Managing smarter: a decision support system for mental health providers

A decision support system for assessing management interventions in a mental health ecosystem: The case of Bizkaia (Basque Country, Spain)

Evidence-informed strategic planning is a top priority in Mental Health (MH) due to the burden associated with this group of disorders and its societal costs. However, MH systems are highly complex, and decision support tools should follow a systems thinking approach that incorporates expert knowledge. The aim of this paper is to introduce a new Decision Support System (DSS) to improve knowledge on the health ecosystem, resource allocation and management in regional MH planning. The Efficient Decision Support-Mental Health (EDeS-MH) is a DSS that integrates an operational model to assess the Relative Technical Efficiency (RTE) of small health areas, a Monte-Carlo simulation engine (that carries out the Monte-Carlo simulation technique), a fuzzy inference engine prototype and basic statistics as well as system stability and entropy indicators. The stability indicator assesses the sensitivity of the model results due to data variations (derived from structural changes). The entropy indicator assesses the inner uncertainty of the results. RTE is multidimensional, that is, it was evaluated by using 15 variable combinations called scenarios. Each scenario, designed by experts in MH planning, has its own meaning based on different types of care. Three management interventions on the MH system in Bizkaia were analysed using key performance indicators of the service availability, placement capacity in day care, health care workforce capacity, and resource utilisation data of hospital and community care. The potential impact of these interventions has been assessed at both local and system levels. The system reacts positively to the proposals by a slight increase in its efficiency and stability (and its corresponding decrease in the entropy). However, depending on the analysed scenario, RTE, stability and entropy statistics can have a positive, neutral or negative behaviour. Using this information, decision makers can design new specific interventions/policies. EDeS-MH has been tested and face-validated in a real management situation in the Bizkaia MH system.

Using information to guide managed behavioral health care

This evaluation of substance abuse and mental health treatment services in Arizona discusses and illustrates the use of data already collected by the State to manage and monitor the public behavioral health sector. The authors utilize a framework that focuses on rate-setting and financial incentives; provider profiling and education; and monitoring of data quality and system-wide performance. Information and analysis can contribute to key management activities and forces that guide behavior in the system toward optimal system performance. Using data from 33,208 Medicaid-covered and uninsured adults, service mix varied substantially by region; for example, spending on residential care ranged from 0% to 40% for substance abuse treatment clients. By focusing on a smaller group of client with functional assessments, it also appears that regional spending levels varied considerably, for reasons not explained by client demographics or clinical measures. Finally, longitudinal data show that the regional managed care organizations are moving in different directions with regard to client mix and spending priorities. All of this variation suggests that there may be considerable latitude to guide and improve system-wide performance.