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Total healthcare cost savings through improved bipolar I disorder identification using the Rapid Mood Screener in patients diagnosed with major depressive disorder.
Current Medical Research and Opinion 2023 Februrary 14
INTRODUCTION: Misdiagnosis of bipolar I disorder (BP-I) as major depressive disorder (MDD) leads to increased healthcare resource utilization and costs. The cost-effectiveness of the Rapid Mood Screener (RMS), a tool to identify BP-I in patients with depressive symptoms, was assessed in patients diagnosed with MDD presenting with depressive episodes.
METHODS: A decision-tree model of a hypothetical cohort of 1000 patients in a US health plan was used to estimate the number of correct diagnoses and overall total, direct healthcare costs over a 3-year timeframe for RMS-screened versus unscreened patients. Model inputs included prevalence of BP-I in patients diagnosed with MDD, RMS sensitivity/specificity, and cost of misdiagnosing BP-I as MDD.
RESULTS: Screening with the RMS resulted in 171, 159, and 143 additional correct BP-I or MDD diagnoses at Years 1, 2, and 3, respectively. Total healthcare plan cost savings were $1279 per patient in Year 1. Cumulative cost savings per patient for RMS screening versus no RMS screening were $2307 over 2 years, and $3011 over 3 years. Scenario analyses showed that the RMS would remain cost-saving assuming lower prevalence of BP-I (20% or 10%) versus the base case (24.3%).
CONCLUSION: The RMS is a cost-effective tool to identify BP-I in patients who would otherwise be misdiagnosed with MDD. Screening with the RMS resulted in cost-savings over 3 years, with model results remaining robust to lower prevalence of BP-I and reduced RMS sensitivity assumptions.
METHODS: A decision-tree model of a hypothetical cohort of 1000 patients in a US health plan was used to estimate the number of correct diagnoses and overall total, direct healthcare costs over a 3-year timeframe for RMS-screened versus unscreened patients. Model inputs included prevalence of BP-I in patients diagnosed with MDD, RMS sensitivity/specificity, and cost of misdiagnosing BP-I as MDD.
RESULTS: Screening with the RMS resulted in 171, 159, and 143 additional correct BP-I or MDD diagnoses at Years 1, 2, and 3, respectively. Total healthcare plan cost savings were $1279 per patient in Year 1. Cumulative cost savings per patient for RMS screening versus no RMS screening were $2307 over 2 years, and $3011 over 3 years. Scenario analyses showed that the RMS would remain cost-saving assuming lower prevalence of BP-I (20% or 10%) versus the base case (24.3%).
CONCLUSION: The RMS is a cost-effective tool to identify BP-I in patients who would otherwise be misdiagnosed with MDD. Screening with the RMS resulted in cost-savings over 3 years, with model results remaining robust to lower prevalence of BP-I and reduced RMS sensitivity assumptions.
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