Journal
CURRENT MEDICAL RESEARCH AND OPINION
Volume 39, Issue 4, Pages 605-611Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/03007995.2023.2177413
Keywords
Rapid Mood Screener; bipolar disorder; major depressive disorder; screening tool; healthcare costs; misdiagnosis
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This study assessed the cost-effectiveness of the Rapid Mood Screener (RMS) in identifying bipolar I disorder in patients with major depressive disorder. The results demonstrated that screening with the RMS can lead to cost savings and reduce misdiagnosis in patients with depressive symptoms.
IntroductionMisdiagnosis 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.MethodsA 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 the prevalence of BP-I in patients diagnosed with MDD, RMS sensitivity/specificity, and the cost of misdiagnosing BP-I as MDD.ResultsScreening 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 a lower prevalence of BP-I (20% or 10%) versus the base case (24.3%).ConclusionThe 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 even with lower prevalence of BP-I and reduced RMS sensitivity assumptions.
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