modeling, and digital monitoring can help identify high-risk patients earlier, support timely treatment interventions with DM-BUP, and ensure both clinical and economic benefits for patients, providers, and health systems.