The Phase 1 project goal is to develop a quantitative model that allows healthcare decision makers to analyze the effectiveness, costs, and cost-effectiveness of interventions for patients with schizophrenia in a state Medicaid population. Furthermore, a discrete-event operational planning submodel will evaluate how schizophrenia and associated interventions affect resource demand and utilization on a societal level, and conversely, how the availability of intervention resources affects the health outcomes in persons with schizophrenia. If Phase 1 proves feasible, the Phase 2 project will develop a model that adapts to any state Medicaid population, analyzing many prevalent mental health conditions (adding depression, anxiety, panic disorder, and bi-polar disorder). The burden of mental illness on health and productivity is substantial and likely to increase considerably in the decades to come. Mental illness is highly prevalent and associated with considerable disability, however, a substantial percentage of individuals with mental illness do not receive any health care for their condition. To this end, MDM proposes the following specific aims: 1. Determine conceptual framework, data needs, and data sources of the schizophrenia disease progression-operational planning model. 2. Extract required data for the model and create parameterized distributions for model inputs. 3. Incrementally develop, verify, and validate disease progression and operational planning submodels. 4. Demonstrate the value of the disease progression-operational planning model by analyzing the effects of increasing key schizophrenia treatment resources on primary outputs (e.g., costs, proportion of days without relapse).
The U.S. spends tens of billions of dollars per year on mental health services, and considerable pressure exists on U.S. payers, especially public payers, to deliver efficient and cost-effective mental health services. In such resource-constrained, cost-conscious environments the proposed discrete-event simulation model can be used to assist in the development of efficient and cost-effective mental health resource allocation plans. ? ? ? ?
