This dataset contains data from the Mental Health System of Gipuzkoa, which are described in the manuscript: “Garcia-Alonso, CR., Almeda, N., Salinas-Pérez, JA., Gutiérrez-Colosía, MR., Iruin-Sanz, A., & Salvador-Carulla., L. (2021). Use of a decision support system for benchmarking analysis and organizational improvement of regional mental health care: The case of Gipuzkoa (Basque Country, Spain)”. This manuscript has been submitted to Plos One journal.

This research focused on developing an analytical process for assessing the performance of the Mental health (MH) system of Gipuzkoa and identifying benchmark and target-for-improvement catchment areas. For doing so a decision support system that integrated data envelopment analysis, Monte Carlo simulation and artificial intelligence was used. The units of analysis, which are considered the decision-making units, were the 13 catchment areas defined by a reference MH centre. The following indicators were assessed: relative technical efficiency, stability and entropy to guide organizational interventions.

Main results of the analyses pointed out that the MH system of Gipuzkoa showed high efficiency scores in each main type of care (inpatient, day and outpatient), but it can be considered unstable (small changes can have relevant impacts on MH provision and performance). With regards to performance improvement, it is recommended to reduce admissions and readmissions for inpatient care, increase workforce capacity and utilization of day care services and increase the availability of outpatient care services.

The Mental Health Network of Gipuzkoa manages its community and rehabilitation MH services (774,700 residents). These services include outpatient and core health day care facilities closely related to both hospital services (inpatient) and the social care network for severe cases. The Mental Health Network of Gipuzkoa is divided into 13 catchment areas, all of which have a mental health centre, considered as a reference, with a distinct orientation to outpatient care (some of them also include health day care facilities). This paper has been submitted to Plos One journal.

Each catchment area was described by 25 variables: service availability, placement capacity and workforce capacity. Most variable values were expressed in rates per 100,000 inhabitants (adults, over 18 years old). Utilization and performance variables were selected to collect information about service use, treated prevalence and incidence. Hospital discharges and readmissions were obtained from the 2015 MH utilization database (Health care Dashboard, Mental Health Network of Gipuzkoa). The structural variables (resource availability) were considered the input set, and utilization and performance variables (outcome proxies) were considered the output set.

The inputs for the inpatient acute hospital care scenario were the number of available services (rate per 100,000 inhabitants), number of beds (rate), number of psychiatrists (rate), number of psychologists and nurses (rate) and total number of professionals (rate), and the outputs were discharges (number of users), readmissions (number of users) and average length of stay (days). For the acute and non-acute core health day care scenario, the inputs were the number of available services (rate), number of psychiatrists (rate), number of psychologists and nurses (rate), total number of professionals (rate) and placement capacity (rate), and the outputs were the utilization of acute core health day care health services (number of places) and utilization of non-acute day care core health services (number of places). Finally, for the non-acute outpatient care scenario, the inputs were the number of available services (rate), number of psychiatrists (rate), number of psychologists (rate), number of nurses (rate) and total number of professionals (rate), and the outputs were the incidence (number of users), prevalence (number of users) and contacts (number of users).

The readme file contains an explanation of each variable in the dataset, including its variable name, units and explanation. This dataset has been used for assessing relative technical efficiency, stability and entropy indicators.