Enhanced Monitoring of Individuals at Risk of Suicide: a Consistent System for Assessing Assistance is Being Developed
Through cooperation between the State Data Agency (Statistics Lithuania), Vilnius University, and the Institute of Hygiene, Lithuania has assessed and continues to develop a monitoring system for the algorithm used to provide assistance to individuals experiencing a risk of suicide. The main objective is to identify the most appropriate structure of the monitoring system, the necessary changes in the calculation of monitoring indicators, and the needs for improving data visualization in information dashboards. Based on the analysis, proposals were made on how a consistent monitoring of the assistance provision process should be organized in order to strengthen a data-driven health system response to suicide prevention challenges in Lithuania.
Need for monitoring: from implementation of the algorithm to its evaluation
In Lithuania, the provision of assistance to individuals experiencing a risk of suicide is regulated by the Description of the Procedure for Providing Assistance to Persons Experiencing a Risk of Suicide, a Suicide Crisis, or Having Experienced a Suicide Crisis, approved by Order No. V‑859 of 26 July 2018 of the Minister of Health of the Republic of Lithuania, and updated in 2022 and 2024. The Description establishes the procedures for identifying suicide risk, organizing, providing, and monitoring assistance, with the aim of ensuring timely, high-quality, and safe support. However, a consistent and systematic monitoring of the implementation of this Description has not yet been fully developed.
A monitoring system creates the conditions for evaluating the assistance provision algorithm, identifying potential gaps in the system, strengthening interinstitutional cooperation, and increasing the robustness and transparency of decision-making. It can also contribute to strengthening public trust in the health care system.
Three main monitoring aspects
Following an analysis of the existing system and available data, it was determined that monitoring of the algorithm should most appropriately be carried out with regard to three interrelated aspects:
1. Monitoring the frequency and profile of suicide risk
This monitoring makes it possible to assess whether suicide risk is properly identified and recorded across different levels of the health care system. Most of the indicators required for this monitoring already exist; however, they need refinement and calibration.
2. Monitoring service provision
This aspect analyzes what types of services, and to what extent, are provided to individuals experiencing a suicide risk at different levels of assistance provision. It allows for an assessment of the health system’s readiness to provide services and their accessibility. However, ensuring high‑quality monitoring in this area is more complex, as it requires broader data integration and the development of additional indicators, which has not yet been fully implemented.
3. Monitoring continuity of assistance
This aspect assesses whether individuals are provided with consistent and continuous assistance across all levels of the health care system. It is one of the most complex yet critically important aspects, as effective prevention depends on an uninterrupted care process. However, ensuring this type of monitoring is challenging, as it requires additional data linkage that makes it possible to track each individual’s pathway across different levels of service provision.
Data challenges and directions for system improvement
The analysis revealed important trends and gaps in the functioning of the suicide prevention system. It was found that the health care system is increasingly identifying individuals experiencing a risk of suicide, especially after the algorithm was updated in 2022, with the greatest progress observed in the recognition of suicidal ideation and in outpatient care.
However, a fundamental problem remains: the majority of individuals who later die by suicide are not identified as experiencing a suicide risk prior to their death. This indicates that further strengthening of the suicide prevention system should focus not only on the expansion of services, but primarily on the consistent and early identification of suicide risk across all levels of service provision.
It was also found that the provision of psychosocial assessment services is expanding; however, there are significant differences in their availability across different population groups, levels of service provision, and municipalities. Reducing these disparities is an important prerequisite for ensuring the effective functioning of the assistance provision algorithm.
To address these challenges, it is recommended to consistently improve data integration across different systems, refine and standardize indicator calculation methodologies, expand the scope of monitoring by incorporating additional data sources, and develop advanced data dashboards aimed at supporting decision-makers.
Role of data in strengthening prevention
The contribution of the State Data Agency in implementing this project – data analysis, calculation of indicators, and their visualization – is essential for creating an effective monitoring system. Integrated, high-quality, and timely accessible data make it possible not only to assess the current situation, but also to forecast risks and plan targeted interventions.
This initiative marks a shift from fragmented data use to a systematic, data‑driven governance model, which is necessary to achieve long-term effectiveness in suicide prevention.
Next steps
Once the proposed improvements to the monitoring system, related to the refinement of indicators, data integration, and their visualization in information dashboards, are implemented, the conditions will be created for consistently assessing the performance of the assistance provision algorithm, identifying priority areas for improvement, and strengthening the health care system’s capacity to respond to suicide risk. This is an important step toward building a coordinated, data‑driven, and people‑centred support system in Lithuania.