Conference Agenda

Quality measures (QMs) are standardized, evidence-based metrics used to evaluate and improve the quality of care provided in healthcare settings. QMs enable the identification of gaps in care, the benchmarking of performance across providers, and the improvement of patient outcomes through evidence-based practices. Given that a significant percentage of individuals who died by suicide had contact with clinical services within the year (i.e., up to 90%) and month (i.e., approximately 50%) prior to their death, developing and using QMs related to suicide assessment and care can be an important targeted suicide prevention effort. However, to be effective, such QMs must be rigorously developed and tested in real-world clinical settings and using large sample sizes, especially when related to health conditions with low base rates, such as suicide and suicidal behaviors. Also, to enhance feasibility, clinical utility, and uptake, such QMs need to incorporate common data elements (CDEs) that are easily collected during routine clinical care. The objective of this presentation is to: 1) discuss the importance of CDEs in targeted suicide prevention research, 2) highlight the challenges in developing QMs related to suicide prevention, 3) highlight how CDEs across settings, providers, and geographic locations informed the development and implementation of QMs related to quality improvement in suicide care, and 4) illustrate how CDEs in QM development research translates into changes in clinical practices and payment policies.

Suicide prevention research has expanded substantially over recent decades, yet the field struggles with a persistent and underappreciated obstacle: the absence of standardized methods across studies. Heterogeneity in how suicidal ideation, self-harm, and suicide attempts are defined, measured, and reported creates fragmented evidence that is difficult to compare, synthesize, or translate into actionable prevention strategies.

Inconsistent use of terminology, assessment instruments, and outcome definitions undermines the cumulative scientific value of suicide prevention research. When studies employ divergent case definitions — conflating suicidal ideation with suicide attempts, or applying inconsistent thresholds for clinical risk — the resulting evidence-base becomes difficult to interpret at the individual study level and near-impossible to synthesize across studies. Meta-analyses and systematic reviews are consequently hampered by methodological rather than substantive heterogeneity, obscuring genuine signals of intervention effectiveness.

A related challenge concerns statistical power. Because suicide and suicide attempts are low-frequency outcomes, demonstrating intervention effectiveness requires extraordinarily large sample sizes, often beyond the reach of any single study due to the long timelines and substantial resources involved. Two complementary strategies are proposed. First, standardized measures and harmonized data collection protocols would enable merging of datasets across studies, institutions, and countries, transforming individually underpowered investigations into a collectively powerful evidence base. Second, consensus on intermediate or proxy outcomes — such as suicidal ideation severity, help-seeking behavior, or risk factor exposure — would allow smaller-scale studies to contribute meaningful, comparable evidence, provided these proxies are consistently defined and shared.

The presentation concludes with a call for coordinated joint action across the international research community, advocating for development of consensus-based standardized measures under the auspices of the International Academy of Suicide Research (IASR) — leveraging IASR's global reach and the scientific expertise of its members to establish shared definitions, instruments, and reporting standards across diverse research contexts and populations worldwide.

Harmonization techniques that combine data from multiple disparate research projects are particularly relevant to the study of rarer outcomes that require larger datasets to adequately power analyses, such as advancing research in suicide prevention. Data harmonization offers promise for examining early interventions in childhood that target upstream childhood and adolescent risk and protective factors. This presentation will focus on data harmonization efforts to understand how data from existing interventions could be leveraged to look for crossover effects on suicide and suicidal thoughts and behaviors (STBs). Crossover effects in suicide prevention refer to unintended, downstream benefits where programs designed for other purposes—such as social-emotional learning, substance use prevention, or family counseling—simultaneously reduce STBs. These programs target common risk factors including poor emotional regulation and lack of social connection. We will introduce a new data harmonization study that includes 32 discrete preventive intervention programs and over 50,000 individuals. The included trials were selected specifically to address known risk and protective factors at the family, school, community, and individual levels, rather than to target STB outcomes. The overarching goal of this collaboration is to systematically examine crossover intervention effects on STBs, as well as the mediators, moderators, and mechanisms underlying these effects. The presentation will showcase preliminary findings and discuss key methodological and conceptual issues facing this harmonization effort. Findings from this project will help to: a) better understand which conditions early preventive interventions can help prevent, such as fatal and nonfatal STB, and b) guide the development of future robust interventions delivered early and scaled to address suicide globally. The presentation will also discuss how Common Data Elements (CDEs) could have improved the efficiency of data harmonization and integration efforts of the study by providing standardized definitions, formats, and values for variables across studies.

Suicide remains a leading public health crisis worldwide, requiring innovative, data-driven solutions to enhance prevention and intervention strategies. A vast body of research has demonstrated that the use of a uniform suicide risk detection method has significantly advanced our understanding across all areas of suicidology, with a gamut of populations and communities. The integration of common data elements (CDEs) into public health suicide prevention and intervention models has facilitated groundbreaking insights into geographical locations with heightened suicide risk, enabling targeted prevention efforts, policy, and care delivery innovations.

The goal of this presentation is to synthesize extensive research findings and real-world case studies that demonstrate the impact of standardized suicide risk detection methodologies at the national and international levels. From being integral to federal policy initiatives, such as the National Suicide Prevention Strategy and the Federal Action Plan, to driving large-scale system integration across healthcare, education, and crisis response delivery, the CDEs have transformed and galvanized suicide prevention efforts within public health. A review of the recent advancements in digital and AI-based suicide risk detection will further illustrate the transformative potential of these standardized measures in crisis care delivery and early intervention strategies.

This presentation will also provide insights into how systematic implementation of CDEs in suicide risk detection has fueled global collaboration, improved risk identification, and shaped the next level of public health intervention. Attendees will gain an understanding of the extensive research behind these methodologies, their integration into large-scale prevention strategies, and future directions to expand their global impact. By fostering public health cross-sector partnerships and embracing innovative models of risk detection, we can drive sustainable changes in suicide prevention and ultimately save lives worldwide.

Science is undergoing a ‘credibility revolution’. As part of this, researchers are encouraged to follow a set of practices, sometimes called “open science” practices, designed to increase the transparency, reproducibility, and replicability of research. These practices include pre- and post-registration of research, Registered Reports, sharing of study materials, code, and data, and pre-printing. All of these are scientific skills requiring time and skill-learning investment from researchers, which is why the use of these practices has sometimes been termed “slow science”. With a global public health problem as pressing as suicide, so-called slow science may seem a luxury we cannot afford. Yet, using open science practices may, in fact, be critical to achieving our suicide prevention research goals faster by facilitating scientific rigour, large-scale collaborative research, and rapid dissemination of knowledge. Open science practices are not the ultimate panacea for all ills in suicide research. However, they can prompt researchers to conduct more conscious research and engage more with pervasive methodological challenges, such as underpowering and measurement issues. They also facilitate replication — a rarity in suicide research — and collaboration, through safe sharing of data and code. In this talk, I will provide an overview of open science practices and the crucial role they have to play in the future of suicide research. I will also discuss the challenges of implementing open science practices and in starting to work in an open science way. Drawing on my experience of moving our lab to adopt open science as standard practice, and my roles as a former open science advisor at Clinical Psychological Science and current Chair of Moderation at the preprint server PsyArXiv, I propose solutions to the challenges of implementing open science in suicide research.